SGIP Market Transformation: Final Report
Submitted to: PG&E and the SGIP Working Group
Prepared by:
330 Madson Place Davis, CA 95618
November, 2015 TABLE OF CONTENTS
TABLE OF CONTENTS ...... i LIST OF FIGURES ...... v LIST OF TABLES ...... vii GLOSSARY ...... ix 1 EXECUTIVE SUMMARY ...... 1-1 1.1 Purpose of This Report ...... 1-1 1.2 Conclusions ...... 1-2 1.3 Recommendations ...... 1-5 2 SUMMARY OF RESULTS ...... 2-1 2.1 Extent to which SGIP is Transforming California’s DG and Energy Storage Markets ...... 2-1 Establishing SGIP Program Theory and Logic 2-2 Assessing SGIP’s Influence 2-3 2.2 Characterizing California’s DG and Energy Storage Markets ...... 2-4 California’s Combined Heat and Power (CHP) Market ...... 2-5 California’s Wind Market ...... 2-7 California’s Biogas Market ...... 2-8 California’s Advanced Energy Storage Market ...... 2-11 2.3 Overcoming Market Barriers ...... 2-14 Key Takeaways from Host Customer Surveys ...... 2-14 Key Takeaways from Manufacturer and Project Developer Interviews 2-14 Overall PA/CPUC Perspective and Key Takeaways 2-15 2.4 Lessons Learned 2-16 3 INTRODUCTION ...... 3-1 3.1 Purpose and Objectives of this Study ...... 3-1 Overall Purpose of this Study ...... 3-1 3.2 Scope ...... 3-2 Focus on SGIP but Comparisons to Other DG Incentive Programs ...... 3-2 3.3 Defining Distributed Energy Resources within the Context of the SGIP ...... 3-3 3.4 California’s Programs for Distributed Energy Resources ...... 3-3 3.5 Overview of California’s Distributed Energy Resources ...... 3-4 3.6 Overview of SGIP ...... 3-5 3.7 Defining Market Transformation ...... 3-6 Market Transformation versus Resource Acquisition...... 3-7 Complementary Nature of Program Drivers, Policies and Market Players in Market Transformation .... 3-7 3.8 Report Organization...... 3-7 4 APPROACH ...... 4-1 4.1 Program Theory and Logic Models ...... 4-2 4.2 Characterizing California’s DG and Energy Storage Markets ...... 4-5 Technical, Economic, and Market Potentials ...... 4-5
TABLE OF CONTENTS | i SGIP Market Transformation Report
DG and Energy Storage Market Dynamics and Major Market Players ...... 4-9 4.3 Collecting Information on Market Impacts and Market Players ...... 4-10 Participant and Nonparticipant Surveys ...... 4-11 Manufacturer Surveys ...... 4-11 Project Developer Surveys ...... 4-12 Stakeholder Surveys ...... 4-13 PA Surveys ...... 4-13 4.4 Evaluating SGIP Influences ...... 4-15 4.5 Assessing Market Trends Toward Self-Sufficiency ...... 4-15 5 PROGRAM LOGIC AND THEORY ...... 5-1 5.1 Results...... 5-1 Defining Transformed Markets ...... 5-3 Role of the SGIP in DG and Energy Storage Market Transformation ...... 5-5 5.2 Information Collected on Barriers and Opportunities ...... 5-6 Program Associated Staff Interviews ...... 5-6 Host Customer Interviews ...... 5-11 Key Takeaways from Host Customer Surveys ...... 5-17 Manufacturer and Developer Interviews ...... 5-17 Key Takeaways from Manufacturer and Project Developer Interviews ...... 5-21 5.3 Barriers Facing SGIP Technologies ...... 5-21 5.4 Perceived Drivers to Growth in SGIP Technologies ...... 5-23 5.5 Expected Outcomes ...... 5-23 6 CHARACTERIZING CALIFORNIA’S DG AND ENERGY STORAGE MARKETS ...... 6-1 6.1 California’s Combined Heat and Power (CHP) Market ...... 6-1 Overall Approach ...... 6-2 Estimated CHP Technical Potential ...... 6-3 CHP Economic Potential ...... 6-10 Potentials and Observed Growth in California’s CHP Market ...... 6-12 CHP Market Potential ...... 6-14 6.2 California’s Wind Market ...... 6-17 Overall Approach ...... 6-17 Wind Technical Potential Results ...... 6-18 Wind Economic Potential Results ...... 6-27 Wind Market Potential ...... 6-29 6.3 California’s Biogas Power Generation Market ...... 6-32 Overall Approach ...... 6-33 Landfill Gas Results ...... 6-34 Dairy Biogas Results ...... 6-37 Wastewater Treatment Plants Biogas Results ...... 6-40 Summary of California’s Biogas Potentials ...... 6-42 6.4 California’s Advanced Energy Storage Market ...... 6-44
TABLE OF CONTENTS | ii SGIP Market Transformation Report
Overall Approach ...... 6-45 Estimated AES Technical Potential ...... 6-46 Estimated AES Economic Potential ...... 6-47 Estimated AES Market Growth and Potential ...... 6-50 6.5 Historical Trends in DG and Energy Storage Growth in California ...... 6-52 7 SGIP’S INFLUENCE ON DG MARKET TRANSFORMATION ...... 7-1 7.1 Assessing Influence through Indicators ...... 7-2 CHP Programs, Policies, and Accomplishments ...... 7-2 CHP in California: Programs and Policies ...... 7-3 CHP in Connecticut: Programs and Policies ...... 7-4 CHP in Illinois: Programs and Policies ...... 7-5 CHP in Massachusetts: Programs and Policies ...... 7-7 CHP in Michigan: Programs and Policies ...... 7-8 CHP in New Jersey: Programs and Policies ...... 7-8 CHP in New York: Programs and Policies ...... 7-9 CHP in Pennsylvania: Programs and Policies ...... 7-11 CHP in Wisconsin: Programs and Policies ...... 7-12 Wind Programs, Policies, and Accomplishments ...... 7-13 Wind in California: Programs and Policies ...... 7-14 Wind in Illinois: Programs and Policies ...... 7-16 Wind in Massachusetts and Programs and Policies ...... 7-17 Wind in Michigan: Programs and Policies ...... 7-18 Wind in New Jersey: Programs and Policies ...... 7-19 Wind in New York: Programs and Policies ...... 7-20 Wind in Pennsylvania: Programs and Policies ...... 7-21 Wind in Wisconsin: Programs and Policies ...... 7-22 Biogas Programs, Policies, and Energy Accomplishments ...... 7-23 Biogas in California: Programs and Policies ...... 7-24 Biogas in Connecticut: Programs and Policies ...... 7-26 Biogas in Illinois: Programs and Policies ...... 7-27 Biogas in Massachusetts: Programs and Policies ...... 7-28 Biogas in Michigan: Programs and Policies ...... 7-30 Biogas in New Jersey: Programs and Policies ...... 7-31 Biogas in New York: Programs and Policies ...... 7-32 Biogas in Pennsylvania: Programs and Policies ...... 7-33 Biogas in Wisconsin: Programs and Policies ...... 7-35 7.2 Statistical Analysis of CHP, Wind, and Biogas Capacity ...... 7-36 Development of Independent Variables ...... 7-37 Model Specifications ...... 7-41 CHP Results ...... 7-42 Result for Wind and Biogas ...... 7-45
TABLE OF CONTENTS | iii SGIP Market Transformation Report
7.3 Comparisons of SGIP to other DG and Energy Storage Programs – Program Administrator Surveys in the Northeast ...... 7-46 DG and Energy Storage Program Goals ...... 7-46 CHP Technology Drivers ...... 7-47 CHP Technology Barriers ...... 7-47 Impact of Regulations and Policies on DG and Energy Storage Adoption ...... 7-48 Best Practices in the Northeast:...... 7-48 APPENDIX A MARKET TRANSFORMATION SURVEY FORMS ...... A-1 A.1 SGIP Host Customer Survey ...... A-2 A.2 SGIP Program Administrator Survey ...... A-14 A.3 SGIP Manufacturer Survey ...... A-35 A.4 SGIP Installer Survey ...... A-46 A.5 SGIP Combined Manufacturer + Installer Survey ...... A-61 APPENDIX B HOST CUSTOMER SURVEY RESULTS ...... B-1 B.1 Site Weighted Survey Results ...... B-1 SGIP Program Experience ...... B-1 Technology Evaluation ...... B-3 Priority ...... B-8 B.2 Capacity Weighted Survey Results...... B-11 APPENDIX C POLICIES IMPACTING DG AND STORAGE ...... C-1 APPENDIX D SITEPRO BUILDING TYPE TO NAICS CODE MAPPING ...... D-1 APPENDIX E CHP POTENTIAL ANALYSIS ...... E-1 E.1 Overview ...... E-1 E.2 Market Segment Host Buildings for CHP Systems ...... E-2 E.3 Building Electrical and Thermal Loads ...... E-5 E.4 CHP Systems Parameters ...... E-5 E.5 CHP System Sizing ...... E-7
TABLE OF CONTENTS | iv SGIP Market Transformation Report
LIST OF FIGURES
Figure 2 1: Preliminary SGIP Program Theory and Logic Diagram ...... 2-2 Figure 2 2: Growth in Cumulative CHP Capacity toward 2024 Market Potential ...... 2-6 Figure 2 3: Wind Market Potential at 20% Average Annual Growth Rate ...... 2-8 Figure 2 4: California’s Biogas Market Growth Forecast ...... 2-11 Figure 2 5: AES Market Growth Forecast ...... 2-13 Figure 4 1: Linkages between DG Market Growth in California, Drivers and Barriers...... 4-2 Figure 4 2: Simplified Program Logic Layout ...... 4-4 Figure 4 3: Different Definitions of Potential...... 4-6 Figure 5 1: Preliminary SGIP Program Theory and Logic Diagram ...... 5-2 Figure 5 2: Customer Satisfaction with Installed Technology ...... 5-12 Figure 5 3: Technology Recommendation to Friends and Associates ...... 5-13 Figure 5 4: Host Customer Considerations for Installing Technologies ...... 5-14 Figure 5 5: Installation of the Technology in the Absence of SGIP Incentives ...... 5-15 Figure 5 6: Satisfaction with Different Aspects of SGIP ...... 5-16 Figure 6 1: How CHP Displaces Host Site Electrical and Thermal Loads ...... 6-4 Figure 6 2: Relationship of Food Manufacturing and Processing Segments to Other Market Segments ...... 6-5 Figure 6 3: 2014 Technical Potential Percentages by Segment and IOU ...... 6-7 Figure 6 4: Total Resource Cost Test Benefit/Cost Ratios for Select Proxy CHP Systems ...... 6-11 Figure 6 5: Cumulative CHP Installed Capacity in CA for Select Prime Mover Technologies (1998-2014) ...... 6-12 Figure 6 6: Annual Capacity Additions in CA for Select Prime Mover Technologies...... 6-13 Figure 6 7: Growth in Cumulative CHP Capacity toward 2024 Market Potential ...... 6-15 Figure 6 8: Growth in Cumulative CHP Capacity and Annual Capacity Additions ...... 6-16 Figure 6 9: Distribution of Wind Speeds by County (50 Meters) ...... 6-22 Figure 6 10: Counties Representing 90% of Wind Technical Potential ...... 6-23 Figure 6 11: Distribution of Wind Class Speeds in Counties with IOU Overlay (50 Meters) ...... 6-26 Figure 6 12: Wind Economic Potential by Nominal Capacity and Year...... 6-27 Figure 6 13: Amount of Wind Energy Installed under the SGIP as of 2014 ...... 6-29 Figure 6 14: Wind Market Potential at 10% Average Annual Growth Rate ...... 6-30 Figure 6 15: Wind Market Potential at 20% Average Annual Growth Rate ...... 6-31 Figure 6 16: Wind Market Potential at 30% Average Annual Growth Rate ...... 6-32 Figure 6 17: Ranking of Landfill Gas Technical Potential by County ...... 6-35 Figure 6 18: TRC Benefit-to-Cost Ratios for Selected Landfill Biogas Generation Technologies ...... 6-37 Figure 6 19: Counties Representing 90% of Dairy Biogas Technical Potential...... 6-39 Figure 6 20: TRC Benefit-to-Cost Ratios for Selected Dairy Biogas Generation Technologies ...... 6-40 Figure 6 21: Ranking of Wastewater Treatment Plants Biogas Potential by County ...... 6-42 Figure 6 22: Total Technical, Economic, and Market Potential Growth 2014-2024 (kW) with SGIP Biogas Projects ...... 6-43 Figure 6 23: Biogas Generation Market Growth Forecast ...... 6-44
LIST OF FIGURES | v SGIP Market Transformation Report
Figure 6 24: TRC Benefit-to-Cost Ratios for Residential AES ...... 6-48 Figure 6 25: TRC Benefit-to-Cost Ratios for Nonresidential AES ...... 6-49 Figure 6 26: AES Market Growth Forecast ...... 6-51 Figure 6 27: Forecasted Cumulative and Annual Year over Year Growth Rates ...... 6-52 Figure 6 28: Trends in DG and Energy Storage Growth in SGIP and Associated Policies ...... 6-53 Figure 7 1: State Level CHP Accomplishments from 1999 to 2014 ...... 7-3 Figure 7 2: CHP California: Programs and Policies ...... 7-4 Figure 7 3: CHP in Connecticut: Programs and Policies ...... 7-5 Figure 7 4: CHP in Illinois: Programs and Policies ...... 7-6 Figure 7 5: CHP in Massachusetts: Programs and Policies...... 7-7 Figure 7 6: CHP in Michigan: Programs and Policies ...... 7-8 Figure 7 7: CHP in New Jersey: Programs and Policies ...... 7-9 Figure 7 8: CHP in New York: Programs and Policies ...... 7-11 Figure 7 9: CHP in Pennsylvania: Programs and Polices ...... 7-12 Figure 7 10: CHP in Wisconsin: Programs and Policies ...... 7-13 Figure 7 11: Wind Capacity by State ...... 7-14 Figure 7 12: California Wind Capacity: Programs and Policies ...... 7-15 Figure 7 13: Illinois Wind Capacity: Programs and Policies ...... 7-17 Figure 7 14: Massachusetts Wind Capacity: Programs and Policies ...... 7-18 Figure 7 15: Michigan Wind Capacity: Programs and Policies ...... 7-19 Figure 7 16: New Jersey Wind Capacity: Programs and Policies ...... 7-20 Figure 7 17: New York Wind Capacity: Programs and Policies ...... 7-21 Figure 7 18: Pennsylvania Wind Capacity: Programs and Policies ...... 7-22 Figure 7 19: Wisconsin Wind Capacity: Programs and Policies ...... 7-23 Figure 7 20: New Landfill and Dairy and Swine Digester Biogas Capacity by State (1999-2013, kW) ...... 7-24 Figure 7 21: California Biogas Capacity: Programs and Policies ...... 7-25 Figure 7 22: Connecticut Biogas Capacity and Programs and Policies...... 7-27 Figure 7 23: Illinois Biogas Capacity: Programs and Policies ...... 7-28 Figure 7 24: Massachusetts Biogas Capacity: Programs and Policies ...... 7-29 Figure 7 25: Michigan Biogas Capacity: Programs and Policies ...... 7-30 Figure 7 27: New Jersey Biogas Capacity: Programs and Policies ...... 7-31 Figure 7 27: New York Biogas Capacity: Programs and Policies ...... 7-33 Figure 7 28: Pennsylvania Biogas Capacity: Programs and Policies ...... 7-34 Figure 7 29: Wisconsin Biogas Capacity: Programs and Policies ...... 7-35 Figure E 1: Food Store Segment Annual Average Weekday Hourly Electric End-Use Loads ...... E-9 Figure E 2: Health Segment Average Annual Weekday Hourly Natural Gas End Use Loads ...... E-10 Figure E 3: Health Segment Average Annual Weekday Hourly Electric End Use Loads ...... E-10 Figure E 4: Technical Potential with and without Absorption Chilling Option ...... E-11 Figure E 5: Technical Potential under Different Annual Operating Hour Constraints ...... E-13 Figure E 6: Comparisons of Market Segment Technical Potentials, ICF 2011 and Itron ...... E-14
LIST OF FIGURES | vi SGIP Market Transformation Report
LIST OF TABLES
Table 2 1: 2014 New CHP Technical Potential (MW) by Segment and IOU ...... 2-5 Table 2 2: Technical Potential Wind Capacity with GCF > 35% by County (MW) ...... 2-7 Table 2 3: Landfill Gas Technical and Economic Potential by County ...... 2-9 Table 2 4: Dairy Biogas Economic and Technical Potential by County ...... 2-9 Table 2 5: Wastewater Treatment Plants Biogas Economic and Technical Potential by County ...... 2-10 Table 2 6: AES Technical Potential Based on Maximum Power by Market Segment and IOU ...... 2-12 Table 2 7: Economic Potential for AES by Market Segment and IOU by 2024 ...... 2-12 Table 3 1: DER Projects and Capacity in California at 12/31/14 ...... 3-5 Table 4 1: Summary of Survey Groups ...... 4-11 Table 4 2: Key Research Topics ...... 4-14 Table 5 1: Summary of Barriers to SGIP Technology Market Adoption ...... 5-22 Table 5 2: Realized or Perceived Drivers to Increased Growth in SGIP Technologies ...... 5-23 Table 5 3: Expected Outcomes by Timeframe ...... 5-24 Table 6 1: 2014 New CHP Technical Potential (MW) by Segment and IOU ...... 6-3 Table 6 2: PG&E 2014 Technical Potential by Segment and System Size Category (MW)...... 6-8 Table 6 3: SCE 2014 Technical Potential MW by Segment and System Size Category (MW) ...... 6-8 Table 6 4: SDG&E 2014 Technical Potential MW by Segment and System Size Category (MW)...... 6-9 Table 6 5: 2014 and Future CHP Technical Potentials by IOU (MW) ...... 6-9 Table 6 6: Wind Power Classes and Speeds ...... 6-19 Table 6 7: Area by Wind Class Speed (square miles): 50 Meter Height ...... 6-19 Table 6 8: Potential Wind Capacity with GCF > 35% by County (MW) ...... 6-22 Table 6 9: Counts of Indicator Businesses for Distributed Wind Systems by Type by County ...... 6-24 Table 6 10: Number of Potential Installations of 50 kW and 1.5 MW Wind Turbines...... 6-25 Table 6 11: Technical Wind Potential by IOU: (50 Meters) ...... 6-26 Table 6 12: Economic Potential for Distributed Wind Energy ...... 6-28 Table 6 13: Landfill Gas Technical and Economic Potential by County ...... 6-34 Table 6 14: Distribution of Nominal 500 kW IC Engines by IOU ...... 6-36 Table 6 15: Dairy Biogas Economic and Technical Potential by County ...... 6-38 Table 6 16: Wastewater Treatment Plants Biogas Economic and Technical Potential by County ...... 6-41 Table 6 17: Technical, Economic, and Market Biogas Generation Potential by IOU ...... 6-44 Table 6 18: AES Technical Potential, Based on Energy Capacity to Shave Top 4 Hours ...... 6-47 Table 6 19: Economic Potential for AES by Market Segment and IOU by 2024 ...... 6-50 Table 6 20: Summary of DG and Energy Storage Potentials vs. Installed Capacities in CA ...... 6-52 Table 7 1: Count of Programs in DSIRE Data by State and Implementation Sector ...... 7-37 Table 7 2: Count of Primary Driver Programs by State and Technology ...... 7-38 Table 7 3: Mapping of DSIRE Program Types to Different Aggregation Levels ...... 7-39 Table 7 4: Parameter Estimates for Model of SGIP CHP Impacts ...... 7-43 Table 7 5: California CHP Capacity and Estimated Influence of SGIP ...... 7-44
LIST OF TABLES | vii SGIP Market Transformation Report
Table C 1: Listing of Programs or Policies Impacting DG and Storage in California ...... C-1 Table C 2: Summary of Primary Programs or Policies Impacting DG and Storage in California ...... C-2 Table C 3: Policies Specifically Related to SGIP ...... C-9 Table D 1: Commercial and Industrial NAICS Codes and Associated SitePro Building Type ...... D-1 Table E-1: Market Segment Annual Growth Rates ...... E-2 Table E-2: Commercial and Industrial Census Data Fields ...... E-2 Table E-3: Residential Energy Consumption Survey Data Fields...... E-3 Table E 4: Estimated Building Populations Market Segment, Climate Region, and IOU ...... E-3 Table E 5: CHP System Performance Assumptions ...... E-4 Table E 6: Engineering Performance Assumptions ...... E-6 Table E 7: Commercial and Industrial 2012 NAICS Codes and Associated SitePro Building Type ...... E-16
LIST OF TABLES | viii SGIP Market Transformation Report
GLOSSARY
Term Definition Global Terms AWEA American Wind Energy Association BOP Balance of plant CAISO California independent System Operator Cal EPA California Environmental Protection Agency CARB California Air Resources Board CCDC California Clean Energy DG Coalition CEA Clean Energy Coalition CEC California Energy Commission CEJP Clean Energy Jobs Program CESA California Energy Storage Alliance CSE Center for Sustainable Energy CPUC California Public Utilities Commission DRP Distributed Resource Plans HHV Higher Heating Value IEPR Integrated Energy Policy Report IOU Investor-owned utility LHV Lower Heating Value LMOP Landfill Methane Outreach Program NAICS North American Industry Classification System NEM Net energy metering NREL National Renewable Energy Laboratory NIMBY Not In My Backyard PA Program Administrator PG&E Pacific Gas & Electric PY Program year SCE Southern California Edison Company SCG Southern California Gas Company SDG&E San Diego Gas & Electric Company SGIP Self-Generation Incentive Program Technologies AES Advanced energy storage CHP Combined heat and power DER Distributed energy resource DG Distributed generation FC Fuel cell GT Gas turbine IC engine Internal combustion engine
GLOSSARY | ix SGIP Market Transformation Report
Term Definition MT Microturbines ORC Organic Rankine Cycle PRT Pressure reduction turbine PV Photovoltaic WD Wind turbine WWTP Wastewater Treatment Plant Economics/Financing EPBB expected performance based buydown ITC Investment Tax Credit LCOE levelized cost of energy MACRS Modified Accelerated Cost-Recovery System PBI performance based incentive PCT participant cost test PPA power purchase agreement PTC Production tax credit STRC societal total resource cost TRC total resource cost Emissions Related GHG greenhouse gas
CO2eq CO2 equivalent
NOx Nitric oxide (NO) and nitrogen dioxide (NO2) PM10 Particulate matter (PM) with diameter of 10 micrometers or less
SO2 Sulfur Dioxide REC renewable energy credit
GLOSSARY | x 1 EXECUTIVE SUMMARY
1.1 Purpose of This Report The Self Generation Incentive Program (SGIP) is a long running California incentive program supplying support to distributed generation (DG) and energy storage technologies deployed on the customer side of the meter. Assembly Bill 970 established the SGIP in 2001 to respond to California’s electricity crisis of 2000-2001, and to help encourage development of DG resources. Since its inception in 2001, the program has been extended several times and has undergone a number of significant changes. Most notably, changes have been made in the types of technologies and fuels eligible to receive incentives under the program, and goals have been added targeting reductions in greenhouse gas (GHG) emissions. In its September 2011 decision modifying the SGIP and implementing Senate Bill 412, the California Public Utilities Commission (CPUC) established market transformation as one of the four goals of the program.1 The primary purpose of this report is to identify the extent to which the SGIP is transforming California’s DG and energy storage markets. In addition, this study has seven research objectives: » Characterize the market for SGIP technologies in California; » Identify specific DG and energy storage market barriers and market strengths; » Identify and describe SGIP and other DG and energy storage policy interventions; » Assess the effects of the SGIP and other policy interventions in reducing identified market barriers and in supporting the transformation of DG and energy storage markets toward self-sufficiency; » Establish and analyze indicators (metrics), that reflect the evolution of the distributed energy markets toward self-sufficiency; » Assess sustainability of these markets in California in the absence of the SGIP; and, » Identify and recommend changes to the SGIP program to support further development of these markets in California. California’s DG and energy storage markets encompass a wide variety of market players and stakeholders who have differing roles. We used a combination of in-depth interviews and online surveys to obtain perspectives from past and current SGIP Program Administrators (PAs), CPUC program staff, DG and energy storage technology manufacturers and project developers, customers who either did or did not participate in the SGIP but may have installed DG or energy storage technologies on their premises, public agencies and nonprofit organizations involved with DG or energy storage. In particular, we wanted to obtain their perspectives on how the DG and energy storage markets function in California, what have been and continue to be key barriers or opportunities, to what extent the SGIP has influenced DG and energy storage market growth, and their thoughts on ways in which the SGIP can further help support DG and energy storage growth.
1 In particular, SGIP is to assist in market transformation of distributed energy resource (DER) technologies. See CPUC Decision D.11-09-015, September 8, 2011.
EXECUTIVE SUMMARY| 1-1 SGIP Market Transformation Report
In assessing the status of California’s progress toward a transformed market, we also estimated technical, economic, and market potentials for different SGIP technologies and compared them to 2014 levels of installed DG and energy storage technologies. To help assess what influence the SGIP has on DG market growth, we compared growth in California’s DG market to that of eight other states (Connecticut, Massachusetts, Michigan, New Jersey, New York, Pennsylvania, and Wisconsin).2 We also conducted statistical analyses to see if we could quantify the influence of the SGIP on DG market growth. Based on the information learned from this work, we offer the following conclusions and recommendations.
1.2 Conclusions 1. The SGIP was not created as a market transformation program and market transformation was not explicitly added as a goal to the SGIP until 2011. In addition, program theory and logic has never been developed for the SGIP. As a result, we developed a preliminary program theory and logic model focused on how the SGIP can play a role in transforming California’s DG and energy storage markets. a. DG and energy storage market transformation can be defined as follows: “long-lasting sustainable changes in the DG/energy storage markets achieved by reducing barriers to the adoption of DG/energy storage technologies and effecting structural changes in the DG/energy storage markets, and behaviors of market actors such that the market can be sustained without publicly-funded intervention.” b. The program theory and logic model includes short, medium and long term outcomes for the program and identifies SGIP related activities and outputs to help achieve DG and energy storage market transformation.. 2. In spite of the lack of existing market transformation goals, the SGIP has had a significant impact on the growth of customer-sited DG resources in California and appears poised to have a significant impact on the growth of customer-sited energy storage. a. Since the inception of the SGIP in 2001, the growth in customer-sited combined heat and power (CHP) has increased from 4 MW in 2001 to nearly 290 MW by the end of 2014. California’s contribution to customer-sited CHP is significantly greater than that found in other states with CHP programs. For example, New York has the second largest amount of installed CHP next to California, but that represents only 43% of the CHP capacity installed in California. b. Historically, there has been limited growth in distributed wind in the SGIP; however, since 2010, there has been an increase in distributed wind capacity installed under the SGIP. In comparison to the eight other states examined, with approximately 25 MW of distributed
2 Our initial intent was also to compare growth of energy storage in California to growth of energy storage in other states. However, as energy storage is a nascent technology, there was insufficient information available on installed energy storage capacities in other states. As a result, we focused only on the DG market for this statistical comparison.
EXECUTIVE SUMMARY| 1-2 SGIP Market Transformation Report
wind, California ranks second only to Massachusetts in the amount of distributed wind generation capacity installed since 1999. c. Based on EPA data, California ranks first for installation of distributed biogas generation between 1999 and 2014. While it cannot be shown statistically that the SGIP by itself led to growth of biogas in California, the SGIP certainly helped contribute to that growth. Growth in onsite biogas generating capacity in California increased from less than 100 kW in 2002 to more than 32 MW by 2014. d. Growth in advanced energy storage (AES) has been rapid under the SGIP. Since 2010, over 10 MW of customer-sited energy storage has been installed under the SGIP and there are over 220 AES projects currently in the SGIP queue to receive incentives. 3. There is substantial opportunity to grow customer-sited DG and energy storage in California. a. The economic potential for customer-sited CHP in California is 15.5 GW. The highest opportunities are associated with food manufacturing/processing operations, restaurants, food stores, the health and hospital market segments, and large office buildings. Prime movers that appear to provide the most cost effective approach for CHP include internal combustion (IC) engines and gas turbines. b. We estimate distributed wind energy economic potential in California at nearly 9.8 GW. This economic potential assumes use of larger wind turbines, rated in the 1-3 MW size range but located next to customer sites to help meet onsite electrical demand. Nearly three fourths of the economic potential is located in the SCE service territory, commensurate with the large amount of area in SCE with higher class wind speeds. c. There is approximately 665 MW of small scale biogas economic potential in California. Small scale landfill gas generation represents about 400 MW or over 60% of the state’s biogas economic potential, with the remainder associated with wastewater treatment digester and onsite dairy digester potential. d. While advanced energy storage has enormous technical potential, especially among the residential sector, we estimate the economic potential prior to 2024 at approximately 5.3 GW. At 2.8 GW, SCE has the largest economic potential while PG&E’s economic potential is estimated only slightly lower at 1.9 GW. The largest economic potential is in the large office sector. 4. Statistical analyses to compare the effect of SGIP on DG growth relative to other states showed that the SGIP has had an impact on market growth in CHP but was inconclusive with respect to distributed wind and biogas generation growth. In addition, due to a lack of consistent data on energy storage installations between the different states, we were unable to analyze the impacts of the SGIP on advanced energy storage. a. Modeling shows that SGIP led to a statistically significant increase in the installed capacity of CHP in California from 2002 to 2007. The statistical model found that local financing programs also had a positive impact on installed CHP capacity. In contrast, net energy metering (NEM) policies were found to result in a decrease in CHP. We theorize that implementation of California’s NEM policy, which has not included CHP, has steered investors toward those resources which do qualify for NEM. For example, implementation of
EXECUTIVE SUMMARY| 1-3 SGIP Market Transformation Report
NEM could lead to more investment in photovoltaics (PV) and a reduction in investment in CHP due to the NEM improving the relative cost effectiveness of PV. b. Growth in distributed wind and biogas generation in California appears to track SGIP funding. However, the statistical modeling was not able to produce stable estimates of the impact of SGIP on wind or biogas capacity beyond the effects of other programs and policies. 5. Interviews with Program Administrators within the SGIP, policy makers and those responsible for similar programs outside of California uncovered several important observations: a. Market transformation goals are critical to ensuring the program is headed in the right direction, ensuring measureable progress and setting a clear end point. b. Addressing unmet market and customer needs are critical in obtaining the widespread market adoption needed to transform the DG and energy storage markets. Customer use cases help identify customer energy usage and needs by classes of customers. In selecting how DG and energy storage technologies are provided through the program, it is important to match technologies to customer use cases. c. Market growth of DG and energy storage will occur more readily when the technologies achieve customer needs, utility needs and technology provider needs. DG and energy storage technologies have the potential to help utilities provide safe, reliable and cost effective energy to their customers. Under the SGIP, the utilities are tasked with managing the program, rather than helping develop DG and energy storage technologies in a partnership arrangement with technology providers. This partnership can result in economic benefits to the customer, the utility, and the technology provider and help accelerate market adoption of DG and energy storage technologies. d. Program Administrators should play a significant role in the strategic planning of how to implement market transformation of DG and energy storage technologies. Because of their market connection to utility customers and understanding of utility operations, the PAs bring a unique perspective on the role of DG and energy storage in a transformed market. e. Technology reliability is important in how prospective program applicants view the market. In the Northeast, the PAs used a “prescriptive” catalog approach that standardized configurations, costs and installation procedures to be used by project developers when installing CHP systems. This standardized approach reduced risk, helped streamline the application and implementation processes, and helped reduce costs. At the same time, this approach may limit a program’s ability to support emerging technologies. f. Successful market transformation involves more than addressing economic barriers. Market barriers must be taken into account and alignment of state policies to promote DG and energy storage is very important. As one respondent pointed out, the correct alignment of policies provides investors with faith in the market and is ultimately what moves market transformation forward.
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1.3 Recommendations This report includes a first attempt at developing an SGIP program theory and logic model that targets transformation of California’s DG and energy storage markets. Based on collected survey information, we identified barriers as well as opportunities to expand California’s DG and energy storage markets. However, there has been little discussion among the SGIP PAs, policy makers, and stakeholders as to what would constitute transformed DG and energy storage markets in California. Transforming California’s DG and energy storage markets will require substantive discussion of the role of the SGIP versus other market interventions and the role of the utilities in pursuing this market transformation. Specific recommendations in this area include the following: 1. The collected survey information in this study along with the preliminary program theory and logic model could be used as a starting point to form the basis of future quantitative goals for the program such as technology cost reductions, penetration levels, or levelized costs of electricity. 2. As some PAs have pointed out, the SGIP as configured may not adequately provide for a partnership between utilities and project developers in addressing both customer and utility needs. However, such a partnership could significantly help make forward progress in transforming California’s DG and energy storage markets. Moreover, the successful transformation of California’s DG and energy storage markets requires a uniform front of policies that meet the needs of the industry and the utilities. Identifying commonalities between customer, project developer and utility needs, and then developing uniform policies to help achieve these needs should be a key objective of a plan for transforming California’s DG and energy storage markets. 3. California PAs have noted that SGIP could be improved by better matching of SGIP objectives to customer use cases. One example is matching of SGIP technology selection and incentives to utility customer energy, financial, and environmental needs. Matching program objectives to customer needs should be another key component of the SGIP moving forward. 4. Based on the vision of what constitutes a transformed DG and energy storage market in California, the California investor-owned utilities (IOU)s, PAs, the CPUC, and other appropriate state agencies should work together to develop the goals of the transformed market and an action plan outlining the strategies, objectives, and tactics that will be employed to achieve these goals. 5. The recently completed SGIPce Cost Effectiveness Model should be used by the SGIP Working Group to develop strategies for combining DG and energy storage technologies that meet customer needs, utility needs, and overall societal needs. Use of the SGIPce cost-effectiveness model will also provide for a better understanding of how incentives can be used as a tactic to achieve the desired goals and objectives. 6. Program outreach is important in helping move toward a transformed market. After market transformation goals are defined, the SGIP PAs should perform targeted outreach to likely adopters of DG and energy storage technologies. For CHP, gas bill data can be leveraged to identify customers with high heating loads. Similarly, wind resource data can be utilized to identify areas on the urban-rural boundary where behind-the-meter wind turbines can be installed. Finally, biogas producers should be identified, especially those facilities that would
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otherwise vent biogas to the atmosphere as they provide the largest potential for greenhouse gas reductions. 7. The ability for DG and energy storage technologies to “take off” requires buy-in from the investor community. In order to create investor confidence, potentially conflicting policies (such as DG and energy storage incentives and standby charges) should be reviewed to ensure alignment toward a strategic goal.
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The primary goal of this study is to identify the extent to which the SGIP is transforming California’s distributed generation and energy storage markets. Based on the findings, this study is also to provide recommendations on ways that the SGIP can help further support DG and energy storage market transformation in California. In addition to the overall goal, this study has seven research objectives: » Characterize the market for SGIP technologies in California; » Identify specific DG/energy storage market barriers and market strengths; » Identify and describe SGIP and other DG/energy storage policy interventions; » Assess the effects of the SGIP and other policy interventions in reducing identified markets barriers and in supporting the transformation of the SGIP—eligible technology markets toward self- sufficiency; » Establish and analyze indicators, or metrics, that reflect the evolution of the distributed energy markets toward self-sufficiency; » Assess sustainability of these markets in California in the absence of the SGIP; and, » Identify and recommend changes to the SGIP program to support further development of these markets in California. This section provides a summary of the results with respect to the overall goal and research objectives.
Extent to which SGIP is Transforming California’s DG and Energy Storage Markets In order to assess the extent to which the SGIP has helped transform California’s DG and energy storage markets, we need a definition of what constitutes a transformed DG and energy storage market. We found no existing definition or characterization of transformed DG and energy storage markets. This is not surprising because the SGIP was not created as a market transformation program. Established in 2001 in response to California’s electricity crises of 2000, the SGIP was primarily focused on developing distributed generation projects as a means to help offset peak demand at utility customer sites. Market transformation first showed up as one of four goals of the SGIP in a 2011 decision by the CPUC.1 In addition, a program theory and logic model had not been developed for the SGIP. Program theory and logic establishes clear expectations about what constitutes the transformed markets, what barriers are preventing transformation of the market and the interventions needed to overcome the barriers. The program logic also can identify desired outcomes associated with transforming the market and activities and outputs that can help market transformation move forward.
1 In decision D.11-09-015 (“Decision Modifying the Self-Generation Incentive Program and Implementing Senate Bill 412,” September 8, 2011), the CPUC established these goals as the “Statement of Purpose” for the SGIP.
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The CPUC has provided guidance on defining transformed energy efficiency markets. We adapted that definition to develop the following definition of transformed DG and energy storage markets: …long-lasting sustainable changes in the DG/energy storage market achieved by reducing barriers to the adoption of DG/energy storage technologies and effecting structural changes in the DG/energy storage market and in the behaviors of market actors such that the market can be sustained without publicly-funded intervention. Establishing SGIP Program Theory and Logic In order to continue forward progress of the SGIP in transforming California’s DG and energy storage markets, we developed a preliminary program theory and logic model for the program. Figure 2-1 shows the preliminary program theory and logic diagram developed for the SGIP.
Figure 2-1: Preliminary SGIP Program Theory and Logic Diagram
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Based on our adopted definition of transformed DG and energy storage markets, we propose that transformed DG and energy storage markets will have the following key elements: » There will be no significant barriers preventing utility customers and utilities from routinely using DG and energy storage technologies as part of their energy solutions; » Changes in market operation along with performance and cost improvements will allow DG and energy storage to be adopted without incentives; » The market will encourage development and adoption of even more efficient DG or energy storage technologies, services and solutions into the market; » The DG and energy storage markets will continue to operate and grow even if public interventions are modified, refocused or reduced;2 and, » The DG and energy storage markets will develop in ways to increase environmental benefits, sustain employment and lead to increased job creation. Based on interviews with past and current SGIP Program Administrators, host customers, key market players, and stakeholders, we propose the following long term outcomes for the SGIP to help transform California’s DG and energy storage markets: » DG and energy storage markets grow without incentives while policies affecting the markets are aligned to help support continued growth; » DG and energy storage make up a significant percentage (e.g.,15%) of California’s electricity mix3; » DG and energy storage help provide valuable environmental benefits that include reductions in criteria air pollutants, improved water quality and land use and reductions in GHG emissions; and, » DG and energy storage markets contribute to sustained employment and new job creation. The complete set of analyses on program theory and logic is contained in Section 5 of the report. Assessing SGIP’s Influence In spite of the lack of existing market transformation goals, we found the SGIP has had a significant impact on the growth of customer-sited DG resources in California and appears poised to have a significant impact on the growth of customer-sited energy storage. Among the impacts are the following: 1. Since the inception of the SGIP in 2001, the growth in customer-sited combined heat and power (CHP) has increased from 4 MW in 2001 to nearly 290 MW by the end of 2014. California’s contribution to customer-sited CHP is significantly greater than that found in other states with CHP programs. For example, New York has the second largest amount of CHP installed next to
2 This characterization comes from Ken Keating’s guidance to the CPUC on transforming the energy efficiency market. See Keating, Ken, “Guidance on Designing and Implementing Energy Efficiency Market Transformation Initiatives,” October 13, 2014, page 2 3 Note that if DG makes up all of the 12,000 MW by 2020 goal of Governor’s Brown Clean Energy Jobs Plan, the 6500 MW goal by 2032 for CHP reaches 3000 MW by 2020, and 200 MW of customer-sited storage is achieved by 2024, this would be represent over 30% of California’s peak demand in 2014 of 45,089 MW. It would also represent approximately 19% of the 78,865 MW of California’s in-state electricity generation capacity installed as of the end of 2014 (see http://energyalmanac.ca.gov/electricity/electric_generation_capacity.html).
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California, but New York’s CHP capacity represents only 43% of the CHP capacity installed in California. 2. Historically, there has been limited growth in distributed wind in the SGIP. However, since 2010, there has been an increase in distributed wind capacity installed under the SGIP. In comparison to eight other states examined, California (with approximately 25 MW of distributed wind) ranks second only to Massachusetts for the amount of distributed wind generation capacity installed since 1999. 3. Based on EPA biogas capacity data, California ranks first for installation of distributed biogas generation between 1999 and 2014. Helped by incentives provided by the SGIP, growth in onsite biogas generating capacity in California increased from less than 100 kW in 2002 to more than 32 MW by 2014. 4. Growth in advanced energy storage (AES) has been rapid under the SGIP. Since 2010, over 10 MW of customer-sited energy storage has been installed under the SGIP, and there are over 220 AES projects currently in the SGIP queue. We also sought to determine the extent of SGIP’s influence on DG market growth by comparing it to similar DG support programs operated elsewhere in the country. We statistically compared growth of CHP, wind and biogas capacity in California against growth of those same technologies in eight other states (New York, Massachusetts, Illinois, Wisconsin, Michigan, New Jersey, Pennsylvania and Illinois). Due to the lack of consistent energy storage data among the different states, we were not able to investigate the impact of the SGIP the growth of energy storage. However, with respect to CHP, wind and biogas, we found the following: 1. Modeling shows that SGIP led to a statistically significant increase in the installed capacity of CHP in California from 2002 to 2007. The statistical model found that local financing programs also had a positive impact on installed CHP capacity. In contrast, net energy metering (NEM) policies were found to result in a decrease in CHP. We theorize that implementation of a NEM policy that does not include CHP may lead to preferential investment in NEM resources. This preferential investment occurs because of the opportunity to recognize financial benefits resulting by netting excess generation. For example, implementation of NEM could lead to more investment in photovoltaics (PV) and a reduction in investment in CHP due to NEM improving the relative cost effectiveness of PV. 2. Growth in distributed wind and biogas generation in California appears to track SGIP funding. However, statistical modeling was not able to produce stable estimates of the impact of SGIP on wind or biogas capacity beyond the effects of other programs and policies. The full set of analyses on assessing SGIP’s influence on DG market transformation is contained in Section 7 of the report.
Characterizing California’s DG and Energy Storage Markets In characterizing California’s DG and energy storage markets, we provide estimates of the amount of DG or energy storage capacity that could be installed in different market segments to meet the energy needs of that market segment. We estimate technical, economic and market potentials. The full set of results on market characterization are presented in Section 6.
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California’s Combined Heat and Power (CHP) Market California has approximately 24 GW of technical potential associated with new CHP smaller than 5 MW in capacity. Table 2-1 summarizes the CHP technical potential by market segment and investor owned utility (IOU) service territory.
Table 2-1: 2014 New CHP Technical Potential (MW) by Segment and IOU Segment PG&E SCE SDG&E TOTAL Percent College 54.7 106 12.79 174 0.72% Food Manufacturing 4,321 3,278 280 7,879 32.51% Food Store 988 1,489 294 2,771 11.43% Health, Hospital 1,120 1,237 222 2,579 10.64% Large Multifamily 360 595 118 1,073 4.43% Lodging, Hotel 644 634 204 1,482 6.11% Office, Large 913 1,169 195 2,277 9.40% Office, Small 52.2 62.38 18.38 133 0.55% Restaurant, Sit-Down 829 1,737 390 2,955 12.19% Retail, Large 539 833 179 1,551 6.40% Small Multifamily 0.00 0.00 0.00 0.00 0.00% School 5.22 5.58 1.49 12.3 0.05% Warehouse 702 570 79.0 1,351 5.57% TOTAL 10,527 11,717 1,992 24,236 100% Percent 43% 48% 8% 100%
Based on a complementary study conducted on SGIP technology cost effectiveness,4 we estimate the economic potential for CHP at 15.5 GW, or 64% of the 24.2 GW of 2014 new technical potential. We use a combination of historical California CHP installation capacity, logistic modeling and a starting assumption of a future market potential in estimating CHP market growth.5 The resulting 2024 market potential is 4.5 GW. Figure 2-2 shows CHP market growth out through 2070 needed to achieve the 4.5 GW target. However, along the way CHP growth hits two important policy targets: the AB32 target of 1,844 MW by 2020 and the Clean Energy Jobs Plan (CEJP) target of 2,594 MW by 2030.6
4 Itron on behalf of PG&E and the SGIP Working Group, “2015 Self-Generation Incentive Program Cost Effectiveness Study,” October 2015; final report pending release 5 Our method is a hybrid Delphi approach wherein we start with target market potentials based on previous potential studies, develop market growth curves using the historical CHP capacities and calculate annual CHP growth rates. We then assess the resulting average annual growth rate against that of other technologies to determine if the growth rate appears reasonable. If not we adjust the future market potential and recalculate the growth rates. 6 The AB 32 CHP target for CHP is 4,000 MW by 2030 while Governor Brown’s Clean Energy Jobs Plan targets 6,500 MW of CHP by 2030. New CHP additions in California under both AB 32 and the CEJP will include capacities greater than the 5 MW upper limit examined in this study. To estimate the portion of the 4,000 MW and 6,500 MW of new additions from AB 32 and the CEJP that will be in the 30-5000 kW capacity range, we consider proportions among new capacity in CA since 1999. About 30% of this new capacity since 1999 has been in the 30-5000 kW range. Consequently, for the AB 32 portion, we estimate that 30% or 1,200 MW of the 4,000 MW target from AB 32 and 30% or 1,950 of the CEJP 6,500 MW target will be in the 30-5000 kW range. When added to the 2014 existing 644 MW of capacity the target is 1,844 for the AB 32 target and 2,594 MW for the CEJP target in the 30-5000 kW range.
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Figure 2-2: Growth in Cumulative CHP Capacity toward 2024 Market Potential
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California’s Wind Market We estimate California’s wind technical potential for wind turbines of less than 5 MW in capacity at approximately 16 GW as shown in Table 2-2 below.
Table 2-2: Technical Potential Wind Capacity with GCF > 35% by County (MW) Potential Capacity Potential Capacity County (MW) County (MW) Alameda 33 Orange 68 Alpine 142 Placer 36 Amador 4 Plumas 105 Butte 22 Riverside 1,788 Calaveras 3 Sacramento 0 Colusa 10 San Benito 7 Contra Costa 24 San Bernardino 4,451 Del Norte 107 San Diego 786 El Dorado 58 San Francisco 1 Fresno 103 San Joaquin 4 Glenn 4 San Luis Obispo 56 Humboldt 268 San Mateo 27 Imperial 374 Santa Barbara 596 Inyo 1,698 Santa Clara 1 Kern 1,378 Santa Cruz 1 Kings 1 Shasta 246 Lake 24 Sierra 77 Lassen 192 Siskiyou 448 Los Angeles 1,127 Solano 139 Madera 33 Sonoma 27 Marin 43 Stanislaus 5 Mariposa 4 Sutter 6 Mendocino 73 Tehama 64 Merced 62 Trinity 83 Modoc 177 Tulare 125 Mono 498 Tuolumne 76 Monterey 65 Ventura 375 Napa 6 Yolo 16 Nevada 19 Grand Total 16,166
However, based on cost effectiveness results and assuming that distributed wind generation is located on the customer side of the meter, we estimate California’s distributed wind economic potential at approximately 9.8 GW. In assessing California’s wind market potential, we investigated different average annual growth rates. Figure 2-3 shows cumulative California wind capacity assuming an average annual growth rate of 20%. Under that scenario, cumulative wind capacity reaches approximately 1.2 GW by 2038. This represents
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over 12% of the statewide economic potential estimated earlier assuming installation of 1.5 MW wind turbines at representative businesses.
Figure 2-3: Wind Market Potential at 20% Average Annual Growth Rate
California’s Biogas Market California’s biogas market is derived primarily from biogas generated at landfills, dairies and wastewater treatment facilities. Overall, we estimate the technical potential of California’s biogas market to be over 720 MW and the economic potential to be over 660 MW. We found that California has over 450 MW of untapped landfill gas technical potential and a little over 400 MW of that capacity is a cost effective resource. Table 2-3 is a summary of the technical and economic potential for landfill gas in California.
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Table 2-3: Landfill Gas Technical and Economic Potential by County
Total Economic Potential Total Technical Potential County Capacity (MW) Capacity (MW) Los Angeles 185 190 Orange 32 32 San Diego 26 28 Alameda 21 27 Sacramento 18 18 Ventura 18 18 Santa Clara 16 23 San Mateo 13 13 Contra Costa 11 11 Sonoma 10 10 Others 53 88 Grand Total 404 457
California has over 1.8 million dairy cows, making it one of the largest dairy producing states in the country. We found that the statewide technical and economic potential of dairy biogas is 137 MW and 136 MW respectively. Table 2-4 is a summary of California’s dairy biogas technical and economic potential by county.
Table 2-4: Dairy Biogas Economic and Technical Potential by County
Total Number of Total Economic Potential Total Technical Potential County Dairies Capacity (kW) Capacity (kW) Tulare 281 37,251 37,251 Merced 228 21,258 21,258 Kings 119 14,137 14,137 Stanislaus 207 13,837 13,837 Kern 51 12,873 12,873 Fresno 79 8,995 8,995 San Joaquin 113 7,918 7,918 Madera 43 6,033 6,033 Imperial 62 3,960 3,960 Riverside 28 3,042 3,042 Sonoma 65 2,148 2,148 Glenn 31 1,287 1,287 Sacramento 30 1,084 1,084 Humboldt 60 1,037 Marin 25 585 585 San Bernardino 3 398 398 Tehama 11 269 269 Del Norte 7 217 217 Yuba 4 215 215 San Diego 3 148 148 Siskiyou 3 51 Grand Total 1,453 135,654 136,742
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California has over 240 wastewater treatment plants that handle wastewater from communities across the state. We estimate the statewide technical and economic potential for biogas from wastewater treatment plants at 130 MW and 125 MW respectively. Table 2-5 is a summary of the technical and economic potential for biogas from California’s wastewater treatment plants.
Table 2-5: Wastewater Treatment Plants Biogas Economic and Technical Potential by County
Total Economic Total Technical County Number of Sites Potential (kW) Potential (kW) Los Angeles 8 33,258 33,305 Orange 6 11,420 11,510 San Diego 6 9,895 10,173 Santa Clara 4 9,533 9,533 Sacramento 3 8,282 8,291 Alameda 7 6,732 6,732 Riverside 13 6,432 6,705 San Bernardino 10 4,354 4,476 Fresno 4 3,252 3,341 San Francisco 2 2,927 3,003 Contra Costa 4 2,704 2,965 Kern 7 2,263 2,552 Ventura 8 2,459 2,459 San Joaquin 4 2,352 2,352 Stanislaus 2 2,057 2,235 San Mateo 7 2,107 2,186 Solano 4 1,714 1,786 Santa Cruz 2 1,328 1,328 Others 45 12,196 15,194 Grand Total 146 125,264 130,127
We establish a biogas market potential based on a logistic model using historical biogas installation capacities and a starting assumption that landfill and wastewater biogas growth achieves 40% of their economic potential and diary biogas growth achieves 20% of its economic potential. This corresponds to an overall market potential of 268 MW and is geared toward achieving the 250 MW target set out by SG 1122. Figure 2-4 shows the corresponding biogas market growth rate. The peak annual growth rate is 22 MW in 2019. The average annual growth rate in capacity is 17% whereas the cumulative generation growth rate is about 34%. Note that the SB 1122 target of 200 MW is hit by 2030.7
7 SB 1122 has an overall target of 250 MW. However, 50 MW of the target is associated with biomass residues from forestry residues. Because we are only considering biogas from landfills, wastewater treatment facilities and dairies, we reduced the target accordingly to 200 MW. Note that SB 1122 discusses biogas from “municipal organic waste diversion.” Due to the manner in which landfill gas to energy projects are designed and operated, we believe this refers to landfill biogas potential that has not yet been recovered.
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Figure 2-4: California’s Biogas Market Growth Forecast
California’s Advanced Energy Storage Market Technical potential for customer-sited AES exists primarily for customer energy management, either through reduced peak demand charges or energy arbitrage (shifting energy consumption to lower bill periods). Using a bottom up analytic approach similar to that employed for CHP, we estimate technical potential for AES in California at close to 19 GW. Table 2-6 in California breaks out the potential by IOU and market segment.
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Table 2-6: AES Technical Potential Based on Maximum Power by Market Segment and IOU
Segment PG&E (MW) SCE (MW) SDG&E (MW) Total (MW) Percent College 52 92 2 146 0.78% Food Manufacturing 17 15 1 32 0.17% Food Store 271 330 16 618 3.28% Health, Hospital 193 214 8 415 2.20% Large Multifamily 209 304 12 525 2.78% Lodging, Hotel 123 120 5 249 1.32% Office, Large 698 1,007 32 1,737 9.21% Office, Small 208 252 13 473 2.51% Restaurant, Sit-Down 368 473 16 857 4.55% Retail, Large 655 935 49 1,639 8.70% School 70 102 4 176 0.94% Single Family 4,845 4,356 273 9,474 50.25% Small Multifamily 879 895 39 1,813 9.62% Warehouse 284 404 10 698 3.70% Total 8,873 9,500 481 18,854 100% Percent 47.06% 50.39% 2.55% 100%
Based on cost effectiveness results from a complementary study on SGIP technologies, we estimate the economic potential of AES at approximately 5.3 GW. Table 2-7 provides estimates of California’s AES economic potential broken down by IOU and market segment.
Table 2-7: Economic Potential for AES by Market Segment and IOU by 2024 Sector PG&E (MW) SCE (MW) SDG&E (MW) Total (MW) Percent College 23 27 6 55 1.04% Food Manufacturing 9 8 1 18 0.34% Food Store 125 144 27 296 5.59% Health, Hospital 124 134 27 285 5.38% Lodging, Hotel 84 84 31 198 3.74% Office, Large 805 1,263 254 2,322 43.79% Office, Small 213 269 55 538 10.14% Restaurant, Sit-Down 174 224 50 449 8.46% Retail, Large 219 426 66 711 13.41% School 29 38 7 73 1.38% Warehouse 135 197 24 357 6.73% Grand Total 1,942 2,814 548 5,303
Percent 36.61% 53.06% 10.33%
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The market potential for AES is a fraction of economic potential. An EPRI energy storage potential study8 stated that “Although it is difficult to predict how consumers will respond to the relatively new energy storage technologies, the analysis assumes that the mid-range of the energy efficient product adoption rate (35%) is a reasonable proxy for direct customer adoption of energy storage systems.” We therefore assumed market potential to be 35% of economic potential or 1,856 MW as our starting assumption in estimating future market potential of AES. Using the 1,856 MW as our starting estimated market potential, we used logistic modeling and historical energy storage installations to calculate annual AES growth rates in the future. Figure 2-5 depicts the market growth forecast for AES associated with meeting a market potential goal of nearly 1.9 GW. This market growth shows AES meeting the 2024 AB 2514 goal for behind the meter energy storage 4 years early—in early 2020.
Figure 2-5: AES Market Growth Forecast
8 Rastler, D, Electricity Energy Storage Technology Options. A White Paper Primer on Applications, Costs, and Benefits, EPRI, 1020676, December 2010; This study based market potential on technical not economic potential. However, the May 12, 2008 California Energy Efficiency Potential Study found market sizes of 27 to 41% of economic potential; in the same range as the 35% used here.
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Overcoming Market Barriers California has significant untapped economic and market potential for DG and energy storage technologies. In order to find out barriers to achieving these potentials, we interviewed past and current SGIP Program Administrators, CPUC staff involved with the program, companies that manufacture and develop DG and energy storage projects, customers who have either participated in the SGIP or those who did not participate but may have installed DG or energy storage systems in their facilities or homes, and other stakeholders such as public agencies or non-profit organizations who are involved with implementation or regulation of DG and energy storage technologies. Based on those interviews, we developed key “takeaways” about market barriers and opportunities from the different groups. Key Takeaways from Host Customer Surveys Within the host customer survey, we asked customers for open-ended responses with respect to their perceptions about market barriers to achieving widespread adoption of SGIP technologies as well as recommended changes in the SGIP to help effect increased market transformation of SGIP technologies. Among the more significant responses were the following: » Most customers felt that high upfront costs represent a barrier to widespread adoption of DG and energy storage technologies. They recognized that SGIP incentives helped to lower upfront costs for them but felt it was important for the SGIP to help find ways to help reduce the cost of these technologies in order to achieve widespread market adoption. » Customers noted that most people are not familiar with SGIP technologies, their costs, their performance, or their benefits. Consequently, in order to help grow the DG and energy storage markets, they recommended that SGIP increase program outreach both with respect to SGIP technologies as well as various aspects of the SGIP itself to help educate consumers. » Customers also noted that there can be a great amount of discrepancy in sizing, costing and installing of SGIP technologies. Customers who noted this recommended that the SGIP help develop standardization in configurations and installations of SGIP technologies. They believe this standardization would help reduce costs and improve customer comfort level in adopting the technologies. There were also recommendations that the SGIP help screen or pre-qualify contractors who install technologies under the program to help ensure high quality services. The expectation from host customers is that contractors associated with installing SGIP technologies provide them with independent and honest assessments. » A number of customers also stressed that streamlining the incentive and utility processes are important in enabling higher adoption of SGIP technologies. Key Takeaways from Manufacturer and Project Developer Interviews Manufacturers and project developers are usually responsible for the design of the SGIP technologies employed at host sites. They are also typically responsible for interacting with the utility on obtaining the utility interconnection, working with the PAs on SGIP applications and acquiring any needed permits from regulatory agencies. As such, they interact with a number of stakeholders including the customers
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and have developed perceptions about the SGIP market in California, its barriers and opportunities for market growth. Among the key takeaways from manufacturers and project developers are the following: » Delays and additional costs associated with utility interconnection is universally cited as a primary barrier to expanded adoption of SGIP technologies. » The prospect of helping customers in avoiding demand charges is viewed by the AES industry as one of the biggest value proposition for AES. However, the lack of clarity in NEM policy is stranding the deployment. » Departing load charges, standby charges and non-by-passable charges are the biggest barriers to CHP adoption as they significantly impact project economics and appear to the CHP industry to be an unjustified cost. » For fuel cell manufacturers and project developers, the “zero” criteria pollutant emissions aspects of fuel cells, along with favorable footprint/power density provides them with a significant market opportunity, but requires additional education of customers. In addition, they believe these characteristics coupled with their ability to provide grid support applications including reactive power should make them attractive to utilities. However, high upfront costs remain a barrier. » For wind manufacturers and project developers, the biggest barriers to increased wind growth in California are the difficulties associated with matching customer load with the availability of adequate wind resources, NIMBY attitudes, and restrictions on wind energy within existing NEM policies. Overall PA/CPUC Perspective and Key Takeaways The PAs are responsible for implementing the SGIP and the CPUC is involved in establishing policies on the SGIP. Both the PAs and CPUC have developed perceptions regarding how the SGIP relates to the DG and energy storage markets in California, what barriers need to be overcome to achieve more widespread adoption of the technologies, and opportunities that exist for achieving that more widespread adoption. Nearly all of the PAs felt that there was a large untapped potential for SGIP to help grow the market for SGIP technologies. Similarly, all expressed that there were a number of challenges involved in making progress towards increased market growth but had different opinions about the types of challenges and ways they could be addressed. For example, at least two of the PAs felt that utility tariffs could impede growth of SGIP technologies by under-valuing the benefits provided by SGIP technologies. The PAs also noted that customer perception that SGIP technologies are expensive or “risky” present significant barriers to be overcome to achieve market growth. Some of the PAs also felt that SGIP funding allocation to technologies was not equitable and may have unfairly advantaged some technologies; thereby impeding growth of those technologies. Several of the PAs felt they were taking a more proactive approach in conducting outreach or marketing technology offerings customized to create mutual benefit for both customers and the utility. However, they noted that more could be done to educate customers as well as policy makers as to the benefits of SGIP technologies.
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Key takeaways from the interviews with the PAs and CPUC staff include the following: » While market transformation was not a goal at the start of the SGIP, market development of SGIP technologies has been occurring under the SGIP; » There is a significant untapped potential for SGIP to help grow the market for SGIP technologies; » The lack of customer familiarity with SGIP technology costs and performance has created the perception among customers that SGIP technologies are costly and risky. This perception poses a significant barrier to additional SGIP technology growth; » It would be beneficial to educate customers and policy makers as to the costs, performance and benefits of SGIP technologies; and, » There is a need for alignment of critical state policies on DG and energy storage in order to make forward progress on market transformation of these technologies. To be successful, the overarching policy goal of a transformed DG/energy storage market needs to be presented clearly and with a unified front. This unified front provides clean tech capital investors with faith that policies and regulations will not present uncertainty or barriers to market growth and will enable them to invest in innovative clean technology projects. This consistent alignment of clean tech policies with the subsequent capital investment from the tech capital investment community will ultimately be a key force in transforming the DG and energy storage markets. Results of the various interviews are contained in Section 5 of the report. Survey forms used in the study are contained in Appendix A while host customer survey results are contained in Appendix B.
Lessons Learned In addition to interviewing SGIP PAs and program staff, we also interviewed Program Administrators responsible for similar programs outside of California. Among the PAs interviewed are those responsible for implementing significant DG and CHP programs in the eastern United States (e.g., New York, Massachusetts, New Jersey, etc.). We wanted to find out their perspectives on DG barriers and what lessons they learned in implementing programs and how that may compare to California. Based on the combined interviews conducted earlier and with our interviews with PAs outside of California, we developed the following list of learned lessons: 1. In setting out to transform a market, it is important to have defined the characteristics of the transformed market, and to have quantitative goals. The quantitative goals help define successful transformation of the market and allow measurement of progress towards the goals. 2. Addressing unmet market and customer needs are critical in obtaining the widespread market adoption needed to transform the DG and energy storage market. Customer use cases help identify customer energy usage and needs by classes of customers. In selecting how DG and energy storage technologies are provided through the program, it is important to match technologies to customer use cases. 3. Market growth of DG and energy storage will occur more readily when the technologies achieve customer needs, utility needs and technology provider needs. DG and energy storage technologies have the potential to help utilities provide safe, reliable and cost effective energy to their customers. Under the SGIP, the utilities are tasked with managing the program, rather than helping develop DG and energy storage technologies in a partnership arrangement with
SUMMARY OF RESULTS | 2-16 SGIP Market Transformation Report
technology providers. This partnership can result in economic benefits to the customer, the utility and the technology provider and help accelerate market adoption of DG and energy storage technologies. 4. Program Administrators should play a significant role in the strategic planning of how to implement market transformation of DG and energy storage technologies. Because of their market connection to utility customers and understanding of utility operations, the PAs bring a unique perspective on the role of DG and energy storage in a transformed market. 5. Technology reliability is important in how prospective program applicants view installing a technology and so affect how technologies are adopted in the market. In the Northeast, the PAs used a prescriptive catalog approach for CHP technologies that reduced risk, helped streamline the application and implementation processes and helped reduce costs. At the same time, this approach needs to be configure in a way to support growth of emerging technologies that could help achieve market transformation. 6. Successful market transformation involves more than addressing economic barriers. Market barriers must be taken into account and alignment of state policies to promote DG and energy storage is very important. As one respondent pointed out, the correct alignment of policies that provides investors with faith in the market is ultimately what moves market transformation forward.
SUMMARY OF RESULTS | 2-17 3 INTRODUCTION
3.1 Purpose and Objectives of this Study The Self Generation Incentive Program (SGIP), established in 2001 to respond to California’s electricity crisis of 2000-2001, and to help encourage development of distributed generation.1 Since its inception in 2001, the SGIP has provided incentives to a wide variety of distributed generation (DG) and other distributed energy technologies including wind turbines, fuel cells, gas turbines, microturbines, internal combustion (IC) engines, waste heat to power systems,2 advanced energy storage (AES) technologies, and pressure reduction turbines.3 As California’s energy landscape changed over time, the goals and objectives of the SGIP evolved to respond to the new needs and challenges. The SGIP’s goals now include lowering greenhouse gas (GHG) emissions, reducing customer electricity purchases, improving electric system reliability, and supporting market transformation of distributed energy resources (DER).4 A 2011 Cost-Effectiveness study5 provided a qualitative consideration of market transformation effects of the SGIP. However, no other effort had been taken to examine the extent to which the SGIP had influenced the growth and market transformation of California’s DG and energy storage markets.6 Overall Purpose of this Study The overall purpose of this market transformation study is to determine the extent to which the SGIP is transforming California’s DG and energy storage markets. Based on the findings, the study also contains recommendations on ways in which the SGIP can help further support DG and energy storage market transformation in California.
1 Senate Bill 970 (Ducheny), September 7, 2000 legislatively established the SGIP. CPUC Decision D.11-09-015, September 8, 2011 provided direction for implementing the SGIP. See http://docs.cpuc.ca.gov/PublishedDocs/WORD_PDF/FINAL_DECISION/6083.PDF 2 Waste heat to power is the process of capturing heat discarded by an existing industrial process and using that heat to generate power. Waste heat to power technologies can include steam turbines, Organic Rankine Cycle (ORC) and the Kalina Cycle. Up through 2014, the only waste heat to power technology active within the SGIP has been ORC. Consequently, for this study, only ORC systems are treated as waste heat to power technologies. 3 Solar photovoltaic (PV) technologies were originally eligible under the SGIP. However, with emergence of the California Solar Initiative (CSI) and in accordance with Assembly Bill (AB) 2778 (Lieber, September 2006), PV technologies were removed from the SGIP. Instead, incentives for PV technologies are provided under the CSI. 4 In decision D.11-09-015 (“Decision Modifying the Self-Generation Incentive Program and Implementing Senate Bill 412,” September 8, 2011), the CPUC established these goals as the “Statement of Purpose” for the SGIP. 5 Itron on behalf of PG&E and the SGIP Working Group, “CPUC Self-Generation Incentive Program: Cost-Effectiveness of Distributed Generation Technologies,” February 9, 2011. 6 Energy and Environmental Economics (E3) proposed a methodology in 2007 on evaluating market transformation effects of the SGIP. The proposed methodology is similar to that used in this study but does not include market characterization or program theory and logic models. See Energy and Environmental Economics, Inc. “SGIP Market Transformation Effects Evaluation Methodology: Discussion Draft,” May 5, 2007 from http://www.cpuc.ca.gov/NR/rdonlyres/9462081A-B273-4014- B159-66E496107099/0/SGIPMarketTransformation5507.doc
INTRODUCTION | 3-1 Document title goes here
Specific Objectives This study has the following seven objectives: » Characterize the market for SGIP technologies in California » Identify specific DG and energy storage market barriers and market strengths » Identify and describe SGIP and other DG and energy storage policy interventions » Assess the effects of the SGIP and other policy interventions in reducing identified market barriers and in supporting the transformation of the SGIP-eligible technology markets toward self-sufficiency » Establish and analyze indicators, or metrics, that reflect the evolution of the DG and energy storage markets toward self-sufficiency » Assess sustainability of these markets in California in the absence of the SGIP » Identify and recommend changes to the SGIP program to support further development of these markets in California
3.2 Scope The focus of this Market Transformation Study is to understand the influence of the SGIP on DG and energy storage market transformation in California. As such, the timeframe for this study runs from the start of the SGIP in 2001 through the program’s currently planned end date of 2020. Technologies being examined include a subset of technologies eligible under the SGIP as of 2014, which includes wind turbines, fuel cells, gas turbines, microturbines, IC engines7 and AES technologies.8 The study examines renewable as well as nonrenewable energy resources. Solar PV technologies were originally an eligible technology and make up a significant portion of California’s DER market. However, solar PV ceased being an SGIP-eligible technology at the start of 2007 and is not examined in this study.9 In addition, the market transformation effects associated with California’s solar PV market has been treated elsewhere.10 Focus on SGIP but Comparisons to Other DG Incentive Programs While the focus on this study is on California, we also examine the growth of DG technologies in other states with DG programs. We specifically examine programs active in Connecticut, Illinois, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Wisconsin. By examining DG growth in other states
7 Fuel cells, microturbines, internal combustion engines, and gas turbines can be operated in either electric only mode or in a combined heat and power configuration. 8 Although waste heat to power and pressure reduction turbines are technologies eligible under the SGIP, they are not examined in this study due to the limited number of these projects that have been installed under the program. In particular, there were only 10 active WHP and PRT projects in the SGIP by the end of 2014, only two of which have received incentive payments. 9 Solar PV technologies were originally eligible under the SGIP. However, with emergence of the California Solar Initiative (CSI) and in accordance with Assembly Bill 2778 (Lieber, September 2006), PV technologies were removed from the SGIP. Instead, incentives for PV technologies are provided under the CSI. 10 Navigant for the California Public Utilities Commission, “California Solar Initiative Market Transformation Study (Task 2),” March 27, 2014.
INTRODUCTION | 3-2 Document title goes here
and programs, our intent is twofold: 1) to isolate the influence of federal program interventions on DG market transformation, and 2) compare and contrast effects of DG market transformation in California against that occurring in other states. By comparing and contrasting DG market growth in California versus other states, we can quantitatively ascribe the influence of the SGIP on transforming California’s DG markets.11
3.3 Defining Distributed Energy Resources within the Context of the SGIP The California Public Utilities Commission (CPUC) decision in September of 2011 set forth the goal that the SGIP should help transform California’s distributed energy resource (DER) markets. In that same September 2011 decision, the CPUC established six guiding principles for the SGIP. Among the guiding principles was the statement “the SGIP should support behind the meter ‘self-generation’ DER technologies, which serve the primary purpose of offsetting some or all of a host-customer’s on-site demand.” DER technologies can include DG, energy storage, energy efficiency and demand response systems. In accordance with the CPUC ruling and for the purposes of this study, we treat the combination of small- scale (i.e., less than 5 MW in rated capacity)12 DG and energy storage technologies as the DERs that form the focus of SGIP market transformation activities. We specifically concentrate on the extent to which the SGIP has helped shape and influence the transformation of DG and energy storage markets in California. In the rest of this section, we provide an overview of DERs in California and examine the role of the SGIP in supporting market transformation of DG and energy storage. We also define what we mean by market transformation and discuss the complementary roles played by policies and programs in supporting market transformation. In the following sections, we address the research questions to be answered with respect to characterizing California’s DG and energy storage markets. These questions include the strengths and weaknesses of the markets; the role of the SGIP and other policy interventions in helping transform the DG and energy storage markets; the extent to which the SGIP has helped transform the markets; indicators that can be used to mark the evolution of the DG and storage markets toward self-sufficiency and the ability of the markets to be self-sustaining in the absence of the SGIP; and lastly, if there are modifications to the SGIP to further support development of a sustainable California DG and energy storage markets.
3.4 California’s Programs for Distributed Energy Resources California has a number of public purpose energy programs that address key energy issues facing the state. The programs provide support to different energy technologies or measures as well as provide support to different market segments. Support provided by public purpose programs range from
11 We initially intended to look at other states to see if we could isolate the influence of the SGIP on the growth of energy storage. However, the energy storage market is still nascent and we were unable to find enough energy storage installation data in other states to be able to conduct a comparative analysis. As such, we limited our investigation to DG technologies. 12 In an effort to be responsive to the guiding principle focused on “behind the meter” applications, we selected a size limit of 5 MW as a reasonable upper limit of DG and storage technologies that could be located behind the meter.
INTRODUCTION | 3-3 Document title goes here
assistance to low-income utility customers (for example, in the form of rate discounts under the California Alternate Rates for Energy program) and energy efficiency and conservation programs targeting such households to broader support for energy efficiency, renewable energy and DER programs. Several programs were established in California to help support development of DG and renewable energy technologies within the state. Examples of some of the earliest programs are the California Energy Commission’s Emerging Renewables Program (ERP), the Biomass Demonstration Program and the Self Generation Incentive Program. More recently established programs include the California Solar Initiative. Assembly Bill 970 established the SGIP in 2001 to address peak electricity demand issues facing California’s grid following the state’s electricity crises of 2000. The SGIP provides incentives to DG and combined heat and power (CHP) systems located at primarily commercial or small industrial sites of utility customers.13 Together, the SGIP and the ERP provided complementary coverage14 of DG technologies to small and medium-sized utility customers and helped provide a broader base of support for what would become California’s growing DER market.
3.5 Overview of California’s Distributed Energy Resources DERs make up a small but growing amount of customer-based power resources in California. At the end of 2014, there were over 215,000 DER projects,15 responsible for over 2,500 MW of customer-based generation.16 Table 3-1 is a summary listing of estimated DER projects and capacity located on the customer side of the meter in California at the end of 2014.
13 The SGIP was established in response to Assembly Bill (AB) 970, which required the California Public Utilities Commission (CPUC) to initiate certain load control and distributed generation program activities. The CPUC issued Decision 01-03-073 (D.01-03-073) on March 27, 2001 outlining provisions of a distributed generation program, which became the SGIP. 14 In general, the SGIP focused on nonresidential customers while the ERP focused on residential customers. 15 This number represents statewide DER projects; not just SGIP projects. 16 The SGIP has continued to grow. However, we use December 31, 2014 as a starting baseline in this study.
INTRODUCTION | 3-4 Document title goes here
Table 3-1: DER Projects and Capacity in California at 12/31/14
PG&E SCE SDG&E POU17 STATEWIDE Technology No. MW No. MW No. MW No. MW No. MW Solar PV18 84,413 950.7 78,816 765.3 23,850 206.1 27,582 228.3 214,661 2,150.3 Wind 12 8.6 9 15.1 1 1.0 0 0.0 22 24.7 Renewable 66 25.8 42 25.7 16 8.5 8 4.9 132 65.0 --Gas Turbines 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 --Microturbines 15 2.1 6 2.5 4 0.8 0 0.0 25 5.4 --IC Engines 17 9.4 10 6.9 2 0.7 0 0.0 29 16.9 --Fuel Cells 34 14.4 26 16.3 10 7.1 8 4.9 78 42.7 Nonrenewables 255 102.8 206 109.5 54 28.6 37 18.7 552 259.6 --Gas Turbines 3 4.0 4 17.0 2 9.1 0 0.0 9 30.1 --Microturbines 49 10.8 52 8.9 13 1.1 9 2.2 123 23.0 --IC Engines 104 57.4 100 70.5 21 12.1 7 3.0 232 143.1 --Fuel Cells 99 30.6 50 13.1 18 6.2 21 13.4 188 63.4 AES 30 2.9 4 1.1 2 0.0 26 0.1 62 4.2 WHP 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 PRT 0 0.0 0 0.0 1 0.5 0 0.0 1 0.5 Totals 84,776 1,091 79,077 917 23,924 245 27,653 252 215,430 2,504
3.6 Overview of SGIP California’s SGIP provides support to distributed generation and storage systems located on the customer side of the electricity meter. Funded by California ratepayers, the SGIP is managed by Program Administrators (PAs) representing California’s major investor owned utilities.19 The California Public Utilities Commission (CPUC) provides program oversight. Incentives are provided under the SGIP to participating customers to help offset the cost of buying and installing SGIP technologies. By the end of 2014, the SGIP had provided over $530 million in incentives to more than 769 projects representing more than 354 MW of rebated generating capacity. Consequently,
17 POU refers to publicly owned utility (e.g., Sacramento Municipal Utility District, Los Angeles Department of Water and Power, etc.) 18 Solar PV estimates include completed projects from California Solar Initiative projects (http://www.californiasolarstatistics.ca.gov/media/public_files/working_sets/WorkingDataSet_1-01-2014.zip); Single- Family Affordable Solar Homes (SASH) sites are apportioned to each IOU based on each IOU’s CSI general market share. Also included are Self-Generation Incentive Program (SGIP), New Solar Homes Partnership (NSHP) and Emerging Renewables Program (ERP) totals from SGIP impact reports and the CSI Go Solar database, and Publically Owned Utilities as reported to the California Energy Commission http://www.energy.ca.gov/sb1/pou_reports/Publicly_Owned_Utilities_Report_Summary_01-01-2013_to_12-31-2013.xls). Note these totals do NOT include systems installed outside of incentive programs since data for those systems is not yet readily available without NDA’s from the Investor Owned Utilities. 19 The Program Administrators are Pacific Gas & Electric (PG&E), Southern California Edison (SCE), Southern California Gas Company (SCG) and the Center for Sustainable Energy (CSE), which administers the program in the San Diego Gas & Electric (SDG&E) service territory.
INTRODUCTION | 3-5 Document title goes here
SGIP technologies make up approximately 14% of the state’s total DER capacity and approximately 16% of the DER capacity located in IOU service territory.
3.7 Defining Market Transformation Market transformation has been defined in a variety of ways. Within the context of the energy efficiency market, the CPUC defined market transformation in a 2009 decision as follows:
Market transformation is long-lasting, sustainable changes in the structure or functioning of a market achieved by reducing barriers to the adoption of energy efficiency measures to the point where continuation of the same publicly-funded intervention is no longer appropriate in that specific market. Market transformation includes promoting one set of efficient technologies, processes or building design approaches until they are adopted into codes and standards (or otherwise substantially adopted by the market), while also moving forward to bring the next generation of even more efficient technologies, processes or design solutions to the market.20 More recently, Keating and others have defined market transformation as efforts…
…designed to induce sustained increases in the adoption and penetration of energy efficient technologies and practices through structural changes in the market and in behaviors of market actors.21 Schiller describes market transformation in a similar means as…
…long-lasting sustainable changes in the structure or functioning of a market achieved by reducing barriers to the adoption of energy efficiency measures to the point where further publicly-funded intervention is no longer appropriate.22 For this report, we adopt a modified definition of market transformation that targets DG and storage:
Long-lasting sustainable changes in the DG/storage market achieved by reducing barriers to the adoption of DG/storage technologies and effecting structural changes in the DG/storage market and in the behaviors of market actors such that the market can be sustained without publicly- funded intervention. It is important to note that market transformation goals have not been established for the SGIP. When the program was established in 2001, it was in response to the electricity crises of 2000-2001. Consequently, the SGIP was primarily established to help meet peak demand through the use of distributed generation resources. It was only in 2011 that market transformation became a goal of the SGIP.23
20 California Public Utilities Commission Decision D.09-09-047, September 24, 2009, Pg. 89 21 Keating, Ken, for the CPUC, “Guidance on Designing and Implementing Energy Efficiency Market Transformation Initiatives,” October 13, 2014 22 Schiller, Steve, Presentation to the Kentucky Public Service Commission, “Market Level Evaluations,” September 11, 2009 23 In CPUC Decision 11-09-015 (September 8, 2011), market transformation became one of four goals for the SGIP. The four goals are: 1) reduce GHG emissions in the electricity sector; 2) help with demand reduction and reduce customer electricity
INTRODUCTION | 3-6 Document title goes here
Market Transformation versus Resource Acquisition Programs like the SGIP are similar in nature to resource acquisition efforts typically undertaken in integrated resource planning efforts by utilities. However, market transformation is significantly different from resource acquisition efforts. Resource acquisition programs target participants to the program; look to gain energy and cost savings primarily from program participants; focus almost exclusively on the program; and, are controlled largely by the Program Administrators.24 In contrast, market transformation targets all consumers; seeks to obtain energy and cost savings from the overall market; and is involved in effecting changes in a dynamic market with changing market players, market forces and drivers. In assessing the role of the SGIP in helping transform California’s DG and energy storage markets, consideration must be given to barriers and opportunities that extend to the entire California DG and energy storage markets. Complementary Nature of Program Drivers, Policies and Market Players in Market Transformation Given that a single program by itself cannot transform a market, we need to examine what combinations of actions lead to a transformed market. Market transformation programs are about strategic partnerships with market players seeking similar goals for their own purpose. These market players are, in turn, influenced by market conditions and policies. Market transformation occurs when there is a convergence of market conditions, policies and market players aligned in a way that market barriers are addressed effectively. In this study, we look at the policies, market conditions and the role of market players within California’s DG and energy storage markets stretching from the start of the SGIP to 2014 with an eye to how these factors complemented or acted in opposition to one another. We overlay SGIP- specific policies and actions on the market events as a way to help visualize SGIP’s role in these events.
3.8 Report Organization This report is organized into seven sections and five appendices as described below: » Section 1 provides an executive summary of the purpose, key findings and recommendations. » Section 2 summarizes results from the report and addresses the primary goal and research objectives of the study. » Section 3 is this introduction, which provides an overview of the SGIP and DER in California, defines market transformation, and identifies the scope, purpose and objectives of the study. » Section 4 describes the approach in linking market transformation program logic and theory to past and current activities in California’s DG and storage markets, which includes characterizing California’s DG and energy storage markets, collecting information on market impacts and market players, and methods assessing the sustainability of California’s DG and energy storage markets.
purchases; 3) improve electricity system reliability through improved transmission and distribution system utilization and 4) help with market transformation of distributed energy resources. 24 Keating, op cite, page 4.
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» Section 5 lays out the program logic and theory for assessing market transformation of the California DG and energy storage markets. The section presents how we use program theory to make a quantitative assessment of the impact of the SGIP on DG market transformation; including a discussion of statistical treatment to isolate the effect of SGIP. » Section 6 characterizes California’s DG and energy storage markets including a substantive look at California’s markets by technology and market segment. Estimates include the technical, economic and market potential of DG and energy storage markets at different time periods. Market characterization also includes identification of important market trends taking into account market interventions at the federal and state level and SGIP-specific policies and DG support mechanisms. » Section 7 presents a discussion on the extent to which the SGIP has influenced California’s DG market transformation. Indicators identified in Section 5 (program theory and logic) and statistical methods are used to quantify the possible influence of the SGIP on DG market transformation. » Appendix A contains the survey forms prepared for collecting market information from DG and energy storage equipment manufacturers and project developers, other key industry stakeholders and PAs as well as over the phone and internet surveys with participants and nonparticipants. » Appendix B consists of the aggregated host survey results resulting from the surveys conducted in the study. » Appendix C provides a consolidated listing of the policies and regulations cited in the study as influencing California’s DG market transformation. » Appendix D is a mapping of 2012 Census data NAICS codes using from 2-digit to 6-digit width to corresponding SitePro building types. » Appendix E provides additional information on the approach used in developing CHP technical and economic potentials.
INTRODUCTION | 3-8 4 APPROACH
This section describes the approach and methodology behind how we assess if California’s DG and energy storage markets are being transformed and the role played by the SGIP in transforming the markets. We start by defining program theory and logic models. Program theory and logic models are used in linking assumptions between causes of market barriers and expectations of how market interventions will resolve the barriers to produce a transformed DG/energy storage market. The program theory and logic model results are presented in Section 5. We then characterize the California DG and energy storage markets using estimates of technical, economic and market potentials. These estimates provide upper level estimates of the size of California’s DG and energy storage markets by market segment and by IOU service territory. By comparing estimated capacities of DG and energy storage installed in California, we are able to identify the progress of market development. We also examine the growth of DG and energy storage capacity in California in context of changes in policies or market conditions that may have influenced the growth of these technologies. The technical, economic, and market potential results are presented in Section 6. In order to understand the role of market interventions better, we also interviewed a number of key players in the DG and energy storage markets as well as Program Administrators responsible for implementing DG and energy storage incentive programs. We describe our approaches in collecting information from these key stakeholders on their perceptions of California’s DG and energy storage market challenges and opportunities and their views on the role of the SGIP in addressing these challenges and opportunities. The collected interview data is used in developing the program theory and logic model and the results are presented in Section 5. The survey forms and the aggregated host site results are presented in Appendix A and B, respectively. The primary focus of this study is to assess the degree to which the SGIP has influenced DER market transformation.1 However, the SGIP is not the only force influencing market transformation of the California DG market. To determine the impact on market transformation that can be assigned to the SGIP, we use statistical means to isolate effects of the SGIP by comparing growth of DG and CHP technologies in California to DG and CHP growth in other states and programs. We use the statistically determined impact of the SGIP on California’s DG and CHP market growth to estimate what California’s DG and CHP market growth might have looked like in the absence of the program. Results of the statistical treatment are presented in Section 7.
1 As discussed in the introduction, DER is defined for the purposes of this study as DG and energy storage technologies. In addition, we discovered early on in the study that there was insufficient data on energy storage technologies to provide a common database upon which to compare energy storage growth among different states. As such, our interstate comparison was limited to certain DG and CHP technologies.
APPROACH | 4-1 SGIP Market Transformation Report
4.1 Program Theory and Logic Models In deciding if a market is transformed, it is important to identify what the transformed market should look like, how it should operate and how those expectations compare to the existing market. Strategies for transforming the market are then based on addressing the barriers preventing such transformation. Program theory and logic models are tools used in monitoring and evaluating a wide range of programs.2 Program theory describes the underlying assumptions about how a program is expected to operate. Ideally, the theory provides a framework that describes the barriers preventing market growth and how the program or interventions will result in intended outcomes. A logic model is a diagram that lays out the key relationships between the program elements and the problems being addressed by the program. The program logic model links desired or expected program outcomes to “who” delivers the solution, “what” specifically is delivered, “when” it is delivered, and “how” it is delivered. Not only does program logic help visualize the program theory but it also provides a means for measuring success of the program in addressing the market barriers. It is helpful to envision market transformation from the perspective of market growth and market saturation. Figure 4-1 depicts the idealized growth of DG technology in California’s DG market starting at 2001 and continuing to 2020.
Figure 4-1: Linkages between DG Market Growth in California, Drivers and Barriers
2 Megdal and Associates, et. al. for the New York State Energy Research and Development Authority “Using program logic model analysis to evaluate & better deliver what works,” European Council for an Energy Efficient Economy (ECEEE) 2005 Summer Study
APPROACH | 4-2 SGIP Market Transformation Report
This concept is based on market diffusion theory.3 Over time, changes in market conditions, drivers (including market interventions, such as SGIP incentives), barriers, perceptions of the technology by market players and technology changes themselves determine the extent to which the technology grows in the market. Ultimately, the policy goal is to develop a sustainable market that can survive and thrive without market interventions. This goal not only requires growth in the DG and energy storage markets but development of technologies and products that are cost-competitive without subsidies or other market interventions. A complication to assessing the influence of the SGIP on California’s DG and energy storage markets is the absence of an existing program theory. The SGIP was not originally conceived as part of a market theory targeting a sustainable DG/energy storage market. The SGIP was established in 2001 as one of a collection of possible solutions to peak electricity problems facing California.4 Moreover, SGIP goals have changed over time. Since its inception, SGIP program goals have been modified to help address key energy issues. For example, legislation passed in 2003 required SGIP technologies to achieve low levels of nitrogen oxides (NOx) emissions starting in 2007 to help establish ultra-clean distributed generation technologies.5 SGIP’s goals now include lowering GHG emissions, reducing customer electricity purchases, improving electric system reliability, and supporting market transformation of distributed energy resources.6 A program theory and logic model developed now for the SGIP must necessarily take into account these different expectations and goals. The approach in developing a program theory for transforming California’s DG and energy storage markets involves identifying the expectations, and identifying barriers that may prevent market adoption of these technologies and achievement of the market transformation expectations. Examples of assumptions about barriers or market failures include beliefs that DG and energy storage costs are too high; that some policies may be preventing adoption of different DG or energy storage technologies;7 that there is a lack of sufficient market players needed to create forward momentum in the market; or, that there is consumer misperception about the performance or acceptability of DG and energy storage technologies within their lifestyle or business mission. In addition, we establish assumptions regarding drivers and opportunities that could propel forward DG and energy storage technology adoption. Such assumptions could include beliefs that DG and energy storage technologies are attractive to consumers as a means of having additional energy choices; that they represent a hedge against possible volatility in energy costs; or represent a way for utilities to help achieve GHG emission
3 For example, see Michelfelder, R. and Morrin, M. “Overview of New Product Diffusion Sales Forecasting Models,” AUS Consultants, from http://law.unh.edu/assets/images/uploads/pages/ipmanagement-new-product-diffusion-sales- forecasting-models.pdf 4 The Self-Generation Incentive Program (SGIP) was established in response to Assembly Bill (AB) 970, which required the California Public Utilities Commission (CPUC) to initiate certain load control and distributed generation program activities. The CPUC issued Decision 01-03-073 (D.01-03-073) on March 27, 2001 outlining provisions of a distributed generation program, which became the SGIP. 5 Assembly Bill 1865 (Leno), February 21, 2003. 6 In decision D.11-09-015 (“Decision Modifying the Self-Generation Incentive Program and Implementing Senate Bill 412,” September 8, 2011), the CPUC established these goals as the “Statement of Purpose” for the SGIP. 7 For example, AB 2778 enacted in September of 2006 limited SGIP eligibility to fuel cells and wind energy systems
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reduction goals more cost effectively than other options. In establishing assumptions regarding DG and energy storage market barriers and opportunities, we rely on literature, legislation, and CPUC decisions extending from inception of the SGIP up through current times. We also rely on interviews with current and past SGIP Program Administrators, CPUC staff involved with administering the program, and key stakeholders in California’s DER market. Program theory also allows us to view the relationships between the desired outcomes and observed outcomes. These distinctions are important when assigning changes in the market to different market components. Program logic provides linkages between specific program elements (e.g., outreach, incentives, etc.) and expected outcomes that develop in the short-term, near-term, or long-term. Figure 4-2 shows a simplified layout of program logic. The diagram also points out that each stage in the logic layout can be used to explain the relationships between the different activities and expected outcomes.
Figure 4-2: Simplified Program Logic Layout8
In developing program logic diagrams, we take into account that the SGIP is not the only market intervention affecting California’s DG and energy storage markets. However, when considering the role of the SGIP on the markets, we examine the expected role of different elements within the SGIP such as incentives, outreach, program rules, etc. Outcomes can act as metrics toward market transformation. For example, one goal of DG and energy storage market transformation is the emergence of technologies that are cost-competitive in the market without incentives. We use changes in technology costs and cost-effectiveness tests as a way to determine if market transformation is occurring. A robust and sustainable DG and energy storage market also requires adequate delivery lines of products and services. Consequently, we use numbers and business sizes of DG and energy storage technology suppliers as metrics for assessing growth in the DG and energy storage supply infrastructure. Timing of outcomes provides a means to measure progress toward market transformation. In particular, market diffusion provides a way to measure the extent to which DG and energy storage technologies are
8 Braverman, M. “Theory and Rigor in Extension Program Evaluation Planning,” June 2009. From: http://www.joe.org/joe/2009june/a1.php
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approaching market saturation at different times. We evaluate market penetration of DG and energy storage technologies relative to their economic potential. Because market deployment in early stages of market transformation may be slow, we use combinations of metrics to measure progress. Such combinations of metrics can include interest in DG and energy storage technologies (manifested by technology orders to suppliers or applications to DG and energy storage incentive programs), backlog of orders, and increased investment in the DG and energy storage markets. Program theory and logic supply the story line behind assumptions about market barriers and expectations on ways we can use interventions to address the barriers. However, it is important to ground assumptions and expectations with information on market characteristics and operation.
4.2 Characterizing California’s DG and Energy Storage Markets The term “market characterization” is generally used to refer to a qualitative assessment of the structure and functioning of a market, the primary purpose of which is to understand how the market functions in order to be able to effect market transformation.9 We begin by developing estimates of the potential size of California’s DG and energy storage markets. Estimates are developed based on the technical and economic potentials. This in turn allows assessment of the changes in growth of California’s DG and energy storage markets by comparing the capacity of deployed DG and energy storage technologies in the market to the overall potential size of the market. In addition to characterizing the market in terms of size, we also describe DG and energy storage market operations. Operations consist of how products and services are offered in the market, how value is determined, and how different players in the market interact. Technical, Economic, and Market Potentials The first part of our approach in characterizing California’s DG and energy storage markets is to estimate the potential size of these markets. This gives us an upper limit by which we can measure relative growth in the DG and energy storage markets over time. We identify DG and energy storage markets by technology and market segment. Because we focus on DG and energy storage technologies located on the customer side of the meter, we discuss these technologies only in the residential, commercial and small industrial segments. We also characterize the DG and energy storage markets at the end of 2014 because it represents close to current conditions and the projected future market at 2020; which coincides with the expected end date of the SGIP. The potential size of the DG and energy storage market can be defined based on increasing levels of constraints as shown in Figure 4-3. Technical potential refers to the potential that is constrained only by technical conditions such as performance or physical requirements. Technical potential is a useful definition as it is links the total amount of potential installed capacity of market size to DG and energy storage performance characteristics. For example, technical potential allows us to estimate the maximum capacity of CHP technologies needed to meet the on-site thermal and electrical needs of different classes of utility customers. Technical potential tends to overestimate market size because it
9 Ridge & Associates for Pacific Gas & Electric, “California Schools Market Characterization,” September 20, 2005, page 1-1
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ignores economic considerations. Economic potential takes into account the cost of different technologies. In the case of the DG and energy storage market, we use cost-effectiveness test results to estimate the capacity of different DG and energy storage technologies that can be deployed in different market segments.10 In developing estimates of market potential, we used a hybrid Delphi approach. In particular, first order estimates of market potential (as percentages of economic potential) were taken from other studies. Using historical market data, we used logistic models to develop S-curves which provided us with annual growth rates associated with achieving the first order market potential target. We examined the average annual growth rates and assessed the reasonableness of the growth rate given the technology. If the growth rate was considered unrealistic, the target market potential was readjusted and a new set of S- curves generated.
Figure 4-3: Different Definitions of Potential11
10 We specifically use total resource cost (TRC) test results for DG and energy storage technologies from the SGIP cost effectiveness study in estimating economic potential. This approach is consistent with the manner in which energy efficiency economic potential is typically estimated. See for example, the 2008 California energy efficiency potential study (http://www.cpuc.ca.gov/NR/rdonlyres/F8F8F799-40A8-4856-869F- 713D6E6FF5E0/0/2008CaliforniaEnergyEfficiencyPotentialStudy.pdf) and the 2015 California energy efficiency potential study (http://www.cpuc.ca.gov/NR/rdonlyres/0C4CF052-0E02-4776-A69A- 88C619AC8DFB/0/2015andBeyondPotentialandGoalsStudyStage1FinalReport92515.pdf) 11 From NREL, “U.S. Renewable Energy Technical Potential,” http://www.nrel.gov/gis/re_potential.html
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While we can examine the technical and economic potential markets by each individual DER technology, it makes sense to group technologies that have similar performance characteristics or meet common market needs. We group all CHP technologies together because they have similar fueling characteristics (e.g., most are fueled by natural gas) but also because they are unique in providing both electrical and thermal energy to customers. Conversely, we group biogas and wind technologies in their own groups because their technical potential is based on the amount of available resources rather than the end use. Technical Potential Estimates Combined Heat and Power To estimate CHP technical potential we establish basic engineering assumptions about the system capacities and performances of commercially available IC engines and microturbine systems as representative CHP systems. Performance characteristics include average electrical and overall system efficiencies and useful heat recovery rates. We model performance of CHP systems to satisfy SGIP requirements (i.e., assumed capacity factors and useful heat recovery rates that help achieve the GHG emission rates associated with SGIP eligibility).12 We then model hourly electrical and thermal loads of a building and identify the largest CHP system to reduce those loads while also having high usage and heat recovery. We consider the basic heating needs that CHP might provide in the California climate for a range of different commercial and residential building types and sizes. These needs are based upon hourly models of heating and electrical end use needs across the state. Hourly heating and electrical end use needs are based on data from the Commercial End Use Survey (CEUS) database13 and modeled using Itron’s SitePro software.14 From these hourly models we identify a “preferable generator technology,” as one which meets the performance requirements needed to meet both thermal loads and electrical loads while simultaneously achieving GHG targets. We use the larger electrical capacity of the IC engine or microturbine that meets these performance criteria for estimating technical potential. We estimate the potential number of these generators based on 2012 Census counts of establishments by facility type, size, and location based on postal service ZIP code. ZIP code spatial resolution of facility location allows association to climate zone as well as to electric utility. While census data provides us counts of different types of facilities, we also need to match facility type with the facility types identified in the SitePro model. Consequently, we use specific North American Industry Classification System (NAICS)15 codes to help match facilities identified in the census data with facility types defined in SitePro. For example, many commercial and some manufacturing sector codes
12 Annual capacity factors assumed sufficient to recover all performance-based incentives. By GHG emission rates that match SGIP eligibility requirements, we mean that CHP systems must achieve no higher than 379 kg/MWh of GHG emissions as specified in the 2014 SGIP Handbook, page 47. 13 Itron for the California Energy Commission, “California Commercial End-Use Survey,” CEC-400-2006-005, March 2006. 14 SitePro is a proprietary end-use load shape tool, based on national and regional energy use data and uses DOE 2 for the simulation of HVAC energy. It can be used in conjunction with load research data or used independently to provide shapes for the residential, commercial, and industrial sectors. 15 NAICS codes represent the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy.
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correspond to an “office” facility. We establish facility size using SitePro’s facility-specific reference values of employment intensity. These values of square foot of facility per employee are multiplied by midpoint values of Census employee count bins (e.g., 34.5 for 20 to 49 employees) to establish facility sizes in terms of square feet of floor area. Different facility sizes are input to the consumption model. ZIP code resolution of Census facility counts establish geospatial location and climate as well as electric utility. This approach accounts for climate and size variables that strongly influence hourly energy use. Using CHP system and facility parameters and our end use consumption model, we develop a CHP system electrical capacity and specific prime mover type for each facility type, size, and climate. This sizing approach is thermally driven, which means we size the CHP system to completely satisfy the heating baseload while using all available waste heat from the CHP system. Where the resulting CHP system thermal capacity results in excess onsite electricity, we reduce the electrical capacity. In this case, the heating baseload will not be completely satisfied by the CHP system. However, in order to optimize the project economics and maximize the rate of GHG emission reductions, we always set the CHP size and operation to use whatever waste heat is available to meet the onsite thermal demand. Renewable Resources Technical potential of renewable resources (wind and biogas) are based on gross estimates of these resources located throughout the state and then restricted by performance, geographical or land-use constraints. Wind technical potential is estimated using California wind resource data16 and wind turbine operating characteristics (e.g., expected capacity factors at given wind speeds and turbine height). We restrict wind applications to single site applications (i.e., wind projects are located at customer sites such that they help offset the customer electricity demands) and turbine hub heights of no more than 80 meters (approximately 260 feet). In addition, we limit wind applications using land use and environmental restrictions developed by NREL.17 With hub heights of up to 80 meters, this leaves the possibility for single wind turbine applications with reasonably high wind speeds that are still proximate to commercial end users. Biogas technical potential is based on numbers of landfills, wastewater treatment facilities and dairies and estimated biogas production from these sources. For example, biogas potential for dairies is based on the number of dairy cows and the estimated amount of methane produced from each dairy herd. We take into account those dairies that have already installed dairy digester systems and generate electricity. Similar treatment is applied to landfills in California.18 Biogas potential for wastewater treatment plants is based on flow rates at each plant along with associated biogas production rates. Larger landfills are required to employ landfill gas-to-energy operations. However, there are a significant
16 Sources of California wind resource data include CEC PIER wind resource data; NREL’s wind data site (http://www.nrel.gov/gis/data_wind.html); and U.S. DOE EERE (http://energy.gov/eere/wind/wind-resource-assessment- and-characterization) 17 NREL, “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” July 2012, Appendix A 18 Sources of landfill gas information include EPA’s Landfill Methane Outreach Program (http://www.epa.gov/lmop/projects- candidates/index.html); the California Biomass Collaborative (http://biomass.ucdavis.edu/); the California Biomass Energy Collaborative (http://www.calbiomass.org/renewable-energy/)
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number of smaller landfills that are required by EPA to capture landfill gas but do not use the landfill gas for energy purposes. In smaller landfills, the captured landfill gas is flared. Energy Storage The potential for on-site energy storage arises from opportunities to reduce demand during peak periods by storing energy during off-peak periods. While energy storage will use more electricity to deliver the same amount of electricity for end use, it will use less valuable electricity. The resulting savings of on-peak demand charges can exceed the extra costs for additional electricity purchase, installation, and operation of the energy storage system. Energy storage is a suitable technology for various types of facilities. Energy storage systems that meet large and critical needs will enjoy economies of scale besides providing other values like greater power quality control. Technical potential is greatest where on-peak demands are high and differ greatly from daily averages. Potential begins where on-peak loads exceed the daily average load. To estimate technical potential we first establish basic engineering assumptions about the hours of operation expected from an energy storage system. We then consider the greatest monthly on-peak demand reductions the system could provide for a range of different facility types and sizes across California’s climate. These on-peak reductions are based upon hourly-resolution models of facility electrical demand across the state developed by Itron and based on the Commercial End Use Survey (CEUS) data.19 From hourly demand models we determine power and energy capacities for storage. The technical constraints are a power specification (kW) and an energy specification (kWh) sufficient to flatten the highest four demand hours in a day to the fifth highest demand hour of that same day. Charge is off-peak only and discharge is on-peak only. Off-peak demands increase accordingly and off- peak demand peaks are allowed to increase. Charge is based on 100% round-trip efficiency. Discharge is presumed to occur only during on-peak hours. Using energy storage system and facility parameters and our hourly consumption model, we develop electrical capacity specifications for each facility type, size, and climate. This sizing approach is driven with aim to reducing the highest four demand hours each day. DG and Energy Storage Market Dynamics and Major Market Players Characterizing California’s DG and energy storage markets also involves understanding how the market functions. Markets are more than collections of providers and consumers of products. Markets represent dynamic financial environments wherein perceptions and market position can be as influential as the price of a commodity. To characterize the DG and energy storage markets, we seek information about what stimulates market growth and what causes market stagnation. We ask, “Who are the key market players and what makes them critical players in the market?” “What drives sales of technologies
19 The Commercial End Use Survey (CEUS) is a comprehensive study of commercial sector energy use, primarily designed to support the state's energy demand forecasting activities. The California Energy Commission maintains CEUS data. See http://www.energy.ca.gov/ceus/
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and services?” “How critical is market share and position relative to product innovation?” “How important are synergies between policies that address market needs or barriers and the ability of market players to act quickly to provide these products?” We use in-depth surveys of key market players20 and stakeholders to gain an understanding of how the DG and energy storage markets in California function.21 We conduct online surveys of utility customers participating in DG and energy storage incentive programs or electing not to participate in these programs to better understand underlying motivations in the market. By combining survey results, we are able to put together a more complete picture of California’s DG and energy storage market. In addition to primary research, we use literature related to California’s DG and energy storage markets, data from technology manufacturers and project developers, policies and regulations that have affected or are influencing the DG and energy storage markets and databases on DG and energy storage growth. We use information on energy policies, regulations and market conditions to gain an understanding of how these factors may have influenced actions by market players or stakeholders. We examine trends in order to help evaluate if they had a sustaining effect on the DG and energy storage markets. We examine trends within and outside of the SGIP.
4.3 Collecting Information on Market Impacts and Market Players California’s DG and energy storage markets encompasses a wide variety of market players and stakeholders who have differing roles.22 We used a combination of in-depth interviews and on line surveys to obtain stakeholder perspectives on how the DG and energy storage markets function in California. We also ask what have been and continue to be key barriers or opportunities, to what extent the SGIP has influenced DG and energy storage market growth in California, and the survey participants’ thoughts on ways in which the SGIP can further help support DG and energy storage growth. While there was no explicit intent to meet statistical significance, we based our sample sizes for all the groups on a target of 90% confidence with 10% precision. However, we also assumed varying levels of bias (50% - 75%) expected in the key question response depending on the group. Table 4-1 provides a summary of the survey groups along with the population and sample estimates for each group.
20 Key market players are businesses or organizations that either conduct a significant volume of business in the market or have a role in defining policies or regulations that can have significant impacts of market growth. Stakeholders are those entities that may have a vested interest in the market but are not necessarily key market players. 21 We consider market players to be companies and firms who are financially vested in the DG and storage markets. This group consists of SGIP technology developers, project developers, service providers and utilities. We define stakeholders as organizations who have an active interest in the outcome of market development but may not be financially vested. 22 Among the groups we considered to be stakeholders included nonprofit organizations such as the American Wind Energy Association (AWEA), the California Energy Storage Alliance (CESA), the California Clean Energy DG Coalition (CCDC) and the Clean Energy Coalition (CEA). Among the public institutions we considered as stakeholders included the California Air Resources Board (CARB), the California Environmental Protection Agency (Cal EPA) and the California Energy Commission (CEC).
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Table 4-1: Summary of Survey Groups Population Survey Group Estimate Sample Description Host Customers in California SGIP Participants 712 Population estimate based on SGIP Tracking data of - Residential 54 27 complete, in process or pending payment applications - Nonresidential 658 48 SGIP Nonparticipants 1828 Population estimate based on SGIP Tracking data of - Residential 635 48 incomplete, cancelled or withdrawn applications - Nonresidential 1193 49 Manufacturers 141 38 Population estimate based on equipment details available in SGIP tracking data plus other known manufacturers Project 151 39 Population estimate based on installers from the SGIP Developers/Installers tracking data plus other significant installers/developers operating in CA or outside Stakeholders 10 Sample based on number of public agencies relevant to self-generation and industry representative groups. Program Administrators 16 Sample based on combination of current and past SGIP Program Administrators as well as Program Administrators in NY, TX, MA and NJ
Participant and Nonparticipant Surveys Participants are California IOU utility customers who applied to the SGIP, received incentives and installed DG or energy storage equipment. We further break participants into market segments of residential and nonresidential (i.e., commercial and small industrial). Nonparticipants are utility customers that intended to install or installed an SGIP eligible technology at their site but did not go through the program. We developed a host customer survey to find out more about how utility customers view DG and energy storage technologies, their level of experience with these technologies, reasons why they decided to participate or not participate in the SGIP, their experience with the SGIP and any suggestions they may have about improving the SGIP. The host customer survey was designed and fielded as an online response form to over 1900 contacts available as part of the SGIP tracking data. Manufacturer Surveys Manufacturers are DG and energy storage equipment manufacturers whose equipment is eligible to receive SGIP incentives or has been installed as part of an SGIP project. We included not only manufacturers of equipment currently being installed under the SGIP but also manufacturers of DG equipment installed in the early years of the program. The list of manufacturers was compiled based on equipment information available in the SGIP tracking database. Due to the longevity of the SGIP, the list of manufacturers who had been active in the program was fairly long. Consequently, we used a shortened list to ensure there was a balance between DG technology representation by type of
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technology as well as timeframe of installations. Each manufacturer on the shortened sample list was contacted individually for an in-depth interview. The in-depth interview was designed to draw out information from manufacturers about the extent to which they do business in California; the policies that they believe help support or act as barriers to increased sales and deployment of their technology in California; the market conditions that they believe either help support or suppress sales and deployment of their technology within the state; how they believe their customers are responding to their technologies; as well as other key questions related to the strengths and weaknesses of the DG and energy storage markets in California. We also asked about their perception of the extent to which the SGIP is influencing DG and energy storage growth in California. Project Developer Surveys We classify project developers as companies that develop DG and energy storage projects at host sites and in many cases install the DG and energy storage technologies used in the projects. As with manufacturers, we wanted to survey not just project developers currently involved with the technologies but also reach back to some of the companies developing projects and installing DG equipment in the earlier years of the SGIP. The list of project developers was compiled based on the information available in the SGIP tracking database. The list was shortened to match the sample design size, and to retain a balance between good representation of technology type and the timeframe of project deployment. Each project developer who was placed on the shortened sample list was then contacted individually for an in-depth interview. The in-depth interview for project developers is similar in nature to the survey designed for manufacturers. Like the manufacturing group, we wanted to find out how project developers perceive the way in which the DG and energy storage markets function in California. However, while manufacturers may not come into direct contact with utility customers, the project developers work hand-in-hand with the customers. Consequently, the in-depth survey was designed to distinguish between different geographical and market segments within California. We were particularly interested in capturing insights from project developers about how they perceive drivers and barriers to DG and energy storage deployment in California, if different DG and energy storage technologies face the same drivers and barriers, if there are differences in perceptions among market segments, and how changes in market conditions seem to be influencing market operations. In some instances, companies who manufacture a DG or energy storage technology also act as project developers. We noted these companies and developed a combined manufacturer and project developer survey for conducting in-depth surveys for these companies. However, prior to conducting the combined interview, we contacted the company to find out if the manufacturing and project development activities were confined to one part of the company or operated in distinct and separate parts of the company. In the latter case, we generally conducted separate in-depth interviews with the two different operations within the company.23
23 In some instances, companies who provide both manufacturing and project development services requested that we conduct only one combined survey even if operations were considered distinct parts of the company.
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Stakeholder Surveys Stakeholders are people, groups or organizations that have an interest in the outcome of a venture. Within the DG and energy storage markets, stakeholders include companies developing or deploying DG and energy storage technologies as well as those companies or people purchasing or using these technologies. However, stakeholders also include public agencies, environmental groups, the academic community or nonprofit groups that have an interest in California’s DG and energy storage market development. Our stakeholder sample included public agency representatives from the CPUC (both staff involved in the SGIP and other DER related activities), the California Air Resources Board (CARB), the California Environmental Protection Agency (Cal EPA) and the California Energy Commission (CEC). Industry and nonprofit representative groups included the American Wind Energy Association (AWEA), the California Energy Storage Alliance (CESA), the California Clean Energy DG Coalition (CCDC), and the Clean Energy Coalition (CEA). The in-depth interview for stakeholders sought perspectives on underlying drivers or barriers to DG and energy storage market development in California. In the case of public energy and environmental agencies, we wanted to learn background perspectives on the genesis of DG and energy storage support programs and their relationship to energy and environmental policies in California. In particular, we wanted to understand the extent to which DG and energy storage was considered to be a key ingredient when the policies were formed and how that has developed over time. In instances where policies have been implemented to manage DG and energy storage operation in the California market (e.g., air quality control policies, nonbypassable fees, etc.), we sought perspectives on the reasoning behind the controls and how this fit in with the expected role of DG and energy storage in California’s energy market. In the case of industry groups, we sought their perspective on how California policies and regulations have helped build or stymie DG and energy storage development in California over time, what progress has been made in developing a sustainable DER market, what are the primary barriers and opportunities to developing a sustainable California DG and energy storage market, and to what extent the SGIP has helped develop that market in California. PA Surveys The Program Administrator (PA) group is a mix of both past and current SGIP Program Administrators. The PA group provides unique insights into SGIP policies and goals, the functioning of the program and intended outcomes as well as observed results. We expanded the PA group to include Program Administrators of DG and energy storage support programs similar to SGIP but located outside of California. These included DG and energy storage programs in New York, Texas, and Massachusetts. The intent of the expanded PA group is to compare perspectives from California PAs to those of PAs with programs in other states and see what commonalities and differences exist. We felt this comparison could help us better understand basic DG and energy storage market operations that are independent of geographical location. We developed an in-depth PA survey that seeks the perspectives of the PA group to define the market and future of DG and energy storage technologies. Table 4-2 is a summary of the key research questions as addressed in the different surveys, along with performance metrics or indicators collected via the surveys.
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Table 4-2: Key Research Topics Groups Being Surveyed
ata
eview
R
D
roups
/
G
ser/
Performance U
ollection Research Topic Metric/Indicator iterature
End End Customers Installers/ Developers Manufacturers Industry PAs PUC + Secondary C L To characterize the market CA Market segments for X X X X X for SGIP technologies in each tech California # and MW To identify distributed # Barriers X X X X X energy market barriers and # Strengths X X X X X market strengths To identify and describe Key policies related to X X X X X SGIP and other distributed DG that changed the energy resource policy market (5) interventions To assess the effects of the Change in barriers and X X X X SGIP and other policy strengths due to policy interventions in reducing identified market barriers and in supporting the transformation of the SGIP eligible technology markets toward self-sufficiency To establish and analyze Change in production X X indicators, or metrics, that volume reflect the evolution of the Change in number of X X X distributed energy markets Manufacturers toward self-sufficiency Change in number of X X X installers Emergence of new X X X business models and financing options Compare # of X X X X applications/sales to completes Diversity in installation X X market sector and geographic. To assess sustainability of Change in customer X X X X X these markets in California satisfaction in the absence of the SGIP Change in customer X X X X X awareness Compare # installations X X X X X with and without incentives How important is cost in X X X X making the decision? To identify and recommend What changes would X X X X X X changes to the SGIP program you want in policy and to support further programs to support the development of these market? markets in California
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4.4 Evaluating SGIP Influences In the previous section, we described our approach in characterizing California’s DG and energy storage markets. We discussed ways to examine trends and interventions in the DG and energy storage markets that may have acted to enhance or impede growth in the technologies. In this section, we look specifically at the SGIP and our approach in evaluating the extent to which the SGIP has influenced California’s DG market transformation.24 Results of the analysis are presented in Section 7. We recognize that a number of factors influence the amount of DG that can be adopted into the market. Consequently, we use linear regression analysis with multiple explanatory or independent variables to isolate and estimate the impacts of the different factors. We use a log-linear regression analyses to analyze the effects of different variables on CHP, wind, and biogas capacity in California and eight other high priority states. By deliberate grouping of technologies together into CHP, wind and biogas, we have developed sets of technology and their newly installed capacity where there is overlap in the likely impact of some of the independent variables and lack of variables in other cases. For example, air quality regulations may have a specific impact on CHP technologies but a different and potentially opposite impact on wind or biogas to energy technologies. However, all three groups of the technology groups are assumed likely to be positively influenced by incentive programs. In order to isolate the impacts of SGIP on newly installed capacity over time, we need to look not only across different DG technologies but also different programs in different states that support DG. This approach allows us to isolate the influence of SGIP policies on DG from the influence of other policies. We use regression techniques to evaluate the influence of independent variables, including SGIP, on the three groups of technologies. In general, this technique involves developing a yearly estimate of newly installed capacity for each technology group, for each state, and each year of the analysis period. The analysis team chose to focus on three technology groups (CHP, wind, and biogas fueled technologies where the biogas was derived from landfill gas or dairy and swine digesters), nine states (California, Connecticut, Massachusetts, Michigan, New Jersey, New York, Pennsylvania, and Wisconsin), and the years 1999 through the most recently available data. The analysis process also required the analysis team to develop information on the independent variables (policies and utility prices) that influenced the installation of DG capacity over these states and time period. By looking at DG installations in multiple states and controlling for DG programs and policies that support DG technologies, we can isolate the influence of SGIP on newly installed capacity in California.
4.5 Assessing Market Trends Toward Self-Sufficiency The statistical methods described above enable us to evaluate the influence of different factors, such as policies and regulations, and to isolate the impact of SGIP-specific policies on new capacity in California. By examining states that do not have DG support programs, the model can attempt to separately
24 Although we originally expected to examine the impact of the SGIP on the storage market, we found that there was very limited data on storage capacity installed in other states. This is likely due to the still nascent stage of the storage energy market. Due to the lack of storage capacity data, we confined our analysis to the DG market.
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identify the impact of market-driven activities from regulations and programs designed to support the installation of new DG capacity. A downside of this approach is that while we rely on historical market data to estimate DG capacity in states with programs supporting DG, there is limited DG market data in states that do not (or have not had) DG support programs. The low and highly variable level of newly installed capacity may make it more difficult for the regression model to isolate the influence of regulations and programs designed to support the installation of new DG capacity.
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This section provides a preliminary program theory and logic model for the SGIP. The program theory and logic model identifies expectations about transformed DG and energy storage markets, about possible interventions to address key market barriers preventing transformation of the DG and energy storage markets and identifies activities that can be used by the SGIP to help with market transformation. We base the model on interviews with California utility customers who installed SGIP technologies (participants), customers who considered installing SGIP technologies but decided against it (nonparticipants), SGIP technology manufacturers and installers, SGIP Program Administrators and Program Administrators of DG programs similar to the SGIP and located in other states, CPUC staff, and with other key players in the DG and energy storage markets. We also relied on secondary information including white papers, reports or presentations from workshops where industry representatives or other DG and energy storage market actors provided comments on DG and energy storage market barriers or opportunities. Because the SGIP has been active since 2001 our interviews include people who were active in the SGIP in the past. Interviews with these people provide insights into how perspectives about the DG and energy storage markets and the role of the SGIP have evolved.
5.1 Results Based on interview results and secondary information, we developed a preliminary program theory and logic diagram for the SGIP as shown in Figure 5-1. The bottom of the diagram is broken down into short, medium and long term outcomes. We define short term outcomes as outcomes that could be expected to occur in the next five years; prior to the current planned end of the SGIP. We view medium term outcomes as occurring between the five and ten years. Long term outcomes occur later than the next ten years. We selected these long term outcomes based on deadlines of existing policy goals that might correspond to goals of transformed DG and energy storage markets (e.g., AB 32 sets GHG goals to be achieved by 2030 and Governor Brown’s Clean Energy Jobs Plan has goals to be achieved by 2020 and 2032). The diagram also contains outputs and activities. Activities correspond to actions that could be taken within the SGIP to help move towards transformed DG and energy storage markets. Outputs are the products or services delivered by the SGIP as a result of the activities. Sources of the outcomes, outputs and activities and additional explanation of their meaning and intent are described in the sections that follow.
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Figure 5-1: Preliminary SGIP Program Theory and Logic Diagram
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Defining Transformed Markets Long Term Outcomes Long-term outcomes should ideally reflect the operation and structure of the transformed DG and energy storage markets. While a number of policies have been enacted to help spur growth of DG and energy storage in California, there has been little discussion of what transformed DG and energy storage markets should look like or how they should operate. In addition, when the SGIP was established in 2001, market transformation was not a goal of the program. Market transformation first showed up as one of four goals for the SGIP in a 2011 CPUC decision.1 In a 2009 decision, the CPUC defined market transformation in context of the energy efficiency market:
Market transformation is long-lasting, sustainable changes in the structure or functioning of a market achieved by reducing barriers to the adoption of energy efficiency measures to the point where continuation of the same publicly-funded intervention is no longer appropriate in that specific market. Market transformation includes promoting one set of efficient technologies, processes or building design approaches until they are adopted into codes and standards (or otherwise substantially adopted by the market), while also moving forward to bring the next generation of even more efficient technologies, processes or design solutions to the market.2 Interviews with the SGIP PAs provide clear insights into some of the key barriers preventing increased adoption of SGIP technologies into the market. When we combine PA perspectives about market barriers with the CPUC’s 2009 definition of transformed markets, we believe there are several key elements that can be used to characterize transformed DG and energy storage markets: » There will be no significant barriers preventing utility customers and utilities to routinely use DG and energy storage technologies as part of their energy solutions; » Changes in market operation along with performance and cost improvements will allow DG and energy storage to be adopted without incentives; » The market will encourage development and adoption of even more efficient DG or energy storage technologies, services and solutions into the market; and » The DG and energy storage markets will continue to operate and grow even if public interventions are modified, refocused or reduced.3 Transformed DG and energy storage markets should also provide nonenergy benefits to be sustainable. By providing nonenergy benefits such as increased employment, cleaner air or water, and enhanced use
1 The CPUC Decision 11-09-015 (September 8, 2011) stated the four goals of the SGIP. Those goals are: 1) reduce GHG emissions in the electricity sector; 2) help with demand reduction and reduce customer electricity purchases; 3) improve electricity system reliability through improved transmission and distribution system utilization; and 4) help with market transformation of distributed energy resources. See http://docs.cpuc.ca.gov/PublishedDocs/WORD_PDF/FINAL_DECISION/143459.PDF 2 D.09-09-047 at 89 3 This characterization comes from Ken Keating’s guidance to the CPUC on transforming the energy efficiency market. See Keating, Ken, “Guidance on Designing and Implementing Energy Efficiency Market Transformation Initiatives,” October 13, 2014, page 2.
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of local resources, DG and energy storage markets build broader acceptance of these technologies. Existing policies supporting the growth of DG and energy storage growth in California include Governor Brown’s Clean Energy Jobs Program, AB 32 (the California Global Warming Solutions Act of 2006), and AB 327 (which revises net energy metering provisions in California and requires utilities to file distributed resources plans). Among the nonenergy benefits targeted in these policies include the following: » Growth in environmental benefits including reductions in criteria air pollutants as well as greenhouse gas (GHG) emissions; and » Creation of jobs and sustained clean energy employment. In light of these considerations, we pose that long-term outcomes of transformed DG and energy storage should include: » DG and energy storage markets grow without incentives; and policies affecting the markets are aligned to help support continued growth; » DG and energy storage make up a significant portion (e.g.,15%) of California’s electricity mix4; » DG and energy storage help provide valuable environmental benefits that include reductions in criteria air pollutants, improved water quality and land use and reductions in GHG emissions; and » DG and energy storage markets contribute to new job creation and sustained employment. It would be valuable to have quantitative goals associated with these long term outcomes. Due to the policy implications, goals should be adopted by policy makers who can take into account and balance the different policies needed to achieve the goals. Short and Medium Term Outcomes Transformed markets emerge through sustained efforts that lead to long lasting changes in the market. Short and medium term outcomes listed in Figure 5-1 support the development of long-term outcomes and include the following: » Outreach materials that identify how SGIP technologies match customer needs and utility mission; » Development of a catalog of “prequalified” SGIP technologies and installation methods that are “proven,” highly replicable and represent certainty in costs and performance; » Strategies that help move SGIP technologies to improved performance and cost targets while simultaneously stimulating growth in selected “high value” market segments; » A coordinated approach with financial institutions to provide SGIP applicants with flexible, long-term financing vehicles that make adoption of SGIP technologies more attractive to a broader audience; » Informational materials that help identify to policy makers how the SGIP is helping transform the DG and energy storage markets, where conflicting policies and regulations may be preventing that transformation and ways in which the policies could be aligned to achieve the desired policy objectives while growing the DG and energy storage markets.
4 Note that if DG makes up all of the 12,000 MW by 2020 goal of Governor’s Brown Clean Energy Jobs Plan, the 6500 MW goal by 2032 for CHP reaches 3000 MW by 2020, and 200 MW of customer-sited storage is achieved by 2024, this would be represent over 30% of California’s peak demand in 2014 of 45,089 MW.
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Interim outcomes are also important in creating milestones that can be measured to assess the progress being made towards a transformed market. Role of the SGIP in DG and Energy Storage Market Transformation In his guidance document advising the CPUC on designing and implementing energy efficiency market transformation initiatives, Keating points out differences between resource acquisition programs and market transformation initiatives.5 A key distinction is the focus of the efforts. Resource acquisition programs focus on achieving program level results. In contrast, market transformation initiatives focus on producing changes in the market. To date, the SGIP has acted primarily as a resource acquisition program. The scale of the SGIP has been targeted at the SGIP program level rather than the entire DG and energy storage markets. For example, the use of incentives targets program participants instead of a broader financial or support mechanism that would target all end use customers. Similarly, the savings are estimated based on the program instead of across the market. Lastly, the PAs largely control the pace and scale of the activities occurring in the SGIP. However, in a market transformation initiative much of the how, when and where market impacts occur requires the close coordination of a number of the key market players. In identifying the role of the SGIP relative to a market transformation initiative, we have associated SGIP activities and outputs to the short, medium and long term outcomes. We group SGIP activities and outputs into six categories: » Outreach: activities and outputs geared to increasing information about the SGIP and its relationship to DG and energy storage market goals » Improved Cost and Performance: efforts to help move SGIP technologies to the point where financial incentives are no longer needed » Improved Environmental Benefits: actions to expand and deepen the level of environmental benefits provided by SGIP technologies, thereby increasing the sustainable operation of the DG and energy storage markets » Broader Financial Vehicles: efforts to increase the attractiveness of SGIP technologies to more end users who may otherwise lack the financial ability to adopt DG and energy storage technologies » Aligned Policies and Regulations: actions to help align otherwise conflicting policies in order to develop sustainable DG and energy storage markets that simultaneously achieve energy, environmental and economic goals for California » Policies Encouraging DG and Energy storage Market Growth: actions and products that pinpoint specific types of policies that can help grow DG and energy storage markets in California. Figure 5-1 identifies specific activities and outputs that can be generated by the SGIP to help further DG and energy storage market transformation. The rest of this section describes the information collected from the surveys and that was used in developing the program theory and logic model.
5 Keating, op. cit., page 14
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5.2 Information Collected on Barriers and Opportunities Program Associated Staff Interviews These interviews included both past and present SGIP PAs, program EM&V staff and CPUC staff. We interviewed all four current PAs. The intent of the surveys was to obtain perspectives on the barriers and opportunities facing market transformation of DG and energy storage technologies and the role of the SGIP. Selected staff associated with the program in the past were also interviewed to provide a historic perspective on the SGIP as it evolved through the years since 2001. The interviews were all conducted as structured in-depth conversations. Program Goals and Expected Outcomes The in-depth interviews were primarily aimed at finding out the PA and CPUC staff understanding of the goals for the SGIP program as well as expected outcomes. All of the interviewed PAs stated that GHG reduction was currently a primary goal of the program. They also mentioned that quantitative goals had not been explicitly defined in the early years of the program. For example, the SGIP started primarily as a load control and peak demand reduction program resulting from the energy crisis in 2000. The PAs pointed out that while there was no explicit MW capacity goal; increasing the overall capacity of DG technologies was an implied outcome that had for the most part met or exceeded expectations. However, not having an explicit capacity goal was also cited as a problem for PAs in that it created difficulties in making a clear assessment of the progress of the SGIP. One PA also pointed out that an implicit goal of the SGIP was to have a diversified portfolio of technologies. Increasing customer awareness of DG technologies was also not an explicit goal of the program. In one of the initial decisions made by the CPUC in implementing the SGIP, the guidance was for program marketing to be “conducted through existing networks of SPC program service providers.”6 The PAs felt it was an implied outcome. The PAs also felt customer awareness had either been met or been exceeded through increased outreach and other activities occurring in both the market and as a result of the program. However, some of the PAs cited lack of marketing budget allocation in the administration funds as the reason for no direct outreach activities. All outreach was leveraged through other existing channels and thus limited in its attribution to the program. Customer satisfaction with technologies funded under the SGIP, while an important component of market transformation, was not cited as an expected outcome. Drivers and Barriers for DG and Energy Storage Technologies Overall, incentives are perceived as a primary driver for promoting DG and energy storage technologies under the SGIP. PAs also stated their perspective that increase in the efficiency of DG and energy storage technologies along with the development of financing models available such as lease and PPA agreements have helped drive the DG and energy storage market. In contrast, PAs indicated that one of the biggest barriers to increased growth in DG and energy storage is the lack of customer familiarity and comfortableness with the technologies. Most of the DG and energy
6 Decision 01-03-073, “Interim Opinion: Implementation of Public Utilities Code 399.15(b), Paragraphs 4-7; Load Control and Distributed Generation Initiatives, March 27, 2001, page 29
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storage are more complex than solar PV or involve more than “flipping a switch.” Consequently, customers are not as comfortable or confident about the reliability of the technologies and do not adopt them as readily as they adopt solar PV systems. The PAs also pointed out that CHP systems and fuel cells require some level of engineering and proactive operations for customers to fully realize the benefits associated with these technologies. In addition, because SGIP marketing is primarily conducted by project developers, there is not an equitable approach to how and what customers are educated about the benefits of SGIP technologies. In some instances, poor matches between customer needs and SGIP technology costs or performance has led to less than optimal economics in spite of the available financing mechanisms. Some PAs expressed the view that steps to match customer needs and use cases to SGIP technologies is one of the biggest untapped opportunities for growing the market. For example, CHP growth could potentially be increased by identifying and marketing to those customers where there is heat load concurrent to electricity loads or available waste heat from ancillary processes. The PAs also discussed untapped market sectors, including the residential market for CHP and schools; and the small commercial market for those DG and energy storage technologies that could provide demand response capabilities. Specific technology barriers are addressed below. Combined Heat and Power (CHP) Some of the PAs viewed gas turbine technologies as a reliable source of power for very specific customers and along specific demographic characteristics. Ideal applications are mostly large scale projects that have large amounts of heat where waste heat utilization potential can be realized round the clock. The economics of the technology also only typically works better for the larger customers. In addition, financing rates are lower for large government entities in comparison to the private sector, making it more practical and affordable. In general, Air Quality Management District (AQMD) requirements and permitting requirements represent significant barriers to further market adoption of CHP in California. Other technologies such as fuel cells are exempt from these air quality permitting requirements due to their inherently low NOx emissions. However, CHP systems have to go through an AQMD process which is viewed as being very elaborate and restrictive. Most customers are reluctant to deal with the air quality permitting process. As such, most CHP incentives are directed to government host customers who are more willing and likely to go through the permitting process. Another PA expressed the belief that a major barrier to CHP market growth is departing load charges. The PA noted that departing load charges can represent a significant economic barrier for CHP and can decrease benefits to host customers by 25%. Customers are billed based on their load, rather than the energy they consume from grid. In addition, the PA noted that incentives provided to CHP technologies are lower and considered to be less competitive than what is provided to other SGIP technologies. In particular, the CHP incentive is based on an assumed capacity factor. The assumed capacity factor is perceived as penalizing customers who may not have the load needed to match the required capacity factor. If the host customers are not able to meet the performance based aspect of the incentive, they may end up receiving only half the expected incentive. The mismatched base load and heat load make this an issue for most customers, thereby making CHP look uneconomical. Also, CHP has more burden when it comes to the GHG screening process and at least one PA indicated the SGIP uses an unrealistic
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GHG reduction requirement for CHP systems. Lastly, CHP projects represents engineered systems and the required amount of commissioning is more intensive and iterative than for simpler systems. The commissioning process may go through many modifications before being finalized. As a result, CHP systems can be viewed by customers and more challenging requiring a greater investment in time and effort to participate in the SGIP. Electric Only Fuel Cells Several of the PAs identified the electric only fuel cell technology as one of the high growth technologies in the SGIP. PAs noted that this growth was primarily driven by higher incentive levels, the presence of motivated vendor(s) along with perceived affordability and reliability of the technology. In addition, at least one PA noted that electric only fuel cells smaller than 1 MW in capacity were eligible for NEM, unlike larger sized CHP technologies and increasing their financial attractiveness. PAs viewed higher upfront cost investment as often being the major barrier; especially when there are no leasing or PPA models available. Wind Wind was cited as one of the low growth technologies served by the SGIP as the PAs found that there was limited resource availability in their regions. Wind energy technology is perceived as having been around for a while and is viewed as a known technology with good customer familiarity. The PAs perceive that major barriers to wind systems are mostly the “not in my backyard” phenomenon which prohibits wind turbine installations. In addition, PAs noted that wind is an intermittent resource that may have been viewed by customers as lacking reliability. Also, the PAs perceive that wind turbines are not easily adjustable to meet capacity needs of customers as they are available in fixed sizes and can only be installed in multiples of that size. Advanced Energy Storage Since becoming eligible in the SGIP, advanced energy storage has seen significant growth in the SGIP. At least one respondent indicated the main reason for growth in energy storage can be attributed to effective marketing by vendor(s). The PAs perceive that the main attraction of energy storage to customers is the potential to reduce demand charges coupled with smart controls. Even though the technology is expensive, effective marketing has been able to increase adoption. The main barrier to advanced energy storage is seen as regulatory uncertainty related to NEM and interconnection of solar paired with energy storage. Some PAs expressed the opinion that the value stream to AES will remain uncertain till these regulatory issues are resolved and the policy is clarified. Influence of Policies and Regulations We asked the PAs their perspectives on how different policies and regulations have affected the growth of SGIP technologies. We also asked them to rank the policies with respect to how favorably or unfavorably the policies have impacted SGIP technology growth.
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Policies External to the SGIP
GHG and RPS policies: For the most part, PAs rated GHG reduction requirements and RPS goals as either favorable or not having much impact for most of the SGIP technologies. However, at least one PA noted the GHG requirement that GHG emissions from SGIP technologies had to be 20% lower than the grid was impeding development of gas fired CHP.
Air pollution permitting policies and regulations: Almost all the PAs indicated that air pollution control permitting policies and regulations appeared to impede growth of gas-fired CHP technologies. However, one PA noted that air permitting requirements were forcing manufacturers to produce more efficient technologies with lower amounts of air pollution emissions.
NEM policies: While PAs indicated that the NEM requirements favored most technologies, at least one PA indicated that existing NEM policies are unfavorable for large CHP. However, some of the PAs noted that because NEM is not available to all SGIP technologies. Because NEM results in increased financial attractiveness and helps to streamline project interconnection processes, NEM results in an “uneven” playing field among SGIP technologies. Utility interconnection policies: PAs generally perceived the interconnection process as having no impact on SGIP technology growth. The PAs indicated that most technologies have been through a learning curve with respect to interconnection but are “now settling into the process”. However, at least one PA noted that advanced energy storage still has unresolved interconnection issues which could impact growth of AES going forward. In addition, at least one PA noted that municipal utilities have not been interconnecting gas fired CHP due to existing renewable power contracts.
ZNE and LEED policies: The PAs indicated that Zero Net Energy (ZNE) and Leadership in Energy and Environmental Design (LEED) policies appeared to act favorably to SGIP technology growth or have no impact.
Tax treatment policies: PAs viewed federal tax credit policies enacted through the Investment Tax Credit (ITC) and the Production Tax Credit (PTC) along with the Modified Accelerated Cost-Recovery System (MACRS) as being very favorable for growth of most SGIP technologies by helping to offset a big part of the technology cost. In addition, the PAs indicated that loss of the ITC could unfavorably impact future growth of SGIP technologies. Several PAs noted that not all SGIP technologies are eligible for federal tax credits. Consequently, they felt that unequal treatment of tax credits created an uneven playing field for SGIP technologies.
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Policies Internal to the SGIP
Outreach policies: The PAs had different opinions regarding the extent of outreach conducted by the SGIP and its impact on SGIP technology growth. In general, the level and rigor of outreach varies across the PA territories. One PA indicated that due to rapid oversubscription of SGIP funds, there was little need to conduct outreach. In this case, the PAs primarily leveraged other outreach efforts. However, two PAs noted that they conduct much more extensive outreach activities. One of the PAs expressed the opinion that even though they were conducting more extensive outreach, there appeared to be little correlation between the amount of outreach and the number of SGIP projects. That PA felt there was a need for more education. Another PA perceived that their outreach efforts were extensive and was yielding more projects and more informed customers.
SGIP incentives: Almost all the PAs indicated that SGIP incentives were a primary factor in helping to grow SGIP technologies. However, one PA noted that there are significant disparities in incentive levels between the SGIP technologies and that technologies receiving lower incentive levels tended to grow at lower rates. The PAs also noted hearing complaints from applicants about the different incentive levels. One PA also pointed out that disparities in incentive levels was acting to reduce the diversity of technologies in the SGIP. Overall PA/CPUC Perspective and Key Takeaways Nearly all of the PAs felt that there was a large untapped potential for SGIP to help grow the market for SGIP technologies. Similarly, all expressed that there were a number of challenges involved in making progress towards increased market growth but had different opinions about the types of challenges and ways they could be addressed. For example, at least two of the PAs felt that utility tariffs could impede growth of SGIP technologies by under-valuing the benefits provided by SGIP technologies. The PAs also noted that customer perception that SGIP technologies are expensive or “risky” are significant barriers to be overcome to achieve market growth. Additionally, some of the PAs also felt that SGIP funding allocation to technologies was not equitable and unfairly advantaged some technologies, thereby impeding growth of certain technologies. Several of the PAs felt they were taking a more proactive approach in conducting outreach or marketing technology offerings customized to create mutual benefit for both customers and the utility. However, they noted that more could be done to educate customers as well as policy makers as to the benefits of SGIP technologies. Key takeaways from the interviews with the PAs and CPUC staff include the following: » While market transformation was not a goal at the start of the SGIP, market development of SGIP technologies has been occurring under the SGIP; » There is a significant untapped potential for SGIP to help grow the market for SGIP technologies;
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» The lack of customer familiarity with SGIP technology costs and performance has created the perception among customers that SGIP technologies are costly and risky and poses a significant barrier to additional SGIP technology growth; » It would be beneficial to educate customers and policy makers as to the costs, performance and benefits of SGIP technologies; and » There is a need for alignment of critical state policies on DG and energy storage in order to make forward progress on market transformation. To be successful, the overarching policy goal of a transformed DG/energy storage market needs to be presented clearly and in a unified front. This unified front is what allows clean tech capital investors to have faith and invest in the innovative clean technology projects. Consistent alignment of clean tech policies with the subsequent capital investment from the tech capital investment community will ultimately be a key force in transforming the DG and energy storage markets. Host Customer Interviews Host customers for SGIP technologies in California were surveyed to get their perspective on market barriers and opportunities for growing these technologies. The surveys included both customers that had received SGIP incentives and those that did not receive incentives. In addition, the host customer surveys spanned both residential and nonresidential market sectors. The surveys were structured to identify the customer’s level of awareness of the SGIP technologies, gauge their satisfaction with the technologies, obtain their perspectives on the market barriers and opportunities facing SGIP technologies and obtain any recommendations they had related to changes in the SGIP or in policies affecting SGIP technologies. The survey was implemented using an on-line format and thus the responses are self-reported. Within the survey, customers were asked to select one of a number of responses that best fit their opinion. However, customers were also provided an opportunity to provide open-ended responses to specific questions. Surveys were emailed to over 1900 customers from the SGIP tracking dataset as one time applicants with interest in program served technologies whether or not they went through with the actual installation or received the incentive. Over 250 complete responses were analyzed to present the following findings. Technology Awareness and Satisfaction The level of customer satisfaction with installed SGIP technology was used as indirect measure of the opportunity for SGIP technology market growth. A high level of customer satisfaction with an SGIP technology is likely to indicate the opportunity to grow the market for the technology. In general, SGIP technologies involve complex operations that are difficult to market through conventional advertising. A large base of mostly satisfied customers can play a significant role in increasing the adoption of technologies which otherwise do not lend themselves to easy advertising. Consequently, word of mouth and recommendations to friends and associates becomes an important aspect of growing the market for SGIP technologies. Figure 5-2 represents the summary findings from host customers regarding their satisfaction with the SGIP type of technology installed at their site. Technologies examined included Advanced Energy Storage (AES), Fuel Cells (FC), IC engines (ICE) and Microturbines (MT). Other technologies were included as “Other.”
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Figure 5-2: Customer Satisfaction with Installed Technology
Customers were asked to rate their satisfaction with the technology as falling into one of the following categories: » Very satisfied (3) » Satisfied (2) » Somewhat satisfied (1) » Not satisfied at all (0) » NA (A third party or installer/vender took care of the application) Note that responses include both customers who participated in the SGIP (“participants”) and received incentives to install the technology as well as customers who installed a technology but did not participate in the SGIP (“nonparticipants”). The scale at the left-hand side of Figure 5-2 reflects the percentage of responses received from the surveyed customers. Note the responses include a “Not Answered” response. Because these were online surveys, if a question was left blank, we recorded the response as “Not Answered.” The scale at the right represents the average response. The average response value is also represented in each category. Overall, customers expressed a high level of satisfaction with whatever was the SGIP technology they had installed at their site. We also categorized responses by residential (“Res”) versus nonresidential (“NonRes”) customers. In general, both residential and nonresidential customers appeared satisfied with the technology they had installed.
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Whether or not a customer would recommend the technology to friends and associates is another measure of satisfaction with the technology. Customers were asked if they would recommend the technology to their friends using the following responses: » Very likely » Not at all A summary of the responses by technology and by customer segment (residential and nonresidential) is shown in Figure 5-3.
Figure 5-3: Technology Recommendation to Friends and Associates
The scale at the left axis refers to the responses by percentage of customers. For example, 100% of customers who installed an AES technology would recommend the technology to a friend or associate. Note that because 10% of the customers answered “NA,” this was not counted as a percentage of the recommendation response. Similarly, “Not Answered” was not counted as a percentage of the recommendation response. Overall, most of the surveyed customers indicated they would very likely recommend the installed SGIP technology to a friend, indicating a high level of satisfaction with the technology. In addition, both residential and nonresidential customers would recommend the technology to a friend or associate. Drivers and Barriers to Adoption of SGIP Technologies Customers consider a number of factors in deciding to install an SGIP technology. Factors that are considered very important in the decision process are possible drivers to adoption of a technology. We asked both customers who participated in the SGIP as well as customers who installed SGIP type technologies but did not participate in the program to rank the importance of different factors that may have influenced their decision to install the technology. The results are shown in Figure 5-4 below.
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Figure 5-4: Host Customer Considerations for Installing Technologies
Customers were asked to rank the importance of five factors in their decision to install a technology. The following are those five factors: » first cost (First Cost); » incentive level (Incentive); » tax benefits (Tax); » reliability of the technology (Reliability); and, » operation and maintenance cost (OM Cost). The customer choices for responses included: » “Extremely Important:” given a score of 5; » “Quite Important:” given a score of 4; » “Moderately Important:” given a score of 3; » “Slightly Important:” given a score of 2; and » “Not At All Important,” given a score of 1. We grouped the customer responses by whether they were participants (Part) or nonparticipants (NP). Figure 5-4 shows the percentage responses for each of the participant and nonparticipant groups with respect to the factor being considered (e.g., First Cost, Incentive, etc.). We also show a “Total Weighted
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Average.” The Total Weighted Average represents the average response weighted by the capacity of the technology installed by participants and nonparticipants.7 Reliability, and cost (both first and O&M costs) were found to be the predominant factors driving the decisions for both participants and nonparticipants. Incentives and tax benefits appeared to be lower considerations. Perspective on the Role of the SGIP A key objective of this market transformation study is to assess the importance that the SGIP may play in moving the DG and energy storage markets forward. As a result, we felt it important to ask customers how they viewed the role the SGIP made in their decision to install a technology. A significant measure of the SGIP’s impact on customer’s intent to install the technology is related to the availability of SGIP incentives. Consequently, we asked customers about their experience with the SGIP and specifically their likelihood of installing the technology in the absence of SGIP incentives. Figure 5-5 below shows the responses grouped by type of technology but also by market segment.
Figure 5-5: Installation of the Technology in the Absence of SGIP Incentives
The left axis of the chart shows the percentage of responses by the customers relative to the installed technology. For example, for those customers who installed AES technology, approximately 50% would
7 Because both participants and nonparticipants in this study installed technologies, we were able to obtain the installed capacities. We used the sum of the nonparticipant capacity and the sum of the participant capacity to develop a “total” capacity, which we then used to weight the responses.
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not have installed the AES technology without SGIP funding. Conversely, less than 20% of customers installing microturbines would not have installed the microturbines without SGIP funding. To help make comparisons easier between the technologies, we developed a “probability” that customers would install the technology in the absence of SGIP funding. The probability is calculated giving a 0 if the customer answered “No, not at all” (they would not have installed the technology without the incentive), 0.75 or 75% for respondents that indicated that “Maybe, at a later time” they would have installed the technology if there were no SGIP incentive, and 1.0 or 100% for respondents who stated that “Yes, definitely” they would have installed the technology even if there were no SGIP incentive. The black dot located in the center of each bar reflects the calculated probability that customers would have installed the technology without the SGIP incentive. For example, we estimated the probability that customers would have installed AES technology without the SGIP incentives at approximately 38%. Overall, most customers would not have installed AES, fuel cells or IC engines without the advantage provided by SGIP incentives. Nonresidential customers were also much less willing than residential customers to install technologies without SGIP funding; which may be tied to the larger and therefore more capital intensive projects installed by nonresidential customers. We also wanted to assess customer satisfaction with different implementation aspects of the SGIP. Consequently, we asked customers to rate their level of satisfaction of the SGIP with respect to the application process (Application), the amount of incentives provided (Amount), the amount of time associated with receiving the incentive (Time) and program requirements (Requirements). Results are shown in Figure 5-6 below.
Figure 5-6: Satisfaction with Different Aspects of SGIP
In general, most participants are satisfied to very-satisfied with the SGIP application process, the amount of the incentives provided, the time associated with receiving the incentive and other program requirements. Even though nonparticipants did not receive incentives through the SGIP, they commented
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on their level of satisfaction with all aspects of the program; some of which may have been tied to their direct experience and some tied to their expectations. Key Takeaways from Host Customer Surveys Within the host customer survey, we asked customers for open-ended responses with respect to recommended changes in the SGIP or to effect increased market transformation of SGIP technologies. Among the more significant recommendations were the following: » Most customers felt that high upfront costs represent a barrier to widespread adoption of DG and energy storage technologies. They felt it was important for the SGIP to help find ways to help reduce the cost of these technologies so incentives were not necessary. » Customers also noted that most people are not familiar with SGIP technologies; their costs, their performance or their benefits. Consequently, in order to help grow the DG and energy storage markets they recommended that SGIP increase program outreach both with respect to SGIP technologies as well as various aspects of the SGIP itself. » Customers also noted that there can be a great amount of discrepancy in sizing, costing and installing of SGIP technologies. Customers who noted this recommended that the SGIP help develop standardization in configurations and installations of SGIP technologies to help reduce costs and improve customer comfort level in adopting the technologies. There were also recommendations that the SGIP help screen or pre-qualify contractors who install technologies under the program to ensure high quality services. The expectation from host customers is that contractors associated with the SGIP need to provide independent and honest assessments; not sales pitches. » A number of customers stressed that streamlining the incentive and utility processes are important in enabling higher adoption of SGIP technologies. Manufacturer and Developer Interviews We held in-depth interviews with companies who manufacture SGIP technologies and companies who develop projects in California using DG and energy storage technologies. While most of the companies we interviewed had been active in the SGIP some were aware of the program but had not been actively involved in it. However, we felt it was important to obtain perspectives from both manufacturers and project developers even if they had not been active in the SGIP but who could have an impact of future DG and energy storage market development. Advanced Energy Storage Six different manufacturers and project developers were contacted to get their perspectives of the market for AES and the SGIP’s impact on the AES market. We specifically wanted to find out what they viewed as market barriers or market opportunities relative to growing the AES market in California. While California was the primary market for these companies, they were all looking to or expanding outside of California to locations such as Hawaii and even internationally (Australia and Japan). Market sectors served by these companies included residential, hotel/lodging, offices, healthcare, retail, academic, warehouses and the industrial sector. Most of the companies were targeting commercial and
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industrial customers; however there were a few targeting multi-tenant properties. Most used power purchase agreements (PPAs) or leases as the preferred mode of financing AES projects. Market Barriers: Most of the AES industry representatives interviewed reported that the utility interconnection process was the most unfavorable policy aspect slowing market adoption of AES technology. NEM was also pointed to as a market barrier as AES does not qualify for NEM; and representatives believe it creates a market advantage for other technologies. These representatives also note that lack of customer understanding of benefits provided by AES along with existing tariff structures were impediments to having a broader group of customer adopt AES. Market Opportunities: The companies identified incentives as the main driver for the California market. At the time of the interviews, California was seen as a large market because of the policy targets for installing AES through IOUs as well as on the customer side of the meter. In addition, the capability of AES to reduce demand charges was perceived by the AES representatives as one of the biggest drivers to host customers and thereby increase adoption of energy storage. Combined Heat and Power Ten different manufacturers and project developers were interviewed for the CHP market. They represented a combination of IC engine, microturbine, gas turbines and waste heat recovery technologies. Many of the interviewed companies have been in operation for more than ten years and do business in a variety of states other than California. Among the other states in which the businesses operate include Texas, Pennsylvania, New Jersey, Connecticut, New York, Maryland and Arizona. Market sectors served by the CHP industry include health care, lodging, retail, universities, warehouses and the industrial sector. Many of the businesses use PPA agreements but a number also target larger businesses that can provide their own financing. One representative noted that the “CHP industry has predominantly done self-funding. Funding typically comes from businesses capital improvement programs; which often is bond funding. Sometimes it is revenue funding from enterprise bonds.” Market Barriers: Almost all the CHP industry representatives identified departing load charges, standby and nonbypassable charges as some of the biggest policy barriers facing increased CHP adoption. However, a number of the representatives noted out that lack of customer awareness of CHP performance and benefits is an important barrier. Several representatives pointed out that the long amount of time for permitting for air quality reasons is also a significant barrier to increased market adoption. They also stated the belief that emissions standards in California are too strict for small engines; and are impeding the development of these smaller CHP applications.
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Market Opportunities: A number of the CHP representatives (i.e., gas turbines, IC engines, microturbines) viewed their technologies as being mature technologies with proven track record. They felt that because of these characteristics, CHP technologies offered several things not available from other technologies including increased reliability and resiliency, cost savings and the ability to reduce carbon emissions. Several representatives also pointed out that with high electricity and low natural gas prices in California, there is significant “spark spread” in the state; making CHP more cost effective than other competing technologies. Some of the representatives pointed out that SGIP incentives have helped support for the technology in the market. Fuel Cells Four different fuel cell manufacturers and project developers were interviewed to obtain their perspectives on the fuel cell market in California. Most of the companies interviewed have been in business for over 10 years. Geographically, most of the companies indicated they targeted California because of the incentives but also were active in a number of other states. Some of the companies were active internationally. The target audience for fuel cells was much the same as for the CHP industry; with a heavy emphasis on health care, lodging, retail, universities, warehouses and the industrial sector. Market Barriers: Representatives pinpointed high capital cost of the technology is one of the biggest barriers to widespread adoption though they noted costs are continuing to come down. Two of the representatives highlighted standby charges, departing load charges and the interconnection process as significant barriers. One representative noted “When you have new building that was never connected to the grid, and you put in CHP you still get hit even though you never were connected.” Because of the barriers, two of the companies were beginning to focus their marketing effort back east due to California’s high standby and departing load charges. The lack of alignment in policy towards natural gas fuel cells was also cited as a significant barrier. The representatives noted that the CEC views fuel cells as a preferred resource in the Integrated Energy Policy Report (IEPR). However, SCE does not consider fuel cells in their Preferred Resources Pilot (PRP) program and the CPUC’s Distributed Resource Plans (DRP) does not even require utilities to consider natural gas fueled DG in their plans. Market Opportunities: Representatives stated that the primary driver for the fuel cells is its attractiveness to customers as a clean technology with high reliability. In addition, these representatives felt that the low to no emissions aspect enables more rapid permitting; which provides fuel cells an edge over competing CHP technologies. They also believe fuel cells can grow in the market once customers realize the benefits associated with the combination of low emissions and small footprint with high power density. They also felt that increased growth was possible if utilities realized the technology’s ability to provide grid support applications, including providing reactive power.
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Wind Six different wind manufacturers and project developers were contacted obtain their perspectives on the market barriers and opportunities facing wind technology. The wind companies interviewed generally had been in business more than ten years. Their geographical markets were generally focused in the United States. The targeted market sectors include residential, agricultural, hotel and lodging, retail and the industrial sector. Market Barriers: The representatives noted that finding a match between the wind resource and customer demand is an inherent challenge to the wind industry. In particular, one representative indicated they need customers with at least 500 kW of constant load to make the economics work. From a siting perspective, representatives stated the “not in my backyard” (NIMBY) attitude is preventing increased adoption of wind. They also stated that lack of “green-friendly” tariffs in many locations was a problem for expanded growth. Moreover, they felt some communities oppose wind installations; viewing them as being obtrusive. Delays with interconnection and uncertainty about the ITC continue to be problems. Representatives also pointed out that wind has a difficult time competing against other DG technologies because wind doesn’t reduce demand charges. In addition, they felt that the wind industry is undercapitalized as compared to the solar industry but felt that could be changing. One of the representatives pointed out that California’s NEM policy is problematic in that it is limited to 1 MW; has too many variations (making it difficult to understand and implement) and does not allow multiple ownership of generation. Market Opportunities: Most of the representatives viewed that wind’s 30 year track record gives customers confidence in the technology. They also felt that wind’s lower cost per kWh is more competitive than most other technologies. They also felt that policies such as AB 32 and the GHG reduction requirements are promoting adoption of clean generation; including wind energy. They also felt that growth in complimenting technology such as energy storage is helping bolster the value of wind energy. They viewed federal tax credits (ITC/PTC) as being among the top drivers for increased market growth; and noted that when wind is coupled with solar it is eligible for tax credit and depreciation. However, they noted the uncertainty about tax credits is a problem. At least one representative pointed out that wind growth opportunity exists if wind can be used as one of the resources to help meet peak demand by allowing import of wind energy produced from states adjacent to California. They pointed out that expansion of the balancing area to outside California for wind can help flatten the high evening peaks shown in the CAISO “Duck Curve.”8 In addition, allowing export of
8 In examining possible impacts of increased intermittent renewable energy generation resulting from California’s Renewable Portfolio Standard, the California Independent System Operator (CAISO) developed a net load shape curve that became known as the “Duck Curve.” The “Duck Curve” shows the possibility that high penetration of intermittent resources such as solar PV could significantly increase generation in the early afternoon (when demand is historically low) but then drop off steeply in the evening, when demand is typically highest. The early afternoon situation would constitute “over generation” issues whereas the steep drop off in generation in the evening would create a significant need for fast ramping generation.
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wind power from California can also help avoid what is thought to be too high penetration; thereby flattening the high afternoon generation part of the “Duck Curve.” Key Takeaways from Manufacturer and Project Developer Interviews » Delays and additional costs associated with utility interconnection is universally cited as a primary barrier to expanded adoption of SGIP technologies. » The prospect of avoiding demand charges is viewed by the AES industry as one of the biggest value proposition for AES. However the lack of clarity in NEM policy is stranding the deployment. » Departing load charges, standby charges and nonbypassable charges are the biggest barriers to CHP adoption as they significantly impact project economics and appear to the industry to be an unjustified cost. » For fuel cells, their zero criteria pollutant emissions aspects, along with favorable footprint/power density provides them with a significant market opportunity, but requires additional education of customers. In addition, they believe these characteristics coupled with their ability to provide grid support applications including reactive power should make them attractive to utilities. However, high upfront costs remain a barrier. » For wind the biggest barriers to increased wind growth are the difficulties associated with matching customer load with the availability of adequate wind resource; NIMBY attitudes and restrictions on NEM policies. 5.3 Barriers Facing SGIP Technologies
Table 5-1 is a summary of perceived or actual barriers to SGIP technology market growth. These perceived or observed barriers are based on information obtained from interviews with a combination of participants in the SGIP, project developers and manufacturers of DG and energy storage equipment and key market players including SGIP PAs as well as PAs of different DG and energy storage incentive programs located throughout the Unites States. This list is not comprehensive but meant to convey some of the key barriers facing increased market growth of SGIP technologies.
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Table 5-1: Summary of Barriers to SGIP Technology Market Adoption
Barriers Perceived Problem or Concern High costs Some SGIP technologies can have high upfront costs, which makes them less attractive to potential users Some DG technologies have fueling costs, which can fluctuate over time, making their costs uncertain Costs of some SGIP technologies with slower learning curves, particularly those technologies that are already mature, will drop slower, making them less attractive future investments Low value SGIP technologies with a narrow band of market application may be perceived as providing lower value in proposition the market Benefits from SGIP technologies may have important but hard to monetize benefits. Examples include increased reliability or resiliency to the grid and lower GHG or criteria air pollution emissions Performance SGIP technologies with lower electrical efficiencies may be less attractive than higher efficiency alternatives Some CHP with lower useful waste heat recovery rates can have lower GHG emission benefits and lower cost effectiveness Some technologies with lower annual average capacity factors may provide less annual energy benefits, which may reduce their financial attractiveness or their ability to provide higher annual GHG emission reductions Technologies with higher annual performance decay provide lower lifetime benefits SGIP technologies with lower ramp rates have greater difficulty in providing load following capability and providing firming Technologies using intermittent resources (e.g., solar and wind) cannot provide capacity on an as-needed basis without supplemental support (e.g., firming generation or storage) Environmental Technologies with higher emission rates of criteria air pollutants may have higher capital and O&M costs impacts (due to required air pollution control equipment); may have longer deployment time (due to permitting requirements) and may be perceived as “less green” Technologies with higher GHG emissions are less attractive as a way to help meet GHG emission targets
Operation of technologies that impact wildlife (e.g., wind turbines and bats) or habitat may be more difficult to deploy and be perceived as being “less green” Technologies that handle solid or liquid wastes (e.g., dairy biogas systems) may require permits, incur treatment costs and be viewed as being “less green” Financing Some SGIP technologies may lack low cost financing that would help increase their attractiveness to potential users There is a lack of third party ownership models in certain SGIP technologies that could otherwise help expand their market adoption Financing institutes lack familiarity with certain SGIP technologies, making it more difficult for these technologies to receive low cost financing Market Many potential users are unaware of SGIP technologies, their performance aspects and their potential awareness benefits Utility Interconnection requirements are still a problem for some SGIP technologies interactions Standby charges, demand charges, departing load charges pose economic challenges to increased growth of many SGIP technologies Utility perspective that increased DG may make it more difficult to maintain system reliability and responsiveness Resource Some technologies require fuel resources (e.g., wind, waste heat, natural gas, biogas, etc.) that must often availability be contractually obligated over the life of the project Some resources (e.g., wind, biogas) are distributed unevenly throughout the state; creating different value propositions for each utility Institutional Lack of clear policies on NEM aspects Lack of cohesive and uniform approach on policies that affect future growth of SGIP technologies Uncertainty on future and longevity of federal tax policies Unfamiliarity of government policy makers with SGIP technologies and lack of connection to customer needs
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5.4 Perceived Drivers to Growth in SGIP Technologies
Interviewees also identified a number of drivers they believe have either increased growth in SGIP technologies or are important to future market growth of the technologies. Table 5-2 is a summary of drivers as expressed by the different groups who were interviewed.
Table 5-2: Realized or Perceived Drivers to Increased Growth in SGIP Technologies
Surveyed Group Realized or Perceived Drivers PAs (both inside and Incentives are viewed as the primary driver to expanding the program and outside CA) and Program promoting market development Staff Matching technologies to customer use cases is viewed as an “untapped” opportunity for expanding market growth Flexible financing options are viewed as increasing the numbers and types of customers who can use the technologies and expanding market growth Progressive environmental policies (e.g., GHG) can help drive “clean” DG and storage market growth Alignment of policies can be a critical driver to increased market development of SGIP technologies by sending a positive signal to the investment market Host Customers Low first costs influence the initial decision to participate; tax benefits were a secondary driver Increased program outreach Increased streamlining of the incentive process as well as processes that require utility approvals Expanded number of technology vendors who provide honest assessments of technology costs and performance Developers and Overall government support of the technologies is viewed as critical in overcoming Manufacturers institutional and market barriers Incentives or other financial support to help overcome first costs or market uncertainties California’s need for “clean” and reliable DG technologies Federal and state tax benefits 5.5 Expected Outcomes
Although helping to transform California’s DG and energy storage markets is one of four primary goals of the SGIP, there has been little concerted discussion among policy makers, the SGIP PAs or stakeholders about what would constitute transformed DG and energy storage markets.9
9 The four goals of the SGIP were stated in CPUC Decision 11-09-015 (September 8, 2011). The four goals are: 1) reduce GHG emissions in the electricity sector; 2) help with demand reduction and reduce customer electricity purchases; 3) improve electricity system reliability through improved transmission and distribution system utilization and 4) help with market transformation of distributed energy resources. See http://docs.cpuc.ca.gov/PublishedDocs/WORD_PDF/FINAL_DECISION/143459.PDF
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We can look to CPUC decisions to see if they help identify how the SGIP should help transform California’s DG and energy storage markets. In its September 8, 2011 decision modifying the SGIP and implementing Senate Bill 412, the CPUC adopted six guiding principles for the SGIP.10 Those six guiding principles are: » The SGIP should only support technologies that produce fewer GHG emissions than they avoid from the grid. » The SGIP should support behind the meter “self-generation” DER technologies, which serve the primary purpose of offsetting some or all of a host-customer’s on-site demand. » The SGIP should only support commercially available technologies. » The SGIP should target best of class DER by paying for performance. » The SGIP incentives should focus on projects that efficiently utilize the existing transmission and distribution system. » The SGIP should complement the structure of and be coordinated with existing ratepayer supported programs, especially the California Solar Initiative (CSI), which is aimed at transforming the market for renewable DG by driving down prices and increasing performance of DER. These guiding principles help determine objectives of the SGIP moving forward and should be taken into account when developing a SGIP market transformation plan. However, the principles constitute only a portion of the expected outcomes of a transformed DG/energy storage market. We can use information from the interviews to develop a more comprehensive but still preliminary list of expected outcomes. Table 5-3 is a preliminary list of expected outcomes broken out by short term (e.g., less than five years), medium term (e.g., five to ten years), and longer term (e.g., beyond ten years).
Table 5-3: Expected Outcomes by Timeframe Timeframe Expected Outcomes Short Term Increase interest in SGIP technologies Improved performance and cost Faster deployment of SGIP technologies Improved ability to fit market needs Increased number of market players Improved ability to reach users Medium Term Increased acceptance of SGIP technologies by customers and utilities Significant improvement in SGIP technology cost and performance Development of financing options that greatly expands market reach Longer Term DG/storage makes up a significant portion of CA's energy mix DG/storage provides valuable environmental benefits DG/storage contributes to high employment and job development
10 Ibid
PROGRAM LOGIC AND THEORY | 5-24 6 CHARACTERIZING CALIFORNIA’S DG AND ENERGY STORAGE MARKETS
In characterizing California’s DG and energy storage markets, we provide estimates of the amount of DG or energy storage capacity that could be installed in different market segments to meet the energy needs of that market segment. For example, the “technical” DG potential represents the amount of DG capacity that would serve the energy needs of the different applications ignoring the cost effectiveness of the technology. The “economic” potential is a subset of the technical potential in that it identifies the amount of capacity that could be installed not only to meet the energy needs of the market segment but also meets specific energy cost targets. Estimates of technical and economic potential are further broken down by specific DG technologies (e.g., fuel cells, IC engines, etc.) and by applications within a market segment (e.g., hospitals, food industry, retail, etc.). We compare estimates of technical and economic potentials to historical and projected capacities of DG and energy storage technologies in California. We then provide estimates of “market” potential, which represents the amount of capacity we expect can be realistically added to the market on an annual basis. Tracking market adoption rates against the technical or economic potentials at different years identifies the extent to which a technology is making inroads into the market segment, and ultimately towards a transformed market. In addition, we overlay historical trends in the installed capacity of these technologies against the backdrop of policies and market changes occurring during the observed timeframe. This overlay helps pinpoint specific drivers that may have influenced technology adoption rates into the market. In characterizing California’s DG markets, we break DG technologies into three broad technology categories based on their distinct performance or fuel considerations: combined heat and power (CHP) systems, wind energy systems, and biogas fueled technologies. Due to the small amount of installed capacity of waste heat to power (WHP) and pressure reduction turbines (PRT) projects within the SGIP, we do not include them in the market characterization.1 In addition, because of the emerging status of energy storage technologies, we do not break the energy storage category down into any sub-technology areas. Each of the following sections provides market characteristics by CHP, wind, biogas, and energy storage categories. 6.1 California’s Combined Heat and Power (CHP) Market
In Section 6.1 we estimate potentials for new customer-sited CHP generating capacity in the service territories of California’s investor-owned utilities (IOU).2 We begin by estimating total technical potential. To identify new potential we subtract existing CHP capacity from the total. Next, we estimate the portion of new technical potential that is economic. Finally, we estimate the portion of this economic potential
1 There is only one WHP project currently “active” in the SGIP queue, two PRT projects that are in the “PBI in payment” stage, and seven other PRT projects are active in the queue. 2 Non-IOU utilities also have CHP potential. A 2012 report by ICF suggested 22% of remaining technical potential in 2030 would be among these utilities. About 60% of that is in the LADWP service territory.
CHARACTERIZING CALIFORNIA’S DG AND ENERGY STORAGE MARKETS | 6-1 SGIP Market Transformation Report
that may be adopted as market potential. We forecast these potentials up to 2024 and distinguish them by market segment, a CHP capacity bin, a climate region, and an electric utility. Overall Approach We use a “bottom-up” approach to estimate CHP potentials.3 We begin with counts of all business establishments and residential units in the state in a base year. We then group them by common market segment, estimate of building size, and climate. We model a group building by daily activity type and size with a full year of hourly electrical and thermal end-use loads.4 We then develop an array of CHP system specifications based on the modeled loads. The specifications include a prime mover technology, an absorption chiller option, and a generating capacity.5 The largest of the generating capacities identifies the group’s average technical potential and an associated prime mover technology and absorption chiller option.6 Finally, we multiply a group average technical potential by its population to get a group total technical potential. To identify new technical potential we subtract existing CHP capacity from group total technical potential. We apportion existing capacity to a group based on market segment, prime mover type, and capacity bin proportions of total technical potential. The remainder is new technical potential by group. We forecast future technical potential for groups using a base year estimate and segment–specific annual growth rates.7 We determine economic potential based on total resource cost (TRC) tests that were conducted under a complementary SGIP cost effectiveness study.8 To be consistent with the results of that study, we assume that CHP systems that achieve TRC ratios of 0.8 or greater are cost effective. We examine the cost effectiveness of over 70 proxy CHP systems to represent the many systems specified for over 500 different groups. The proxy systems differ by prime mover type, capacity bin, climate region, and IOU. If a group’s proxy system is cost effective in a particular year, the group’s economic potential equals its technical potential in that year. If a group’s proxy system is not cost effective, we identify the next largest capacity among its other CHP specifications that has a cost effective proxy system. That system’s capacity is the basis of the group’s economic potential. We consider all the prime mover and absorption chiller options before concluding a group has no economic potential.
3 A “top-down” approach might begin with total natural gas and electricity consumption by market segments and then estimate what portions might be offset by CHP. 4 We use Itron’s SitePro software to model hourly loads by end-use based on market segment, building size, and climate region. 5 Appendix E describes development of system specifications in detail. 6 Technical potential for CHP exists where onsite thermal loads can be served with waste heat captured from an electric generator serving onsite electric loads. Thermal loads may include cooling loads where CHP system includes an absorption chiller. 7 Annual growth rates based on a 2012 ICF report: “Combined Heat and Power: Policy Analysis and 2011-2030 Market Assessment,” prepared for the California Energy Commission, ICF International, Inc., February 2012. See Appendix E for list of assumed growth rates. 8 Itron on behalf of PG&E and the SGIP Working Group, “2015 Self-Generation Incentive Program Cost Effectiveness Study,” October 2015; final report pending release
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We also estimate market potentials using historical data on installed CHP capacity in California, logistic models (S-curves) and a target level of market potential. We start by adopting a target market potential from other previous potential studies. Using the logistic models and historical installation data, we develop annual growth rates. We assess the reasonableness of the average annual growth and, if necessary, readjust the target market potential. A wide variety of market barriers from alternative investment strategies to uncertainty about air quality regulations, prevent some CHP with economic potential from achieving market potential status. We take these factors, as well as the ability of the CHP technology to respond to improvements in manufacturing, in assessing the reasonableness of the average annual growth rate. Estimated CHP Technical Potential We estimate new CHP technical potential for over 500 groups categorized by market segment, building size, climate region, and electric utility. Below we list 2014 technical potentials summarized by group characteristics. Technical Potential by Market Segment and IOU Service Territory
We estimate a 2014 new CHP technical potential of 24,236 MW across the service area of the three IOUs: PG&E, SCE, and SDG&E. Table 6-1 lists new CHP technical potential subtotals in capacity (MW) for each market segment as well as for each IOU. The market segments in Table 6-1 are primarily commercial apart from large and small multifamily and food manufacturing segments.
Table 6-1: 2014 New CHP Technical Potential (MW) by Segment and IOU Segment PG&E SCE SDG&E TOTAL Percent College 54.7 106 12.79 174 0.72% Food Manufacturing9 4,321 3,278 280 7,879 32.51% Food Store 988 1,489 294 2,771 11.43% Health, Hospital 1,120 1,237 222 2,579 10.64% Large Multifamily 360 595 118 1,073 4.43% Lodging, Hotel 644 634 204 1,482 6.11% Office, Large 913 1,169 195 2,277 9.40% Office, Small 52.2 62.38 18.38 133 0.55% Restaurant, Sit-Down 829 1,737 390 2,955 12.19% Retail, Large 539 833 179 1,551 6.40% Small Multifamily 0.00 0.00 0.00 0.00 0.00% School 5.22 5.58 1.49 12.3 0.05% Warehouse 702 570 79.0 1,351 5.57% TOTAL 10,527 11,717 1,992 24,236 100% Percent 43% 48% 8% 100%
9 Food manufacturing includes food processing and handling
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Table 6-1 shows SCE has over 11.7 GW and 48% of the total technical potential. PG&E has just over 10.5 GW and 43% of the total. In the smaller SDG&E territory, there is almost 2.0 GW of technical potential. As identified in Table 6-1, the highest technical potential for CHP applications falls into five segments: food manufacturing, the health and hospital industry, food stores (e.g., Safeway, Albertsons, etc.,) large offices, and sit-down restaurants. Within these applications, CHP systems are typically used to offset electricity demands (e.g., lighting or HVAC in offices, restaurants and hospitals, electric chillers and motors in food stores, and food manufacturing) and to offset thermal needs (e.g., space heating in offices, domestic hot water needs for laundries in hospitals and the health industry, cooling with absorption chillers in food manufacturing and food stores). There are a variety of prime movers and configurations of heat recovery systems for these different applications.10 Figure 6-1 shows how CHP systems can be used in these applications to help meet electricity and thermal energy demands.
Figure 6-1: How CHP Displaces Host Site Electrical and Thermal Loads
BASELINE CHP
Baseline Electricity Electrical Load Grid
CHP CHP Prime Mover Fuel BaseCCC line Boiler Prime Mover NaturalNaturalNaturalBoiler Heat Heating Heat Rejected Heat Boiler Exchanger Load Exchanger FuelGasGasGas
Baseline Chiller
Electric Cooling Absorption Chiller Load Chiller GridGrid
10 Additional information on the different prime movers, their characteristics and the application of CHP to different market segments can be found in the EPA report “Catalog of CHP Technologies.” United States Environmental Protection Agency. Combined Heat and Power Partnership, March 2015; from http://www3.epa.gov/chp/documents/catalog_chptech_full.pdf
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Table 6-1 shows the food manufacturing segment has the most technical potential with almost 7.9 GW, one-third of total technical potential. This is the one manufacturing sector segment we included in the study. Its potential is greatest for PG&E. A roadmap that identifies the different pathways in which the food manufacturing and processing industries can begin to approach the technical potential is beyond the scope of this report. However, due to the significant technical potential associated with California’s food manufacturing industry, it is important to have a better understanding of this industry. California’s food manufacturing/process industry is highly diversified. It comprises over 3,000 plants processing commodities from over 88,000 farms. Over 240 commodity and trade associations represent food and agricultural interests in California.11 The diversity of California’s agriculture across all sectors of food operations is reflected in the range in size of the processing facilities. They include all types and sizes, from the “Mom and Pop” shops to some of the largest single site operations in the world. In addition, the food manufacturing and processing industries are closely associated with other market segments, which affects their overall economics. Figure 6-2 shows the relationship and degree to which food manufacturing and processing are interconnected to other market segments.
Figure 6-2: Relationship of Food Manufacturing and Processing Segments to Other Market Segments12
Water supply, energy supply, and sewage removal are essential to most food manufacturing and processing facilities. These services, once inexpensive and taken for granted, have become expensive and sometimes unreliable, placing California food processors at a serious disadvantage in the face of intense
11 Food Industry Advisory Committee and California Institute of Food and Agricultural Research University of California at Davis, “California Food Processing Industry Technology Roadmap,” prepared for the California Energy Commission, July 2004. From http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.466.6676&rep=rep1&type=pdf 12 KMPG International, “The Agricultural and Food Value Chain: Entering the New Era of Cooperation,” May 2013. From https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/agricultural-food-value-chain-report.pdf
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competition from both domestic and foreign producers.13 Globalization of the food manufacturing and processing industries is adding complexity to an already complex market. Food manufacturing and processing companies in California contemplating CHP face a variety of challenges including high upfront costs of CHP systems, internal competition for securing project financing (versus use of capital for facility maintenance or expansion), concerns over potential project risks and effects on project payback, perceived or real permitting and regulatory constraints, and concerns over changing environmental or other regulatory policies.14 At the same time, CHP systems can provide real relief to food manufacturing and processing companies facing increasing energy costs, concerns over future volatility in energy costs, and the need for highly reliable power and resiliency. In addition, the ability of certain food manufacturing and processing facilities to install biogas to energy CHP systems that use food processing wastes as a feedstock to an anaerobic digestion process, can also help address waste disposal issues. After food manufacturing, Table 6-1 shows a cluster of four segments each with between 9.4% and 12.2% of technical potential. They include the two traditional CHP markets of health and office segments, and two of the nontraditional CHP markets of restaurant and food store segments. All four segments have between 2.5 and 3.0 GW of potential. The three segments of retail, lodging, and warehousing each have about 6% of the total potential. Technical potential falls below 5% with the large multifamily segment. College, school, small office, and small multifamily segments together have 1.4% of total. Note that Table 6-1 shows zero technical potential for the small multifamily segment. This segment includes buildings with fewer than 50 units. These buildings are so small that technical potential for all of them was less than the 30 kW threshold for technical potential.
Figure 6-3 shows segment percentages of IOU technical potential and MW of each IOU’s largest market segment. The percentages differ between IOUs in part due to different concentrations and sizes of market segments and in part due to different climates.
13 Food Industry Advisory Committee, ibid 14 Chittum, A. and Kaufman, N., American Council for an Energy-Efficient Economy (ACEEE), “Challenges Facing Combined Heat and Power Today: A State-by-State Assessment,” September 2011. From http://aceee.org/sites/default/files/publications/researchreports/ie111.pdf
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Figure 6-3: 2014 Technical Potential Percentages by Segment and IOU
Food Manufacturing
Restaurant, Sit-Down 4,321 MW
Food Store 3,278 MW
390 MW Health, Hospital
Office, Large PG&E Lodging, Hotel
Retail, Large SCE
Warehouse SDG&E
Large Multifamily
College
Office, Small
School
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Percent of New Technical Potential
Market Segments and CHP Capacity Bins Technical potential differs not only between market segments, but also between capacity bins. Segments with larger buildings typically require larger systems. We summarize 2014 technical potential by capacity bins for the different market segments and each IOU. Table 6-2, Table 6-3, and Table 6-4 show the breakout of 2014 CHP technical potential by market segment and capacity for PG&E, SCE, and SDG&E respectively.
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Table 6-2: PG&E 2014 Technical Potential by Segment and System Size Category (MW)
Under 50 to 500 to 1 to Segment 50 kW 500 kW 1000 kW 5 MW TOTAL College 1.02 14.3 4.29 35.1 54.7 Food Manufacturing 0.00 0.00 126 4195 4321 Food Store 0.00 148 369 471 988 Health, Hospital 0.00 120 388 612 1120 Large Multifamily 0.00 339 21.3 0.00 360 Lodging, Hotel 0.00 347 161 136 644 Office, Large 131 432 0.00 350 913 Office, Small 52.2 0.00 0.00 0.00 52.2 Restaurant, Sit-Down 276 553 0.00 0.00 829 Retail, Large 79.4 392 47.7 19.9 539 Small Multifamily 0.00 0.00 0.00 0.00 0.00 School 5.22 0.00 0.00 0.00 5.22 Warehouse 79.4 372 79.8 170 702 TOTAL 624 2,717 1,197 5,989 10,527
Table 6-3: SCE 2014 Technical Potential MW by Segment and System Size Category (MW) Under 50 to 500 to 1 to Segment 50 kW 500 kW 1000 kW 5 MW TOTAL College 2.37 11.8 8.6 83.5 106 Food Manufacturing 0.00 0.00 107 3170 3278 Food Store 0.00 178 696 615 1489 Health, Hospital 0.00 94.8 473 670 1237 Large Multifamily 0.00 595 0.00 0.00 595 Lodging, Hotel 0.00 284 189 161 634 Office, Large 150 532 0.00 488 1169 Office, Small 62.4 0.00 0.00 0.00 62.4 Restaurant, Sit-Down 376 1361 0.00 0.00 1737 Retail, Large 0.00 701 93.1 38.8 833 Small Multifamily 0.00 0.00 0.00 0.00 0.00 School 5.58 0.00 0.00 0.00 5.6 Warehouse 58.1 295 114 104 570 TOTAL 654 4,052 1,680 5,330 11,717
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Table 6-4: SDG&E 2014 Technical Potential MW by Segment and System Size Category (MW)
Under 50 to 500 to 1 to Segment 50 kW 500 kW 1000 kW 5 MW TOTAL College 0.421 2.63 0.96 8.8 12.8 Food Manufacturing 0.00 0.00 10.8 269 280 Food Store 0.00 44.8 119 130 294 Health, Hospital 0.00 15.0 86.7 120 222 Large Multifamily 0.00 114 3.64 0.00 118 Lodging, Hotel 0.00 76.6 53.9 73.8 204 Office, Large 30.7 105 0.00 58.8 195 Office, Small 18.4 0.00 0.00 0.00 18.4 Restaurant, Sit-Down 79.6 310 0.00 0.00 390 Retail, Large 0.00 137 29.6 12.2 179 Small Multifamily 0.00 0.00 0.00 0.00 0.00 School 1.49 0.00 0.00 0.00 1.49 Warehouse 10.2 43.1 12.6 13.1 79.0 TOTAL 141 849 316.7 686 1,992
PG&E and SCE have the greatest technical potential in the 1 to 5 MW size category, with food manufacturing dominating the category. SDG&E has greatest potential in the 50 to 500 kW category and the second greatest potential in the 1 to 5 MW category. PG&E and SCE have the second greatest potential in the 50 to 500 kW size category, with the restaurant and several other segments making up the greatest potential. The third highest potential for all IOUs is the 500 to 1,000 kW category. Food store and health segments dominate the 500 to 1,000 kW categories. All IOUs have least potential in the under 50 kW size category, with the restaurant segment comprising the largest potential, followed by the office segment. CHP technical potential will grow as new buildings are constructed. We estimated future technical potentials using segment-specific cumulative growth rates. Table 6-5 lists the resulting current and future year technical potentials.
Table 6-5: 2014 and Future CHP Technical Potentials by IOU (MW) Technical Technical Technical Technical Technical IOU Potential 2014 Potential 2017 Potential 2020 Potential 2024 Potential 2030 PG&E 10,527 10,835 11,153 11,592 12,286 SCE 11,717 12,055 12,404 12,886 13,648 SDG&E 1,992 2,050 2,110 2,193 2,324 Total 24,236 24,940 25,666 26,671 28,258
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CHP Economic Potential We estimate CHP economic potential as the portion of new technical potential that meets a cost effectiveness criterion. The criterion is based on a complementary study conducted on the cost effectiveness of SGIP technologies. To be consistent with the results of that study, we assume that technologies with a Total Resource Cost (TRC) benefit-to-cost ratio of 0.8 or greater represent cost effective resources.15 The criterion is the same for every group and every specified CHP system regardless of size or prime mover technology or inclusion of absorption chilling. System costs, on the other hand, differ substantially between prime mover technologies. Emerging prime mover technologies like microturbines and fuel cells currently have higher unit capital costs than IC engines and gas turbines. Absorption chillers also add to system capital costs. System benefits also differ between inland and coastal climate regions with inland regions showing a small advantage. System costs and benefits differ between IOUs due to different electric and gas tariffs. Meanwhile, with time, all costs and benefits may change. As more systems are sold, system capital costs tend to decline. As natural gas prices fall, system operating costs decrease. These considerations were taken into account in the SGIP cost effectiveness study and model16. We use the SGIP cost effectiveness model to estimate benefit-to-cost ratios in TRC tests for current and future years. We develop ratios for over 70 proxy systems distinguished by prime mover type, capacity climate region, and IOU territory. Systems that met the criterion are included in economic potential for the year. Figure 6-4 shows a select set of TRC test benefit-to-cost ratios from our cost effectiveness model. The ratios are for proxy CHP systems located in the coastal climate region of PG&E territory. Ratios are very similar in other inland and coastal climate regions and for other IOUs. Each series of vertical bars in Figure 6-4 represents a proxy CHP system and shows its TRC benefit-to-cost ratios for years 2014, 2017, 2020, and 2024.17 Several of the system specifications include absorption chillers as indicated by the suffix ‘AbsChlr’.
15 For additional description of why a TRC ratio of 0.8 is used for establishing cost effectiveness and the details on the approach, please see “2015 Self-Generation Incentive Program Cost Effectiveness Study,” October 2015; final report pending release 16 A publicly available cost effectiveness model entitled “SGIPce” accompanies the study and is pending release. 17 The cost effectiveness model does not extend to the year 2030.
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Figure 6-4: Total Resource Cost Test Benefit/Cost Ratios for Select Proxy CHP Systems
1.1
1.0
0.9
0.8 TRC Benefit/Cost Ratio Benefit/Cost TRC
0.7
0.6
2014 2017 2020 2024
Figure 6-4 shows that in each year fuel cells and microturbines have ratios above 0.6 but below 0.8. Systems with these two emerging technology prime movers do not meet the cost effectiveness criterion and so do not contribute to economic potential from 2014 through 2024. Figure 6-4 also shows that in each year the other CHP systems have ratios above 0.8. Technical potential based upon these other systems do contribute to economic potential from 2014 through 2024. Since none of the proxy systems from 2014 to 2024 has TRC ratios that progress across the 0.8 criterion, we consider economic potential in those years to arise solely from IC engine and gas turbine capacity. New 2014 CHP technical potential includes 11.7 GW of fuel cells and microturbines that do not meet the cost effectiveness criterion. As described earlier, however, for economic potential we do not consider simply a group’s technical potential based upon its largest specified CHP system capacity and associated prime mover. We also consider a reduced technical potential for the group based upon a smaller specified system with a different prime mover. We then define a group’s economic potential based upon the largest specified CHP system capacity that is cost effective.18
18 For example, a group’s CHP specifications may include a 500 kW fuel cell, a 400 kW microturbine, and a 300 kW IC engine. Technical potential is 500 kW based on the fuel cell. Economic potential, however, is 300 kW based upon the IC engine because it is the largest capacity that is cost effective.
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For groups whose largest specified CHP system is a fuel cell or microturbine, we consider the smaller IC engines and gas turbines specified for them as a downwardly adjusted technical potential. For those groups, the smaller systems total 3.0 GW, or almost 25% of the 11.7 GW based upon fuel cells and microturbines. As a result, 8.7 GW of new technical potential does not meet the cost effectiveness criterion. Economic potential in 2014 then is 15.5 GW, or 64% of the 24.2 GW of 2014 new technical potential. Since prime mover specifications for groups and prime mover TRC outcomes do not change over the future years of interest, economic potential in those future years likewise is 64% of their technical potential. Potentials and Observed Growth in California’s CHP Market California clearly has substantial CHP technical and economic potential in the 30 kW to 5000 kW system capacity range. CHP in this range includes several different prime mover technologies. Figure 6-5 shows observed CA growth since 1998 in cumulative CHP capacity for the different prime mover technologies SGIP supported.
Figure 6-5: Cumulative CHP Installed Capacity in CA for Select Prime Mover Technologies (1998-2014)19
0.45
0.40
0.35
0.30
0.25
0.20
0.15 Cumulative Installed Capacity (GW) Capacity Installed Cumulative
0.10
0.05
0.00 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 IC Engine Gas Turbine Fuel Cell Microturbine
19 Source: DOE ICF data for CA, includes natural gas and biogas fueled CHP capacity
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Figure 6-5 shows that by 2014 almost 0.45 GW of cumulative capacity has been installed among the prime mover technologies SGIP supported. During this period, the SGIP supported 0.33 GW of this new capacity. Cumulative growth peaked around 2005 and slowed until 2012. Growth sped up through 2013 then slowed again. IC engines have been the predominant prime mover technology. IC engines currently contribute over three times the capacity of the next largest contributor, gas turbines. Fuel cell and microturbine technologies contribute smaller capacities. Annual additions of these two emerging technologies have grown in proportion to IC engines. Additions, in general, are unsteady. Figure 6-6 shows annual additions by prime mover technology.
Figure 6-6: Annual Capacity Additions in CA for Select Prime Mover Technologies
50
45
40
35
30
25
20
Annual Capacity ddition (MW) ddition Capacity Annual 15
10
5
0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 IC Engine Gas Turbine Fuel Cell Microturbine
Figure 6-6 shows that in recent years, annual additions of IC engines have declined sharply both in absolute and relative terms. Additions among the other technologies have cycled up and down. In 2012 and 2013, fuel cell and gas turbine technology contributions peaked respectively. Both then fell back sharply but the peaks suggest greater rates of adoption exist.
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Technical Potential of All-electric Fuel Cells When specifying CHP technical potentials for population groups we develop system sizes for several prime mover technologies. One of these (electric only fuel cells) does not fall into the typical CHP configuration since it does not recover waste heat for onsite use. Instead, heat from the process is captured and used in the internal reformers to help increase the electrical efficiency of the fuel cell. The all-electric fuel cell is recognized in the SGIP for its higher electric conversion efficiency despite not using waste heat to serve thermal loads. Here we discuss the special treatment of all-electric fuel cells. Most population groups have substantial periods with low thermal loads that sharply limit system capacity specification. A large system with low thermal loads can waste heat. When we size a system as an all- electric fuel, thermal loads do not matter. There is no constraint against wasting heat. Only electric loads matter. This tends to bias all-electric fuel cell technical potential upwards relative to true CHP systems. We attempt to address the upward bias of technical potential for all-electric fuel cells by recognizing an operational reality of fuel cells generally. We typically see fuel cell operation with 365/24/7 generation very near its ideal of full capacity, and with infrequent and small modulations in output. While both all- electric and CHP fuel cells operate in this fashion, and many gas turbines too, we tighten an annual operational constraint for the all-electric fuel cell only. The constraint is operation with a minimum of 7,000 equivalent full load hours of operation rather 5,000 used for CHP technologies. So, while only hourly electric and not thermal loads matter for all-electric fuel cells, many more of those hours matter. All-electric fuel cells contribute 4.3% to new technical potential. CHP fuel cells contribute 41%. Fuel cells are a preferred prime mover technology where there is a high ratio of power to heat. With both fuel cell types subject to the same 5,000 hour constraint, fuel cell contributions would change little but more would be all-electric and fewer would be CHP. With neither type of fuel cell meeting the cost effectiveness criterion during 2014 to 2024, they do not contribute to economic potential during that time. This does not mean fuel cells will not be adopted during that period. The continued adoption of this emerging technology, however, may require continued policy support. CHP Market Potential CHP market potential is new CHP that actually may be adopted over time. As a starting point, we assume market potential to be 25% of economic potential.20 Based on our cost effectiveness examination, 64% of technical potential is economic in 2014 and that percentage will not change in 2024. In absolute terms, new technical and economic potential both continue to grow. When considering market potential and these ‘moving targets’, we set our sights on a fixed year of 2024. We then examine growth of cumulative CHP capacity from its existing 2014 capacity to its 2024 market potential. The 2024 market potential is 4.5 GW. Figure 6-7 shows growth in existing and forecast cumulative capacity.
20 Adoption is not instant, taking from years to decades. The 2012 ICF “Combined Heat and Power: Policy Analysis and 2011- 2030 Market Assessment” describes adoption rates for base, medium, and high scenario forecasts where higher scenarios include more policy support. Adoption rates are 11.8%, 22.7%, and 38.3% respectively for base, medium, and high. These percentages are based upon new technical potential. When adjusted for 64% economic potential they are 18.4%, 35.5%, and 59.8%.
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Figure 6-7: Growth in Cumulative CHP Capacity toward 2024 Market Potential 5,000
4,500 2024 Market Potential 4,000 4,521 MW
3,500 Clean Energy Jobs Plan Target 3,000 2,594 MW
2030 2,500 3,086 MW AB32 Target 1,844 MW
2,000 Cumulative Capacity (MW) Capacity Cumulative 1,500 2020 1,725 MW
1,000
500 2014 644 MW 0 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 Existing Forecast
Figure 6-7 shows forecast growth in cumulative capacity based on actual history from before 1980 up until 2014. We use market saturation curves (known as sigmoid or “S-Curves”) in forecasting annual market growth in CHP.21 The S-Curve growth model is a logistic model frequently used to forecast the growth of a technology. The initial growth forecast is approximately exponential until almost 2030. Then as market saturation begins, the growth slows and more gradually reaches the 2024 market potential. Figure 6-7 shows it is not until after 2060 that growth reaches the 2024 market potential. Although the market potential will be greater in 2060 than in 2024, the forecast demonstrates the steep growth that would be required to reach market potential. Figure 6-7 includes two important policy targets based on new CHP capacity additions: the AB32 target for 2020 and the Clean Energy Jobs Plan (CEJP) target for 2030.22 The growth forecast undershoots the AB32 target slightly but then surpasses the CEJP by almost 500 MW, or 19% of the CEJP target. Slower
21 See for example, Michelfelder, R. and Morrin, M. “Overview of New Product Diffusion Sales Forecasting Models,” AUS Consultants, from http://law.unh.edu/assets/images/uploads/pages/ipmanagement-new-product-diffusion-sales- forecasting-models.pdf 22 Both policy’s grand total targets include CHP capacity beyond the maximum 5000 kW capacity considered here. We take only a portion of the grand totals in order to represent the limited range of 30 kW to 5000 kW discussed here.
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growth may hit the CEJP target but would do so at the expense of missing the AB32 target. To match the growth forecast of Figure 6-7 will require considerable acceleration in CHP adoption. Figure 6-8 demonstrates this acceleration in terms of both cumulative capacity growth and annual capacity additions.
Figure 6-8: Growth in Cumulative CHP Capacity and Annual Capacity Additions
3,000 150 2024 2,279 MW 2,500 125
2,000 100
1,500 75
1,000 50
Cumulative Capacity (MW) Capacity Cumulative Annual Capacity Addition (MW) Addition Capacity Annual
500 25
0 0
Existing Forecast
The lines and left vertical axis of Figure 6-8 show growth of cumulative CHP capacity in CA in the 30 kW to 5000 kW range. The red line shows historic growth that includes all existing capacity. The dashed yellow line shows a growth forecast that adds new capacity. The forecast annual increase is an average of 13.5%. This is the growth rate required for CHP to go beyond the 2014 cumulative capacity of 644 MW to the forecast 4,500 MW capacity at 2024 on the logistic curve of Figure 6-7. This cumulative growth rate is large but not unprecedented with energy technologies.23 Such rates sustained for a decade suggest a substantial shift in energy market investment and focus.
23 For example, the solar PV market in the Unites States grew from 2009 through 2014 at a compound annual growth rate of 60%. See http://www.greentechmedia.com/articles/read/shayle-kanns-solar-predictions. We recognize that one of the factors allowing rapid PV growth is the capability to mass manufacture PV modules and other components and this may not be the case for CHP technologies. However, for smaller CHP systems, modular sizing may make mass production similar to PV more practical.
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The vertical bars and right vertical axis of Figure 6-8 show annual capacity additions in CA in the 30 kW to 5000 kW range. The red bars show existing historic additions that include capacity added during the SGIP years 2000 through 2014. The yellow bars show capacity additions forecast that are new capacity. These additions follow the logistic curve of Figure 6-7 to hit the 2024 market potential target after 2060. The yellow bars indicate that new additions will be twice as large by 2020 as seen during the SGIP years. With these additions, by 2024 the cumulative capacity reaches 2,279 MW. While this is still below the assumed 2024 market potential, as Figure 6-4 shows, this cumulative forecast surpasses the CEJP target. 6.2 California’s Wind Market
This section estimates the total technical, economic and market potentials for distributed wind energy in California and in the three major IOU service territories. The potential estimates target wind turbines 50 kW to 5 MW that could be located on the customer side of the meter. Overall Approach We estimate wind technical potential using California wind resource data24 and wind turbine operating characteristics (e.g., expected capacity factors at given wind speeds and turbine height) for wind turbines in the size range of 50 kW to 5 MW.25 We restrict wind applications to single-site applications (i.e., instead of grouping wind turbines into “farms,” a single-site application associates one or more wind turbines directly to a customer site to help meet that customer’s electricity demands) and turbine hub heights of no more than 80 meters (approximately 260 feet).26 In addition, we follow land use exclusions set forth by NREL for wind resource modeling, which excludes wilderness areas, parks, urban areas, and bodies of water.27 Distributed wind energy systems under SGIP are located behind the customer meter, so we use Census information on numbers of select business types in each county as possible host sites for distributed wind turbine installations.
24 Sources of California wind resource data include CEC PIER wind resource data; and NREL’s wind data site (http://www.nrel.gov/gis/data_wind.html); and U.S. DOE EERE (http://energy.gov/eere/wind/wind-resource-assessment- and-characterization) 25 Wind turbine capacity is based on wind speed. The maximum power output (the nameplate capacity) coincides with the maximum wind speed at which the wind turbine can safely operate. Power output from wind turbines vary with wind speed and wind turbine manufacturers provide power curves that provide estimated power output of the turbine based on wind speed at the hub height. Note that at very high wind speeds (generally above 50 to 55 mph, the rotor on the wind turbine is placed into a locked position and the turbine rotates without load (no production of power). For additional details on wind turbine performance, please see Erich Hau’s book “Wind Turbines: Fundamental Technologies, Application, Economics: 2nd edition, 2010 or Appendix A of 2015 Self-Generation Incentive Program Cost Effectiveness Study,” October 2015; final report pending release 26 We adopt a maximum “hub” height of 80 meters as this is the height needed to get into higher wind resources but still economically viable for host customers. 27 NREL environmental exclusions include federal, state or private lands designated as park, wilderness, wilderness study area, national monument, national battlefield, recreation area, national conservation area, wildlife refuge, wildlife area, wild and scenic-river or inventoried roadless area. Excluded lands also include those lands with slopes greater than 20%. See Department of Energy’s Wind Exchange site at http://apps2.eere.energy.gov/wind/windexchange/windmaps/resource_potential.asp
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We estimate wind economic potential using the total resource cost (TRC) results generated from the SGIPce model28 for wind turbines at nominal capacities of 50 kW and 1.5 MW with no incentives.29 Our criteria is that wind systems are economic for all instances where TRC results are equal to or exceed 0.8. Wind Technical Potential Results In developing wind technical potential, we first examine the amount of area in each county with sufficient wind resource to generate power. Wind resource is typically classified by wind speeds and estimated wind power densities. Table 6-6 lists wind power classes from 1 through 7 based on associated wind speeds at different heights.30 In general, wind speeds in class 3 and above are considered minimums for commercial wind energy systems.
28 See “2015 Self-Generation Incentive Program Cost Effectiveness Study,” October 2015; final report pending release 29 While we are investigating wind resource potential up through 5 MW in capacity, we use the nominal 50 kW and 1.5 MW ratings as these were the capacities modeled in the SGIP cost effectiveness study and have cost effectiveness results. In addition, we currently see most distributed wind turbine installations coming in at no more than the 1.5 – 2 MW size range for “single-site” applications. 30 Wind power classes from NREL: http://www.nrel.gov/gis/wind_detail.html. Note that wind power classes are meant to overlap two power density groups.
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Table 6-6: Wind Power Classes and Speeds 10 m (33 ft) 50 m (164 ft) Wind Power Wind Power Speed (b) m/s Wind Power Speed (b) m/s Class Density (W/m2) (mph) Density (W/m2) (mph)
0 0 0 1
100 4.4 (9.8) 200 5.6 (12.5) 2 150 5.1 (11.5) 300 6.4 (14.3) 3 200 5.6 (12.5) 400 7.0 (15.7) 4 250 6.0 (13.4) 500 7.5 (16.8) 5 300 6.4 (14.3) 600 8.0 (17.9) 6 400 7.0 (15.7) 800 8.8 (19.7) 7 1000 9.4 (21.1) 2000 11.9 (26.6)
Table 6-7 lists the approximate amount of land area available by county (in square miles) and by average annual wind speed categorized by wind class speed. We limit estimates of potential to areas of class 3 or greater wind speeds.31
Table 6-7: Area by Wind Class Speed (square miles): 50 Meter Height Area by Class Wind Speed (sq. miles) County Class 3 Class 4 Class 5 Class 6 Class 7 Total Alameda 14.1 3.8 3.9 0.2 22.0 Alpine 43.6 21.5 12.8 10.8 5.8 94.4 Amador 1.8 0.6 0.3 0.2 2.9 Butte 12.1 2.0 0.4 14.4 Calaveras 1.8 0.0 1.9 Colusa 5.5 0.7 0.1 0.0 6.4 Contra Costa 12.8 2.6 1.0 0.0 16.3 Del Norte 48.4 15.8 5.1 1.8 0.1 71.2 El Dorado 16.3 7.9 5.0 5.4 3.7 38.4 Fresno 40.0 15.9 7.3 4.4 1.3 68.9 Glenn 2.2 0.4 0.3 0.0 2.9 Humboldt 90.9 39.3 22.9 16.3 9.0 178.4 Imperial 184.8 42.1 11.6 9.6 1.2 249.4 Inyo 671.9 241.1 115.0 75.6 28.8 1,132.3 Kern 268.4 242.8 167.2 172.9 67.7 919.0
31 Wind classes 1 and 2 are excluded because wind speeds in these classes generally do not have sufficient speed to meet the cut-in speed requirements of turbines.
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Area by Class Wind Speed (sq. miles) County Class 3 Class 4 Class 5 Class 6 Class 7 Total Kings 0.7 0.1 0.9 Lake 13.8 1.9 0.2 16.0 Lassen 89.9 24.2 8.7 4.3 1.0 128.1 Los Angeles 285.3 241.6 144.2 64.7 15.6 751.4 Madera 10.8 5.0 2.8 2.6 1.1 22.3 Marin 16.6 7.6 4.0 0.6 28.8 Mariposa 1.4 0.6 0.2 0.2 0.0 2.4 Mendocino 40.9 6.8 1.0 0.1 48.9 Merced 31.4 6.7 2.7 0.5 41.3 Modoc 60.5 24.9 13.7 12.3 6.8 118.2 Mono 149.8 68.4 41.4 41.8 30.8 332.2 Monterey 30.0 10.0 2.2 0.8 0.2 43.2 Napa 3.5 0.4 0.2 0.1 4.2 Nevada 8.8 2.5 0.9 0.4 0.1 12.7 Orange 33.2 9.7 2.2 0.2 45.3 Placer 13.7 5.4 2.3 1.9 0.6 24.0 Plumas 48.5 13.9 4.4 2.5 0.4 69.7 Riverside 645.2 292.1 125.0 84.5 45.4 1,192.2 Sacramento 0.1 0.1 San Benito 4.3 0.5 0.0 4.9 San Bernardino 2,008.2 645.1 186.7 101.1 25.9 2,967.0 San Diego 238.5 141.0 71.1 48.7 24.8 524.0 San Francisco 0.4 0.4 San Joaquin 1.6 0.8 0.0 2.5 San Luis Obispo 30.6 5.2 1.5 0.1 37.3 San Mateo 14.4 2.5 0.8 0.1 17.7 Santa Barbara 169.1 115.3 57.5 39.5 15.7 397.1 Santa Clara 0.5 0.5 Santa Cruz 0.5 0.5 Shasta 99.0 33.5 15.2 11.5 5.0 164.1 Sierra 32.6 11.0 4.3 2.7 0.6 51.2 Siskiyou 179.7 64.8 25.7 17.8 10.7 298.6 Solano 37.1 53.0 1.8 0.6 92.4 Sonoma 14.4 2.9 0.7 0.1 18.1 Stanislaus 2.9 0.1 3.0 Sutter 3.4 0.6 0.1 0.0 4.1 Tehama 36.4 4.9 1.0 0.2 42.5 Trinity 36.0 11.8 5.2 2.4 0.2 55.6 Tulare 43.9 19.3 9.4 8.4 2.3 83.2 Tuolumne 29.1 9.8 5.3 4.9 1.4 50.5 Ventura 139.6 64.8 29.5 14.4 1.8 250.1 Yolo 9.0 1.7 0.2 10.9 State Totals 11,837.9 6,618.5 3,489.7 4,701.3 882.5 27,530.0
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Figure 6-13 is a map showing the distribution of wind speeds with hub heights of 50 meters (approximately 164 feet) by county.32 At hub heights of 50 meters, most of the state consists of low wind resources. For the most part, those regions of California with class 4 wind speeds and higher were developed in the 1980’s as wind parks.33 Economic considerations limited development of the lower class wind areas until recently. Improvements in wind turbine technology have made it possible for wind energy developers to build projects in the lower wind speed resource areas. Wind energy projects developed under the SGIP in recent years indicate the increased interest shown by wind developers in these lower wind-speed regions. As indicated earlier, we are interested in the wind energy potential associated with wind projects in the 50 kW to 5 MW size range. For these types of wind projects, we also look at wind energy potential where the gross annual capacity factor is 35% or greater.34
32 While we place a maximum hub height of 80 meters as the ceiling, our wind resource assessment is limited to 50 meter hub height due to limited availability of granular wind resource information at 80 meters. 33 In particular, high wind resource areas located at San Gorgonio, Altamont Pass and Tehachapi were the focus of large wind parks. Total installed capacities in these areas are 615 MW at San Gorgonio, Altamont at over 570 MW and Tehachapi at over 700 MW. Additional large scale wind farms have been developed in Kern County (Alta Wind Farm) and Solano County (Montezuma Hills) but post 1980’s. 34 Gross annual capacity factor represents the ratio of its actual output over the year to its potential annual output if it were possible for it to operate at full nameplate capacity. Our use of 35% gross capacity is conservative as wind turbine technologies being developed currently can be expected to have annual capacities of 40% or more, depending on the wind resource. Because we are using average annual wind resource estimates, we are able to associate the gross annual capacity factor to the average annual wind speeds.
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Figure 6-9: Distribution of Wind Speeds by County (50 Meters)
Table 6-8 provides estimates of the potential capacity for wind turbines in the 50 kW to 5 MW size range with associated annual gross capacity factor (GCF) of greater than 35%.
Table 6-8: Potential Wind Capacity with GCF > 35% by County (MW) Potential Capacity Potential Capacity County (MW) County (MW) Alameda 33 Orange 68 Alpine 142 Placer 36 Amador 4 Plumas 105 Butte 22 Riverside 1,788 Calaveras 3 Sacramento 0 Colusa 10 San Benito 7 Contra Costa 24 San Bernardino 4,451 Del Norte 107 San Diego 786 El Dorado 58 San Francisco 1 Fresno 103 San Joaquin 4 Glenn 4 San Luis Obispo 56 Humboldt 268 San Mateo 27
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Potential Capacity Potential Capacity County (MW) County (MW) Imperial 374 Santa Barbara 596 Inyo 1,698 Santa Clara 1 Kern 1,378 Santa Cruz 1 Kings 1 Shasta 246 Lake 24 Sierra 77 Lassen 192 Siskiyou 448 Los Angeles 1,127 Solano 139 Madera 33 Sonoma 27 Marin 43 Stanislaus 5 Mariposa 4 Sutter 6 Mendocino 73 Tehama 64 Merced 62 Trinity 83 Modoc 177 Tulare 125 Mono 498 Tuolumne 76 Monterey 65 Ventura 375 Napa 6 Yolo 16 Nevada 19 Grand Total 16,166
Figure 6-10 shows a ranking of the counties that represent 90% of the technical wind energy potential by capacity (MW). According to the information in Figure 6-10, counties in Southern California appear to be better prospects for low wind speed project development (from a technical potential perspective).
Figure 6-10: Counties Representing 90% of Wind Technical Potential
The gross potential of Table 6-8 assumes installation of wind turbines in this size range regardless of availability of host customer site. However, we assume that distributed wind turbines are located at host customer sites to help displace onsite electricity demand. There are still only a limited number of distributed wind turbine installations in California. It is difficult to determine business types that would be
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the best candidates for hosting wind turbines. We selected two general categories of possible candidate business types based on the limited amount of historical data on distributed wind in California: manufacturing/industrial facilities and retail trade businesses.35 Table 6-9 provides a list of “indicator” businesses that represent possible host sites for distributed wind turbine installations.
Table 6-9: Counts of Indicator Businesses for Distributed Wind Systems by Type by County 2013 2013 County Manufacturing Retail Trade County Manufacturing Retail Trade Alameda 1,800 4,245 Orange 4,637 9,461 Alpine 0 2 Placer 249 1,236 Amador 38 121 Plumas 15 84 Butte 176 736 Riverside 1,477 5,044 Calaveras 37 135 Sacramento 742 3,503 Colusa 24 59 San Benito 65 106 Contra Costa 535 2,502 San Bernardino 1,768 4,750 Del Norte 8 58 San Diego 2,861 9,297 El Dorado 157 522 San Francisco 684 3,595 Fresno 575 2,481 San Joaquin 511 1,595 Glenn 25 75 San Luis Obispo 382 1,158 Humboldt 128 573 San Mateo 611 2,041 Imperial 52 468 Santa Barbara 451 1,505 Inyo 10 87 Santa Clara 2,319 4,925 Kern 396 1,879 Santa Cruz 298 877 Kings 59 280 Shasta 134 641 Lake 32 171 Sierra 2 7 Lassen 1 80 Siskiyou 32 170 Los Angeles 12,478 28,442 Solano 251 1,069 Madera 96 317 Sonoma 828 1,795 Marin 226 1,033 Stanislaus 395 1,399 Mariposa 15 65 Sutter 57 284 Mendocino 118 448 Tehama 37 147 Merced 106 537 Trinity 10 53 Modoc 1 30 Tulare 236 1,046 Mono 7 71 Tuolumne 62 180 Monterey 270 1,313 Ventura 869 2,592 Napa 448 531 Yolo 169 466 Nevada 145 398 Yuba 39 131
We can use the count of indicator businesses to develop an estimate of the countywide and statewide capacity if wind turbines in the nominal 50 kW or 1.5 MW size range could be installed in each of the businesses. Table 6-10 lists the potential wind capacity associated with deployment of nominal 50 kW or
35 To date, distributed wind energy systems in California have generally been located at manufacturing/industrial sites (e.g., Anheuser-Busch, Cemex, etc.), retail trade spaces (e.g., Walmart) or large agricultural operations (e.g., Superior Farms).
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1.5 MW wind turbines associated with each business. Overall, locating one wind turbine of 50 kW or 1.5 MW capacity at the representative businesses corresponds to a stateside technical potential of approximately 1.4 GW and 9.8 GW, respectively.
Table 6-10: Number of Potential Installations of 50 kW and 1.5 MW Wind Turbines Potential Capacity (MW) Potential Capacity (MW) County At 50 kW At 1.5 MW County At 50 kW At 1.5 MW Alameda 33 33 Orange 68 68 Alpine 0 0 Placer 12 36 Amador 2 4 Plumas 1 23 Butte 9 22 Riverside 74 1,788 Calaveras 2 3 Sacramento 0 0 Colusa 1 10 San Benito 3 7 Contra Costa 24 24 San Bernardino 88 2,652 Del Norte 0 12 San Diego 143 786 El Dorado 8 58 San Francisco 1 1 Fresno 29 103 San Joaquin 4 4 Glenn 1 4 San Luis Obispo 19 56 Humboldt 6 192 San Mateo 27 27 Imperial 3 78 Santa Barbara 23 596 Inyo 1 15 Santa Clara 1 1 Kern 20 594 Santa Cruz 1 1 Kings 1 1 Shasta 7 201 Lake 2 24 Sierra 0 3 Lassen 0 2 Siskiyou 2 48 Los Angeles 624 1,127 Solano 13 139 Madera 5 33 Sonoma 27 27 Marin 11 43 Stanislaus 5 5 Mariposa 1 4 Sutter 3 6 Mendocino 6 73 Tehama 2 56 Merced 5 62 Trinity 1 15 Modoc 0 2 Tulare 12 125 Mono 0 11 Tuolumne 3 76 Monterey 14 65 Ventura 43 375 Napa 6 6 Yolo 8 16 Nevada 7 19 Grand Total 1,410 9,759
Technical Potential by IOU Service Territory Table 6-11 is a summary list of the technical wind energy potential by IOU service territory. At nearly 12,000 MW, SCE represents nearly 3/4ths of the state’s technical distributed wind energy potential.
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Table 6-11: Technical Wind Potential by IOU: (50 Meters) Potential Capacity (MW) Statewide PG&E SCE SDG&E 16,166 3,401 11,979 786
100.0% 21.0% 74.1% 4.9%
The reason why SCE dominates the wind energy technical potential is more clearly seen in Figure 6-11. This figure shows a map of the wind resource areas at 50 meters with an overlay of the IOU service territories. It is clear that much of the higher wind resource in California at 50 meters is located in the SCE service territory.
Figure 6-11: Distribution of Wind Class Speeds in Counties with IOU Overlay (50 Meters)
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Wind Economic Potential Results We use results from the SGIPce cost effectiveness model to assess wind energy economic potential. Consistent with our treatment of all the DG and energy storage technologies, we assume wind energy technologies with a TRC benefit-to-cost ratio of 0.80 or better are cost effective. Under that condition, the technical potential becomes the economic potential. Figure 6-12 is a bubble chart showing the TRC ratios of the nominal 50 kW and 1.5 MW wind turbines modeled using the SGIPce model.
Figure 6-12: Wind Economic Potential by Nominal Capacity and Year
Based on the TRC ratios, only the larger wind turbine systems in the nominal 1.5 MW capacity have economic potential prior to 2024. We also assume that the 1.5 MW wind turbines are associated with the representative businesses that were shown earlier in Table 6-9. Table 6-12 shows the economic potential for distributed wind energy in California by county. For the larger wind turbine systems with nominal capacity of 1.5 MW, the economic potential is the same as the estimated 9.8 GW technical potential (assuming one 1.5 MW turbine located at a host site). Per our earlier assumptions, it would require the installation of over 6,500 wind turbines rated at 1.5 MW of capacity to achieve the distributed wind energy potential.
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Table 6-12: Economic Potential for Distributed Wind Energy
Economic No of Installs Economic No. of Installs County Capacity (MW) at 1.5 MW County Capacity (MW) at 1.5 MW Alameda 33 22 Orange 68 45 Alpine 0 0 Placer 36 24 Amador 4 3 Plumas 23 15 Butte 22 14 Riverside 1,788 1,192 Calaveras 3 2 Sacramento 0 0 Colusa 10 6 San Benito 7 5 Contra Costa 24 16 San Bernardino 2,652 1,768 Del Norte 12 8 San Diego 786 524 El Dorado 58 38 San Francisco 1 0 Fresno 103 69 San Joaquin 4 3 Glenn 4 3 San Luis Obispo 56 37 Humboldt 192 128 San Mateo 27 18 Imperial 78 52 Santa Barbara 596 397 Inyo 15 10 Santa Clara 1 1 Kern 594 396 Santa Cruz 1 0 Kings 1 1 Shasta 201 134 Lake 24 16 Sierra 3 2 Lassen 2 1 Siskiyou 48 32 Los Angeles 1,127 751 Solano 139 92 Madera 33 22 Sonoma 27 18 Marin 43 29 Stanislaus 5 3 Mariposa 4 2 Sutter 6 4 Mendocino 73 49 Tehama 56 37 Merced 62 41 Trinity 15 10 Modoc 2 1 Tulare 125 83 Mono 11 7 Tuolumne 76 50 Monterey 65 43 Ventura 375 250 Napa 6 4 Yolo 16 11 Nevada 19 13 Grand Total 9,759 6,506
Since inception of the SGIP in 2001, nearly 25 MW of distributed wind energy capacity has been installed under the program. Figure 6-13 shows the growth of wind capacity under the SGIP by year. However, this represents only a small fraction of the state’s economic wind potential.
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Figure 6-13: Amount of Wind Energy Installed under the SGIP as of 2014
Wind Market Potential In assessing the market potential for wind, we examine different market growth rates using market saturation curves. In particular, using historical wind turbine capacity data and logistic modeling we assumed 10%, 20% and 30% average annual growth rates to calculate future market potential levels. This allowed us to estimate the future market potential as a percentage of the economic potential and assess the reasonableness of both the growth rate and percentage penetration of the economic potential. Figure 6-14 depicts how cumulative installed wind capacity could look in California assuming an average annual growth rate of 10% using a logistic modeling approach. Wind capacity would grow from approximately 25 MW of capacity in 2015 to approximately 250 MW by 2038. The maximum annual growth rate of approximately 14 MW would occur shortly after 2018.
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Figure 6-14: Wind Market Potential at 10% Average Annual Growth Rate
Figure 6-15 shows cumulative wind capacity in California under an average annual growth rate of 20%. Under this scenario, cumulative wind capacity reaches approximately 1.2 GW by 2038. This represents over 12% of the statewide economic potential estimated earlier assuming installation of 1.5 MW wind turbines at representative businesses.
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Figure 6-15: Wind Market Potential at 20% Average Annual Growth Rate
Figure 6-16 represents cumulative wind capacity in California assuming an average annual growth rate of 30%. In this situation, California realizes a cumulative installed wind capacity of approximately 2.5 GW by 2038. This represents nearly 26% of the statewide economic potential. While a 30% average annual growth rate may be a high target for distributed wind, 20% may be achievable. However, this magnitude of growth rate for wind would require significant changes in policies and support.
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Figure 6-16: Wind Market Potential at 30% Average Annual Growth Rate
6.3 California’s Biogas Power Generation Market
In this section, we estimate technical, economic and market potential for California’s distributed generation biogas market.36 Sources of biogas include anaerobic digesters used in dairy operations, landfills, wastewater treatment plants and food processing facilities. We focus our estimates on landfills, wastewater treatment plants and dairy operations. These are among the main sources of biogas for distributed generation applications. We examine the potential by county and IOU service territory. We further discuss potential based on the nominal generator sizes and show how many potential projects could be installed if the specific generation technology was used and the potential distribution of projects based on the nominal sizes. The nominal sizes used in the SGIP cost effectiveness model (SGIPce) for internal combustion engines are 1,500 kW and 500 kW; for fuel cells 500 kW and 1,200 kW; for gas turbines 2,500 kW; and, for microturbines 200 kW. In identifying economic potential, we assume any technology with a TRC benefit-to-cost ratio of greater than 80% is economic.
36 We refer to distributed generation applications to distinguish them from larger-scale (e.g., 20 MW and larger) direct combustion biomass projects that were installed in California in the 1980’s and 1990’s.
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Overall Approach Landfill data used is obtained from EPA’s Landfill Methane Outreach Program (LMOP) database.37 This data includes landfill gas to energy sites that are operational or under construction. These sites have generators planned or already in operation. The generation potential for sites not explicitly specified in the database are estimated based on the daily gas production information, which is provided in the database. In addition to these sites, there are other sites that are identified as candidate sites for future biogas development. These aggregations of operational sites and candidate sites are used to determine the overall technical potential. Dairy biogas estimates are obtained using historical dairy animal population data available from the California Department of Food and Agriculture (CDFA).38 These data show the number of milking cows in California each year by county. We use this population data to estimate the technical potential based on an estimate of 30 cubic feet of methane produced per day per animal.39 We further assume that the generators operate 20 hours per day. Economic potential is estimated by filtering the same data to remove operations that have less than 250 animals. This is the minimum number of dairy cows needed to generate enough biogas to operate a 25 kW internal combustion engine. The CDFA data specifically tracks dairy milking cows. This approach produces a conservative estimate of technical and economic potential because it ignores dry lot and replacement animals present in most operations. If these animals were also included, we could increase the potential biogas production by 30-50%. Wastewater treatment plants and food processing facilities biogas estimates are obtained using data from the California State Water Resources Control Board.40 These data show daily flow, annual flow and biosolid loads, which are used to estimate potential biogas production. We estimate generation capacity based on a 30% conversion efficiency and further assume the generators operate 20 hours per day. California has over 240 wastewater treatment plants and some of them actively recover biogas to generate electricity. The economic potential is based on the TRC results from the SGIPce model for biogas operations. Consistent with that model and study, we assume that a technology is a cost effective resource if it has a TRC benefit-to-cost ratio of 0.8 or greater. In the case of dairy operations, the collection costs include the digester cover needed to capture the gas. For landfill and wastewater applications, we do not include cover costs because these are required by statute at landfills. The nominal cases tested are 500 kW and 1,500 kW for IC engines, 2,500 kW for gas turbines, 500 kW and 1,200 kW for fuel cells, and the 200 kW for microturbines. All nominal cases passed the TRC criteria of exceeding 0.8 and were considered cost effective.
37 EPA’s database is found at: http://www.epa.gov/lmop/ 38 California Department of Food and Agriculture Statistics, from http://www.cdfa.ca.gov/statistics/ 39 Donald L. Van Dyne and J. Alan Weber, Biogas Production from Animal Manures: What Is the Potential? Industrial Uses/TUS- 4/December 1994 40 http://www.swrcb.ca.gov/
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Landfill Gas Results To determine the landfill biogas economic potential capacity we use the EPA LMOP database and segment the available resources by county. The economic potential shows systems that are already operating or in the planning stage. The technical potential is determined using the same database with the addition of candidate sites. Candidate sites are expected to produce landfill biogas in the near future (i.e., within five years). Table 6-13 shows the potential resource generation capacity ranked by quantity and county as well as the statewide totals. Total technical and economic potential are 457 MW and 404 MW respectively. Los Angeles County has the highest capacity by far with an expected economic capacity of over 185 MW. The statewide landfill gas technical potential is nearly 460 MW and the statewide economic potential is about 404 MW.
Table 6-13: Landfill Gas Technical and Economic Potential by County
Total Economic Potential Total Technical Potential County Capacity (MW) Capacity (MW) Los Angeles 185 190 Orange 32 32 San Diego 26 28 Alameda 21 27 Sacramento 18 18 Ventura 18 18 Santa Clara 16 23 San Mateo 13 13 Contra Costa 11 11 Sonoma 10 10 Others 53 88 Grand Total 404 457
Figure 6-17 shows the span of landfill gas technical potential among the top fifteen counties, representing 90% of the statewide technical potential. Again, Los Angeles has far greater technical potential; primarily due to the number of large landfill operations in the county.
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Figure 6-17: Ranking of Landfill Gas Technical Potential by County
Table 6-14 breaks down the resource available within each IOU service area and illustrates the distribution of nominal 500 kW IC engine systems. The number of installs are determined by assessing the potential resource at each site and determining how many multiple installations of a 500 kW IC engine could use the available biogas. Empirical evidence shows that most operators install multiple modular generators. This distribution shows that 50% of available sites would need one to four-500 kW units to make full use of the available landfill gas resource.
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Table 6-14: Distribution of Nominal 500 kW IC Engines by IOU Pacific Gas & San Diego Gas & Southern California Resource Electric Co Electric Co Edison Co Total Availability Possible Total Possible Total Possible Total Possible Total Bin based on No. of Possible No. of Possible No. of Possible No. of Possible 500 kW 500kW Capacity 500kW Capacity 500kW Capacity 500kW Capacity Increments Installs (kW) Installs (kW) Installs (kW) Installs (kW) 0-500 13 1,376 3 490 16 1,866 500-1000 11 9,092 3 1,650 14 10,742 1000-1500 14 17,600 2 2,754 4 4,742 20 25,096 1500-2000 11 18,026 1 1,700 12 19,726 2000-2500 5 10,295 5 11,206 10 21,501 2500-3000 5 13,821 4 10,701 9 24,522 3000-3500 8 26,083 4 13,000 1 3,000 13 42,083 3500-4000 2 7,400 1 3,700 3 11,100 4000-4500 1 4,300 3 12,350 4 16,650 4500-5000 1 4,649 2 9,900 3 14,549 >5000 8 58,752 4 38,025 16 172,850 28 269,627 Grand Total 79 171,394 12 59,179 41 226,890 132 457,463
The economic potential for landfill gas technologies is determined using the 80% TRC cost to benefit ratio minimum derived from the SGIPce model. Figure 6-18 summarizes the TRC test results for the different prime movers that could be used for landfill biogas to energy operations by nominal capacities used in the SGIPce model. In general, all the prime movers achieve a TRC ratio of 80% or greater starting in 2014. The TRC ratios also increase moving into the future. Figure 6-18 shows that the nominal 5 MW gas turbine technology has the highest TRC ratios for landfill biogas applications.
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Figure 6-18: TRC Benefit-to-Cost Ratios for Selected Landfill Biogas Generation Technologies
Dairy Biogas Results We use milking animal population data from the California Department of Food and Agriculture41 to estimate the potential biogas production from dairy animals. For the technical potential criteria, we use all dairy operations. For the economic potential, we limit our analysis to dairies that have more than 250 milking cows. A minimum of 250 milking dairy cows is assumed to be the number of animals needed to generate enough biogas to operate a 25 kW IC engine unit operating 20 hours per day. Table 6-15 shows a summary of the CDFA Dairy Statistics data by county for 2014 ranked by technical potential. The total technical and economic potential are 137 MW and 135 MW respectively. Tulare, Merced, and Kings County account for over 50% of the technical potential.
41 http://www.cdfa.ca.gov/dairy/pdf/Annual/ California Dairy Statistics 2014_Data.pdf
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Table 6-15: Dairy Biogas Economic and Technical Potential by County
Total Economic Potential Total Technical Potential County Total Number of Dairies Capacity (kW) Capacity (kW) Tulare 281 37,251 37,251 Merced 228 21,258 21,258 Kings 119 14,137 14,137 Stanislaus 207 13,837 13,837 Kern 51 12,873 12,873 Fresno 79 8,995 8,995 San Joaquin 113 7,918 7,918 Madera 43 6,033 6,033 Imperial 62 3,960 3,960 Riverside 28 3,042 3,042 Sonoma 65 2,148 2,148 Glenn 31 1,287 1,287 Sacramento 30 1,084 1,084 Humboldt 60 1,037 Marin 25 585 585 San Bernardino 3 398 398 Tehama 11 269 269 Del Norte 7 217 217 Yuba 4 215 215 San Diego 3 148 148 Siskiyou 3 51 Grand Total 1,453 135,654 136,742
Table 6-15 shows the counties that represent the 90% of California’s dairy technical potential. All of the counties with potential are in the PG&E service territory except for San Bernardino (SCE).
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Figure 6-19: Counties Representing 90% of Dairy Biogas Technical Potential
The economic potential is determined using the 250 milking dairy cows’ minimum criteria and the 80% TRC minimum derived from the SGIPce model. The TRC gives the cost benefit ratio for each generation technology. The costs for the dairy biogas case includes the cost of the digester cover and this is not included the landfill biogas case. Furthermore, because we only consider onsite biogas generation, no resource transportation costs are included in the TRC. Onsite dairy biogas generation is limited by the number of animals and the scale of dairy biogas operations, and thus, is much smaller than for a landfill or wastewater operation. Figure 6-20 shows that the nominal 200 kW microturbine technology has the highest TRC for dairy biogas applications and this matches what we would expect given that the average milking dairy herd in California42 is 1,100 animals. In general, the size and capital cost of a 200 kW microturbine system matches the scale of most dairy operations in California. Fuel cells have the lowest TRC cost benefit ratios because of the high capital costs associated with the fuel cell as well as the required gas clean-up system.
42 http://www.cdfa.ca.gov/dairy/pdf/Annual/ California Dairy Statistics 2014_Data.pdf
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Figure 6-20: TRC Benefit-to-Cost Ratios for Selected Dairy Biogas Generation Technologies
Wastewater Treatment Plants Biogas Results We used data from the California State Water Resource Board to estimate the biogas production technical potential for wastewater treatment plants. The electric generation capacity is estimated from the estimated biogas at a conversion efficiency of 30% and a generator capacity of 83%. We estimate the economic potential by filtering out all sites that have an estimated generation capacity of less than 100 kW. Table 6-16 shows the potential by county ranked by capacity. Total economic and technical potential are estimated as 125 MW and 130 MW, respectively. Los Angeles County has the highest capacity at 33 MW for both economic and technical potential.
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Table 6-16: Wastewater Treatment Plants Biogas Economic and Technical Potential by County
Number of Total Economic Total Technical County Sites Potential (kW) Potential (kW) Los Angeles 8 33,258 33,305 Orange 6 11,420 11,510 San Diego 6 9,895 10,173 Santa Clara 4 9,533 9,533 Sacramento 3 8,282 8,291 Alameda 7 6,732 6,732 Riverside 13 6,432 6,705 San Bernardino 10 4,354 4,476 Fresno 4 3,252 3,341 San Francisco 2 2,927 3,003 Contra Costa 4 2,704 2,965 Kern 7 2,263 2,552 Ventura 8 2,459 2,459 San Joaquin 4 2,352 2,352 Stanislaus 2 2,057 2,235 San Mateo 7 2,107 2,186 Solano 4 1,714 1,786 Santa Cruz 2 1,328 1,328 Others 45 12,196 15,194 Grand Total 146 125,264 130,127
Figure 6-21 shows the span of wastewater treatment plants biogas technical potential among the top counties, which represent about 90% of the statewide technical potential. Los Angeles County accounts for about 26% of the total technical biogas potential.
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Figure 6-21: Ranking of Wastewater Treatment Plants Biogas Potential by County
Summary of California’s Biogas Potentials Figure 6-22 summarizes the technical, economic and market potential of distributed biogas generation capacity in California. For 2014, we estimate a technical potential of about 724 MW, an economic potential of about 665 MW and a market potential of 234 MW. The market potential is that fractional portion of the economic potential that meets the SGIPce TRC criteria. It also meets a saturation target modeled using a logistic curve as shown below in Figure 6-23. We assume a market penetration of 40% for landfill and wastewater treatment plant biogas and 20% for dairy biogas43 of the economic potential. With these limits, we estimate a market potential of 268 MW. The logistic curve indicates we can expect to meet the SB 1122 target of 200 MW of biogas generation in California by 2024.44 Existing SGIP biogas projects represent approximately 36 MW or about 5% of the state’s economic biogas potential. Figure 6-23 shows the actual installations in SGIP and the projected market growth based on a 268 MW market potential. The actual SGIP biogas installations are used as the template for modeling the market growth and meeting the SB 1122 target. The logistic curve model indicates that the growth in
43 California Public Utilities Commission, “Final Consultant Report-Small Scale Bioenergy: Resource Potential, Costs and Feed- in Tariff Implementation Assessment”, October 31, 2013 44 SB 1122 has an overall bioenergy target of 250 MW. However, 50 MW is to be derived from “byproducts of sustainable forest management.” As we are focusing solely on biogas derived energy from landfills, wastewater treatment systems and dairies, we exclude that 50 MW from the target.
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biogas generation exceeds what would be expected in the beginning, which is presumably the effect of the biogas adder in the earlier years of the program. The relative proportion of the different SGIP technologies is shown in Figure 6-22. SGIP generation technology consists of 50% internal combustion engines, 34% fuel cells, and 16% microturbines and consists predominantly of generation units less than 5 MW. Landfill biogas sources have the quantity of biogas needed to run gas turbines and larger generation capacities are not represented in the SGIP.
Figure 6-22: Total Technical, Economic, and Market Potential Growth 2014-2024 (kW) with SGIP Biogas Projects
Figure 6-23 shows the market growth based on the market potential of 268 MW. The curve shows biogas generation meeting the SB 1122 target in 2030. In addition, the figure shows the annual growth rate needed to attain the market potential. The peak growth rate is 22 MW in 2019. The average annual growth rate in capacity is 17%, whereas the cumulative generation growth rate is about 34%.
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Figure 6-23: Biogas Generation Market Growth Forecast
Table 6-17 shows resource potential by IOU. PG&E has the highest biogas generation potential.
Table 6-17: Technical, Economic, and Market Biogas Generation Potential by IOU Total Technical Total Economic Total Market Potential IOU Potential Capacity (kW) Potential Capacity (kW) Capacity (kW) Pacific Gas & Electric 360,221 314,288 110,059 San Diego Gas & Electric 81,009 79,735 36,355 Southern California Edison 283,101 271,886 122,165 Grand Total 724,332 665,910 268,579
6.4 California’s Advanced Energy Storage Market
In this section, we estimate the total technical, economic, and market potential for customer-sited advanced energy storage (AES) capacity at buildings in the service territories of California’s electric IOUs. We consider market segments in the residential, commercial, institutional, and industrial sectors but restrict the analysis to AES systems sized between 1 kW and 5 MW.
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Overall Approach Technical potential for customer-sited AES exists primarily for customer energy management,45 either through reduced peak demand charges or energy arbitrage (shifting energy consumption to lower bill periods).46 We estimate the technical potential for AES to be the capacity sufficient to reduce every hour’s load to the average load of the greatest usage day of the month. In the future, the use of AES to firm renewables and provide grid support may become viable value streams, but those do not currently exist in California. We use a bottom-up approach to estimate the AES technical potential for buildings served by California’s electric IOUs. We begin with counts of all business establishments and residential units in the state in a base year. We then group them by common market segment, size, and location. Next, for each group, we model a full year of hourly electric loads.47 Then we model AES to flatten the top four hourly loads on any given day. We define a building’s hourly electrical demands as the sum of its hourly electrical end use demands. Demand during on-peak hours is the primary target for discharge from the AES. We use Itron’s SitePro software model to estimate electric demand. SitePro estimates hourly electricity consumption year-round by end use for a large number of market segments, building sizes, and climates. Electricity end use loads are then summed for individual hours. The AES discharges to reduce demand during the four hours with the highest demand per day and charges only during off-peak hours. This demand reduction approach to technical potential fundamentally serves to enhance AES economics. It does so by moving demand from generally more expensive on-peak to lower cost off-peak hours but without regard to any system costs. C or Power to Energy Ratio AES is unique from other SGIP technologies in that it is rated on both power (MW) and energy (MWh) capacities. System costs are a function of both power and energy but are more heavily driven by energy. Battery sizing requires matching both the power and energy requirements needed to meet a site’s needs. The ratio of power to energy is termed ‘C’ for batteries. For example, a battery that requires 4 hours to discharge has a C of 1:4. A battery that could fully discharge in a single hour has a C of 1. A battery that could discharge in 15 minutes has a C of 4. The SGIP sets C at 0.5 (1:2) setting the rated power capacity of the battery at one-half the rated energy capacity of the battery. Many commercially available lithium ion systems can far exceed the SGIP C of 0.5, and some can approach a C of 4, fully discharging in 15 minutes.
45 AES could also provide grid support and intermittent renewable firming. However, current California rules do not provide any incentive for behind the meter AES to provide these services. 46 AES also has the potential to improve reliability by providing backup power for critical circuits during grid outages. This is not a use that is supported by the SGIP so this study does not investigate the potential of backup and increased grid reliability. 47 We use Itron’s SitePro software to model hourly loads by end-use based on market segment, building size, and climate region.
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For example, a 40 kWh CODA Core UDP Tower would have a SGIP rating of 20 kW. However, it is capable of 40 kW continuous discharge (C=1) and can produce up to 160 kW (C=4) for short periods.48 To match C ratios seen in the field, we calculated the technical potential by sizing the AES with enough energy capacity to meet the difference between the highest and the fifth highest hourly energy consumption during each site’s highest consumption day. This resulted in nearly a 1:4 power to energy ratio.49 This means the AES would be essentially sized to flatten or ‘shave’ the top four peak hours on the highest usage day at each site. We then added a 20% buffer to provide a margin that allows the controller to preserve battery life better by not fully discharging the battery on that peak day. Again, we did this to be consistent with the Cost Effectiveness Model and current industry practice. To calculate an equivalent SGIP power capacity, we divided this energy capacity by 2 (i.e., a power to energy ratio of 1:2). Estimated AES Technical Potential As done for CHP, we estimate AES technical potential for over 500 combinations of market segment, size, and location. Total technical potential is the sum of the products of all these technical potentials and their associated building counts. Below we list technical potentials for various combinations of market segment as well as by electric IOU. Potential by Market Segment and IOU Service Territory Table 6-1Table 6-18 presents the technical potential based on the sums of energy storage needed by segment. This technical potential is nearly 15 GW, as shown in Table 6-18. The majority of the technical potential (approximately 53%) is in the single family residential sector. The total statewide potential is 18,854 MW.
48 https://www.sustainablesv.org/ecocloud//uploads/solutions/40_kWh_Single_DC_Tower_2014-1-31_Public.pdf 49 We chose 1:4 to match summer peak where the majority of the highest loads occur for any site.
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Table 6-18: AES Technical Potential, Based on Energy Capacity to Shave Top 4 Hours50 Sector PG&E (MW) SCE (MW) SD&G (MW) Total (MW) Percent College 23 27 6 55 0.37% Food Manufacturing 9 8 1 18 0.12% Food Store 125 144 27 296 1.98% Health, Hospital 124 134 27 285 1.91% Large Multifamily 174 225 44 444 2.97% Lodging, Hotel 84 84 31 198 1.33% Office, Large 805 1,263 254 2,322 15.54% Office, Small 213 269 55 538 3.60% Restaurant, Sit-Down 174 224 50 449 3.00% Retail, Large 219 426 66 711 4.76% School 29 38 7 73 0.49% Single Family 3,538 3,671 636 7,846 52.51% Small Multifamily 585 623 140 1,347 9.02% Warehouse 135 197 24 357 2.39% Grand Total 6,239 7,333 1,368 14,940 Percent 41.76% 49.08% 9.16% Comparison of Potential Estimates to Other Studies A number of studies have examined the potential of energy storage but few have focused on California. A Sandia National Labs study51 assumed that for demand charge management, one-third of the total California peak load “is in play” for demand charge management, which means that is the potential amount of energy storage that could be installed to flatten the load and additional energy storage would not be utilized. That would result in slightly higher potential (16,589) than shown in Table 6-18.52 However, doing so would require significantly longer discharge periods (an average of 7 hours or C~0.14) than systems are being designed for.53 Estimated AES Economic Potential The economic potential of AES is the portion of the technical potential that meets a cost effectiveness criterion, in this case a TRC test benefit-to-cost ratio of 0.8 or higher. The cost effectiveness criterion is the same for each AES system regardless of size. System costs across sizes, on the other hand, differ somewhat. Benefits from AES also differ between inland and coastal climate regions as well as between IOUs. As system costs decline through time, more of the technical potential meets the criterion, assuming no other changes in costs or benefits.
50 MW rating based on SGIP Requirements of 2 hours of discharge (i.e., C=0.5 or MW=MWH/2) 51 Sandia Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide sand2010sand 2010sand2010-0815 52 Based on the maximum load CAISO ever recorded, 50,270 MW in 2006: (https://www.caiso.com/Documents/CaliforniaISOPeakLoadHistory.pdf) 53 If we size AES to reduce all loads to the monthly average per site, our technical potential is 132,492 MWh and 18,854 MW.
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We used the SGIPce cost effectiveness model to identify systems with technical potential that met the cost effectiveness criterion and so contribute to economic potential. Figure 6-24 shows the TRC test results for residential energy storage from 2014 through 2024. Based on the results, none of the locations is expected to become cost effective by 2024, so the economic potential for this sector is zero. However, the SGIPce cost effectiveness model does not include AES projects paired with other technologies, such as PV, that can increase benefits to society. Additionally, the Cost Effectiveness Model does not value system backup that could change these results, as could the potential development of residential demand charges and the potential to monetize grid support services.
Figure 6-24: TRC Benefit-to-Cost Ratios for Residential AES
Figure 6-25 shows the TRC test results for nonresidential energy storage. Some of the 30 kW systems at some locations show a TRC result greater than 0.8 starting as early as 2018. However, both system sizes and all locations have TRC ratios greater than 0.8 by 2024, meaning that all of these options should be cost effective by that time. If future costs fall faster than predicted, this cost effectiveness point may come earlier. Conversely, if future costs do not fall as quickly as expected, the cost effectiveness point may be delayed.
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Figure 6-25: TRC Benefit-to-Cost Ratios for Nonresidential AES
We can then filter the results of Table 6-18 to leave only the locations and system sizes that are expected to achieve a TRC of 0.8 by 2024, based on the data of Figure 6-24 and Figure 6-25. This yields Table 6-19, which shows our estimated economic potential for AES in 2024. Essentially, all residential potential is removed but all nonresidential potential remains. This assumes that government and nonprofit entities take advantage of energy services agreements that allow a third party to take advantage of tax and other benefits to help the host reduce costs.
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Table 6-19: Economic Potential for AES by Market Segment and IOU by 2024 Sector PG&E (MW) SCE (MW) SDG&E (MW) Total (MW) Percent College 23 27 6 55 1.04% Food Manufacturing 9 8 1 18 0.34% Food Store 125 144 27 296 5.59% Health, Hospital 124 134 27 285 5.38% Lodging, Hotel 84 84 31 198 3.74% Office, Large 805 1,263 254 2,322 43.79% Office, Small 213 269 55 538 10.14% Restaurant, Sit-Down 174 224 50 449 8.46% Retail, Large 219 426 66 711 13.41% School 29 38 7 73 1.38% Warehouse 135 197 24 357 6.73% Grand Total 1,942 2,814 548 5,303 Percent 36.61% 53.06% 10.33%
The statewide economic potential for nonresidential AES by 2024 is approximately 5.3 GW. SCE shows the greatest economic potential, as it did with technical potential. The largest economic potential is in the large office sector, which is quite different from the leading sector for technical potential of single family residential. Changes in residential rates or the ability to monetize a number of other value streams (e.g., firming of renewables and ancillary services for grid support), or faster than expected cost reductions, could cause the residential sectors to be cost effective. Estimated AES Market Growth and Potential The actual market potential is a fraction of economic potential. An EPRI energy storage potential study54 stated that “Although it is difficult to predict how consumers will respond to the relatively new energy storage technologies, the analysis assumes that the mid-range of the energy efficient product adoption rate (35%) is a reasonable proxy for direct customer adoption of energy storage systems.” We therefore estimate market potential to be 35% of economic potential or 1,856 MW. Using the 1,856 MW market potential, we can fit past energy storage installations to an adoption S-curve to estimate market growth in the future. Figure 6-26 shows actual installations and market growth based on this market potential. This curve shows AES meeting the 2024 AB 2514 goal for behind the meter energy storage 4 years early—in early 2020.
54 Rastler, D, Electricity Energy Storage Technology Options. A White Paper Primer on Applications, Costs, and Benefits, EPRI, 1020676, December 2010; This study based market potential on technical not economic potential. However, the May 12, 2008 California Energy Efficiency Potential Study found market sizes of 27 to 41% of economic potential; in the same range as the 35% used here.
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Figure 6-26: AES Market Growth Forecast
To meet the forecasted growth, the AES market will have to grow quickly, but at a slower rate than has been seen to date in the SGIP. The average year over year growth would be 28%, which is close to the 31% used in the SGIP Cost Effectiveness Model. This is also a similar rate of growth to the 29% average growth seen in the California PV industry since the start of the CSI.55 Figure 6-27 shows the annual installs needed to meet the cumulative forecasts in Figure 6-26.
55 Based on annual installations in the SGIP and CSI tracking databases for years 2007 through 2014
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Figure 6-27: Forecasted Cumulative and Annual Year over Year Growth Rates
6.5 Historical Trends in DG and Energy Storage Growth in California Table 6-20 presents a comparison between estimates of the technical and economic potential of DG and energy storage technologies in California and the quantities of these resources that were installed as of the end of 2014. With installed DG and energy storage capacities making up less than 1% of the economic potential, it is clear that there is significant opportunity to develop additional DG and distributed energy storage capacity in California. However, these numbers also raise the question of why so little DG and energy storage have been developed to date in California.
Table 6-20: Summary of DG and Energy Storage Potentials vs. Installed Capacities in CA56
2020 Technical 2020 Economic Capacity Installed Percent of Technology Area Potential (MW) Potential (MW) in CA at 2014 (MW) Economic Potential CHP 24,025 17,900 287 1.6% Wind 16,166 16,166 25 < 0.2% Biogas 593 539 33 6% Energy storage 14,940 5,303 4 < 0.1% Totals: 54,658 39,460 349 < 1% To better understand operation of the DG and energy storage markets in California, we can examine the relationship between the growth of these technologies in the SGIP against policies impacting the SGIP and
56 Note that we have included 60 MW of all-electric fuel cells into the CHP installed capacity estimates.
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the technologies. Figure 6-28 shows the growth in DG and energy storage technologies funded under the SGIP on a timeline that also shows policies influencing the program.
Figure 6-28: Trends in DG and Energy Storage Growth in SGIP and Associated Policies
The timeline Figure 6-28 shows potential associations between growth in the DG and energy storage resource. However, it provides little insight into the barriers and opportunities associated with developing DG and energy storage in California and the role of the SGIP. The information obtained from interviews with the SGIP PAs, program staff, project manufacturers and developers, and other key stakeholders identify critical barriers and opportunities associated with transforming California’s DG and energy storage markets and the possible role of the SGIP. While it is evident that the SGIP has played a key role in the growth of these technologies, we still need to assess the degree to which SGIP has influenced the market in California. To do that, we looked at other DG programs operated outside of California. Those findings are the focus of the next section.
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Several states have implemented incentive programs, grants, and regulatory policies to encourage the installation of DG technologies. To isolate the influence of SGIP on the installation of DG technologies in California and in other states, this section begins by assessing the installation of DG technologies and the implementation of incentive, grant, and regulatory policies that may potentially influence the likelihood of implementing DG technologies in eight comparison states from 1999 through 2014. This section of the report will focus on nine states: California (CA), Connecticut (CT), Illinois (IL), Massachusetts (MA), Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA), and Wisconsin (WI). These states were chosen for analysis and comparison to DG installations in California because they represent states in different phases of DG development for the technologies included in the analysis. Some of these states are just beginning to develop a DG policy. These states provide information on the market without policy intervention. Other states have advanced DG policies. Including states with advanced DG policies helps to compare the potential influence of SGIP with the influence of other policies in states that have prioritized the implementation of DG. This section of the report focuses on the installed capacity of small CHP, wind, and landfill and dairy and swine digester projects and the programs and policies that may have influenced the implementation of these types of DG. The focus on these technologies is due to their eligibility in SGIP and the availability of historical, state specific information on the installed capacity of these technologies. Capacity information was derived from the following sources: CHP capacity data (US DOE Combined Heat and Power Installation Database); wind capacity data (USGS Wind Installation Database); landfill gas to energy capacity information (US Environmental Protection Agency (USEPA) Landfill Methane Outreach Program); and, Dairy and Swine Digester data (USEPA Livestock Anaerobic Digester Database). The section does not include an analysis of the installed capacity of energy storage technologies due to the lack of available data on historic, state-specific information on the installed capacity of energy storage. The information on programs and policies was gathered from the Database of State Incentives for Renewables & Efficiency (DSIRE) database.1 The DSIRE data consist of information on programs and policies related to renewables for a variety of “Implementation Sectors.” In addition to the state and program name, the DSIRE data include a number of fields to group programs and summary descriptions of each program’s history and its relevance to different technologies. In most cases, the data include information on the relevant time period associated with the program. The first subsection of this chapter presents information on the qualitative relationships between policies and growth (or lack of growth) in CHP, wind and biogas capacity by state and year. However, it is difficult to determine from this qualitative approach the extent to which the SGIP or other state and utility policies have influenced growth in CHP, wind or biogas markets. In the second subsection of this chapter, we use a statistical approach to determine if there are discernable impact of SGIP on these markets and to determine the impacts of other programs on installed capacity. The last subsection compares the practices used by Program Administrators in other states who run DG support programs similar to the SGIP. The
1 These data are available from the DSIRE database located on the North Carolina Clean Energy web site: http://www.dsireusa.org/
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intent is to identify if there are practices in other states and programs that could help improve DG market transformation activities in California.
Assessing Influence through Indicators CHP Programs, Policies, and Accomplishments Using data from the US DOE Combined Heat and Power Installation Database,2 Figure 7-1 illustrates the CHP capacity installed in California (CA), Connecticut (CT), Illinois (IL), Massachusetts (MA), Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA), and Wisconsin (WI). The data are disaggregated by state and by type of CHP prime mover technology. The data are limited to installations of 5 MWs or less of capacity that are fueled by natural gas or fuel oil. These data represent CHP technology likely to be located behind the customer meter and of a size consistent with eligibility for SGIP. Technologies fueled with biogas are analyzed separately. The data in Figure 7-1 clearly indicates that California has installed substantially more CHP capacity between 1999 and 2014 than other states included in this analysis. New York has installed approximately 43% of the capacity installed in California. The data show that reciprocating engines (IC engines) represent the largest share of capacity for both California and New York. Wisconsin has a small amount of microturbine CHP capacity, but the installed capacity is substantially smaller than for other states such that it appears that Wisconsin has no capacity.
2 These data are available from the DOE’s web site: https://doe.icfwebservices.com/chpdb/. The data collection effort is funded by the U.S. DOE and maintained by ICF international. The data provides a comprehensive listing of CHP projects in the U.S. For this analysis, CHP installations have been limited to 5 MW or less. As with all large data collection efforts of this type, it is possible that some CHP installations in the U.S. are missing from this data source. Given the nature of these data, it is possible that information on CHP capacity specific to SGIP may differ from the data in the DOE dataset. For consistency across all states, the analysis uses the DOE data for all states’ CHP capacity information.
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Figure 7-1: State Level CHP Accomplishments from 1999 to 2014 400,000
350,000
300,000 Reciprocating Engine 250,000 Other Microturbine 200,000 Fuel Cell 150,000 Combustion Turbine Combined Cycle 100,000 Boiler/Steam Turbine
50,000
- CA CT IL MA MI NJ NY PA WI
CHP in California: Programs and Policies Figure 7-2 summarizes CHP capacity sized 5 MW and less installed in California from 1999 to 2014. The chart also identifies CHP related programs and policies implemented during this time period in California. The data on the CHP programs and policies were derived from the DSIRE database. Figure 7-2 illustrates that the installed capacity of CHP increased substantially after 2001. The 2002 increase in the installed capacity of CHP occurred at the same time the Self-Generation Incentive Program began providing incentives, the Public Leadership Solutions for Energy or PULSE program begin providing loans, and the Energy Financing Industrial Development Bond Program began. These programs were instituted, at least in part, to help California develop capacity during and following the state’s energy crisis of 2000 and 2001.3 These programs were quickly followed by policies on Net Metering and the development of a California Renewable Portfolio Standard.
3 In the summer of 2000 and continuing through early 2001, California experienced severe rolling blackouts that affected much of the state. For example, see http://www.pbs.org/wgbh/pages/frontline/shows/blackout/california/timeline.html
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In 2006 the California legislature revised the SGIP by limiting incentives to only fuel cells and wind turbines starting January 1, 2008.4 The decline in newly installed CHP capacity observable in Figure 7-2 from 2009- 2011 is likely due, at least in part, to the elimination of SGIP incentives and their impact on customers’ decisions to implement CHP. It is also likely that the recession of 2008 negatively impacted the installation of CHP capacity. In 2012, SGIP eligibility for CHP to receive incentives was restored and the level of CHP installations increased. The timeline of SGIP and the yearly changes in CHP installed capacity in California support the importance of SGIP in customers’ decisions to install CHP.
Figure 7-2: CHP California: Programs and Policies
CHP in Connecticut: Programs and Policies Figure 7-3 shows the amount of CHP capacity 5 MW and less installed in Connecticut from 1999 to 2014. The chart also overlays the timing of CHP related programs and policies implemented during this same time period. The fall in CHP capacity observed between 2012 through 2014 may be associated with net metering provisions that drew away potential customer investment in CHP. As shown in Figure 7-3, Connecticut implemented many new policies designed to assist the installation of CHP beginning in 2003, including the Connecticut Clean Energy Fund (CCEF) Project 150 Initiative, which had a goal to install 150 MW of clean energy. The CCEF rebated many fuel cell projects.
4 Assembly Bill 2778, Lieber, February 24, 2006. The technologies had to meet emission standards established by the California Air Resources Board’s Distributed Generation certification standards.
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Figure 7-3: CHP in Connecticut: Programs and Policies
The numerous policies implemented from 2003 to 2007 likely contributed to the substantial growth in installed CHP capacity observed from 2007 through 2009. It is possible that the recession of 2008 and the completion of the 150 MW CCEF goal impacted new installed capacity in 2010. In 2011 Connecticut implemented Net Metering and the CCEF Alpha Project. The CCEF Alpha Project was designed to fund the development and implementation of clean energy technologies. CHP was one of several options within the Alpha Project. Net metering in Connecticut requires the state’s investor-owned utilities to net meter “Class 1” renewable-energy resources including solar, wind, landfill gas, fuel cells, sustainable biomass, ocean-thermal power, wave or tidal power, low-emission advanced renewable- energy conversion technologies, and hydropower facilities up to 2 MW.5 In 2012 and 2013, Connecticut expanded the number of programs designed to facilitate the implementation of CHP technologies though there was a drop in CHP installations between 2011 and 2012. As noted earlier, the fall in capacity in 2012 through 2014 may be associated with a potential negative impact of net metering on CHP by essentially drawing interest towards other technologies that benefited by net metering provisions. CHP in Illinois: Programs and Policies Figure 7-4 illustrates the amount of CHP capacity 5 MW and less installed in Illinois from 1999 to 2014 and the CHP related programs and policies implemented during this time period. Illinois implemented the
5 Information on net metering in Connecticut was derived from information provided on the clean energy authority web site: http://www.cleanenergyauthority.com/solar-rebates-and-incentives/connecticut/connecticut-net-metering/.
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Clean Energy Community Foundation Grants in 1999. As shown in the chart, customers in Illinois installed approximately 24 MW of CHP capacity from 1999 to 2003. The data also shows that from 2004 to 2013 Illinois installed approximately 5 MW of new CHP capacity. During this time period, Illinois began a net metering policy and a renewable portfolio standard. Illinois’ net metering policy qualified residential and commercial electric generators up to 2 MW if the generation of electricity was generally for the customer’s use and if the generator was powered by photovoltaics, wind, dedicated crops grown for electricity generation, anaerobic digestion of livestock or food processing waste, fuel cells or microturbines powered by renewable fuels, or hydroelectricity. The CHPs analyzed in this section of the report are those fueled by natural gas and oil, and would generally have been ineligible for Illinois’ net metering policy. The ineligibility of traditional CHP for net metering may have made CHP relatively less cost-effective when compared with other forms of distributed generation that were eligible for net metering in Illinois. In 2014, Illinois passed the Public Sector Combined Heat and Power Pilot Program in an attempt to increase the CHP capacity in public sector buildings. The ICF CHP data, however, did not indicate that this policy had led to the installation of any capacity in 2014. Given the long planning times required for CHP projects, the Public Sector CHP Pilot Program may lead to increased CHP capacity in the future.
Figure 7-4: CHP in Illinois: Programs and Policies
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CHP in Massachusetts: Programs and Policies The amount of CHP installed from 1999 to 2014 in Massachusetts is shown in Figure 7-5 along with CHP related programs and policies implemented during this time period. Massachusetts implemented a Renewable Portfolio Standard (RPS) in 2002 and an Alternative Energy Portfolio Standard (AEP) in 2008. The Massachusetts RPS mandated a minimum percentage of electricity sales had to come from renewable resources including solar, wind, small hydro, landfill and anaerobic digester gas, marine or hydrokinetic energy, geothermal energy or eligible biomass fuel. The Massachusetts AEP provided an opportunity for businesses, institutions, and government to receive incentives for installing CHP, flywheel storage, coal gasification, and efficient steam technologies. The AEP obligation was initially set at 1% of electricity supplies in 2009, increasing by 0.5% points each following year until 2014; at which time the growth rate would be reduced to 0.25% per year.6 The Massachusetts CHP installed capacity data in Figure 7-5 indicates that while the Massachusetts RPS may not have led to substantial increase in CHP capacity, the AEP appears to be positively associated with an increase in CHP capacity in Massachusetts.
Figure 7-5: CHP in Massachusetts: Programs and Policies
6 Information on Massachusetts’ RPS and AEP were derived from http://www.mass.gov/eea/energy-utilities-clean- tech/renewable-energy/rps-aps/rps-and-aps-program-summaries.html.
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CHP in Michigan: Programs and Policies Figure 7-6 shows the amount of CHP capacity 5 MW and less installed in Michigan from 1999 to 2014 and CHP related programs and policies implemented during this time period. Michigan implemented an Alternative Energy Personal Property Tax Exemption in 2002. This law exempted industrial and commercial property from increases in taxes if increases in property value were due to improvements associated with energy efficiency or distributed generation additions. Michigan also enacted a Refundable Payroll Tax Credit for businesses that locate in a NextEnergy Zones and conduct research and development or manufacture alternative energy technologies. It also enacted a Nonrefundable Business Activity Tax Credit that provided tax credits for businesses engaged in alternative energy research, development, and manufacturing. From the data presented in Figure 7-6, none of these programs appear to have had a substantial impact on the amount of CHP installed capacity in Michigan. Based on the CHP data in Figure 7-6, the installation of new CHP capacity appears relatively random, with positive capacity installed in one year and then one or two years with no new CHP capacity installed.
Figure 7-6: CHP in Michigan: Programs and Policies
CHP in New Jersey: Programs and Policies The amount of CHP installed in New Jersey sized at 5 MW and less from 1999 to 2014 is shown in Figure 7-7. CHP related programs and policies implemented during this time period also are so shown in the chart. New Jersey implemented a net metering policy and a Societal Benefits Charge in 1999. The New
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Jersey net metering policy was not applicable to CHP fueled by natural gas or oil. In 1999 and 2000 New Jersey installed approximately 5 MW of CHP capacity (based on the criteria for this analysis) each year. In 2001 New Jersey implemented a Renewable Portfolio Standard and the New Jersey Renewable Energy Incentive Program. While fuel cells fueled by biomass are eligible for these programs, traditional CHP fueled by natural gas and oil are typically not eligible. In New Jersey, new CHP capacity was relatively small in 2001 but grew to approximately 5 MW of new CHP capacity in 2002. During the 2008-2012 time period New Jersey implemented several new programs and policies designed to encourage the implementation of CHP technologies. High priority programs and policies included property tax exemptions in 2008, the Clean Energy Solutions Capital Investment Loans and Grants and the Edison Innovation Clean Energy Fund in 2009, the ARRA Clean Energy Solutions CHP program in 2010, and the Clean Energy Solutions for Large and Small CHP and Fuel Cells in 2012. The passage of multiple programs and policies in New Jersey from 2008 to 2012 appears to have led to a more consistent level of CHP capacity installation. In addition, the Fuel Cell programs implemented in 2012 appear to have led to an increase in fuel cell installed capacity in 2013.
Figure 7-7: CHP in New Jersey: Programs and Policies
CHP in New York: Programs and Policies Figure 7-8 shows the amount of CHP capacity 5 MW and less installed in New York from 1999 to 2014 along with CHP programs and policies implemented during this time period. New York implemented an Interconnection Policy in 1999 to establish rules and regulations governing customer’s ability to connect
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electric generators to the grid. In 2000 and 2001, New York’s installed CHP capacity grew from approximately 2 MW in 1999 to over 8 MW in each of the following two years. In 2002 New York implemented a Fuel Cell Rebate and Performance Incentive, though their CHP capacity continued to be dominated by reciprocating (IC) engines. A substantial increase in fuel cell capacity occurred in 2006. Since 2000, the installation of new CHP capacity in New York has exceeded 6 MW per year for every year except 2011. Several programs and policies have been adopted since the Fuel Cell Program in 2002 to support the continued growth of CHP in New York. New York implemented a Renewable Portfolio Standard in 2004, while a customer-sited tier of the New York RPS is focused on supporting customer sited smaller-scale renewables including fuel cells. In 2009 NYSERDA began a CHP and Renewable Generation Technology Assistance program. In 2011 New York also implemented several programs designed to increase the installed capacity of CHP. In 2011 the Anaerobic Digester Gas-to-Electric Rebate and Performance Incentive Program and the Distributed Generation Combined Heat and Power Program were instituted. These programs, combined with the NYSERDA’s Operating Plan for Technology and Market Development Programs have helped to increase the capacity of CHP installed in New York since 2011. In 2011 the New York Public Service Commission (PSC) reaffirmed its decision to continue to use the System Benefits Charge to help fund the Technology and Market Development Portfolio (T&MD). The mission of the T&MD fund is to “test, develop, and introduce new technologies, strategies and practices that build the statewide infrastructure to reliably deliver clean energy to New Yorkers.”7 In New York, through the T&MD and other programs, substantial funds have been provided for CHP development and installation. As illustrated in Figure 7-8, the yearly installed capacity of CHP 5 MW and smaller in New York has grown every year since 2011. The newly installed CHP capacity was approximately 5 MW in 2011, growing to over 18 MW of newly installed CHP capacity in 2014.
7 “Operating Plan for Technology and Market Development Programs (2012-2016)”, second revision 2/15/2013, case 10-m- 0457, NYSERDA.
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Figure 7-8: CHP in New York: Programs and Policies
CHP in Pennsylvania: Programs and Policies The amount of CHP capacity 5 MW and less installed in in Pennsylvania from 1999 to 2014 along with CHP related programs and policies implemented during this time period is shown in Figure 7-9. Pennsylvania implemented a large number of programs and policies from 1999 to 2001 applicable to CHP. These policies included utility specific programs associated with sustainable energy fund loans and grants and a statewide Renewable Portfolio Standard. Following the implementation of these policies, there was a small increase in CHP installations from 2003 through 2005. In 2005 Pennsylvania implemented an Alternative Energy Portfolio Standard (AEPS). Pennsylvania’s AEPS was designed to encourage reliance on more diverse and environmentally friendly sources of energy. The energy requirements under Pennsylvania’s AEPS included different tiers or classifications for alternative technologies: Tier 1, Tier 2, and solar specifications. The AEPS specifies a requirement for the share of energy allowed in each tier to satisfy the AEPS standard. Pennsylvania’s AEPS fuel cells are a Tier 1 resource and general distributed generation systems are a Tier 2 option. Pennsylvania also implemented net metering and Interconnection Standards in 2006. In 2007 Pennsylvania experienced a small increase in the installation of CHP resources. In 2009, Pennsylvania instituted the Green Energy Load Fund, which provides financing for high-performance energy systems in existing nonresidential buildings. In 2011 through 2013, Pennsylvania experienced a substantial increase in installed CHP capacity, though 2014 installation fell far short of those in the preceding years.
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Figure 7-9: CHP in Pennsylvania: Programs and Polices
CHP in Wisconsin: Programs and Policies Figure 7-10 shows the amount of CHP capacity 5 MW and less installed in Wisconsin from 1999 to 2014 along with CHP related programs and policies implemented during this time period. Figure 7-10 illustrates that Wisconsin has had very little CHP installed capacity and few programs or policies specific to CHP. In 2009 Wisconsin implemented a Renewable Portfolio Standard requiring the state to obtain 10% of its electricity from renewable sources by 2015. Wisconsin met their RPS goal in 2013. However, CHP fueled by oil or natural gas does not contribute to satisfying Wisconsin’s RPS. The programs and policies implemented in Wisconsin were designed to encourage electricity generation using alternative fuels and generally do not apply to CHP fueled by more conventional fuels.
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Figure 7-10: CHP in Wisconsin: Programs and Policies
Wind Programs, Policies, and Accomplishments Using data from the USGS Wind Installation Database,8 Figure 7-11 shows the different capacity (in MWs) of wind energy systems installed in California (CA), Connecticut (CT), Illinois (IL), Massachusetts (MA), Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA), and Wisconsin (WI). The data are limited to installations with 7.5 MWs or less of capacity per wind site by year of installation for the years 1999 to 2008 and 9 MW or less for years 2009-2013.9 The data is designed to represent wind installation
8 These data are available from the USGS’s web site: http://pubs.usgs.gov/ds/817/downloads/. This data set provides industrial-scale onshore wind turbine locations in the United States through July 22, 2013, corresponding facility information, and turbine technical specifications. The wind turbine data was created through a process that synthesized existing information and combined existing data with the Federal Aviation Administration’s Digital Obstacles File (DOF) (Federal Aviation Administration, 2013). The USGS data also uses information from databases developed by the U.S. Energy Information Administration (U.S. Energy Information Administration, 2013) and the no longer maintained Wind Energy Data and Information (WENDI) data set from the Oak Ridge National Laboratory. 9 The analysis also removed capacity associated with Wethersfield Wind Farm. The capacity associated with this customer was found to represent capacity that was not behind the customer’s meter. During the data development phase, the analysis team reviewed sites with more than 7 MW of capacity installed in a single year. Wind installations without capacity information were eliminated from eligibility for the analysis. Many of the wind installations without capacity data in the USGS data, though not all of them, were associated with site names that were clearly large scale wind farms and not eligible for this analysis due to their size and in-front of the meter status.
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behind the customer’s meter. Given that there is no clear indication that a wind turbine installation is behind the customer meter, the two capacity restrictions are designed to restrict installations to those that are likely to be behind the customer meter. During the data development phase, we reviewed the name of customer sites in the USGS data in an attempt to limit installations to customer sited wind turbines. The data represent capacity installations from 1999 to 2013. The data illustrated in Figure 7-11 clearly indicates that Massachusetts has installed the most behind-the- meter wind capacity during the analysis period, followed by California. These data also indicate that Connecticut had not installed any behind-the-meter wind capacity from 1999 to 2013.
Figure 7-11: Wind Capacity by State 70
60
50
40
30
MW of Wind Capacity Wind of MW 20
10
- CA CT IL MA MI NJ NY PA WI
Wind in California: Programs and Policies Figure 7-12 shows the amount of small-sized, behind-the-meter wind capacity installed in California from 1999 to 2013. The chart also overlays wind energy related programs and policies implemented during this time period. Data on the wind programs and policies were derived from the DSIRE database. As represented in Figure 7-12 installation of small wind capacity occurred primarily in two distinct time periods: one from 2003 to 2005 and one from 2009 through 2012. Small wind capacity installed in the 2003 to 2005 time period represents individual installations of a single turbine each year. The capacity
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installed in the later time period represents installations at 15 sites with many sites installing multiple turbines. In 2001 California implemented the Solar and Wind Energy System Credit, a state tax credit valued at 7.5% to 15% of the net, nonrebated cost of the system for systems sized up to 200 kW. California also implemented an Energy Financing Industrial Development Bond that allowed solar and wind manufacturers to borrow money exempt from federal taxes and a Public Leadership Solutions for Energy (PULSE) fund to help finance renewable energy installations on public buildings. The Self-Generation Incentive Program was also implemented in 2002 to provide incentives for the installation of distributed generation technologies, including wind, that could help provide electricity resources at time of the system peak. These early programs may have contributed to the early installations of wind, but the later wind installations represent substantially more capacity and customers. In 2009 the California legislature passed Assembly Bill 45 authorizing counties to adopt ordinances to provide for the installation of small wind systems (50 kW and smaller) outside urbanized areas but within the county’s jurisdiction. The bill allows counties to be more lenient than the ordinance, but establishes maximum restrictions that a county may implement. During 2007 the SGIP was modified to provide only incentives to wind and fuel cells. The SGIP restrictions were in place until 2012. These restrictions may have provided additional funds for wind projects that no longer needed to compete for funds with a wider array of distributed generation technologies. The increase in installed capacity from 2009 through 2012 is timed with the implementation of the county wind ordinance and the modifications of SGIP.
Figure 7-12: California Wind Capacity: Programs and Policies
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Wind in Illinois: Programs and Policies Figure 7-13 illustrates the small-sized, behind-the-meter, wind capacity installed in Illinois from 1999 to 2013 and the timing of wind related programs and policies implemented during this time period. Illinois has implemented many programs and policies designed to encourage the development of small wind capacity. The Small Wind Grant Program was established to provide grants for wind projects between 1 and 50 kW while the Wind Energy Production Program provided financial assistance for the development of new wind projects up to 500 kW. According to the USGS data, the first, small-sized behind-the-meter wind capacity installed in Illinois were two sites in 2005. The joint capacity across these first installations was 2.31 MW. In 2007 Illinois developed net metering protocols. Illinois’ net metering applies to renewable distributed generation with an operating capacity of less than 2 MW and intended primarily to offset the electric requirements of the owner of the generation. Small, behind-the-meter wind in Illinois is eligible for net metering. In 2008 Illinois established standard interconnection requirements for distributed generation systems including wind. In 2009 Illinois established a maximum setback limit for wind turbines designed for onsite energy generation. The rule was amended in 2013 to allow municipalities to prohibit wind devices with a capacity of 100 kW or greater. The initial intent of the energy setback standards was to standardize and simplify the location of wind turbines to encourage their installation while the latter amendment limits the installation of larger (i.e., greater than 100 kW) wind turbines. In 2009 there was a substantial increase in new, small wind capacity in Illinois. This increase is consistent with the anticipated impact of the net metering, interconnection, and the original intent of the energy setback standards. Illinois also saw installations of new wind capacity in 2010, 2011, and 2012. During this time period, Illinois implemented the Large-Distributed Solar and Wind Grant Program providing incentives for nonresidential customers installing wind as well as the Solar and Wind Rights Policy prohibiting homeowners’ associations from preventing homeowners from installing solar or wind systems.
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Figure 7-13: Illinois Wind Capacity: Programs and Policies
Wind in Massachusetts and Programs and Policies Data on the amount of small, behind-the-meter, wind capacity installed in Massachusetts from 1999 to 2013 is shown in Figure 7-14. In 2000 Massachusetts implemented the Commonwealth Wind Program. The Program was designed to offer communities and developers assistance in site assessments, feasibility studies, and development grants for appropriately-sited wind energy development.10 Massachusetts also implemented a Renewable Portfolio Standard in 2002, obligating electricity suppliers to provide their customers with 1% of their energy from renewable sources in 2003. In 2008 the RPS was broken into Class 1 and Class 2 obligations with Class 1 obligations increasing 1% point annually. Wind energy qualifies as a Class 1 resource for the Massachusetts RPS.11 In 2007 and 2008 Massachusetts implemented several policies designed to encourage wind energy development. The Massachusetts Green Communities Act set in motion many policies designed to shift the state’s energy policy and energy consumption. The Green Communities Act expanded the RPS by
10 Information on Massachusetts’ Commonwealth Wind Program was found on the Mass CEC’s web site. http://www.masscec.com/programs/commonwealth-wind-program. 11 Information on Massachusetts’ RPS was found on the Mass.gov Energy and the Environmental Affairs web site. http://www.mass.gov/eea/energy-utilities-clean-tech/renewable-energy/rps-aps/rps-and-aps-program-summaries.html.
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providing customers the ability to own and benefit from energy producing technologies, and allowing customers to take advantage of net metering programs. The Model As-of-Right Zoning Ordinance was developed by the Department of Energy Resources to assist cities and towns in establishing reasonable standards for wind-power development.12 Massachusetts also adopted interconnection standards in 2007 mandating that all distributed generation installations must be reviewed to qualify for interconnection. The many policies implemented in 2007 and 2008 appear to have contributed to a substantial growth in wind energy capacity from 2009 to 2012.
Figure 7-14: Massachusetts Wind Capacity: Programs and Policies
Wind in Michigan: Programs and Policies Figure 7-15 shows the amount of small, behind-the-meter, wind capacity installed in Michigan from 1999 to 2013 and the timing of wind energy related programs and policies implemented during this time period. Michigan has implemented many programs and policies associated with wind energy, but few small, behind-the-meter wind systems have been installed. The wind installations depicted in Figure 7-15 represent multiple turbines installed at two customer sites. The installation in 2001 occurred prior to the implementation of high priority wind energy programs and policies. The wind installation in 2006 occurred following the implementation of tax policies, Michigan’s interconnection regulations and the development of net metering. In 2006, Michigan also implemented Renewable Energy Renaissance Zones
12 Information on Massachusetts’ model zoning ordinances for wind was found on the Mass.gov web site. http://www.mass.gov/eea/docs/doer/green-communities/grant-program/wind-model-bylaw-mar-2012.pdf.
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created around a facility that generates renewable energy. The designation of a Renewable Energy Renaissance Zone eliminates the customer’s requirement to pay state education taxes, personal and real property taxes and local income taxes. Since 2006, Michigan has implemented many policies applicable to wind systems, but no additional small, behind-the-meter systems have been installed.
Figure 7-15: Michigan Wind Capacity: Programs and Policies
Wind in New Jersey: Programs and Policies The amount of small, behind-the-meter, wind capacity installed in New Jersey from 1999 to 2013 and wind related programs and policies is shown in Figure 7-16. New Jersey has implemented many programs and policies associated with wind energy, but few small, behind-the-meter wind systems have been installed. Prior to the installation of a substantially sized wind energy system in 2006, New Jersey implemented net metering, a Renewables Portfolio Standard, and Interconnection regulations. From 2007 to 2011 New Jersey implemented the Clean Energy Solutions Capital Investment Loan/Grant Program, Edison Innovation Clean Energy Fund, developed permitting laws and instituted new tax regulations. However, only one additional wind system was implemented in 2012.
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Figure 7-16: New Jersey Wind Capacity: Programs and Policies
Wind in New York: Programs and Policies Figure 7-17 shows the amount of small wind capacity installed in New York from 1999 to 2013 overlaid with wind energy related programs and policies implemented during this time period. New York has implemented programs and policies associated with wind energy, but few small, behind-the-meter wind systems have been installed. In 1999 New York implemented interconnection regulations and in 2002 the New York State Research and Development Authority (NYSERDA) developed Guidance for Local Wind and Energy Ordinances. The Guide provides information to help communities address issues they may encounter with the development of wind systems. In 2004 New York developed a Renewable Portfolio Standard and in 2006 NYSERDA instituted a wind incentive program. To help encourage a network of wind energy installers, the NYSERDA program provides incentives to eligible installers. The installers are directed to pass through the incentives to the owners of the wind system.13 In 2009 New York implemented net metering and in 2011 remote net metering. Remote net metering allows a nonresidential customer to apply the excess energy produced by their system to another meter on their account. The remote net metering system allows the nonresidential customer to potentially receive a higher value from their excess energy production.14 In 2010 and 2012, New York experienced the
13 Information on the NYSERDA Wind Incentive Program is derived from data available on the DSIRE database. http://programs.dsireusa.org/system/program/detail/956. 14 Information on New York’s Net Metering and Remote Net Metering policies were found on NYSERD’s web site. http://www.nyserda.ny.gov/Cleantech-and-Innovation/Power-Generation/Net-Metering-Interconnection.
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installation of their first small, behind-the-meter wind capacity since 2002. In 2012, NYSERDA implemented an On-Site Wind Incentive Program providing incentives for installation of on-site or behind- the-meter wind systems.
Figure 7-17: New York Wind Capacity: Programs and Policies
Wind in Pennsylvania: Programs and Policies Data on small, behind-the-meter, wind capacity installed in Pennsylvania from 1999 to 2013 and wind energy related programs and policies implemented during this time are shown in Figure 7-18. Pennsylvania’s utilities implemented a number of wind energy related programs and policies from 1999 to 2004, but no small, behind-the-meter wind systems were installed according to the USGS wind capacity data. Pennsylvania also implemented a Renewable Portfolio Standard in 2001, an Alternative Energy Portfolio Standard in 2005, and net metering in 2006. A fairly significant wind energy system was installed in 2011. However, these regulations appear to have had little overall impact on behind-the-meter wind systems in Pennsylvania.
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Figure 7-18: Pennsylvania Wind Capacity: Programs and Policies
Wind in Wisconsin: Programs and Policies Wisconsin instituted a number of small wind energy related programs and policies beginning in 1999. However, as the wind capacity data shown in Figure 7-19 illustrates, there have been few installations of small, behind-the-meter wind systems. Wisconsin developed Interconnection Standards for behind-the- meter generation in 2004, a Green Power Purchasing agreement in 2006 and a Renewable Portfolio Standard (RPS) in 2009. Wind systems are an eligible source of renewable power under Wisconsin’s RPS. In 2012, the Renewable Energy Competitive Incentive Program was also implemented to encourage the installation of renewable sources of energy. To date, the Renewable Energy Competitive Incentive Program has largely funded PV installations.15
15 Information on the distribution of incentives within the Wisconsin Renewable Energy Competitive Incentive Program were derived from https://focusonenergy.com/business/renewable-energy.
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Figure 7-19: Wisconsin Wind Capacity: Programs and Policies
Biogas Programs, Policies, and Energy Accomplishments We use data from the US Environmental Protection Agency Landfill Methane Outreach Program and the Livestock Anaerobic Digester Database in determining installed capacity of biogas projects. Figure 7-20 shows the capacity (in kWs) of biogas fueled generation projects installed in California (CA), Connecticut (CT), Illinois (IL), Massachusetts (MA), Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA), and Wisconsin (WI). The data are limited to installations with 5 MWs or less of capacity per biogas site by year of installation for the years 1999 to 2014. The data is designed to represent capacity fueled by biogas installation behind the customer’s meter. The EPA data indicate that California, closely followed by Michigan, has the highest level of new biogas capacity installed from 1999 to 2014. However, New York’s and Wisconsin’s newly installed biogas capacity are also very similar and not substantially less than California and Michigan’s new capacity. The newly installed capacity in Connecticut and New Jersey are substantially less and are restricted to landfill gas capacity. Wisconsin has the largest new dairy and swine digester capacity.
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Figure 7-20: New Landfill and Dairy and Swine Digester Biogas Capacity by State (1999-2013, kW)
120,000
100,000
80,000
60,000 LandFill Digester
40,000 KW of BioGas Capacity BioGas of KW
20,000
- CA CT IL MA MI NJ NY PA WI
Biogas in California: Programs and Policies Figure 7-21 shows the amount of small (less than 5 MW) landfill and dairy and swine digester biogas capacity installed in California from 1999 to 2014. The figure is overlaid with biogas energy related programs and policies implemented during this time period.16 As shown in Figure 7-21, the capacity of new biogas projects was relatively high in 1999. California has had long running programs designed to capture the gases created by landfills. Capturing these gases helps to limit the likelihood of fires or explosions at California landfills. According to the EPA Landfill Methane Outreach Program database, the first landfill gas to energy project was installed in California in 1982. By 1995 California had 56 landfill gas recovery facilities; 14 collecting landfill gas and 42 collecting landfill gas and producing energy.17
16 The data on biogas programs and policies were derived from the DSIRE database. 17 The historic information on landfill gas energy production in California was derived from the CA.Gov web site. http://www.energy.ca.gov/biomass/landfill_gas.html.
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Figure 7-21: California Biogas Capacity: Programs and Policies
A number of policies potentially impacting biogas to energy project development have been enacted in California. In 1996 California passed the Public Benefits Fund for Renewables and Energy Efficiency. The Public Benefits Fund provided early funding for biogas fueled on-site generation and likely contributed to the growth in capacity from 1999 to 2012. In 2000, California instituted Interconnection Standards and the Solar Energy and Distributed Generation Grant Program to encourage on-site generation. California’s energy crisis of 2000 and 2001 encouraged the state to implement additional policies and programs to encourage on-site generation to help reduce peak generation. The new programs include the Self Generation Incentive Program, Public Leadership for Energy (PULSE) Loans and the Energy Financing Industrial Development Bond Program. The substantial increase in biogas capacity following the implementation of these programs is illustrated in Figure 7-21. In 2003 California also implemented net metering, the Agricultural Biomass to Energy Program and the California Renewable Portfolio Standard; all programs and policies designed to increase the implementation of renewable energy. At the utility level, the SCE Biomass Standard Contract is a resource procurement process through which SCE contracts with biomass projects in their territory with 20 MW or less of capacity. In 2006 the California legislature revised the SGIP by limiting incentives to only fuel cells and wind turbines starting January 1, 2008. The substantial decline in new biogas capacity observed from 2010 to 2011 may be due to changing rules of the Self-Generation Incentive Program combined with the economic recession of 2008 and 2009. California experienced an upturn in new biogas capacity from 2012 through 2014. California has a long history of facilitating the use of biogas to produce energy. This history,
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however, illustrates the importance of maintaining a partnership between biogas energy producers and state programs to preserve the productive use of these resources.18 Biogas in Connecticut: Programs and Policies Connecticut has only begun recently to use landfill gas to produce electricity. The amount of small biogas energy capacity installed in Connecticut from 1999 to 2014 is shown in Figure 7-22. The first landfill gas recovery project in Connecticut occurred in 1998. Figure 7-22 illustrates that new installations of biogas capacity in Connecticut occurred primarily from 2007 through 2009 and that all biogas projects were associated with landfills. Connecticut has instituted several programs and policies designed to increase the capacity of renewable energy production. In 2003 the Connecticut Clean Energy Fund (CCEF) Project 150 Initiative was enacted with a goal of installing 150 MW of clean energy. The DPUC Capital Grants and Low-Interest Loans for Customer-Side Distributed Resources and the On-Site Renewable DG Program were designed to stimulate demand for behind-the-meter installations of renewable energy at nonresidential facilities in Connecticut. Connecticut also implanted a Renewable Portfolio Standard in 2006 and net metering in 2011. In 2013 Connecticut also implemented an RFP for Renewable Power Produced by Biomass, landfill Gas, and Run- of-River Hydropower to help meet Connecticut’s Class 1 renewable energy requirements within Connecticut’s RPS.
18 See http://www.energy.ca.gov/biomass/biomass.html for a description of the history and current state of biomass energy production in California.
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Figure 7-22: Connecticut Biogas Capacity and Programs and Policies
Biogas in Illinois: Programs and Policies Illinois has a medium-sized but significant dairy industry with over 690 licensed dairies, making it the 22nd largest milk producing state in the country.19 In addition, Illinois was ranked as the fourth largest producer of pork products in 2011.20 As of 2014, Illinois had 39 active landfills.21 The amount of small biogas to energy projects installed in Illinois from 1999 to 2014 and biogas energy related programs and policies implemented during this time is shown in Figure 7-23. The data illustrates that all biogas project installations involved landfill gas other than a digester biogas installation in 2002 and a very small digester biogas installation in 2008. Illinois has a history of using landfill gas to produce electricity, with the first landfill gas project occurring in 1988. Illinois Clean Energy Community Foundation Grants, implemented in 1999, provide grants for renewable energy projects including anaerobic digester installations. Illinois’ Renewable Portfolio Standard was implemented in 2007 and landfill gas and energy from anaerobic digestion is eligible under the RPS standard. In 2007 Illinois implemented net metering regulations and in 2008 Illinois established
19 From https://www.midwestdairy.com/farm-life/dairy-in-the-midwest/ 20 From http://www.ilpork.com/?104 21 From http://www.epa.illinois.gov/topics/waste-management/landfills/landfill-capacity/2014/index
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Interconnection Standards. Additional landfill gas projects have been completed in Illinois since 2011, however, these projects exceed the 5 MW limit for this analysis.
Figure 7-23: Illinois Biogas Capacity: Programs and Policies
Biogas in Massachusetts: Programs and Policies Massachusetts has a relatively small diary industry with 137 dairy farms, primarily family run operations.22 Similarly, Massachusetts has a fairly small swine industry, ranking 37th in pork production in 2004.23 Massachusetts has developed policies governing recycling and disposal of solid wastes and, as of 2014, there were only 17 landfills in the state accepting household and business wastes.24 Figure 7-24 shows the amount of new and small landfill, dairy and swine digester biogas capacity installed in Massachusetts from 1999 to 2014 along with biogas energy related programs and policies implemented during this time period. The chart shows that all biogas project installations represent landfill gas to energy production other than digester biogas projects in 2011 and 2013.
22 From http://semaponline.org/resources/dairy-farming/ 23 From http://www.stuffaboutstates.com/agriculture/livestock/hogs.htm 24 From http://www.mass.gov/eea/docs/dep/recycle/solid/swminma.pdf; see page 17 for a description of landfill operations
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Figure 7-24: Massachusetts Biogas Capacity: Programs and Policies
Massachusetts’ first landfill gas to energy installation occurred in 1995. Massachusetts implemented a Renewable Portfolio Standard in 2002 and capacity fueled by landfill gas or digester gas is eligible for Class 1 qualified generation. In 2007 Massachusetts implemented a Green Power Purchasing Commitment, which directs state government agencies to purchase 15% of annual electricity consumption from renewable sources by 2012 and 30% by 2020. In 2011, Massachusetts developed the Green Communities Grant Program to help communities meet the five criteria required to become a Green Community and qualify for grants to finance additional renewable energy projects. The first criterion for a Green Community is to provide as-of-right siting in designated locations for renewable/alternative energy generation, research and development, or manufacturing facilities.25 Through these programs, Massachusetts is developing policy to expand its capacity of electricity generated from landfill and digester biogas. Biogas in Michigan: Programs and Policies Michigan has a robust dairy industry with over 1,900 dairy herds; ranking it 6th in milk production in the country as of 2014.26 Michigan also has a significant swine industry, with over 2,600 swine farms.27 In
25 Information on Massachusetts Green Communities Grant Program was gathered from the Mass.gov web site. http://www.mass.gov/eea/energy-utilities-clean-tech/green-communities/gc-grant-program/ 26 From http://www.milkmeansmore.org/local-milk/dairy-facts 27 From http://i2.wp.com/www.mipork.org/wp-content/uploads/2015/03/MI-Economic-2_SM_Version.jpg
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2010, Michigan had approximately 70 to 80 landfills.28 Figure 7-25 shows the amount of new small dairy, swine digester and landfill biogas capacity installed in Michigan from 1999 to 2014. The data shows that while the majority of the newly installed capacity was fueled with landfill gas, new capacity fueled by digester biogas was substantial. Michigan has a history of assisting with biogas to energy project development. Michigan’s first landfill gas to energy project occurred in 1986. Michigan implemented several tax regulations for renewable generation in 2002 and formalized Interconnection Standards in 2003. In 2006, Michigan implemented the Biomass Gasification and Methane Digester Property Tax Exemption. That policy exempts energy production related to farm facilities from real and personal property taxes. In 2006 the city of Ann Arbor set a goal of 30% renewable energy for all municipal operations by 2010. While Ann Arbor fell short of the 2010 goal, the city passed a new resolution in 2011 to reach the 30% renewables goal by 2015.29 Michigan also implemented net metering and a Renewable Energy Standard in 2008. The success of these policies is observable in the newly installed capacity illustrated in Figure 7-25.
Figure 7-25: Michigan Biogas Capacity: Programs and Policies
28 From http://www.michigan.gov/documents/deq/DEQ-OWMRP-SWS-SolidWasteAnnualReportFY2014._481071_7.pdf. 29 Information on Ann Arbors Green Communities objects was found at energy.gov. http://energy.gov/savings/city-ann-arbor- green-power-purchasing
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Biogas in New Jersey: Programs and Policies New Jersey has a dairy industry composed on over 110 dairy farms.30 The state’s swine operations are very small with less than 1,000 pigs.31 New Jersey has over 400 registered landfills.32 Figure 7-26 shows the amount of new small landfill, dairy and swine digester biogas capacity installed in New Jersey from 1999 to 2014 as well as biogas related programs and policies implemented during this time. As the data shows, all new biogas capacity in New Jersey occurred after 2000 and all of the newly installed biogas capacity was fueled with landfill gas. New Jersey’s first landfill gas to energy project was installed in 1987. In 2000 New Jersey developed net metering regulations and in 2001 implemented a Renewable Portfolio Standard in which landfill and biomass gas are Class 1 renewable energy sources. New Jersey also implemented a Renewable Energy Incentive Program in 2001 to promote the installations of renewable electric generation. In 2004 New Jersey witnessed a substantial increase in installed new capacity from biogas projects. The recession in 2008 and 2009 may help explain the lack of biogas projects during 2009 and 2010 but the state was moving forward, implementing many programs to help encourage clean energy solutions.
Figure 7-26: New Jersey Biogas Capacity: Programs and Policies
30 From http://www.nj.gov/agriculture/news/hottopics/Topics050104.html 31 From http://nationalhogfarmer.com/site- files/nationalhogfarmer.com/files/archive/nationalhogfarmer.com/images/NHF_SOIReport__2011.pdf 32 From http://cdn.intechopen.com/pdfs-wm/27162.pdf; see page 158
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Biogas in New York: Programs and Policies New York has a vibrant and growing dairy industry, with over 5,000 family-owned farms making it the 4th ranked milk producing state in the nation.33 New York’s swine industry ranked 30th in pork production in 2010.34 At the end of 2014, there were 27 active MSW landfills in New York State.35 Figure 7-27 shows the amount of new and small landfill, dairy and swine digester biogas capacity installed in New York from 1999 to 2014. The data indicates that the newly installed biogas capacity is dominated by landfill gas energy projects but that New York also has substantial new capacity derived from digester biogas. New York’s first landfill gas to energy project was installed in 1982. In 1999 New York instituted Interconnection Standards to help facilitate the connection of behind-the-meter energy production to the electric grid. New York passed a Renewable Portfolio Standard in 2004 and electricity produced from landfill or digester gas qualifies as a renewable resource. In 2009 NYSERDA passed the CHP and Renewable Generation Technical Assistance Program to help cost-share the technical assistance studies needed prior to the implementation of renewable generation projects. New York also implemented net metering in 2009. In 2012 substantial resources supporting the installation and operation of new Anaerobic Digester Gas to Electricity systems using dairy farm wastes were made available by NYSERDA. The programs and regulations implemented by New York and NYSERDA are designed to encourage the continued expansion of the use of biogas to fuel electricity production.
33 From http://www.nyanimalag.org/an-overview-of-nys-dairy-industry/ 34 From http://www.stuffaboutstates.com/agriculture/livestock/hogs.htm 35 From http://www.dec.ny.gov/chemical/23682.html
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Figure 7-27: New York Biogas Capacity: Programs and Policies
Biogas in Pennsylvania: Programs and Policies Pennsylvania has a growing dairy industry with nearly 7,400 dairy farms, making it the 5th ranked milk producing state in the nation in 2014.36 With over 100,000 pigs, Pennsylvania ranked as the 12th largest pork producer in the nation in 2010.37 Pennsylvania has 46 active landfills.38 Figure 7-28 shows the amount of new and small-sized landfill, dairy and swine digester biogas capacity installed in Pennsylvania from 1999 to 2014. As shown in the chart, the newly installed biogas capacity is dominated by landfill gas projects. However, Pennsylvania also has substantial new capacity derived from dairy and swine digester biogas. The Pennsylvania Department of Environmental Protection identifies Pennsylvania landfill methane projects; with 37 landfill projects listed as having either closed or operational landfill methane projects as of 2011, one landfill with a project in development, eight landfills as candidates and only five landfills as
36 From http://centerfordairyexcellence.org/pennsylvania-dairy-industry-overview/ 37 From http://www.stuffaboutstates.com/agriculture/livestock/hogs.htm 38 From http://www.pareportcard.org/PDFs/Solid%20Waste%20FINAL%20NATL.pdf
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undetermined. Pennsylvania’s methane landfill projects appear to have a mix of electricity production and direct use of high BTU landfill gas to be blended into existing natural gas pipelines.39 Pennsylvania’s utilities have enacted a number of grant and loan programs designed to encourage the development of renewable energy and the state passed a Renewable Portfolio Standard in 2002. In Pennsylvania, biologically derived methane gas and biomass energy are Tier 1 alternative energy sources. Pennsylvania also developed Interconnection Standards and net metering regulation in 2006 that apply to electricity produced by biogas if the nonresidential customer’s system is less than 3 MW.
Figure 7-28: Pennsylvania Biogas Capacity: Programs and Policies
Biogas in Wisconsin: Programs and Policies Wisconsin has 10,290 dairy farms and nearly 1.3 million dairy cows, ranking it as the 2nd largest milk producing state in the nation.40 Similarly, with over 45,000 pigs, Wisconsin ranked 16th in pork production in the nation in 2010.41
39 Information on Pennsylvania’s landfill methane gas projects is from the Pennsylvania state web portal. http://www.portal.state.pa.us/portal/server.pt/community/landfill_methane_outreach_program/14091/pa_landfill_metha ne_projects/589657 40 From http://www.wmmb.com/assets/images/pdf/WisconsinDairyData.pdf 41 From http://www.stuffaboutstates.com/agriculture/livestock/hogs.htm
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The amount of new small-sized landfill, dairy and swine digester biogas capacity installed in Wisconsin from 1999 to 2014 is shown in Figure 7-29. Landfill gas projects dominate the biogas capacity installed in Wisconsin but there has also been substantial new capacity derived from dairy and swine digester biogas. The new installed capacity of dairy and swine biogas digesters is largest from 2007 through 2013.
Figure 7-29: Wisconsin Biogas Capacity: Programs and Policies
Wisconsin’s first landfill gas to energy projects occurred in 1986. Wisconsin also has an extensive number of dairy and swine biogas digester projects, highlighting the importance of Wisconsin’s dairy economy. In 1999 Wisconsin instituted a Public Benefits charge and created Focus on Energy to raise money to fund energy efficiency, low-income programs, and renewable energy programs. In 2004 Wisconsin developed Interconnection Standards designed to regulate the interconnection of distributed generation 15 MW and less to the utility grid. In 2006 We Energies developed a special tariff for energy developed from biogas resources to be sold back to the utility. The biogas buy-back rate is similar to net metering but designed specifically for customers using biogas to produce electricity. The buy-back program works to improve the cost effectiveness of distributed generation systems. In 2009 Wisconsin implemented a Renewable Portfolio Standard, mandating that 10% of electricity be derived from renewable sources including biogas. In 2012 Focus on Energy developed the Renewable Energy Competitive Incentive Program (RECIP). RECIP provides incentives to cost-effective renewable energy systems through a competitive RFP process including systems fueled by biogas. The consistent
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increase in landfill and digester biogas projects in Wisconsin illustrates Wisconsin’s interest in using the state’s biogas resources to produce energy
Statistical Analysis of CHP, Wind, and Biogas Capacity This section of the report focuses on the statistical evaluation of the influence of programs and policies on installed capacity. The analysis continues to analyze nine states: California (CA), Connecticut (CT), Illinois (IL), Massachusetts (MA), Michigan (MI), New Jersey (NJ), New York (NY), Pennsylvania (PA), and Wisconsin (WI). The analysis are divided into the three technology groupings reviewed above: CHP, wind, and biogas fueled capacity. The CHP capacity data is derived from the U.S. DOE Combined Heat and Power Installation Database. The development of these data is sponsored by the U.S. DOE and maintained by ICF International. While the database tracks CHP installations of all sizes, the analysis for this report is limited to installations with 5 MWs or less of capacity that are fueled by natural gas or fuel oil. These data represent CHP technology likely to be behind-the-meter and a size consistent with eligibility for SGIP. CHP technologies fueled with bio-gas are analyzed separately.42 The wind capacity data is derived from the U.S.G.S. Wind Installation Database. The data include onshore wind turbine locations and sizes through July 22, 2013. Turbine information from the Federal Aviation Administration Digital Obstacle File were combined with facility information from databases developed by the U.S. Energy Information Administration and the no longer maintained Wind Energy Data and Information data set from Oak Ridge National Laboratory as the primary sources of data for the database. For this analysis, these data are limited to installations with 7.5 MWs or less of capacity per wind site by year of installation for the years 1999 to 2008 and 9 MW or less for years 2009-2013.43 The data is designed to represent wind installation behind the customer’s meter. Given that there is no clear indication that a wind turbine installation is behind the customer meter, the two capacity restrictions are designed to restrict installations to those that are likely to be behind the customer meter. The landfill gas to energy capacity information is derived from the US Environmental Protection Agency (USEPA) Landfill Methane Outreach Program. The Dairy and Swine Digester data is from the USEPA Livestock Anaerobic Digester Database. The data are limited to installations with 5 MWs or less of capacity per biogas site by year of installation for the years 1999 to 2014. The data is designed to represent capacity fueled by biogas installation behind the customer’s meter.
42 Information on the U.S. DOE CHP database is available at a joint DOE ICF web site. https://doe.icfwebservices.com/chpdb/about . The database relies on self-reporting of CHP installations and may not be comprehensive or may need data updates. These data, however, represent a comprehensive set of yearly new capacity data for CHP installations across the U.S. Incompleteness that may occur in the data is likely to be unbiased across technologies and states. 43 The analysis also removed capacity associated with Wethersfield Wind Farm. The capacity associated with this customer was found to represent capacity that was not behind the customer’s meter. During the data development phase, the analysis team reviewed sites with more than 7 MW of capacity installed in a single year. Wind installations without capacity information were eliminated from eligibility for the analysis. Many of the wind installations without capacity data in the USGS data, though not all of them, were associated with site names that were clearly large scale wind farms and not eligible for this analysis due to their size and in-front of the meter status. Information on the wind database is available from the USGS web site: http://pubs.usgs.gov/ds/817/ .
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Given that the installed capacity shows substantial differences in magnitude from state to state and year to year, the modeled dependent variable was the log transformation of the installed capacity. Development of Independent Variables The primary source for the independent variables was the Database of State Incentives for Renewables & Efficiency (DSIRE). The DSIRE data contain information for a large number of programs. As shown in Table 7-1, there is information on 362 unique nonfederal programs for the nine focus states. For each technology, the objective was to transform the DSIRE program information into a set of independent variables with a potentially predictive relationship with the annual installed capacity in each state. The idea was that by controlling for these program impacts across all states, it would be possible to estimate the influence of the SGIP on installed capacity above and beyond what would have happened due to other programs and policies. Given the large amount of data, however, this transformation called for multiple steps, which are described below.
Table 7-1: Count of Programs in DSIRE Data by State and Implementation Sector
Implementation Sector State Local Nonprofit State Utility All CA 29 0 26 8 63 CT 2 1 32 9 44 IL 4 1 24 2 31 MA 1 1 40 5 47 MI 7 0 24 2 33 NJ 0 0 26 0 26 NY 3 0 40 5 48 PA 13 0 19 3 35 WI 4 0 22 9 35 All 63 3 253 43 362
The first step was to assess all the programs to determine their relevance to the technologies in question. For example, the “We Energies - Biogas Buy-Back Rate” in Wisconsin would have no obvious influence on installed wind capacity. Likewise, there were many programs that were relevant to wind but not biogas or CHP. This called for a rigorous review that included research beyond the information in the DSIRE data to find descriptions of the program that would clarify each program’s scope in terms of technologies. Additionally, this research helped to populate or refine the information on the start and end dates of many programs, which is a critical element for determining a program’s influence. Other than general categorization to allow some differentiation, all programs in the DSIRE data are treated equally, irrespective of level of incentive. In reality, this is highly unlikely, so as the second step, the programs were flagged as “primary drivers,” meaning those programs that were known to be of more relevance. This step was conducted by a small team of subject matter experts with decades of experience
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in renewable energy. While not a purely objective exercise, this step was necessary to further reduce the DSIRE data to a manageable and theoretically more meaningful set of programs and policies. After the first two steps to reduce the programs in the DSIRE data down to those relevant to the technologies in question with primary relevance, the original set of programs was reduced substantially. These counts are shown in Table 7-2, where the total number of wind programs was 125. Biogas and CHP programs were reduced more substantially, with 88 and 89 programs, respectively, across three implementation sectors.
Table 7-2: Count of Primary Driver Programs by State and Technology
Implementation Sector Local State Utility All State Bio CHP Wind Bio CHP Wind Bio CHP Wind Bio CHP Wind CA 0 0 2 8 9 14 2 1 1 10 10 17 CT 0 0 0 13 18 15 0 1 0 13 19 15 IL 0 0 2 7 5 14 1 1 2 8 6 18 MA 0 0 0 5 7 8 0 1 0 5 8 8 MI 1 0 2 10 9 13 0 0 0 11 9 15 NJ 0 0 0 8 13 9 0 0 0 8 13 9 NY 0 0 0 7 6 8 1 1 3 8 7 11 PA 10 9 12 6 4 9 0 0 0 16 13 21 WI 0 0 1 8 4 10 1 0 0 9 4 11 All 11 9 19 72 75 100 5 5 6 88 89 125
The next step was to take these programs and reduce them to a set of variables that represent the effects of different types of programs. To be clear, the objective of this analysis was not to identify the influence of individual programs, but rather to model the influence of different program types across states. That is, the objective was not to assess the effect of program A in state J, but rather to control for the effects of programs A, B, and C, which represent some category of program, in states J, K, L, etc. Controlling for the influence of the various programs and policies allows for the isolation of the influence of the SGIP program. One challenge with this step is determining the appropriate level aggregation for establishing program types. In terms of level of granularity, at one end there is the DSIRE “program type” field and at the other the DSIRE “program category,” which is simple grouping of programs into financial incentives versus regulatory policy. After examination of the data (i.e., looking at the counts of different program types by state over time), it was clear that defining variables by the 32 DSIRE program types would be too granular, with far too many variables to easily specify models and interpret the results. At the same time, the program category would be too general, with nearly identical presence across all the states, which would have made it impossible for a statistical model to isolate the different effects. As a result, a map was created with three alternate levels of aggregation with Summary Level 1 (11 variables), Summary Level 2 (8 variables), and Summary Level 3 (6 variables) levels to create and test different variable configurations. The mapping is shown in Table 7-3.
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Table 7-3: Mapping of DSIRE Program Types to Different Aggregation Levels DSIRE Program DSIRE Program Type Summary Level 1 Summary Level 2 Summary Level 3 Category Bond Program Financing Financing Financing Financial Incentive Building Energy Code Code and Standard Policy Other Policy Other Regulatory Policy Climate & Environment Policy Other Policy Other Policy Other Regulatory Policy Corporate Depreciation Tax Tax Tax Financial Incentive Corporate Tax Credit Tax Tax Tax Financial Incentive Corporate Tax Deduction Tax Tax Tax Financial Incentive Corporate Tax Exemption Tax Tax Tax Financial Incentive Energy Efficiency Resource Policy Other Policy Other Policy Other Regulatory Policy Standard Energy Standards for Public Code and Standard Policy Other Policy Other Regulatory Policy Buildings Feed-in Tariff Policy Other Policy Other Policy Other Regulatory Policy Grant Program Grant Grant Incentive Financial Incentive Green Building Incentive Incentive Other Incentive Incentive Financial Incentive Green Power Purchasing Green Power Policy Other Policy Other Regulatory Policy Industry Grant Grant Incentive Financial Incentive Recruitment/Support Interconnection Net Energy NEM NEM Regulatory Policy Metering (NEM) Line Extension Analysis Policy Other Policy Other Policy Other Regulatory Policy Loan Program Financing Financing Financing Financial Incentive Net Metering NEM NEM NEM Regulatory Policy Other Incentive Incentive Other Incentive Incentive Financial Incentive PACE Financing Financing Financing Financing Financial Incentive Performance-Based Incentive Incentive Incentive Incentive Financial Incentive Personal Tax Credit Tax Tax Tax Financial Incentive Property Tax Incentive Tax Tax Tax Financial Incentive Public Benefits Fund Incentive Other Incentive Other Policy Other Regulatory Policy Rebate Program Incentive Incentive Incentive Financial Incentive Renewables Portfolio RPS RPS RPS Regulatory Policy Standard Sales Tax Incentive Tax Tax Tax Financial Incentive Solar Renewable Energy Incentive Other Incentive Incentive Financial Incentive Credit Program Solar/Wind Access Policy Policy Other Policy Other Policy Other Regulatory Policy Solar/Wind Contractor Policy Other Policy Other Policy Other Regulatory Policy Licensing Solar/Wind Permitting Permitting Policy Other Policy Other Regulatory Policy Standards Utility Rate Discount Policy Other Policy Other Policy Other Regulatory Policy
The objective in using different levels of aggregation is twofold. First, it is necessary for simplification of the model specification. Second, it is necessary to define variables that represent a theoretical influence of programs or policies across the different states. This influence does not have to be present in every state, but it is important that they apply to more than just one so that they did not just represent a state specific impact. In practice, most of the key programs (NEM, Renewable Portfolio Standards, etc.)
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maintained their individuality, but there are enough other programs to justify the use of different types of aggregation. The substantial number of programs in the DSIRE data meant that in many cases there were multiple programs associated with a single level of aggregation. While the years in which they were active could, and usually did vary, there were still many years where multiple programs were relevant at the same time. This meant that there were two options for creating the independent variables. One option was simply to define dummy variables, where a 1 represents the presence of any program. The other was to define the variables as the sum of programs that are active in a given time period. For example, if one program started in 2004 and another started in 2006, the variable would be equal to 1 for 2004 and 2005 but increase to 2 in 2006. Because the goal of the analysis is to assess any effects of the SGIP, it is important to note that the program is excluded from all of the other independent variables. This raises the question of how to represent the program’s impacts in the model and many different strategies were used. The most straightforward approach for modeling SGIP was the use of a single dummy variable, set to 1 in the state of California during the program’s active years starting in 2002 and 0 otherwise. However, as discussed previously, the specific mechanism through which SGIP incentivized the adoption of different technologies changed over time, making its effect in different time periods inconstant. To account for this, another option was to use multiple dummy variables reflecting different “eras” of the program. One example of this, and the approach that was ultimately deemed most accurate, was the use of a dummy variable for the years 2002 to 2007, another for 2008 to 2010, and a third for 2011 to the present. Another question in assessing the impacts was whether to configure the variables to account for influence in other states, so dummy variables representing the program years or eras could be interacted with the different states. We also explored lagged program and policy impacts as well as a number of other ways of specifying how impacts might manifest themselves. For example, program start dates were often in the middle of the year, so we looked at configuring variables that did not include that partial year at all or included that partial year only when the program was in effect for more than eight months. We also looked at variables where the effects started only in the second year of the program. Additionally, another hypothesis might be that programs have an influence only in the first X years, when they get the “low hanging fruit,” so we looked at variables where the dummy/count was only counted for a limited horizon. The upshot is that these different variable configurations did not dramatically affect the results, so we went with the models that were the most “stable” and easy to explain. It’s likely that some programs and policies would have worked better with lagged effects or with effects that did not persist. However, with the sheer number of programs, it simply would not have been possible to look at program-specific impact configurations efficiently. Model Specifications The final analysis set was created by merging the series of installed capacity for each technology with its respective set of independent variables. This data set presented an opportunity and a challenge in that it permitted thousands of different alternate model specifications. The main considerations in determining the most appropriate model specification included:
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» Level of aggregation of dependent variables. » Implementation sector(s) to include (state, utility, and local). » Fixed effects (state and year). » Binary (dummy) or count-based variables. » SGIP specification (single dummy, era dummy variables, etc.). » Time frame (15 or 20 years). At the outset of the analysis, thousands of different models were estimated to assess these various considerations. Automated review of the model outputs and other diagnostics helped reduce the options to a more manageable subset based on decisions that could be determined more objectively. For example, the 15-year time frame consistently generated higher indicators of model fit (R2). Additionally, the inclusion of fixed effects for both state and year in the final model specification was determined to be superior based on the substantial number of statistically significant parameter estimates associated with the impacts and the much higher indicators of model fit for models that included those effects. This latter decision was supported by visualization of the data that showed clear differences by state and over time in the dependent variables that were highly unlikely to be causally related with the independent variables. Other decisions were less objective, depending more on professional judgment. For example, for the CHP model, the final SGIP specification was to use three dummy variables, which was based on knowledge of how the program changed over time and that the impacts were not likely to be constant over the analysis time frame. For wind, in contrast, there was no theoretical reason for assuming any era-specific impacts, so a single SGIP dummy variable was employed in the final model. The criteria for selection of the final model specification included the model fit, the presence of intuitive and statistically significant parameter estimates for key variables, and a lack of variables that would lead to serial correlation and volatile parameter estimates. CHP Results The final model for estimating SGIP’s impact on CHP – presented in EQUATION 1 below – included state- level variables on the count of programs based on the most granular level of aggregation, utility and local level variables for regulatory policy and financing, and a single variable to capture the count of NEM programs across state, utility, and local jurisdictions. The model was a two-way fixed effects model by state and year estimated for the 15 years from 2000 to 2014.44
44 A two way fixed effects model includes dummy variable for each state and each year. The year fixed effect controls for influences on capacity that are similar across all of the states for a specific year but can vary from year to year. These types of effects would include federal policies and the macro-economy. The state fixed effect controls for influences on capacity that are fixed over time but specific to a state. These types of effects could include state or utility policies that were enacted prior to the analysis period.
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Equation 1: Final Model for CHP
푙푛(푘푊)푠푡 =∝푡+ 훾푠 + 훽1 × 퐶푆퐹𝑖푛푎푛푐푒푠푡+ 훽2 × 퐶푆퐺푟푎푛푡푠푡+ 훽3 × 퐶푆퐺푟푒푒푛푃표푤푒푟푠푡+ 훽4 × 퐶푆퐼푛푐푒푛푡𝑖푣푒푠푡+ 훽5 × 퐶푆푁퐸푀푠푡+ 훽6 × 퐶푆푃표푙𝑖푐푦푂푡ℎ푒푟푠푡+ 훽7 × 퐶푆푅푃푆푠푡+ 훽8 × 퐶푆푇푎푥푠푡 + 훽9 × 퐶푈푅푒𝑔푃표푙𝑖푐푦푠푡+ 훽10 × 퐶퐿퐹𝑖푛푎푛푐푒푠푡+ 훽11 × 푃푟𝑖푐푒푠푡 + 훽12 × 푆퐺퐼푃0207푠푡+ 훽13 × 푆퐺퐼푃0811푠푡+ 훽14 × 푆퐺퐼푃1214푠푡+휀
Where:
Ln(kW)st is the natural log of the installed capacity in state s and year t
∝푡 is the time fixed effect for year t
훾푠 is the state fixed effect for state s
CSFinancest is the Count of State Financing Programs in state s and year t
CSGrantst is the Count of State Grant Programs in state s and year t
CSGreenPowerst is the Count of State Green Power Programs in state s and year t
CSIncentivest is the Count of State Incentive Programs in state s and year t
CSNEMst is the Count of State and Utility NEM Policies in state s and year t
CSPolicyOtherst is the Count of State Policy Other in state s and year t
CSRPSst is the Count of State RPS in state s and year t
CSTaxst is the Count of State Tax Programs in state s and year t
CURegPolicyst is the Count Utility Regulatory Policy in state s and year t
CLFinancest is the Count of Local Financing in state s and year t
Pricest is the average industrial electricity price in cents per kWh in state s and year t
SGIP0207st is a dummy variable for the time period 2002 to 2007 for California
SGIP0811st is a dummy variable for the time period 2008 to 2011 for California
SGIP1214 st is a dummy variable for the time period 2012 to 2014 for California
β1 – β14 are the estimated coefficients associated with the independent variables and ε is the error This final CHP model resulted in an R2 of 0.72 (F = 6.88, p. <.0001), indicating that the variables in the model accounted for 72% of the variability in the installed capacity. While this represents a relatively good fit for the model, of more interest are the parameter estimates, particularly those associated with SGIP as presented in Table 7-4.
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Table 7-4: Parameter Estimates for Model of SGIP CHP Impacts
Parameter Variable Estimate Standard Error t Value Pr > |t| Intercept 1.387 3.15866 0.44 0.6615 SGIP 2002 to 2007 4.459 2.16769 2.06 0.0423 SGIP 2008 to 2011 4.150 2.47835 1.67 0.0972 SGIP 2012 to 2014 3.710 2.62342 1.41 0.1604 Count State Financing 0.094 0.89423 0.10 0.9167 Count State Grant 0.206 0.48256 0.43 0.6710 Count State Green Power 2.021 2.23236 0.91 0.3675 Count State Incentive -1.196 1.56004 -0.77 0.4453 Count All NEM -1.411 0.67003 -2.11 0.0377 Count State Policy Other -1.004 0.67154 -1.49 0.1382 Count State RPS 0.480 0.71472 0.67 0.5038 Count State Tax 0.848 1.49728 0.57 0.5723 Count Utility Regulatory Policy 0.104 1.37013 0.08 0.9394 Count Local Financing 5.440 1.98115 2.75 0.0072 Industrial Price 0.316 0.35262 0.90 0.3719
With respect to the non-SGIP variables in the model, only net metering (NEM) and the count of local financing policies and programs were statistically different from zero with a 95% confidence. The estimated impacts of these two variables were mixed in terms of their signs.45 The model found that local financing programs had a positive, statistically significant impact on installed capacity. In contrast, NEM policies were found to result in a statistically significant decrease in CHP. For all other variables, the signs were mixed and none were statistically significant. For most of the variables in the model, there were no a priori expectations about what the signs should be for the parameter estimates. While all of the programs in the DSIRE data were related to CHP, they also had relevance to other technologies, so in some cases a program could take finite funds away from CHP for other investments. NEM is perhaps one of the more clear cases of this, where the implementation of a NEM policy could lead to investment resources going to technologies that were more likely to have the opportunity to sell generation back to utilities. Implementation of NEM could lead to more investment in PV and a reduction in investment in CHP due to the NEM improving the relative cost effectiveness of PV.
45 Statistical significance can be determined in Table 7-4 using the last column. If the value in the last column is less than 0.1 then the estimated coefficient is statistically different from zero with a 90% confidence.
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As for the parameter estimates for SGIP impacts, the first era from 2002 to 2007 generated a statistically significant parameter estimate with a 95% confidence, indicating that SGIP led to a statistically significant increase in the installed capacity of CHP in California from 2002 to 2007. The estimated parameter on the second era of SGIP, from 2008 to 2011, is positive and statistically significant with a 90% confidence. All three parameter estimates for SGIP are positive and their slight decline over time is consistent with expectations about changes in SGIP’s incentives for CHP would have led to a diminishing influence on installed capacity. Using the log installed capacity as the dependent variable was necessary given the distribution of the untransformed capacity values, but it makes the direct interpretation of the parameter estimates less straightforward. As one way of interpreting the results, Table 7-5 shows how the three parameter estimates translate into estimates of average annual installed capacity (kW) in California that is influenced by SGIP for the three SGIP eras. These data indicate that the average annual estimated installed capacity influenced by SGIP is largest during the first time period when SGIP provided incentives to many types of technologies and smallest during the second SGIP era when SGIP incentives were limited to fuel cells. The estimated average annual installed capacity influenced by SGIP rises during the third SGIP era (2012-2014) when the SGIP expanded the list of eligible CHP technologies. The second column of data is the yearly average actual installed CHP capacity in California as derived from the DOE data. The last column of data compares the SGIP influenced installed capacity to the actuals. SGIP is estimated to have influenced the installation of nearly 94% of the installed CHP capacity in California from 2002 to 2007, 78% from 2008 to 2011, and by 2012 to 2014, the share had declined to 67%. During the first SGIP era, the program is estimated to have influenced a large share of installed capacity as the program incentivized many technologies and likely helped to expand the market’s knowledge and awareness. During the second SGIP era, the program is estimated to have influenced over 75% of a much smaller installed capacity. The influence during the second era may represent actual incentivized projects and a maintenance of the knowledge and awareness developed during the first era.
Table 7-5: California CHP Capacity and Estimated Influence of SGIP Average Annual Estimated Installed Capacity Average Actual Installed Estimated SGIP as Percent SGIP “Era” Influenced by SGIP Capacity from DOE Data of Actual 2002 to 2007 37,025 39,565 93.6% 2008 to 2011 8,760 11,226 78.0% 2012 to 2014 13,812 20,615 67.0%
Result for Wind and Biogas Of the hundreds of models estimated for wind and biogas capacity, there were only a few cases (for wind, with none for biogas) where the estimated SGIP impacts were associated with statistically significant parameter estimates. While these might imply that SGIP has not had a substantial impact on wind or biogas capacity, these models were highly volatile or unstable, with parameter estimates changing in sign and statistical significance with only minor changes to the model specifications or input data. Consequently, the prudent approach is to say that the modeling was not able to produce stable estimates of the impact of SGIP on wind or biogas capacity beyond the effects of other programs and policies.
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These results, while possibly disappointing from the perspective of the program, are not totally inconsistent with expectations given the data. In the case of wind capacity, the sparseness of installations and variability in magnitude (both within and across states) were such that they did not lend themselves to predictive association with any of the available DSIRE data. This can be seen in Figure 7-11 through Figure 7-19, where most states had gaps of multiple years with no new capacity and the magnitude of new installations showed considerable variation. In the case of biogas, the issues with the data are less glaring compared to wind. The biogas data is more consistent across states and time and fewer states have several years without new biogas capacity. Landfill biogas to energy capacity, however, is driven in part by the need to meet federal clean air requirements. Landfill gas-to-energy projects have been ongoing in many states since the early to mid-1980s. While state and local policy may have some impact on the timing of new capacity installations, many of the installations appear to be more influenced by federal policy and the availability of landfills that are candidates for landfill gas to energy projects. While it is likely that state and local policies have a larger impact on dairy and swine digester biogas capacity, this capacity was overwhelmed in the analysis by the landfill biogas capacity. Given the dominance of landfill gas capacity in the model and the importance of federal regulations combined with landfill candidates, it is not surprising that models of biogas capacity did not lead to the development of a stable model emphasizing the importance of state, local, and utility policies.
Comparisons of SGIP to other DG and Energy Storage Programs – Program Administrator Surveys in the Northeast To better understand the influence of DG and energy storage programs on the implementation of SGIP technologies, all four current Program Administrators (PAs) for SGIP in California were interviewed, as were select staff formally associated with SGIP and three PAs from DG and energy storage incentive programs in New York and Massachusetts. Interviewing former SGIP staff provides historic perspective on the SGIP as it has evolved since 2001 while interviewing PAs from New York and Massachusetts provides comparisons with other DG and energy storage programs across the country. The interviews were conducted as structured in-depth conversations. The conversations gathered information on the PAs’ perspective of the program goals and objectives and the programs’ progress toward meeting its’ goals; the primary market barriers and opportunities; the utilities opportunities for growing DG and energy storage and the barriers to growing DG and energy storage in the market. DG and Energy Storage Program Goals Program goals and objectives can have a substantial impact on how a program is organized, marketed, and implemented. The PAs understanding of the program’s goals can be crucial to the program successfully achieving its objectives. The survey of PAs responsible for DG and energy storage programs began by asking PAs to describe their understanding of their program’s goals. The PA from New York described their DG and energy storage program goals stressing objectives consistent with market transformation. The DG and energy storage program goals for New York include trying to move the market forward while also installing more projects, making the market more self- sustaining and building more competencies with the program served technologies. The New York PA
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stated that the program goal was to simplify the customer acquisition of DG and energy storage technologies, making the installation process more efficient by eliminating misunderstandings and mistakes. Reaching these goals helps drive costs down, thereby helping towards development of a self- sustaining market. In Massachusetts, cost effective energy savings is a primary goal for DG and energy storage programs. Specific, quantitative goals are set and the PAs work to meet these goals through the strategic use of incentives and targeting of customers and technologies. One example of this policy is the targeting of CHP technologies installed at projects with high thermal loads throughout the year. These sites help obtain the biggest energy savings for both the customer and the utility. While increasing customer awareness of program served technologies is not a goal of the Northeast programs, customer and vendor education of how targeted technologies can reduce energy operation costs is seen as an integral part of their programs. Customer satisfaction is also not necessarily a goal, but is seen as a program outcome if the energy savings goals are reached. CHP Technology Drivers In the Northeast, the PAs feel that there are a number of factors behind the rise of certain technologies. The following factors were seen as CHP drivers: » Reliability of technology » Ability to understand how technology works » Good track records » Replicability » High volume of installations (Prolific) » Availability of multiple vendors » Stable infrastructure » Overall reasonable costs » Justifiable technology - can compete on its own merit » Educated Consumers This set of drivers were repeated frequently during our interviews, often regardless of the technology being discussed. One respondent stated the importance for emerging technologies to become more widespread and prolific in the territory for the technology to become considered as a viable market option. They used an analogy of snowboarding. Once snowboard was seen as a fringe sport, but as more and more people took up the sport it became a viable option on the slopes with an entire industry and infrastructure put in place to support its growth. CHP Technology Barriers A number of factors were also seen as limiting the growth of certain CHP technologies. The following factors are seen as having a detrimental effect on the growth of CHP technologies:
SGIP’S INFLUENCE ON DG MARKET TRANSFORMATION | 7-46 SGIP Market Transformation Report
» High maintenance requirements » Skilled staff necessary to maintain and operate » Ability of customer to defer purchase decision (discretionary) » Aesthetic concerns (i.e. noise and vibration) » Historically bad track record » Specific, limited applications for optimum operation » Long lead times and a large amount of time and resources required for purchase decision and installation The barriers faced by the respondents were seen as program challenges which could and should be overcome through program design and provision of incentives. For example, customer issues regarding the noise and vibration of newly installed systems was a commonly heard complaint. These stories resonated with customers and even though many of these issues were resolved when the correct equipment was installed, horror stories about vibration problems continued spreading from customer to prospective customer. The PAs elected to address these types of problems by standardizing the project design for small scale systems based on proven approaches. By standardizing the project design, this prevented the likelihood that project developers would make the same wrong installation mistakes over again and again. While some installation problems still occur and lead to stories among the customer population, new success stories are also being broadcast among prospective customers. Unfortunately, more work needs to be done as the weight of a bad experience more than offsets a large number of project successes. Impact of Regulations and Policies on DG and Energy Storage Adoption While some policies and regulations vary across the country, there are many similarities in the regulatory environments. What often differs, however, is how the PAs and their program designs embrace these policies and work to promote their programs within the regulatory parameters. Similarities are leading to identification of best practices in how PAs of DG and energy storage incentive programs are approaching implementation of their programs. Best Practices in the Northeast: The following are some of the best practices that emerged during our interviews with the Program Administrators in the Northeast. A key takeaway is the importance in having an overarching goal, such as market transformation, which guides the direction of all programs. Secondary goals are also viewed as very important. Setting a goal, whether it is number of installations or energy savings is critical in the operation of DG and energy storage programs. As the market gears up and barriers are overcome and goals are met, the PAs are able to ramp incentive levels down and move the market towards self-sufficiency. The importance of understanding the drivers and barriers that customers contemplate when deciding whether or not to adopt DG and energy storage technologies and incorporating these barriers and drivers into the development of the program is critical. For instance, Massachusetts’ PA’s understand that one of the barriers customers face is the maintenance issues related to technologies such as CHP. These
SGIP’S INFLUENCE ON DG MARKET TRANSFORMATION | 7-47 SGIP Market Transformation Report
maintenance hurdles must be overcome if the program is going to be successful and reach program goals. Rather than leaving this to each customer to work out on their own, the programs incorporate maintenance procedures and requirements directly into the program. By requiring that customers follow a set 3-year maintenance plan, the program not only results in less equipment failure, but also ensures that the technology is running up to its potential. It also results in a set demand for maintenance that allows the service market to grow to meet these maintenance needs. Another barrier is the high and often fluctuating cost of technologies. Creating a market where at least several vendors compete helps bring costs down and results in much more stable technology costs. Another best practice uncovered in our research was the separation of the CHP program into two distinct programs based on installation size. In order to help promote the goal of a self-sustaining market, this utility realized that they needed to capitalize on the lessons learned over the past decade and document the obtained competencies. For small to medium installations, this was done through a “prescriptive” cataloging of technologies. While not possible for larger installations, putting together a catalog of types of equipment installations for small to medium CHP units has proven very successful in reducing problems with wrong-sizing of equipment. Project developers become more of a support resource and “personalized shopper” as the vendor is now responsible for choosing the right-sized equipment for small to medium sized installations. This new approach raises customer confidence that they are, indeed, installing a system that has been proven to work for specific business needs. Finally, incentives should not be the main reason that customers opt for program served technologies. Rather, the incentives are drivers to program entry for qualified customers whose participation will not only benefit their business, but also help the utility meet its program savings goals. In New York, the incentives were set at a level to offset the utility interconnection fees. It is felt that these incentives helped compensate for the “frictions” or barriers still found in the marketplace. It is hypothesized that as the frictions are reduced and the program becomes more streamlined and efficient, that the incentives can be scaled back. The importance of private capital investments were also mentioned as a driver in market transformation. As barriers to technological adoption are reduced, the motivation for private capital to enter the marketplace increase. This private capital investment should offset at least some of the incentives that are now in place for program served technologies.
SGIP’S INFLUENCE ON DG MARKET TRANSFORMATION | 7-48 APPENDIX A MARKET TRANSFORMATION SURVEY FORMS
The following pages contain images of Market Transformation customer surveys under the Self- Generation Incentive Program.
MARKET TRANSFORMATION SURVEY FORMS | A-1 SGIP Market Transformation Report
A.1 SGIP Host Customer Survey
MARKET TRANSFORMATION SURVEY FORMS | A-2 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-3 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-4 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-5 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-6 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-7 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-8 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-9 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-10 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-11 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-12 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-13 SGIP Market Transformation Report
A.2 SGIP Program Administrator Survey
MARKET TRANSFORMATION SURVEY FORMS | A-14 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-15 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-16 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-17 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-18 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-19 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-20 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-21 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-22 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-23 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-24 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-25 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-26 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-27 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-28 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-29 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-30 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-31 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-32 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-33 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-34 SGIP Market Transformation Report
A.3 SGIP Manufacturer Survey
MARKET TRANSFORMATION SURVEY FORMS | A-35 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-36 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-37 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-38 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-39 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-40 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-41 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-42 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-43 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-44 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-45 SGIP Market Transformation Report
A.4 SGIP Installer Survey
MARKET TRANSFORMATION SURVEY FORMS | A-46 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-47 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-48 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-49 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-50 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-51 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-52 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-53 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-54 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-55 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-56 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-57 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-58 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-59 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-60 SGIP Market Transformation Report
A.5 SGIP Combined Manufacturer + Installer Survey
MARKET TRANSFORMATION SURVEY FORMS | A-61 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-62 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-63 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-64 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-65 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-66 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-67 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-68 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-69 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-70 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-71 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-72 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-73 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-74 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-75 SGIP Market Transformation Report
MARKET TRANSFORMATION SURVEY FORMS | A-76 APPENDIX B HOST CUSTOMER SURVEY RESULTS B.1 Site Weighted Survey Results SGIP Program Experience Please rate your experience with the SGIP program - Overall Application Process nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 1 0% 1 1% 1 3% 2 11% 5 2% 2 3% Dissatisfied 4 2% 1 1% 2 8% 1 16% 8 3% 3 5% Neutral 4 2% 7 12% 8 26% 1 7% 20 10% 10 18% Satisfied 9 53% 20 25% 11 41% 10 70% 4 31% 54 45% 10 18% Very Satisfied 5 36% 10 57% 5 20% 1 7% 3 46% 24 34% 8 15% NA 8 6% 3 4% 1 3% 1 4% 1 7% 14 5% 22 38% Not Answered 1 2% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%
Please rate your experience with the SGIP program - Incentive Amount nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 1 1% 1 3% 1 7% 3 2% 1 2% Dissatisfied 4 32% 2 1% 1 5% 1 4% 8 15% 2 3% Neutral 3 1% 4 12% 6 22% 3 15% 1 16% 17 11% 8 15% Satisfied 9 23% 23 28% 10 36% 9 70% 6 55% 57 33% 11 19% Very Satisfied 7 38% 11 57% 9 33% 1 4% 1 22% 29 36% 10 19% NA 7 4% 1 2% 1 7% 9 3% 19 33% Not Answered 1 2% 1 2% 2 1% 5 9% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%
HOST CUSTOMER SURVEY RESULTS | B-1 SGIP Market Transformation Report
Please rate your experience with the SGIP program - Incentive Payout Time Frame nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 3 1% 1 1% 2 8% 1 7% 7 3% 3 6% Dissatisfied 5 21% 3 4% 1 3% 1 4% 1 16% 11 11% 4 7% Neutral 5 2% 8 15% 6 20% 4 19% 2 16% 25 12% 11 20% Satisfied 7 35% 20 21% 9 40% 8 66% 4 39% 48 37% 8 14% Very Satisfied 2 34% 9 57% 7 24% 1 4% 1 22% 20 33% 7 14% NA 7 4% 1 2% 1 2% 1 7% 10 3% 20 35% Not Answered 2 2% 2 4% 4 2% 3 5% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%
Please rate your experience with the SGIP program - Program Requirements nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 1 1% 2 8% 2 11% 5 3% 2 3% Dissatisfied 4 20% 2 2% 3 13% 2 32% 1 16% 12 16% 3 6% Neutral 6 3% 11 19% 8 23% 3 15% 1 8% 29 13% 13 23% Satisfied 7 35% 19 20% 9 37% 8 42% 5 47% 48 33% 10 18% Very Satisfied 5 36% 8 56% 4 15% 1 22% 18 31% 6 12% NA 8 5% 1 2% 1 7% 10 3% 19 33% Not Answered 1 2% 2 4% 3 2% 3 5% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%
Would you have installed the technology in the absence of SGIP funding? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No, not at all 20 41% 25 33% 13 47% 5 26% 3 40% 66 39% 21 24% Maybe, at a later 9 57% 14 56% 9 28% 10 70% 5 42% 47 51% 23 29% time Yes, definitely 2 2% 3 6% 6 21% 1 8% 12 7% 10 15% Not Answered 2 5% 1 4% 1 4% 2 11% 6 3% 27 32% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-2 SGIP Market Transformation Report
Technology Evaluation
Please rate your level of satisfaction with each of the technologies that you installed. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not satified at all 1 0% 2 3% 3 11% 2 7% 8 4% 2 3% Somewhat satified 2 1% 13 15% 6 22% 2 7% 1 5% 24 10% 10 11% Satisfied 9 6% 11 15% 6 15% 6 37% 5 42% 37 15% 20 26% Very satified 11 88% 17 62% 10 41% 5 44% 4 45% 47 65% 41 50% NA 7 5% 1 6% 1 5% 9 4% 7 8% Not Answered 1 0% 3 7% 1 4% 1 8% 6 3% 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Based on your experience, how likely are you to recommend the technology to your friends? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all 1 0% 13 16% 10 34% 4 15% 28 13% 6 8% Very Likely 20 92% 28 74% 17 62% 12 85% 10 92% 87 80% 67 83% NA 8 6% 1 6% 1 2% 10 4% 8 9% Not Answered 2 1% 2 5% 1 3% 1 8% 6 2% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-3 SGIP Market Transformation Report
Describe your current level awareness and knowledge of Solar PV nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 2 2% Heard about it 2 1% 2 0% Know and understand 2 1% 2 3% 5 18% 1 4% 2 16% 12 6% 4 5% the technology Have done some 7 3% 5 8% 8 24% 5 41% 1 16% 26 14% 10 12% research into learning about the technology Very well informed 11 40% 21 68% 8 31% 4 22% 4 34% 48 42% 9 12% about the technology Have installed and 7 35% 16 21% 5 17% 5 30% 3 18% 36 26% 55 67% operate the technology on my site Not Answered 2 20% 3 9% 1 4% 1 16% 7 11% 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Describe your current level awareness and knowledge of the Energy Storage? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 1 1% Heard about it 5 8% 3 7% 3 21% 11 4% Know and understand the 2 1% 4 5% 9 33% 4 22% 4 37% 23 13% 9 11% technology Have done some research 6 5% 11 11% 7 23% 7 52% 31 16% 13 16% into learning about the technology Very well informed about 10 57% 19 70% 6 26% 2 11% 3 26% 40 46% 12 15% the technology Have installed and operate 13 37% 3 4% 16 15% 45 56% the technology on my site Not Answered 2 1% 4 11% 3 15% 1 16% 10 5% 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-4 SGIP Market Transformation Report
Describe your current level awareness and knowledge of Fuel Cells. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 5 2% 5 1% 11 14% Heard about it 3 3% 3 11% 2 16% 8 4% 17 19% Know and understand the 6 4% 2 4% 7 29% 4 22% 4 37% 23 14% 8 11% technology Have done some research 7 4% 1 2% 5 13% 6 44% 19 10% 7 8% into learning about the technology Very well informed about 6 67% 10 59% 7 25% 1 4% 2 21% 26 46% 9 12% the technology Have installed and operate 30 34% 3 10% 3 19% 2 11% 38 13% 1 1% the technology on my site Not Answered 4 20% 1 1% 4 11% 2 11% 1 16% 12 13% 28 34% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Describe your current level awareness and knowledge of IC Engines. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 8 5% 8 15% 2 4% 3 24% 21 7% 34 42% Heard about it 6 4% 5 4% 1 2% 2 21% 14 4% 6 8% Know and understand the 4 3% 3 5% 2 11% 2 13% 11 4% 5 6% technology Have done some research 4 35% 6 49% 1 3% 3 33% 1 16% 15 30% 5 6% into learning about the technology Very well informed about 3 32% 8 6% 9 34% 4 22% 1 5% 25 24% 2 2% the technology Have installed and operate 1 0% 11 18% 15 56% 7 33% 1 5% 35 21% the technology on my site Not Answered 5 21% 3 3% 1 2% 1 16% 10 10% 29 36% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-5 SGIP Market Transformation Report
Describe your current level awareness and knowledge of Gas Turbines. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res
No Idea 5 4% 2 1% 2 6% 1 8% 10 3% 21 26% Heard about it 5 3% 6 12% 2 4% 2 11% 1 5% 16 6% 15 18% Know and understand the 6 4% 6 7% 5 18% 2 11% 2 24% 21 10% 11 14% technology Have done some research 6 37% 9 53% 4 15% 5 41% 2 21% 26 35% 3 3% into learning about the technology Very well informed about 4 32% 8 10% 8 34% 2 11% 3 18% 25 25% 2 2% the technology Have installed and operate 10 14% 4 12% 2 11% 1 8% 17 8% the technology on my site Not Answered 5 21% 3 3% 4 11% 3 15% 1 16% 16 14% 29 36% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Describe your current level awareness and knowledge of Mcroturbines. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 8 5% 4 6% 1 8% 13 4% 29 36% Heard about it 5 2% 9 14% 4 13% 3 29% 21 8% 15 18% Know and understand the 4 3% 4 3% 5 18% 3 33% 2 13% 18 10% 4 5% technology Have done some research 6 37% 9 52% 4 15% 1 4% 2 24% 22 31% 2 2% into learning about the technology Very well informed about 3 32% 8 9% 7 27% 2 11% 20 21% 2 2% the technology Have installed and operate 7 12% 6 19% 12 63% 25 14% the technology on my site Not Answered 5 21% 3 3% 3 9% 1 16% 12 12% 29 36% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-6 SGIP Market Transformation Report
Describe your current level awareness and knowledge of Wind Turbines. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 3 1% 2 29% 5 4% 8 9% Heard about it 2 2% 2 1% 2 10% 1 4% 2 16% 9 4% 10 11% Know and understand the 6 4% 18 26% 12 40% 6 33% 3 32% 45 22% 21 26% technology Have done some research 7 4% 5 6% 6 23% 4 15% 22 10% 6 7% into learning about the technology Very well informed about 5 36% 15 60% 5 16% 1 7% 1 16% 27 33% 8 10% the technology Have installed and operate 2 5% 3 16% 5 2% 1 2% the technology on my site Not Answered 8 53% 2 3% 4 11% 2 11% 2 21% 18 26% 27 34% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Describe your current level awareness and knowledge of Pressure Reduction Turbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 12 8% 15 20% 10 41% 4 41% 4 26% 45 22% 39 48% Heard about it 9 5% 9 9% 5 14% 7 33% 30 11% 10 12% Know and understand the 3 3% 4 15% 1 4% 1 8% 9 5% technology Have done some research 4 66% 3 48% 2 4% 1 4% 1 16% 11 39% 1 1% into learning about the technology Very well informed about 1 0% 9 12% 2 5% 1 7% 2 11% 15 5% 1 1% the technology Have installed and operate 2 5% 3 12% 3 40% 8 6% 1 1% the technology on my site Not Answered 5 21% 3 3% 3 9% 2 11% 13 12% 29 36% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-7 SGIP Market Transformation Report
Describe your current level awareness and knowledge of Waste Heat to Power. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 6 4% 4 3% 1 8% 11 3% 26 31% Heard about it 6 3% 7 12% 6 18% 1 4% 1 8% 21 9% 16 19% Know and understand the 4 2% 11 12% 3 16% 7 59% 3 18% 28 14% 6 8% technology Have done some research 7 39% 5 49% 5 14% 2 7% 2 32% 21 32% into learning about the technology Very well informed about 3 32% 8 10% 6 26% 2 11% 3 18% 22 23% 4 6% the technology Have installed and operate 7 11% 5 16% 1 4% 13 7% 1 2% the technology on my site Not Answered 5 21% 2 3% 4 10% 3 15% 1 16% 15 14% 28 34% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Priority
Please rate the priority of the FIRST COST when selecting a technology. nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important Slightly Important 1 1% 1 0% 1 1% Moderately 3 1% 4 3% 2 8% 4 19% 1 8% 14 5% 14 17% Important Quite Important 10 7% 10 16% 13 50% 4 19% 4 34% 41 21% 23 27% Extremely Important 18 91% 29 80% 14 43% 8 63% 6 58% 75 73% 42 53% Not Answered 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-8 SGIP Market Transformation Report
Please rate the priority of the INCENTIVE LEVEL when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important 1 0% 1 0% 3 3% Slightly Important 1 3% 1 4% 2 1% 3 4% Moderately 3 1% 6 5% 5 14% 3 11% 3 24% 20 7% 19 23% Important Quite Important 14 40% 19 23% 14 58% 7 55% 3 26% 57 42% 34 43% Extremely Important 13 58% 19 72% 9 25% 5 30% 5 50% 51 50% 21 25% Not Answered 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Please rate the priority of the TAX BENEFITS when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important 2 1% 8 14% 5 15% 3 15% 5 37% 23 10% 7 8% Slightly Important 3 3% 2 3% 1 3% 2 7% 1 8% 9 3% 8 10% Moderately 10 54% 12 14% 6 29% 4 37% 3 32% 35 36% 18 22% Important Quite Important 9 6% 15 15% 11 35% 2 11% 37 15% 30 37% Extremely Important 5 36% 7 55% 5 17% 4 26% 2 24% 23 34% 16 20% Not Answered 2 1% 1 2% 1 4% 4 1% 2 2% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-9 SGIP Market Transformation Report
Please rate the priority of the RELIABILITY RECORD when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important 1 0% 1 0% Moderately 3 2% 3 1% 3 3% Important Quite Important 13 38% 8 9% 6 15% 4 19% 3 32% 34 24% 30 36% Extremely Important 13 59% 36 91% 23 85% 12 81% 8 68% 92 75% 45 57% Not Answered 1 0% 1 0% 3 3% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Please rate the priority of the OPERATION AND MAINTENANCE COST when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Slightly Important 1 1% 1 16% 2 1% 1 2% Moderately 1 0% 1 2% 2 1% 3 3% Important Quite Important 18 28% 21 31% 12 41% 9 41% 3 21% 63 33% 26 31% Extremely Important 12 71% 21 68% 16 57% 6 52% 7 63% 62 65% 49 61% Not Answered 1 1% 1 7% 2 1% 2 2% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-10 SGIP Market Transformation Report
B.2 Capacity Weighted Survey Results Please rate your experience with the SGIP program - Overall Application Process nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 1 0% 1 1% 1 3% 2 5% 5 2% 2 5% Dissatisfied 4 1% 1 0% 2 3% 1 2% 8 1% 3 4% Neutral 4 21% 7 12% 8 30% 1 3% 20 17% 10 16% Satisfied 9 56% 20 31% 11 34% 10 82% 4 38% 54 40% 10 26% Very Satisfied 5 17% 10 44% 5 21% 1 6% 3 49% 24 31% 8 13% NA 8 5% 3 12% 1 9% 1 4% 1 10% 14 9% 22 36% Not Answered 1 1% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%
Please rate your experience with the SGIP program - Incentive Amount nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 1 1% 1 3% 1 5% 3 2% 1 3% Dissatisfied 4 16% 2 2% 1 2% 1 0% 8 3% 2 3% Neutral 3 0% 4 20% 6 31% 3 21% 1 2% 17 18% 8 20% Satisfied 9 47% 23 36% 10 37% 9 69% 6 66% 57 46% 11 17% Very Satisfied 7 32% 11 39% 9 27% 1 5% 1 21% 29 28% 10 18% NA 7 2% 1 2% 1 10% 9 3% 19 31% Not Answered 1 3% 1 0% 2 0% 5 8% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%
HOST CUSTOMER SURVEY RESULTS | B-11 SGIP Market Transformation Report
Please rate your experience with the SGIP program - Incentive Payout Time Frame nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 3 0% 1 1% 2 3% 1 5% 7 2% 3 11% Dissatisfied 5 44% 3 4% 1 9% 1 4% 1 2% 11 10% 4 4% Neutral 5 6% 8 14% 6 19% 4 18% 2 49% 25 22% 11 21% Satisfied 7 22% 20 28% 9 34% 8 68% 4 18% 48 30% 8 14% Very Satisfied 2 16% 9 51% 7 28% 1 5% 1 21% 20 30% 7 11% NA 7 7% 1 2% 1 4% 1 10% 10 5% 20 33% Not Answered 2 4% 2 3% 4 2% 3 5% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%
Please rate your experience with the SGIP program - Program Requirements nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Very Dissatisfied 1 1% 2 3% 2 5% 5 2% 2 4% Dissatisfied 4 44% 2 2% 3 16% 2 48% 1 2% 12 15% 3 13% Neutral 6 6% 11 23% 8 25% 3 18% 1 24% 29 22% 13 22% Satisfied 7 22% 19 30% 9 39% 8 29% 5 42% 48 35% 10 17% Very Satisfied 5 17% 8 41% 4 15% 1 21% 18 22% 6 10% NA 8 7% 1 2% 1 10% 10 3% 19 29% Not Answered 1 3% 2 3% 3 2% 3 5% Total 31 100% 42 100% 28 100% 15 100% 9 100% 125 100% 56 100%
HOST CUSTOMER SURVEY RESULTS | B-12 SGIP Market Transformation Report
Please rate the priority of the FIRST COST when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important Slightly Important 1 2% 1 1% 1 1% Moderately Important 3 15% 4 3% 2 15% 4 13% 1 24% 14 14% 14 14% Quite Important 10 11% 10 26% 13 35% 4 6% 4 21% 41 25% 23 26% Extremely Important 18 74% 29 69% 14 49% 8 82% 6 56% 75 60% 42 57% Not Answered 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Please rate the priority of the INCENTIVE LEVEL when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important 1 0% 1 0% 3 3% Slightly Important 1 1% 1 1% 2 0% 3 6% Moderately Important 3 15% 6 3% 5 14% 3 8% 3 52% 20 18% 19 20% Quite Important 14 21% 19 36% 14 58% 7 59% 3 16% 57 41% 34 48% Extremely Important 13 63% 19 60% 9 28% 5 32% 5 32% 51 41% 21 23% Not Answered 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-13 SGIP Market Transformation Report
Please rate the priority of the TAX BENEFITS when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important 2 0% 8 21% 5 12% 3 8% 5 30% 23 16% 7 7% Slightly Important 3 15% 2 3% 1 1% 2 2% 1 24% 9 7% 8 9% Moderately Important 10 46% 12 18% 6 26% 4 51% 3 31% 35 29% 18 22% Quite Important 9 16% 15 24% 11 39% 2 7% 37 23% 30 36% Extremely Important 5 22% 7 34% 5 20% 4 28% 2 15% 23 24% 16 25% Not Answered 2 0% 1 2% 1 5% 4 1% 2 2% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Please rate the priority of the RELIABILITY RECORD when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all Important 1 0% 1 0% Moderately Important 3 12% 3 1% 3 3% Quite Important 13 33% 8 10% 6 17% 4 22% 3 51% 34 24% 30 38% Extremely Important 13 55% 36 90% 23 83% 12 78% 8 49% 92 75% 45 56% Not Answered 1 0% 1 0% 3 3% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Please rate the priority of the OPERATION AND MAINTENANCE COST when selecting a technology nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Slightly Important 1 1% 1 2% 2 1% 1 7% Moderately Important 1 0% 1 3% 2 1% 3 3% Quite Important 18 64% 21 41% 12 44% 9 37% 3 34% 63 43% 26 28% Extremely Important 12 36% 21 58% 16 53% 6 61% 7 64% 62 55% 49 60% Not Answered 1 1% 1 3% 2 0% 2 2% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-14 SGIP Market Transformation Report
Please rate your level of satisfaction with each of the technologies that you installed nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not satified at all 1 15% 2 5% 3 4% 2 4% 8 5% 2 6% Somewhat satified 2 0% 13 26% 6 27% 2 3% 1 7% 24 18% 10 9% Satisfied 9 16% 11 17% 6 14% 6 32% 5 77% 37 28% 20 31% Very satified 11 62% 17 49% 10 50% 5 59% 4 13% 47 44% 41 47% NA 7 7% 1 3% 1 2% 9 2% 7 7% Not Answered 1 0% 3 4% 1 2% 1 3% 6 2% 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Based on your experience, how likely are you to recommend the technology to your friends? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res Not at all 1 0% 13 29% 10 28% 4 11% 28 19% 6 9% Very Likely 20 90% 28 61% 17 68% 12 89% 10 99% 87 76% 67 83% NA 8 10% 1 3% 1 0% 10 2% 8 8% Not Answered 2 0% 2 7% 1 3% 1 1% 6 3% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-15 SGIP Market Transformation Report
Describe your current level awareness and knowledge of the Solar PV nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 2 2% Heard about it 2 0% 2 0% Know and understand 2 0% 2 3% 5 17% 1 2% 2 27% 12 12% 4 4% the technology Have done some 7 1% 5 13% 8 20% 5 54% 1 3% 26 15% 10 13% research into learning about the technology Very well informed 11 53% 21 53% 8 29% 4 15% 4 53% 48 41% 9 18% about the technology Have installed and 7 16% 16 31% 5 20% 5 26% 3 15% 36 22% 55 63% operate the technology on my site Not Answered 2 29% 3 14% 1 4% 1 2% 7 9% 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Describe your current level awareness and knowledge of Energy Storage? nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 1 1% Heard about it 5 8% 3 7% 3 32% 11 11% Know and understand 2 0% 4 4% 9 26% 4 13% 4 37% 23 19% 9 15% the technology Have done some 6 4% 11 19% 7 21% 7 62% 31 18% 13 15% research into learning about the technology Very well informed 10 52% 19 61% 6 29% 2 4% 3 28% 40 38% 12 15% about the technology Have installed and 13 43% 3 5% 16 6% 45 53% operate the technology on my site Not Answered 2 3% 4 17% 3 21% 1 2% 10 9% 1 1% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-16 SGIP Market Transformation Report
Describe your current level awareness and knowledge of Fuel Cells nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 5 1% 5 0% 11 12% Heard about it 3 1% 3 6% 2 25% 8 7% 17 16% Know and understand 6 21% 2 6% 7 29% 4 13% 4 37% 23 22% 8 17% the technology Have done some 7 16% 1 2% 5 18% 6 59% 19 13% 7 8% research into learning about the technology Very well informed 6 32% 10 39% 7 29% 1 1% 2 21% 26 29% 9 15% about the technology Have installed and 30 51% 3 11% 3 10% 2 14% 38 21% 1 1% operate the technology on my site Not Answered 4 29% 1 2% 4 8% 2 17% 1 2% 12 8% 28 31% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Describe your current level awareness and knowledge of IC Engines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 8 2% 8 10% 2 4% 3 49% 21 14% 34 43% Heard about it 6 10% 5 6% 1 0% 2 10% 14 5% 6 9% Know and understand 4 12% 3 8% 2 7% 2 10% 11 6% 5 5% the technology Have done some 4 31% 6 31% 1 3% 3 48% 1 14% 15 19% 5 6% research into learning about the technology Very well informed 3 16% 8 14% 9 26% 4 11% 1 7% 25 17% 2 2% about the technology Have installed and 1 0% 11 26% 15 64% 7 35% 1 7% 35 34% operate the technology on my site Not Answered 5 30% 3 5% 1 3% 1 2% 10 6% 29 35% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-17 SGIP Market Transformation Report
Describe your current level awareness and knowledge of Gas Turbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 5 1% 2 1% 2 4% 1 1% 10 2% 21 28% Heard about it 5 1% 6 9% 2 3% 2 8% 1 7% 16 5% 15 18% Know and understand 6 21% 6 7% 5 18% 2 7% 2 7% 21 13% 11 14% the technology Have done some 6 32% 9 45% 4 18% 5 53% 2 21% 26 30% 3 4% research into learning about the technology Very well informed 4 16% 8 13% 8 35% 2 4% 3 39% 25 26% 2 2% about the technology Have installed and 10 20% 4 15% 2 8% 1 24% 17 16% operate the technology on my site Not Answered 5 30% 3 5% 4 7% 3 22% 1 2% 16 9% 29 35% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Describe your current level awareness and knowledge of Mcroturbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 8 2% 4 6% 1 25% 13 6% 29 35% Heard about it 5 12% 9 11% 4 8% 3 34% 21 14% 15 21% Know and understand 4 9% 4 3% 5 18% 3 50% 2 10% 18 14% 4 5% the technology Have done some 6 32% 9 39% 4 9% 1 0% 2 15% 22 20% 2 2% research into learning about the technology Very well informed 3 16% 8 18% 7 32% 2 14% 20 21% 2 2% about the technology Have installed and 7 17% 6 25% 12 50% 25 17% operate the technology on my site Not Answered 5 30% 3 5% 3 7% 1 2% 12 7% 29 35% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-18 SGIP Market Transformation Report
Describe your current level awareness and knowledge of the Wind Turbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 3 0% 2 48% 5 3% 8 8% Heard about it 2 1% 2 2% 2 12% 1 4% 2 25% 9 10% 10 10% Know and understand 6 10% 18 26% 12 45% 6 21% 3 30% 45 32% 21 25% the technology Have done some 7 16% 5 15% 6 18% 4 8% 22 13% 6 6% research into learning about the technology Very well informed 5 34% 15 47% 5 17% 1 3% 1 14% 27 25% 8 11% about the technology Have installed and 2 7% 3 21% 5 6% 1 7% operate the technology on my site Not Answered 8 40% 2 3% 4 8% 2 17% 2 9% 18 11% 27 33% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Describe your current level awareness and knowledge of Pressure Reduction Turbines nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 12 17% 15 17% 10 27% 4 55% 4 63% 45 32% 39 46% Heard about it 9 7% 9 16% 5 23% 7 18% 30 15% 10 16% Know and understand 3 3% 4 16% 1 2% 1 3% 9 8% the technology Have done some 4 41% 3 32% 2 5% 1 5% 1 14% 11 18% 1 1% research into learning about the technology Very well informed 1 6% 9 18% 2 6% 1 3% 2 14% 15 11% 1 1% about the technology Have installed and 2 9% 3 14% 3 6% 8 9% 1 1% operate the technology on my site Not Answered 5 30% 3 5% 3 7% 2 17% 13 8% 29 35% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-19 SGIP Market Transformation Report
Describe your current level awareness and knowledge of the Waste Heat to Power nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No Idea 6 1% 4 4% 1 1% 11 1% 26 26% Heard about it 6 1% 7 10% 6 11% 1 1% 1 25% 21 11% 16 18% Know and understand 4 17% 11 13% 3 19% 7 68% 3 17% 28 20% 6 11% the technology Have done some 7 35% 5 34% 5 24% 2 4% 2 17% 21 25% research into learning about the technology Very well informed 3 16% 8 19% 6 21% 2 4% 3 38% 22 22% 4 7% about the technology Have installed and 7 18% 5 16% 1 2% 13 11% 1 7% operate the technology on my site Not Answered 5 30% 2 3% 4 9% 3 22% 1 2% 15 9% 28 31% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
Would you have installed the technology in teh absence of SGIP funding nAES %AES nFC %FC nICE %ICE nMT %MT nOther %Other nNonRes %NonRes nRes %Res No, not at all 20 50% 25 43% 13 57% 5 18% 3 6% 66 40% 21 19% Maybe, at a later time 9 47% 14 39% 9 22% 10 81% 5 57% 47 40% 23 37% Yes, definitely 2 3% 3 10% 6 18% 1 24% 12 14% 10 17% Not Answered 2 7% 1 2% 1 1% 2 14% 6 5% 27 27% Total 31 100% 44 100% 29 100% 16 100% 11 100% 131 100% 81 100%
HOST CUSTOMER SURVEY RESULTS | B-20 POLICIES IMPACTING DG AND STORAGE
Table C-1 is a listing of programs or policies identified by our research as having possible impacts on DG or storage development in California.
Table C-1: Listing of Programs or Policies Impacting DG and Storage in California
Programs or Policies Programs or Policies Marin County - Green Building Requirements Renewable Market Adjusting Tariff (ReMAT) Palo Alto Utilities - Remote Renewables Program County of San Bernardino - Green Building Incentive Small Business Energy Loan Program City of San Francisco - Green Building Requirement for City Buildings Energy Technology Export Program City of San Francisco - Green Building Code Energy Innovations Small Grant (EISG) Program Sonoma County - Energy Independence Program City of San Diego - Green Power Purchasing Anaheim Public Utilities - Low-Interest Energy Efficiency Loan Program City of San Diego - Sustainable Building Policy City of Palo Alto - Green Building Requirement City of Santa Monica - Green Building Grant Program County Wind Ordinance Standards Small Wind Access Law SMUD - Feed-in Tariff City of San Francisco - Renewable Energy Purchasing Sales and Use Tax Exclusion for Advanced Transportation and Alternative Energy Manufacturing Program Net Metering Marin Clean Energy - Feed-In Tariff City of Santa Monica - Green Power Purchasing City of Los Angeles - Green Building Retrofit Requirement Public Benefits Funds for Renewables and Efficiency LADWP - Net Metering City of San Jose - Municipal Green Building Program Los Angeles County - Green Building Program Emerging Renewables Program Los Angeles County - LEED for County Buildings Self-Generation Incentive Program San Diego County - Wind Regulations Solar Energy and Distributed Generation Grant San Diego County - Design Standards for County Program Facilities Solar or Wind Energy System Credit - Corporate Orange County - Small Wind Energy Systems Solar or Wind Energy System Credit - Personal Santa Clara County - Zoning Ordinance Energy Financing Industrial Development Bond Renewable Auction Mechanism (RAM) Program Interconnection Standards Santa Clara County - Green Building Policy for County Government Buildings Public Leadership Solutions for Energy (PULSE) Loan Energy Efficiency Financing for Public Sector Projects Renewables Portfolio Standard Los Angeles County - Commercial PACE Agricultural Biomass to Energy Program Western Riverside Council of Governments - Home Energy Renovation Opportunity (HERO) Financing
POLICIES IMPACTING DG AND STORAGE | C-1 SGIP Market Transformation Report
Programs or Policies Programs or Policies Program Supplemental Energy Payments (SEPs) Western Riverside Council of Governments - Large Commercial PACE City of Oakland - Self Certification for Renewables CaliforniaFIRST Energy Efficiency Financing Program LADWP - Feed-in Tariff (FiT) Program Burbank Water & Power - Green Building Incentive California Enterprise Development Authority (Figtree Program PACE) - Statewide PACE Program Green Building Action Plan for State Facilities Feed-in-Tariff City of Berkeley - Green Building Standards for City California Natural Gas Rates Owned and Operated Projects City of Santa Monica - Expedited Permitting for Green Palm Desert - Energy Independence Program Buildings SCE - Biomass Standard Contract City of San Francisco - PACE Agriculture and Food Processing Energy Loans Emissions Performance Standard City of Anaheim - Green Building Program Climate Change Scoping Plan City of Palo Alto Utilities - Renewable Energy Credit Purchase Program
Certain of the programs and policies were considered to have more influence on DG and storage development in California and were designated as “primary” programs or policies. Table C-2 is a listing of the primary programs and policies along with a summary.
Table C-2: Summary of Primary Programs or Policies Impacting DG and Storage in California Primary Programs or Policies Summary of Program or Policy Energy Innovations Small The Energy Innovations Small Grant (EISG) Program is administered by the Grant (EISG) Program California Energy Commission. The EISG provides up to $150,000 for hardware projects and $75,000 for modeling projects to small businesses, non-profits, individuals and academic institutions to conduct research that establishes the feasibility of new, innovative energy concepts. Research projects must target one of the PIER R&D areas, address a California energy problem and provide a potential benefit to California electric and natural gas ratepayers. Small Wind Access Law Solar and wind access laws are designed to establish a right to install and operate a solar or wind energy system at a home or other facility. Some solar access laws also ensure a system owner’s access to sunlight. AB 1207 (2001) in California established certain rights for small wind applications. Net Metering California's net-metering law originally took effect in 1996 and applies to all utilities with one exception (LADWP due to size). The law has been amended numerous times since its enactment, most recently by AB 327 of 2013. The original law applied to wind-energy systems, solar-electric systems and hybrid (wind/solar) systems. In September 2002, legislation (AB 2228) allowed biogas-electric facilities up to 1 megawatt (MW) to net meter until December 31, 2005, under a pilot program. This pilot program was extended until December 31, 2009, upon the
POLICIES IMPACTING DG AND STORAGE | C-2 SGIP Market Transformation Report
Primary Programs or Policies Summary of Program or Policy enactment of AB 728 in September 2005. SB 489 did away with the pilot program, and instead allowed for biomass and all other RPS-eligible technologies to participate in net metering under the same terms available for solar and wind. Other legislation enacted in October 2003 (AB 1214) made fuel cells eligible for net metering until the cumulative rated generating capacity of net-metered fuel cells reaches 112.5 MW statewide. AB 2165 increased the statewide maximum to 500 MW, and requires each utility to provide net metering for eligible fuel cells until it reaches its proportionate share of the 500 MW cap. Previously restricted to fuel cells that begin operation prior to January 1, 2014, AB 2165 of 2012 extended the eligibility deadline to January 1, 2015, and AB 327 of 2013 further extended the deadline to January 1, 2017 Public Benefits Funds for California's 1996 electric industry restructuring legislation (AB 1890) directed the Renewables and state’s three major investor-owned utilities (Southern California Edison, Pacific Gas Efficiency and Electric Company, and San Diego Gas & Electric) to collect a "public goods charge" (PGC) on ratepayer electricity use from 1998 through 2001 to create public benefits funds for renewable energy, energy efficiency, and research, development & demonstration (RD&D). Subsequent legislation in 2000 extended the programs for 10 years from 2002 to 2012. This fund is used by the California Energy Commission (CEC) to administer renewable energy and RD&D programs, and by the electric utilities to administer energy efficiency incentive programs. The California Public Utilities Commission separately collects funds to administer the California Solar Initiative, the Self-Generation Incentive Program, the Renewables Portfolio Standard and others. Emerging Renewables Assembly Bill 1890 (Brulte, Chapter 854, Statutes of 1996) and Senate Bill 90 (Sher, Program Chapter 905, Statutes of 1997) created the Energy Commission's Renewable Energy Program. The Legislature directed that a portion of the funds collected from the customers of the three major investor-owned utilities (IOUs) be used for statewide public benefit programs, including incentives for renewable electricity systems. Under this legislation, the program continued through 2002 to provide financial incentives to support existing, new, and emerging renewable resources in a market environment. In 2002, Senate Bill 1038 (Sher, Chapter 515, Statutes of 2002), reauthorized Renewable Energy Program through 2006. Its approach, however, for supporting renewable energy development was impacted by the energy crisis of 2000 and 2001. In accordance with the changes to law under Senate Bill 1018, the Energy Commission took taking immediate steps to close out the Emerging Renewables Program (ERP). Technologies that were eligible for the ERP became eligible for the SGIP when the ERP ended. Self-Generation Incentive The CPUC's Self-Generation Incentive Program (SGIP) provides incentives to Program support existing, new, and emerging distributed energy resources. The SGIP provides rebates for qualifying distributed energy systems installed on the customer's side of the utility meter. Qualifying technologies include wind turbines, waste heat to power technologies, pressure reduction turbines, internal combustion engines, microturbines, gas turbines, fuel cells, and advanced energy storage systems. The Self Generation Incentive Program (SGIP) - with 544 completed projects for a total capacity of 252 megawatts - is one of the longest-running and most successful distributed generation incentive programs in the country. In 2011 alone, these facilities provided over 760,000 MWh of
POLICIES IMPACTING DG AND STORAGE | C-3 SGIP Market Transformation Report
Primary Programs or Policies Summary of Program or Policy electricity to the California, enough electricity to meet the needs of over 116,000 homes. The program continues to make strides toward a cleaner, distributed- energy future. Solar Energy and In 2000, Senate Bill 1345 (Statutes of 2000, Chapter 537, Peace) directed the Distributed Generation Energy Commission to develop and administer a grant program to support the Grant Program purchase and installation of solar energy and selected small distributed generation systems. Solar energy systems include solar energy conversion to produce hot water, swimming pool heating, and battery backup for photovoltaic (PV) applications. Funding for the program had to be renewed annually by the Legislature. The state's budget crisis essentially ended the program Solar or Wind Energy The federal business energy investment tax credit available under 26 USC § 48 was System Credit - Corporate expanded significantly by the Energy Improvement and Extension Act of 2008 (H.R. 1424), enacted in October 2008. This law extended the duration -- by eight years -- of the existing credits for solar energy, fuel cells and microturbines; increased the credit amount for fuel cells; established new credits for small wind-energy systems, geothermal heat pumps, and combined heat and power (CHP) systems; allowed utilities to use the credits; and allowed taxpayers to take the credit against the alternative minimum tax (AMT), subject to certain limitations. The credit was further expanded by the American Recovery and Reinvestment Act of 2009, enacted in February 2009. (30% for solar, fuel cells, small wind) Solar or Wind Energy Established by The Energy Policy Act of 2005, the federal tax credit for residential System Credit - Personal energy property initially applied to solar-electric systems, solar water heating systems and fuel cells. The Energy Improvement and Extension Act of 2008 extended the tax credit to small wind-energy systems and geothermal heat pumps, effective January 1, 2008. Other key revisions included an eight-year extension of the credit to December 31, 2016; the ability to take the credit against the alternative minimum tax; and the removal of the $2,000 credit limit for solar- electric systems beginning in 2009. The credit was further enhanced in February 2009 by The American Recovery and Reinvestment Act of 2009, which removed the maximum credit amount for all eligible technologies (except fuel cells) placed in service after 2008. A taxpayer may claim a credit of 30% of qualified expenditures for a system that serves a dwelling unit located in the United States that is owned and used as a residence by the taxpayer. Expenditures with respect to the equipment are treated as made when the installation is completed. If the installation is at a new home, the "placed in service" date is the date of occupancy by the homeowner. Expenditures include labor costs for on-site preparation, assembly or original system installation, and for piping or wiring to interconnect a system to the home. If the federal tax credit exceeds tax liability, the excess amount may be carried forward to the succeeding taxable year. The excess credit may be carried forward until 2016, but it is unclear whether the unused tax credit can be carried forward after then. The maximum allowable credit, equipment requirements and other details vary by technology. 30% Energy Financing Industrial Development Bonds (IDBs) are tax-exempt securities issued up to $10 Industrial Development million by a governmental entity to provide money for the acquisition, Bond Program construction, rehabilitation and equipping of manufacturing and processing facilities for private companies. IDBs can be issued by the I-Bank, local Industrial Development Authorities, or by Joint Powers Authorities. IDBs are relevant to clean
POLICIES IMPACTING DG AND STORAGE | C-4 SGIP Market Transformation Report
Primary Programs or Policies Summary of Program or Policy energy development in two major ways. First, manufacturers in the supply chain of clean energy (e.g., solar panel, wind turbine and biomass equipment manufacturers) can receive tax-exempt financing. States or regions looking to cultivate a clean energy manufacturing cluster can look at (a) targeting IDB marketing to such companies and (b) creating an enhancement or similar program to facilitate the use of IDBs by clean energy manufacturers. Second, changes to the federal tax code for IDBs could allow any manufacturer to place clean energy generation equipment on their facilities. Whole-facility clean energy generation is not a currently permitted use of IDB proceeds, but was temporarily allowed from 2009-2010. Congress could act to authorize a permanent rule change, making tax-exempt bonds an option for all small- to mid-sized American manufacturers seeking lower-cost, clean energy. Interconnection California's "Rule 21" generally applies to systems connecting to an investor-owned Standards utility’s distribution grid, non-export generating facilities connecting to an investor- owned utility’s transmission grid and all net metered facilities in an investor-owned utility’s service territory. Systems connecting to an investor-owned utility’s distribution grid for the purpose of participating in a wholesale transaction must apply under the investor-owned utility’s Wholesale Distribution Access Tariff. Systems connecting to the transmission grid must apply to the California Independent System Operator for interconnection. Systems connecting to the grid of a municipal or cooperative utility must follow the interconnection procedures adopted by that utility. Rule 21 clearly defines a series of screens meant to filter applicants into the study path most suited for their project. It also establishes fixed timelines for the screens intended to speed the process of approval. Also defined in the tariff are a variety of fees and deposits required at various stages of the interconnection process. Net metered facilities are exempt from most of these fees Public Leadership The California Consumer Power and Conservation Financing Authority (the Power Solutions for Energy Authority or CPA) has created the Public Leadership Solutions for Energy (PULSE) (PULSE) Loan fund to provide loans to help finance energy efficiency and renewable energy projects in public buildings. The program is available to cities, counties, special districts, schools and community colleges across California, as well as State and other local agencies. This loan pool overcomes limitations of other State energy loan programs by supporting larger transactions, a broader range of eligible technologies, and longer loan terms. Loan amounts of $2 million or more with no maximum are available for up to the expected life of the project. Tax-exempt market rates: for short-term or variable rate loans are currently as low as 3%; low longer-term rates range from 4.5-5% up to projects’ useful life. Fixed or variable rate debt options are available and bonds issued under the program will be insured. Agencies still can claim rebates, buydowns, and grants from other sources. Eligible projects include Energy efficiency. Advanced building metering and controls. Thermal storage. On-site renewable energy (solar PV, small scale wind, biogas and landfill gas recovery). On-site distributed generation (fuel cells, micro- turbines, combined heat and power). Incremental costs of exceeding Title 24 building energy standards in new construction and major renovations (for green or sustainable buildings). Financeable costs include feasibility and engineering design, performance guarantees, equipment warranties, project management (managing bidding, equipment procurement, construction management, and commissioning
POLICIES IMPACTING DG AND STORAGE | C-5 SGIP Market Transformation Report
Primary Programs or Policies Summary of Program or Policy the final outcome). Renewables Portfolio California’s Renewables Portfolio Standard (RPS) was originally established by Standard legislation enacted in 2002. Subsequent amendments to the law have resulted in a requirement for California’s electric utilities to have 33% of their retail sales derived from eligible renewable energy resources in 2020 and all subsequent years. The law established interim targets for the utilities. Publicly Owned Municipal Utilities (POUs) are not regulated by the CPUC but are affected by the law nonetheless, and their governing boards are charged with establishing procurement requirements. AB 327 (2013) allows the CPUC to establish procurement requirements in excess of the percentages. Agricultural Biomass to The California Energy Commission (CEC) initiated the Agricultural Biomass-to- Energy Program Energy Program following the enactment of state Senate Bill 704 (SB 704) in September 2003. SB 704 directed the CEC to design and implement a program to help improve California’s air quality in 1 agricultural communities by reducing open-field burning of agricultural residues. Through this program, the CEC offered grants of $10 per ton of biomass to eligible facilities converting biomass to electricity from July 1, 2003 to June 30, 2004. To be eligible for support under this solicitation, facilities were required to increase their purchase of qualified agricultural biomass by at least 10% above their five-year average of qualified purchases. The CEC allocated $6 million for this one-year program, and provided payments upon demonstration that these requirements had been met. However, the $6 million was entirely allocated to nine facilities for purchases occurring by February 2004, and purchases made later in the year will not receive support. There are currently no plans for a continuation of this program. Dairy Power Production "Signed into law on April 11, 2001, Senate Bill 5X created the Dairy Power Program Production Program and authorized the CEC to expend $9.64 million (roughly $8.6 million of which is available for project awards, with the rest covering administrative expenses) to encourage the development of anaerobic digestion and gasification (“biogas”) electricity generation projects on California dairies. The CEC in turn signed a contract with the Western United Resource Development Corporation (WURD) to administer the program. WURD is a non-profit entity created to administer the CEC program and is associated with the Western United Dairymen, a trade association of dairy farmers and producers in California. The original goal of the program was to install over five megawatts of dairy biogas systems capable of generating over 30 million kWh annually by September 30, 2002 (the CEC subsequently extended the timeline in recognition of several barriers discussed below). The CEC has estimated that approximately 100 MW of near term biogas production potential from livestock manure exists in the state, with only 370 kW in place today. As of May 2004, roughly 50 projects had applied to the program, and a total of $5,792,370 had been set aside to fund 14 projects with a total estimated generating capacity of 3.5 MW. SCE - Biomass Standard SCE's Biomass Standard Contract process was made available in 2007 offering three Contract contact vehicles based on capacity (100 kW to 1 MW; 1 - 5 MW; and greater than 5 MW). The contract provided a faster, simpler way for biomass projects under 20 MW to see power to utility customers. SCE’s new contracts were to be available until Dec. 31, 2007, but could be withdrawn after a total of 250 MW was enrolled. SMUD - Feed-in Tariff Under a Feed-In Tariff (FIT), utilities such as SMUD offer standard published rates
POLICIES IMPACTING DG AND STORAGE | C-6 SGIP Market Transformation Report
Primary Programs or Policies Summary of Program or Policy and contract terms for the purchase of electricity made from renewable energy resources and combined heat-power installations. SMUD's Feed-In Tariff is for eligible renewable energy resources and qualifying combined heat and power installations up to 5 MW. Effective August 4, 2010, SMUD suspended the acceptance of new feed-in-tariff applications. LADWP - Net Metering LADWP allows its customers to net meter their photovoltaic (PV), wind, and hybrid systems with a capacity of not more than one megawatt. LADWP will provide the necessary metering equipment unless an installation requires atypical metering equipment. In these cases the customer must cover the additional metering expenses. The customer must also pay any related interconnection fees. Excess kilowatt-hours (kWh) generated by the customer's system will be credited toward their future bills. Excess bill credits, however, may not be used to offset taxes, minimum charges, or other charges which are not based on energy. If a bill credit still remains when the customer terminates service, the balance will be granted to the utility. San Diego County - Wind The County of San Diego has established zoning guidelines for wind turbine Regulations systems of varying sizes in the unincorporated areas of San Diego County. Wind turbine systems can be classified as small, medium, or large, and have different siting requirements. Turbines of all sizes must abide by Noise Abatement and Control laws, must have restricted public access using locked fences, non-climbable towers, or other restrictions, and must have appropriate warning signs posted. A wind turbine is considered non-operational if it produces less than 10% of the expected power output for 12 consecutive months. Small Wind Turbine System: An installation consisting of no more than one wind turbine and a blade sweep area of 220 square feet or less. Small systems must be installed on a parcel of at least 1 acre and should be set back from property lines and roads at least 2 times the height of the wind system. The height of the turbine may not exceed 60 feet. Specific zone and fire setback requirements also apply. Non-operational turbines must be removed within 12 months after becoming non-operational. Medium Wind Turbine System: Installation consisting of one to five turbines with a combined blade sweep area of 850 square feet or less. A medium system must be installed on a parcel of at least 1 acre and requires an Administrative Permit. Turbines must not exceed 60 feet in height and must be set back from property lines and roads at least three times the height of the tower. Specific zone and fire setback requirements also apply. Non-operational turbines must be removed within 12 months after becoming non-operational. Orange County - Small In December 2010, the County of Orange Board of Supervisors adopted small wind Wind Energy Systems performance and development standards (Ord. No. 10-020) in order to promote distributed generation systems in non-urbanized areas (as defined in Government Code Section 65944(d)(2)) within the unincorporated territory. Permitting standards are for systems of 50 kW or less per customer site, for which the energy is primarily for on-site consumption. Height: For systems 45 feet tall or less, a use permit must be approved by the Zoning Administrator, and for systems between 45 and 80 feet in height, the use permit must be approved by the Planning Commission. Systems of up to 100 feet may be considered if special circumstances can be demonstrated. Number of Units: For systems of 60 feet or less, there may not be more than 2 systems on a lot of 5 acres or less. For every additional 5 acres
POLICIES IMPACTING DG AND STORAGE | C-7 SGIP Market Transformation Report
Primary Programs or Policies Summary of Program or Policy of land, 1 more system may be added, for up to 5 total systems. For systems taller than 60 feet, only 1 system is allowed per 10 acres of land. No more than 3 systems total are allowed. Western Riverside Council The Western Riverside Council of Governments (WRCOG) Large Commercial PACE of Governments - Large Financing Program encourages Residential customers to borrow money for the Commercial PACE following energy efficiency projects: Wind, Geothermal Heat Pumps, Solar Water Heat, Photovoltaic, Caulking/Weather-stripping, Duct/Air sealing, Building Insulation, Windows, Roofs, Custom/Others pending approval, Whole House Fans, Pool Pumps, Evaporative Coolers, Water Efficiency. Lighting, Furnaces, Boilers, Heat pumps, Central Air conditioners, and Programmable Thermostats. The customer can receive from $5k min to $1million be repaid in 25 years. Emissions Performance SB 1368 (Perata, Chapter 598, Statutes of 2006) limits long-term investments in Standard baseload generation by the state's utilities to power plants that meet an emissions performance standard (EPS) jointly established by the California Energy Commission and the California Public Utilities Commission. Among the requirements are the following: Establish a standard for baseload generation owned by, or under long-term contract to publicly owned utilities, of 1,100 lbs CO2 per megawatt-hour (MWh). This will encourage the development of power plants that meet California's growing energy needs while minimizing their emissions of greenhouse gases; Require posting of notices of public deliberations by publicly owned utilities on long-term investments on the Energy Commission website. This will facilitate public awareness of utility efforts to meet customer needs for energy over the long-term while meeting the State's standards for environmental impact, and Establish a public process for determining the compliance of proposed investments with the EPS. Climate Change Scoping Assembly Bill 32 (AB 32) required the California Air Resources Board (ARB or Board) Plan to develop a Scoping Plan that describes the approach California will take to reduce greenhouse gases (GHG) to achieve the goal of reducing emissions to 1990 levels by 2020. The Scoping Plan was first considered by the Board in 2008 and must be updated every five years. The Board approved the First Update to the Climate Change Scoping Plan on May 22, 2014. In the Update, nine key focus areas were identified (energy, transportation, agriculture, water, waste management, and natural and working lands), along with short-lived climate pollutants, green buildings, and the cap-and-trade program.
POLICIES IMPACTING DG AND STORAGE | C-8 SGIP Market Transformation Report
Table C-3 is a listing of policies or legislation specific to the SGIP and a summary.
Table C-3: Policies Specifically Related to SGIP Policies Specifically Related to SGIP Summary of Policies or Legislation AB 970 Assembly Bill required the CPUC to initiate load control and distributed generation activities D. 01-03-073 CPUC Decision complying with Assembly Bill 970 and establishing the Self Generation Incentive Program. Implementation of PU Code Section 399.15(b), Paragraph 4-7; Load Control and Distributed Generation Initiatives D. 01-06-035 CPUC Decision establishing waste heat recovery standards for SGIP. Requires Energy Branch to develop reliability criteria. D. 02-02-26 CPUC Decision addressing eligibility of customers served by electric municipalities, maximum size and annual program budget. D. 02-04-004 CPUC Decision clarifying Applicant’s ability to receive incentive funding from multiple sources. Addressing SCAQMD’s PTM of Decision 01-03-073 D. 02-09-051 CPUC Decision adding technology level 3-R, which establishes a new level of incentives. Contains specific requirements for projects using renewable fuels for level 3-R. Addressing Capstone’s PTM AB 1684 Extended the SGIP through 2007; Required that projects commencing January 1, 2005 meet a NOx emission standard; Required that projects commencing January 1, 2007 meet a more stringent NOx emission standard and a minimum system efficiency standard; Established a NOx emission credit that can be used by combined heat and power (CHP) units to meet minimum system efficiency standard D. 04-12-045 Modified SGIP to incorporate provisions of AB 1685: Eliminates maximum percentage payment limits; Reduces incentive payments for several technologies; Expands opportunities for public input regarding developing a declining incentive schedule, developing an exit strategy and adopting a data release format; Required an application fee for all projects received after 1/1/2005 in order to deter against “phantom projects”. This requirement was removed beginning in 2007 except in the case of new technologies that are in the process of certification. D. 06-01-047 Established the California Solar Initiative (CSI) and ordered changes in the 2006 SGIP to accommodate the transition of solar program elements to the CSI beginning January 1, 2007. AB 2778 Extended SGIP until January 1, 2012; Limited eligible technologies beginning January 1, 2008 to fuel cells and wind systems that meet emissions standards required under the distributed generation certification program adopted by the State Air Resources Board; Requires that eligibility of non-renewable fuel cell projects be determined either by calculating electrical and process heat efficiency according to PU Code 216.6 or by calculating overall electrical efficiency D. 08-04-049 Removed the 1 MW cap on incentives for 2008 and 2009 allowing projects to receive lower incentives on a tiered structure for the portion of a system over 1 MW. AB 2267 Requires an additional 20% incentive for the installation of eligible distributed generation resources from a California Supplier. This additional incentive is applied only to the technology portion of the incentive; the additional incentive for renewable fuels is not included in calculating the 20%. D. 08-11-044 Determined that Advanced Energy Storage systems coupled with eligible SGIP technologies will receive an incentive of $2/watt of installed capacity; Revises the process for the review of SGIP
POLICIES IMPACTING DG AND STORAGE | C-9 SGIP Market Transformation Report
Policies Specifically Related to SGIP Summary of Policies or Legislation program modification requests D. 09-09-048 Grants a petition to modify SGIP policies expanding eligibility for Level 2 incentives to include “directed biogas” projects where renewable fuel is nominated via contract D. 10-02-017 Revises Decision 08-11-044 so that Advanced Energy Storage systems coupled with fuel cells must meet the site specific requirements for on-site peak demand reduction and be capable of discharging fully at least once per day in order to be eligible for the$2/watt incentive from the self-generation incentive program; Determines that Advanced Energy Storage systems coupled with eligible technologies under the SGIP must install metering equipment capable of measuring and recording interval data on generation output and Advanced Energy Storage system charging and discharging D. 11-09-015 Adds eligibility requirements based upon greenhouse gas reductions; Establishes an on-site emission rate that projects must beat to be eligible for SGIP participation of 379 kg CO2/MWh; Adds Waste Heat to Power, Pressure Reduction Turbine, Internal Combustion Engine – CHP, Microturbine – CHP, Gas Turbine – CHP, Stand-Alone AES technologies to the list of eligible technologies; Revises the incentive levels for all technologies and adds a $2.00/Watt biogas adder; Directs that Directed Biogas can only be procured from in-state suppliers; Eliminates maximum size restrictions given a project meets on-site load. Sets a 30 kW minimum for wind and renewable fueled fuel cell projects; Adopts a hybrid payment structure with 50% upfront, 50% PBI based on kWh generation of on-site load for projects 30 kW and larger. Projects under 30 kW will receive the entire incentive upfront; D. 11-09-015 Adopts the following assumed capacity factors to be used in PBI calculations: 10% for AES, 25% (cont'd) for wind, and 80% for all other distributed energy resources; Implements incentive decline in the following manner 10% per year for emerging technologies and 5% per year for all other technologies, beginning 1/1/2013; Adopts a supplier concentration limit where no more than 40% of the annual statewide budget available on the first of a given year may be allocated to any single manufacturer’s technology during that year; Establishes a maximum project incentive of $5 million; Establishes that the minimum customer investment in a project must be 40% of eligible project costs; Establishes an SGIP incentive budget allocation of 75% for renewable and emerging technologies, and 25% for non-renewable technologies; Determines that the Program Administration Budget will be reduced to 7%; Establishes that projects exporting to the grid are eligible for SGIP incentives as long as they do not export more than 25% on an annual net basis; Makes an energy efficiency audit mandatory for participation in SGIP unless an extensive audit has been conducted within five years of the date of the reservation request; Establishes an application fee that is 1% of the amount of incentive requested; Limits all projects to one six month extension. Request for a second extension may be made to the Working Group; Extends the warranty period to 10 years. D. 12-05-037 Orders that all technologies previously eligible for the Emerging Renewables Program should be immediately eligible for the SGIP; Determines that consolidating the ERP and SGIP programs now is preferable to perpetuating two competing programs that serve the same types of technologies and policy purposes.
POLICIES IMPACTING DG AND STORAGE | C-10 APPENDIX D SITEPRO BUILDING TYPE TO NAICS CODE MAPPING
We mapped 2012 Census data NAICS codes using from 2-digit to 6-digit width to corresponding SitePro building type. Table D-1 lists the NAICS codes, establishment descriptions, and assigned SitePro building type. Many commercial and some industrial NAICS codes were assigned with SitePro’s large or small office building types. We differentiate large versus small office based on employee count bins in Census data.
Table D-1: Commercial and Industrial NAICS Codes and Associated SitePro Building Type NAICS Code NAICS Description SitePro Building Type 6112 Junior Colleges College 6113 Colleges, Universities, and Professional Schools College 311 Food Manufacturing Food Manufacturing 312 Beverage and Tobacco Product Manufacturing Food Manufacturing 445 Food and Beverage Stores Food Store 622 Hospitals Health, Hospital 623 Nursing and Residential Care Facilities Health, Hospital 721 Accommodation Lodging, Hotel 71391 Golf Courses and Country Clubs Lodging, Hotel 71394 Fitness and Recreational Sports Centers Lodging, Hotel 92214 Correctional institutions Lodging, Hotel 53111 Lessors of Residential Buildings and Dwellings MultiFamily 23 Construction Office (large/small) 52 Finance and Insurance Office (large/small) 54 Professional, Scientific, and Technical Services Office (large/small) 55 Management of Companies and Enterprises Office (large/small) 488 Support Activities for Transportation Office (large/small) 511 Publishing Industries (except Internet) Office (large/small) 512 Motion Picture and Sound Recording Industries Office (large/small) 515 Broadcasting (except Internet) Office (large/small) 517 Telecommunications Office (large/small) 518 Data Processing, Hosting, and Related Services Office (large/small) 519 Other Information Services Office (large/small) 561 Administrative and Support Services Office (large/small) 621 Ambulatory Health Care Services Office (large/small) 624 Social Assistance Office (large/small) 921 Executive, legislative and general government Office (large/small)
SITEPRO BUILDING TYPE TO NAICS CODE MAPPING | D-1 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type 923 Administration of human resource programs Office (large/small) 924 Administration of environmental programs Office (large/small) 925 Community and housing program administration Office (large/small) 926 Administration of economic programs Office (large/small) 927 Space research and technology Office (large/small) 928 National security and international affairs Office (large/small) 42511 Business to Business Electronic Markets Office (large/small) 42512 Wholesale Trade Agents and Brokers Office (large/small) 53119 Lessors of Other Real Estate Property Office (large/small) 53121 Office (large/small)s of Real Estate Agents and Brokers Office (large/small) 53131 Real Estate Property Managers Office (large/small) 53132 Office (large/small)s of Real Estate Appraisers Office (large/small) 53139 Other Activities Related to Real Estate Office (large/small) Construction, Transportation, Mining, and Forestry Machinery and 53241 Equipment Rental and Leasing Office (large/small) 53242 Office (large/small) Machinery and Equipment Rental and Leasing Office (large/small) Other Commercial and Industrial Machinery and Equipment Rental and 53249 Leasing Office (large/small) 53311 Lessors of Nonfinancial Intangible Assets (except Copyrighted Works) Office (large/small) 71131 Promoters of Performing Arts, Sports, and Similar Events with Facilities Office (large/small) 71132 Promoters of Performing Arts, Sports, and Similar Events without Facilities Office (large/small) Agents and Managers for Artists, Athletes, Entertainers, and Other Public 71141 Figures Office (large/small) 71151 Independent Artists, Writers, and Performers Office (large/small) 71211 Museums Office (large/small) 71212 Historical Sites Office (large/small) 71329 Other Gambling Industries Office (large/small) 92211 Courts Office (large/small) 92212 Police protection Office (large/small) 92213 Legal counsel and prosecution Office (large/small) 811211 Consumer Electronics Repair and Maintenance Office (large/small) 811212 Computer and Office (large/small) Machine Repair and Maintenance Office (large/small) 811213 Communication Equipment Repair and Maintenance Office (large/small) 811219 Other Electronic and Precision Equipment Repair and Maintenance Office (large/small) 811412 Appliance Repair and Maintenance Office (large/small) 811420 Reupholstery and Furniture Repair Office (large/small) 811430 Footwear and Leather Goods Repair Office (large/small) 811490 Other Personal and Household Goods Repair and Maintenance Office (large/small)
SITEPRO BUILDING TYPE TO NAICS CODE MAPPING | D-2 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type 812921 Photofinishing Laboratories (except One-Hour) Office (large/small) 812922 One-Hour Photofinishing Office (large/small) 812990 All Other Personal Services Office (large/small) 813110 Religious Organizations Office (large/small) 813211 Grantmaking Foundations Office (large/small) 813212 Voluntary Health Organizations Office (large/small) 813219 Other Grantmaking and Giving Services Office (large/small) 813311 Human Rights Organizations Office (large/small) 813312 Environment, Conservation and Wildlife Organizations Office (large/small) 813319 Other Social Advocacy Organizations Office (large/small) 813410 Civic and Social Organizations Office (large/small) 813910 Business Associations Office (large/small) 813920 Professional Organizations Office (large/small) 813930 Labor Unions and Similar Labor Organizations Office (large/small) 813940 Political Organizations Office (large/small) Other Similar Organizations (except Business, Professional, Labor, and 813990 Political Organizations) Office (large/small) 722 Food Services and Drinking Places Restaurant, Sit-Down 71111 Theater Companies and Dinner Theaters Restaurant, Sit-Down 441 Motor Vehicle and Parts Dealers Retail, Large 442 Furniture and Home Furnishings Stores Retail, Large 443 Electronics and Appliance Stores Retail, Large 444 Building Material and Garden Equipment and Supplies Dealers Retail, Large 446 Health and Personal Care Stores Retail, Large 447 Gasoline Stations Retail, Large 448 Clothing and Clothing Accessories Stores Retail, Large 451 Sporting Goods, Hobby, Musical Instrument, and Book Stores Retail, Large 452 General Merchandise Stores Retail, Large 453 Miscellaneous Store Retailers Retail, Large 454 Nonstore Retailers Retail, Large 53211 Passenger Car Rental and Leasing Retail, Large 53212 Truck, Utility Trailer, and RV (Recreational Vehicle) Rental and Leasing Retail, Large 53221 Consumer Electronics and Appliances Rental Retail, Large 53222 Formal Wear and Costume Rental Retail, Large 53223 Video Tape and Disc Rental Retail, Large 53229 Other Consumer Goods Rental Retail, Large 53231 General Rental Centers Retail, Large
SITEPRO BUILDING TYPE TO NAICS CODE MAPPING | D-3 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type 71311 Amusement and Theme Parks Retail, Large 71312 Amusement Arcades Retail, Large 71321 Casinos (except Casino Hotels) Retail, Large 71395 Bowling Centers Retail, Large 71399 All Other Amusement and Recreation Industries Retail, Large 812111 Barber Shops Retail, Large 812112 Beauty Salons Retail, Large 812113 Nail Salons Retail, Large 812191 Diet and Weight Reducing Centers Retail, Large 812199 Other Personal Care Services Retail, Large 812210 Funeral Homes and Funeral Services Retail, Large 812220 Cemeteries and Crematories Retail, Large 812310 Coin-Operated Laundries and Drycleaners Retail, Large 6111 Elementary and Secondary Schools School 6114 Business Schools and Computer and Management Training School 6115 Technical and Trade Schools School 6116 Other Schools and Instruction School 6117 Educational Support Services School 481 Air Transportation Warehouse, Non-Refr 483 Water Transportation Warehouse, Non-Refr 484 Truck Transportation Warehouse, Non-Refr 485 Transit and Ground Passenger Transportation Warehouse, Non-Refr 486 Pipeline Transportation Warehouse, Non-Refr 487 Scenic and Sightseeing Transportation Warehouse, Non-Refr 562 Waste Management and Remediation Services Warehouse, Non-Refr 42311 Automobile and Other Motor Vehicle Merchant Wholesalers Warehouse, Non-Refr 42312 Motor Vehicle Supplies and New Parts Merchant Wholesalers Warehouse, Non-Refr 42313 Tire and Tube Merchant Wholesalers Warehouse, Non-Refr 42314 Motor Vehicle Parts (Used) Merchant Wholesalers Warehouse, Non-Refr 42321 Furniture Merchant Wholesalers Warehouse, Non-Refr 42322 Home Furnishing Merchant Wholesalers Warehouse, Non-Refr 42331 Lumber, Plywood, Millwork, and Wood Panel Merchant Wholesalers Warehouse, Non-Refr 42332 Brick, Stone, and Related Construction Material Merchant Wholesalers Warehouse, Non-Refr 42333 Roofing, Siding, and Insulation Material Merchant Wholesalers Warehouse, Non-Refr 42339 Other Construction Material Merchant Wholesalers Warehouse, Non-Refr 42341 Photographic Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr
SITEPRO BUILDING TYPE TO NAICS CODE MAPPING | D-4 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type 42342 Office (large/small) Equipment Merchant Wholesalers Warehouse, Non-Refr Computer and Computer Peripheral Equipment and Software Merchant 42343 Wholesalers Warehouse, Non-Refr 42344 Other Commercial Equipment Merchant Wholesalers Warehouse, Non-Refr Medical, Dental, and Hospital Equipment and Supplies Merchant 42345 Wholesalers Warehouse, Non-Refr 42346 Ophthalmic Goods Merchant Wholesalers Warehouse, Non-Refr 42349 Other Professional Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr 42351 Metal Service Centers and Other Metal Merchant Wholesalers Warehouse, Non-Refr 42352 Coal and Other Mineral and Ore Merchant Wholesalers Warehouse, Non-Refr Electrical Apparatus and Equipment, Wiring Supplies, and Related 42361 Equipment Merchant Wholesalers Warehouse, Non-Refr Household Appliances, Electric Housewares, and Consumer Electronics 42362 Merchant Wholesalers Warehouse, Non-Refr 42369 Other Electronic Parts and Equipment Merchant Wholesalers Warehouse, Non-Refr 42371 Hardware Merchant Wholesalers Warehouse, Non-Refr Plumbing and Heating Equipment and Supplies (Hydronics) Merchant 42372 Wholesalers Warehouse, Non-Refr Warm Air Heating and Air-Conditioning Equipment and Supplies Merchant 42373 Wholesalers Warehouse, Non-Refr 42374 Refrigeration Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr Construction and Mining (except Oil Well) Machinery and Equipment 42381 Merchant Wholesalers Warehouse, Non-Refr 42382 Farm and Garden Machinery and Equipment Merchant Wholesalers Warehouse, Non-Refr 42383 Industrial Machinery and Equipment Merchant Wholesalers Warehouse, Non-Refr 42384 Industrial Supplies Merchant Wholesalers Warehouse, Non-Refr 42385 Service Establishment Equipment and Supplies Merchant Wholesalers Warehouse, Non-Refr Transportation Equipment and Supplies (except Motor Vehicle) Merchant 42386 Wholesalers Warehouse, Non-Refr 42391 Sporting and Recreational Goods and Supplies Merchant Wholesalers Warehouse, Non-Refr 42392 Toy and Hobby Goods and Supplies Merchant Wholesalers Warehouse, Non-Refr 42393 Recyclable Material Merchant Wholesalers Warehouse, Non-Refr 42394 Jewelry, Watch, Precious Stone, and Precious Metal Merchant Wholesalers Warehouse, Non-Refr 42399 Other Miscellaneous Durable Goods Merchant Wholesalers Warehouse, Non-Refr 42411 Printing and Writing Paper Merchant Wholesalers Warehouse, Non-Refr 42412 Stationery and Office (large/small) Supplies Merchant Wholesalers Warehouse, Non-Refr 42413 Industrial and Personal Service Paper Merchant Wholesalers Warehouse, Non-Refr 42421 Drugs and Druggists' Sundries Merchant Wholesalers Warehouse, Non-Refr 42431 Piece Goods, Notions, and Other Dry Goods Merchant Wholesalers Warehouse, Non-Refr 42432 Men's and Boys' Clothing and Furnishings Merchant Wholesalers Warehouse, Non-Refr
SITEPRO BUILDING TYPE TO NAICS CODE MAPPING | D-5 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type Women's, Children's, and Infants' Clothing and Accessories Merchant 42433 Wholesalers Warehouse, Non-Refr 42434 Footwear Merchant Wholesalers Warehouse, Non-Refr 42441 General Line Grocery Merchant Wholesalers Warehouse, Refr 42442 Packaged Frozen Food Merchant Wholesalers Warehouse, Refr 42443 Dairy Product (except Dried or Canned) Merchant Wholesalers Warehouse, Refr 42444 Poultry and Poultry Product Merchant Wholesalers Warehouse, Refr 42445 Confectionery Merchant Wholesalers Warehouse, Refr 42446 Fish and Seafood Merchant Wholesalers Warehouse, Refr 42447 Meat and Meat Product Merchant Wholesalers Warehouse, Refr 42448 Fresh Fruit and Vegetable Merchant Wholesalers Warehouse, Refr 42449 Other Grocery and Related Products Merchant Wholesalers Warehouse, Refr 42451 Grain and Field Bean Merchant Wholesalers Warehouse, Non-Refr 42452 Livestock Merchant Wholesalers Warehouse, Non-Refr 42459 Other Farm Product Raw Material Merchant Wholesalers Warehouse, Non-Refr 42461 Plastics Materials and Basic Forms and Shapes Merchant Wholesalers Warehouse, Non-Refr 42469 Other Chemical and Allied Products Merchant Wholesalers Warehouse, Non-Refr 42471 Petroleum Bulk Stations and Terminals Warehouse, Non-Refr Petroleum and Petroleum Products Merchant Wholesalers (except Bulk 42472 Stations and Terminals) Warehouse, Non-Refr 42481 Beer and Ale Merchant Wholesalers Warehouse, Non-Refr 42482 Wine and Distilled Alcoholic Beverage Merchant Wholesalers Warehouse, Non-Refr 42491 Farm Supplies Merchant Wholesalers Warehouse, Non-Refr 42492 Book, Periodical, and Newspaper Merchant Wholesalers Warehouse, Non-Refr 42493 Flower, Nursery Stock, and Florists' Supplies Merchant Wholesalers Warehouse, Non-Refr 42494 Tobacco and Tobacco Product Merchant Wholesalers Warehouse, Non-Refr 42495 Paint, Varnish, and Supplies Merchant Wholesalers Warehouse, Non-Refr 42499 Other Miscellaneous Nondurable Goods Merchant Wholesalers Warehouse, Non-Refr 49211 Couriers and Express Delivery Services Warehouse, Non-Refr 49221 Local Messengers and Local Delivery Warehouse, Non-Refr 49311 General Warehousing and Storage Warehouse, Non-Refr 49312 Refrigerated Warehousing and Storage Warehouse, Refr 49313 Farm Product Warehousing and Storage Warehouse, Refr 49319 Other Warehousing and Storage Warehouse, Non-Refr 53112 Lessors of Nonresidential Buildings (except Miniwarehouses) Warehouse, Non-Refr 53113 Lessors of Miniwarehouses and Self-Storage Units Warehouse, Non-Refr 811111 General Automotive Repair Warehouse, Non-Refr
SITEPRO BUILDING TYPE TO NAICS CODE MAPPING | D-6 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type 811112 Automotive Exhaust System Repair Warehouse, Non-Refr 811113 Automotive Transmission Repair Warehouse, Non-Refr 811118 Other Automotive Mechanical and Electrical Repair and Maintenance Warehouse, Non-Refr 811121 Automotive Body, Paint, and Interior Repair and Maintenance Warehouse, Non-Refr 811122 Automotive Glass Replacement Shops Warehouse, Non-Refr 811191 Automotive Oil Change and Lubrication Shops Warehouse, Non-Refr 811198 All Other Automotive Repair and Maintenance Warehouse, Non-Refr Commercial and Industrial Machinery and Equipment (except Automotive 811310 and Electronic) Repair and Warehouse, Non-Refr 811411 Home and Garden Equipment Repair and Maintenance Warehouse, Non-Refr 812910 Pet Care (except Veterinary) Services Warehouse, Non-Refr 812930 Parking Lots and Garages Warehouse, Non-Refr
SITEPRO BUILDING TYPE TO NAICS CODE MAPPING | D-7 APPENDIX E CHP POTENTIAL ANALYSIS E.1 Overview We use a “bottom-up” approach to estimate CHP potentials. We begin with state population counts of business establishments from 2012 Census data and of residential units from the 2009 Residential Energy Consumption Survey. We group establishments by common market segment, establishment size, and climate region. These characteristics define the group’s building by size and daily activity. Based on these characteristics, we develop a year of hourly estimates of electrical and thermal end-use loads. We then develop 9 CHP system specifications for each group based on the modeled loads and 5 different CHP system prime movers and two absorption chiller combinations. The technologies are internal combustion (IC) engines, microturbines, gas turbines, heat –recovering fuel cells, and all-electric fuel cells. Absorption chillers are optional except for all-electric fuel cells. Absorption chillers tend to increase CHP technical potential.1 The largest of the generating capacities from these 9 specifications is the group’s average technical potential. We multiply the group’s average technical potential by its population to get its total technical potential. To identify new technical potential, we must subtract existing CHP capacity from our estimates of total technical potential. We identify existing capacity by market segment and prime mover type. We apportion existing capacity to groups according to their proportions of total technical potential. The remainder after subtraction is new technical potential for each group. When estimating CHP potentials we consider only specific market segments from the residential, commercial, institutional, and industrial market sectors. We also consider only the 30 to 5000 kW total system capacity market niche. That is the range for which the SGIP provides incentives.2 This market niche effectively excludes most of the industrial sector and the single-family residential segment. When specifying CHP systems we constrain system operations such that both a minimum number of equivalent full-load operating hours occur annually and a minimum overall fuel efficiency is always achieved. We specify a capacity sufficient to frequently serve but not always satisfy thermal and electric loads. The system capacity, defined in terms of kilowatts of nominal electric generating capacity, depends on daily and seasonal dynamics of a building’s thermal and electric loads.3 We estimate technical potential for a base year of 2012 since the Census data are for that year. We then forecast future technical potentials for groups using the base year estimates and annual growth rates.
1 Absorption chillers increase thermal load but decrease electric load needing to be met. Despite greater thermal load the decrease in electric load may reduce technical potential due to capacity exceeding electrical load and permissible export. 2 Fuel cells units of 5 kW capacity were supported under the SGIP program. Several single-family residences installed one or more of these units. Technical problems led to many being removed. While the single-family residential sector has substantial technical potential for small fuel cells we do not expect the under 30 kW market to be a future program target. 3 An all-electric fuel cell system is an exception. Its technical potential depends on electric loads alone. In general, all-electric fuel cells have very similar target audiences to CHP fuel cells whose waste heat is recovered.
CHP POTENTIAL ANALYSIS | E-1 SGIP Market Transformation Report
The growth rates are based on those published in a 2012 ICF report.4 Table E-1 lists the annual growth rates by market segment.
Table E-1: Market Segment Annual Growth Rates
Segment Annual Growth Rate College 0.63% Food Manufacturing 0.92% Food Store 1.03% Health, Hospital 1.42% Large Multifamily 0.56% Lodging, Hotel 1.26% Office, Large 1.15% Office, Small 1.15% Restaurant, Sit-Down 0.96% Retail, Large 0.13% School 0.63% Warehouse 0.78%
E.2 Market Segment Host Buildings for CHP Systems Estimates of CHP potential depend on the number and sizes of systems. We assume all buildings can host a CHP system of some size. We estimate populations of buildings by market segment and assume one CHP system per building. We estimate the sizes of CHP systems based upon a combination of key characteristics of host buildings described below. We use 2012 Census data to develop population estimates of commercial, institutional, and industrial sector buildings grouped by key building characteristics. For the residential sector we use 2009 Residential Energy Consumption Survey data. Table E-2 and Table E-3 show the key characteristics and corresponding source data fields from those two data sources.
Table E-2: Commercial and Industrial Census Data Fields
Key Characteristic Census Data Field Building type 2012 North American Industry Classification System (NAICS) code Building conditioned area Employment size bin Climate region and IOU 5-digit ZIP code Number of CHP systems Number of establishments
4 “Combined Heat and Power: Policy Analysis and 2011-2030 Market Assessment,” prepared for the California Energy Commission, ICF International, Inc., February 2012.
CHP POTENTIAL ANALYSIS | E-2 SGIP Market Transformation Report
Table E-3: Residential Energy Consumption Survey Data Fields
Key Characteristic RECS Data Field Building conditioned area Unit count in building bin Climate region and IOU State Climate region and IOU Climate region Number of CHP systems Count of units characterized, Unit count in building
Census data provide populations for groups of establishments with common NAICS code, employee count bin, and location. Census data do not provide actual building counts. An establishment may be the sole occupant of a building or it could be one of many establishments in a building. An establishment also may occupy multiple buildings in a campus or complex. We develop building population estimates by assuming an establishment as a proxy for a building. Where multiple establishments actually occupy a single building the establishment as proxy approach overestimates the building population. This over-counting introduces upward bias on technical potential. In the same case, however, a downward bias is introduced on specification of CHP system capacities. CHP capacities specified for establishments with few employees often are below the minimum technical potential threshold of 30 kW. To recognize certain market segments frequently have buildings occupied by many establishments we include these undersized CHP systems in technical potential estimates. We include such undersized systems for the office and health market segments only. Compared to other market segments, both of these segments have relatively high technical potential and relatively low percentages of buildings with a single establishment. While some portions of the retail market segment also have very low percentages of buildings occupied by one establishment, the segment generally has low technical potential and a high ratio of very small buildings to very large buildings. Table E-4 lists estimated building populations by market segment, climate region, and IOU.
CHP POTENTIAL ANALYSIS | E-3 SGIP Market Transformation Report
Table E-4: Estimated Building Populations Market Segment, Climate Region, and IOU
PG&E SCE SDG&E Segment Coastal Inland Coastal Inland Coastal Inland College 89 78 81 118 33 6 Food Manufacturing 542 462 321 454 47 21 Food Store 809 792 750 1,174 278 97 Health, Hospital 777 671 525 895 170 74 Large Multifamily 2,405 1,099 2,198 3,209 970 183 Lodging, Hotel 1,058 379 450 561 237 54 Office, Large 4,347 2,682 4,022 4,796 1,471 338 Office, Small 10,348 8,534 9,581 13,000 3,497 1,111 Restaurant, Sit-Down 8,035 6,716 7,276 10,569 2,916 734 Retail, Large 2,828 2,848 2,848 4,166 1,029 315 Small Multifamily 108,680 33,506 86,901 75,319 22,771 5,116 School 650 376 517 716 192 51
We associate one of 12 non-residential market segments from Itron’s SitePro software with a large number of 2012 NAICS codes. The SitePro market segments are listed here. » College » Food Manufacturing » Food Store » Healthcare » Large multifamily » Lodging » Office (large/small) » Restaurant » Retail » Small multifamily » School » Warehouse (refrigerated/not) Table E-7 at the end of this section lists a complete mapping of market segments and NAICS codes. The specificity of the NAICS codes ranges from 2-digit to 6-digit wide to more exactly associate a SitePro market segment. We estimate sizes of host buildings in terms of conditioned floor area. We base estimates on midpoints of employment size bin and market segment-specific employee intensities per square foot from SitePro software. For residential multifamily segment we estimate host building size in terms of unit counts per building. We use midpoints from unit count binsin RECS data. A limitation of Census data used here is an upper limit of employment size bins. The largest is 1,000 or more employees. Establishments with 10,000 employees therefore cannot be distinguished from those with 1,000. For example 2012 Census data show 29 colleges with 1,000 or more employees. To address this limitation we estimate technical potential of either 3 or 5 MW where there are 1,000 or more employees in certain market segments.
CHP POTENTIAL ANALYSIS | E-4 SGIP Market Transformation Report
We determine host building locations from 5-digit ZIP codes in Census data. We map a climate region, either coastal or inland, as well as an IOU using the ZIP codes. Climate region influences CHP technical and economic potential. IOU influences only economic potential due to different tariffs.
E.3 Building Electrical and Thermal Loads Different building types have different daily routines and so have different electric and thermal load schedules. Larger buildings have larger loads, and buildings in hotter climates have more air conditioning load. We use key characteristics of buildings when estimating electric and thermal loads. We use Itron’s SitePro software to estimate hourly electric and thermal loads. The load shapes vary depending on market segment, building size, and climate region. We then size CHP systems based on these load shapes and hourly performance minima for the CHP systems. Using SitePro we develop electric and thermal load shapes for over 500 combinations of market segment, size, and climate. The thermal loads represent heating loads served by conventional natural gas heating service that might be served by CHP. They can include certain electric cooling end-use loads that CHP systems with absorption chillers might serve. From SitePro’s end-use hourly load models we estimate two annual thermal baseloads for a building- one with and another without inclusion of an absorption chiller as a thermal load. A building with higher and more frequent thermal loads that can be served by CHP has a greater annual thermal baseload and thus has greater technical potential. If electric loads are very low during hours where the thermal load is at or above the annual thermal baseload, however, technical potential may be reduced to limit excessive electric export to the grid. To include absorption chillers when sizing CHP systems we treat certain electric cooling end-uses as thermal loads. We consider both electric space cooling and refrigeration end-uses among thermal loads for absorption chillers. The absorption chiller then is assumed to reduce the loads of an electric chiller. By treating electric cooling end-uses as thermal loads and including absorption chillers, CHP technical potential may increase. We exclude absorption chillers for the residential sector where there are lower thresholds for CHP system physical footprint and maintenance requirements. We size CHP systems for specific combinations of prime mover with and without absorption chiller. The sizes are sufficient to serve and, subject to performance and electric export constraints, satisfy the annual thermal baseload. In the exceptional case of all-electric fuel cells it is an annual electric baseload that is satisfied. This combined hourly and annual approach assures annual operating runtime targets and hourly overall fuel efficiency targets both are met.
E.4 CHP Systems Parameters We use several technical parameters to define CHP systems and operations regardless of market segment, building size, or climate region. We size CHP systems to meet specific hourly performance minima while satisfying as much annual thermal load as possible. We assume most generation and all recovered heat are used onsite. These parameters are based on associated performance minima for CHP described in the SGIP Handbook. We begin by assuming:
CHP POTENTIAL ANALYSIS | E-5 SGIP Market Transformation Report
» a minimum generator electrical capacity of 30 kW, and » achievement of 60 percent HHV overall efficiency (except all-electric fuel cells). We use a minimum annual operating target in terms of equivalent full load hours (EFLH) when sizing CHP systems. Technical potential is sensitive and inversely related and can be sensitive to this minimum performance parameter. For IC engines, gas turbines, microturbines, and fuel cells with heat recovery we use 5000 EFLH, equivalent to a 57 percent annual capacity factor. For all-electric fuels we use 7000 EFLH, equivalent to an 80 percent annual capacity factor, to recognize typical operation of these prime movers is continuous and very near rated capacity. A lower EFLH allows for greater technical potential as well as greater operational flexibility. A 5000 EFLH allows CHP systems with multiple independent units to operate in various combinations as loads change while still easily meeting that annual performance minimum. For example, a system composed of five units could have two units remain idle and three units reach 95 percent annual capacity factor and thus achieve 5000 EFLH minimum for the system as a whole. During hours when thermal loads exceed the annual thermal baseload and electrical loads are sufficiently large, the fourth and fifth units also could operate while still meeting hourly performance minima. We define distinct performance parameters for each of the CHP prime mover technology options. They differ in terms of electrical conversion efficiency as well as power to heat ratio, or how much waste heat is available for every kWh of electricity generated. These differences are important when sizing CHP to serve as much of the annual thermal baseload as possible without generating excess electricity and needing to export beyond a permissible level. Table E-5 lists performance assumptions for the prime mover technologies.
Table E-5: CHP System Performance Assumptions 5
Fuel Cell with Heat Fuel Cell Prime Mover Technology IC Engine Gas Turbine Microturbine Recovery All-Electric Electric Conversion Efficiency (HHV) 31% 24% 24% 42% 52% Heat Recovery Efficiency (HHV) 29% 36% 36% 18% 0% Overall Efficiency (HHV) 60% 60% 60% 60% 52% Hourly Capacity Factor Minimum 50% 50% 50% 80% 80% Annual Equivalent Full Load Hour Minimum 5000 5000 5000 5000 7000 Hourly Generation Export Maximum 25% 25% 25% 25% 25%
All-electric fuel cells have the highest electric conversion efficiency of the prime mover technologies, followed by fuel cells with heat recovery, IC engines, and finally microturbines. These last three technologies share the same overall efficiency assumption, so their heat recovery efficiencies also differ. Their heat recovery efficiencies are simply the difference between the common overall efficiency and their individual electric conversion efficiencies.
5 Source: 2014 EPA CHP Technology Catalog
CHP POTENTIAL ANALYSIS | E-6 SGIP Market Transformation Report
Table E-2 lists performance assumptions related to absorption chillers and the displacement of electric cooling loads as well as the assumed efficiency of a conventional heating service boiler.
Table E-6: Engineering Performance Assumptions 6
Description Parameter Value Absorption Chiller (single-effect) Coefficient of Performance 0.7 Btu/Btu Electric Chiller Efficiency 0.68 kWh/Ton Electric Space Cooling End-Use Load CHP Displaceable Fraction 100% Electric Refrigeration End-Use Load CHP Displaceable Fraction 50% Conventional Boiler Fuel Efficiency (HHV) 87%
E.5 CHP System Sizing Specifying appropriate CHP system sizes and technologies for various market segments, building sizes, and climate regions is critical to identifying CHP technical potential. We develop specifications in terms of generating capacity in kilowatts and technology in terms of prime mover type as well as whether or not an absorption chiller is included. General and technology-specific CHP system parameters along with performance minima are fit to whole-year hourly thermal and electric load shapes to determine capacities for each prime mover technology. The largest CHP size and its corresponding prime mover define a building’s technical potential. We estimate annual thermal baseloads from hourly load shapes. We define annual thermal baseload as the 5,000th lowest hourly thermal load. Thermal load includes heating end-use loads that CHP can serve as well as cooling loads where an absorption chiller is included. With this approach there are 5,000 hours in a year that have at a minimum the thermal baseload. CHP systems sized to serve this annual thermal baseload can run at full capacity in all 5,000 hours. This satisfies a performance minimum of 5,000 equivalent full load hours wherein all available heat is used. During hours with lower thermal loads the CHP still may run but at reduced output in order to continue to meet an hourly overall efficiency minimum. For all-electric fuel cell prime movers we define an annual electric rather than thermal baseload. This is a building’s 7,000th lowest hourly sum of its electric end-use loads. Thus there are 7,000 hours in the year that have at least that total electric load. All-electric fuel cells sized to serve the annual electric baseload can run at full capacity in those 7,000 hours to meet a 7,000 equivalent full load hour performance minimum. This 7,000 hour target recognizes that typical operation of an all-electric fuel cell system or individual fuel cell unit is continuous and very near rated generating capacity, unlike IC engine and microturbine unit operations that may be stopped and restarted or modulated over a wider output range. A thermally-driven approach to CHP system sizing aims to recover heat sufficient to completely satisfy the annual thermal baseload while at the same time reaching the minimum overall efficiency target. We
6 Ibid.
CHP POTENTIAL ANALYSIS | E-7 SGIP Market Transformation Report
must reduce system size where satisfying the thermal baseload results in excess electric export to the grid. In such cases the system then does not completely satisfy the annual thermal baseload. Whether or not size is reduced to avoid excess export, heat is recovered sufficient to reach the target overall efficiency. The strictly electric-driven sizing approach used for all-electric fuel cells aims to satisfy the annual electric baseload. There are no constraints regarding heat recovery and no target overall efficiency besides the electric conversion efficiency of the fuel cell. Despite using the 7,000th lowest electric load as the annual electric baseload, this approach often results in greater technical potential than the thermally-driven approach. The all-electric fuel cell then becomes the prime mover associated with the technical potential. A few technology and segment-specific details are worth mentioning to better understand how we arrived at these technical potentials. We begin with waste-heat driven absorption chillers and the non- traditional CHP markets of the restaurant and food store segments. Including an absorption chiller as a thermal load in a CHP system allows space cooling and refrigeration electric end-use loads to be served. This often leads to a higher annual thermal baseload.7 A higher annual thermal baseload can increase CHP technical potential. The restaurant and food store segments both have fairly large refrigeration and space cooling electric end-use loads. The dark blue upper band across Figure E-1 shows that, on an average weekday, refrigeration is the largest electrical end-use load for a food store. The lighter blue band just below refrigeration is the third largest load, space cooling. Both these electric end-use loads can be served by an absorption chiller. A number of food store pilot projects recently have demonstrated success with the addition of absorption chillers to serve cooling and refrigeration loads.8
7 Annual thermal baseload is a metric of hourly thermal needs reached in no less than a minimum number of hours per year. In this analysis it is used to define target thermal output capacity of a CHP system. 8 CHP project reports on food store are available at these web pages http://files.harc.edu/Sites/GulfCoastCHP/CaseStudies/WaldbaumsHauppagueNY.pdf http://files.harc.edu/Sites/GulfCoastCHP/CaseStudies/APFreshMarketMountKiskoNY.pdf http://www.pewtrusts.org/en/research-and-analysis/fact-sheets/2015/06/combined-heat-and-power-helps-groceries- reduce-costs-and-increase-resiliency http://www.nyserda.ny.gov/About/Publications/Case-Studies/NCP-Case-Studies/Whole-Foods-Market.aspx
CHP POTENTIAL ANALYSIS | E-8 SGIP Market Transformation Report
Figure E-1: Food Store Segment Annual Average Weekday Hourly Electric End-Use Loads
The health care segment is a traditional CHP target because of its characteristically high and year round thermal loads for domestic hot water as well as its high summertime space cooling electric loads. Although it has fewer numbers than food stores, the health segment has larger buildings. Figure E-2 shows an average weekday’s hourly natural gas end-use loads for a health facility. Figure E-3 shows corresponding hourly electric end-use loads for a health facility.
CHP POTENTIAL ANALYSIS | E-9 SGIP Market Transformation Report
Figure E-2: Health Segment Average Annual Weekday Hourly Natural Gas End Use Loads
Figure E-3: Health Segment Average Annual Weekday Hourly Electric End Use Loads
CHP POTENTIAL ANALYSIS | E-10 SGIP Market Transformation Report
For the health segment we sized CHP thermally to serve hot water and space heating natural gas end- uses shown by the bright yellow and red bands respectively in Figure E-2. By including absorption chillers as thermal loads we also sized CHP thermally to serve space cooling and refrigeration electric end-uses shown by light and dark blue bands in Figure E-3. Any electric end-use of course can by served by CHP’s electric generation. Contribution of Absorption Chilling to Technical Potential Like electric chillers, absorption chillers serve cooling loads but unlike other chillers they are fueled by heat. An absorption chiller can be an additional thermal load for a CHP system to serve. This can increase a building’s CHP technical potential. By displacing electric chiller operation, however, an absorption chiller reduces electric loads. Reduced electric loads potentially reduce technical potential. To determine the effect on technical potential of including an optional absorption chiller as a thermal load, we specified non-residential sector CHP systems both with and without absorption chillers. Large and small multifamily market segments were sized without absorption chilling. Figure E-4 shows technical potential with absorption as a system option and with no absorption included for the various market segments.
Figure E-4: Technical Potential with and without Absorption Chilling Option
CHP POTENTIAL ANALYSIS | E-11 SGIP Market Transformation Report
All non-residential segments had at least some systems where absorption chilling increased technical potential. The largest effects were in the food store, restaurant, and retail segments. The addition of an absorption chiller option increased total technical potential by more than 35%. For absorption chiller performance we could assume a single-effect or a double-effect system. Single- effect is less efficient at cooling, and also less expensive, than double-effect. The coefficients of performance are 0.7 and 1.1 for single and double respectively. We assumed the less efficient absorption chiller so possible increases in annual thermal load would be greater. This assumption also served to increase technical potential compared to the more efficient chiller. The less efficient chiller’s lower costs meanwhile might increase economic potential. Influence of Annual Operating Hour Requirement To avoid having excess generating capacity that gets little use over the course of a year we specify CHP systems using annual performance minima. We express these in terms of equivalent full load hours (EFLH) of operation. They also can be expressed in terms of annual capacity factors. The SGIP’s performance-based incentive schedule assumes CHP to have an 80 percent annual capacity factor. An 80% annual capacity factor is equivalent to 7,000 EFLH.9 As an operating minimum 7,000 EFLH reduces technical potential relative to lower EFLH. This is because to run more hours at full load while maintaining high overall efficiency the CHP capacity must be smaller. We assumed a less conservative minimum of 5,000 EFLH for all prime mover technologies except all-electric fuel cells. This assumption served to increase technical potential compared to a higher minimum value. Figure E-5 shows differences in total technical potential using 5,000, 6,000, and 7,000 EFLH as annual operating hour constraints.
9 Annual capacity factor is the quotient of the equivalent full load hour value divided by 8760 hours per year.
CHP POTENTIAL ANALYSIS | E-12 SGIP Market Transformation Report
Figure E-5: Technical Potential under Different Annual Operating Hour Constraints
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The vertical axis of Figure E-5 is truncated to better show differences in segments apart from the food manufacturing segment where different annual minimum made no difference in technical potential. Figure E-5 shows that increasing the annual minimum from 5,000 to 6,000 EFLH reduces total technical potential dramatically in food store, retail, and restaurant segments. An increase from 6,000 to 7,000 further reduces potential in those segments as well as in the lodging segment. Comparisons with Earlier Studies We compare results of this analysis with results of two earlier studies that described CHP technical potential in California. Both studies examined the CHP market for a wider range of system capacity than the 30 kW to 5 MW range considered in this analysis. They considered technical potential of CHP with capacities from 50 kW to above 100 MW. In making comparisons to those studies we account for the different market segments and maximum capacities considered.10 Both studies divide CHP markets into system capacity bins and market segments such that reasonable comparisons can be made with this analysis.
10 This analysis of CHP potentials is preceded by earlier studies “Assessment of California CHP Market and Policy Options for Increased Penetration,” Electric Power Research Institute, prepared for the California Energy Commission, CEC-500-2005- 060, April 2005; and “Combined Heat And Power: Policy Analysis and 2011-2030 Market Assessment,” ICF International, prepared for the California Energy Commission, CEC-200-2012-002, February 2012.. These prior studies included estimates of technical and market potential but examined a wider range of CHP system sizes than the 30 kW to 5000 kW range examined here.
CHP POTENTIAL ANALYSIS | E-13 SGIP Market Transformation Report
The estimate developed here is much larger than the 4,123 MW estimated in a 2005 EPRI study11 for an approximately equivalent group of commercial segments.12 The EPRI study did not size systems based upon hourly end-use load shape data. It used annual electric and gas consumption by market segment. A second, later study provides greater market segment detail and so allows for more thorough comparison. ICF estimated technical potential of 3,522 MW in 2011 for an equivalent group of market segments in its study.13 Figure E-6 above shows the segments and technical potentials common to both the ICF and this study.
Figure E-6: Comparisons of Market Segment Technical Potentials, ICF 2011 and Itron
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11 “Assessment of California CHP Market and Policy Options for Increased Penetration,” Prepared by EPRI for CEC PIER, CEC- 500-2005-060-D, April 2005. 12 EPRI’s ‘CCHP market’ includes absorption chilling and in that sense is equivalent only to the commercial sector addressed by this study since residential and industrial sectors are presumed without absorption chilling. 13 “Combined Heat and Power: Policy Analysis and 2011-2030 Market Assessment,” prepared for the California Energy Commission, ICF International, Inc., February 2012. We included system sizes up to 5 MW from tables C-3 to C-8 of the ICF report and the following combinations of NAICS/Application: 311-312/Food, 452/Retail, 493/Refrigerated Warehouses, 445/Food Stores, 722/Restaurants, 531/Commercial Buildings , 531/Multifamily Buildings , 721/Hotels , 518/Data Centers , 512131/Movie Theaters , 71394/Health Clubs , 71391/Golf/Country Clubs , 623/Nursing Homes , 622/Hospitals , 6111/Schools , 6113/College/Univ. , 612/Museums
CHP POTENTIAL ANALYSIS | E-14 SGIP Market Transformation Report
ICF found the greatest technical potential to be in the large office segment and found little potential in the food store segment. ICF’s estimate for the traditional CHP market of the health segment was substantial yet less than for the food manufacturing and lodging segments. Differences in technical potential estimates can arise from different approaches to specifying CHP systems as well as from different population counts of buildings. It is not clear which difference may be more influential in any of the market segments. Below we discuss both areas of difference and their possible contributions. Differences in CHP system sizing approaches influence technical potential. This study used a 30 kW threshold capacity for technical potential whereas ICF used a 50 kW. ICF did not count facilities with systems sized between 30 and 49 kW. Almost 13 percent of this study’s estimated potential is from systems sized between 30 and 49 kW. Different CHP system sizing approaches can include different minimum numbers of annual hours of operation. This study required a minimum number of equivalent full load hours from every CHP system. For CHP systems we required a minimum 5,000 EFLH. ICF segmented the CHP market in several ways including in terms of high or low annual ‘load factor’ that indicated the number of hours of annual operation. ICF used load factors of 7,500 and 5,000 hours depending on market segment. This analysis differs from ICF’s in terms of the time resolution of data underlying CHP system sizing. Here we use SitePro hourly end use load shapes. ICF used annual and monthly resolution data from DOE’s Commercial Buildings Energy Consumption Survey (CBECS) and Manufacturing Energy Consumption Survey (MECS), IHS’s Commercial Energy Profile Database (CEPD), and for the commercial sector, the California Commercial End‐Use Survey (CEUS). CEUS was used to make annual thermal demand estimates more indicative of a California climate. ICF’s modeling may have suggested smaller annual thermal baseloads than this analysis did. CHP sizing then would be correspondingly smaller. Another difference between studies is the counting of building populations. ICF’s primary data source is the Dun & Bradstreet Hoovers Database acquired in October 2011. Itron uses 2012 Census establishment-level data. The potential for differences between these sources is not clear.
CHP POTENTIAL ANALYSIS | E-15 SGIP Market Transformation Report
Table E-7: Commercial and Industrial 2012 NAICS Codes and Associated SitePro Building Type
NAICS Code NAICS Description SitePro Building Type14 6112 Junior Colleges College 6113 Colleges, Universities, and Professional Schools College 311 Food Manufacturing Food Manufacturing 312 Beverage and Tobacco Product Manufacturing Food Manufacturing 445 Food and Beverage Stores Food Store 622 Hospitals Health, Hospital 623 Nursing and Residential Care Facilities Health, Hospital 721 Accommodation Lodging, Hotel 71391 Golf Courses and Country Clubs Lodging, Hotel 71394 Fitness and Recreational Sports Centers Lodging, Hotel 92214 Correctional institutions Lodging, Hotel 53111 Lessors of Residential Buildings and Dwellings MultiFamily 23 Construction Office (large/small) 52 Finance and Insurance Office (large/small) 54 Professional, Scientific, and Technical Services Office (large/small) 55 Management of Companies and Enterprises Office (large/small) 488 Support Activities for Transportation Office (large/small) 511 Publishing Industries (except Internet) Office (large/small) 512 Motion Picture and Sound Recording Industries Office (large/small) 515 Broadcasting (except Internet) Office (large/small) 517 Telecommunications Office (large/small) 518 Data Processing, Hosting, and Related Services Office (large/small) 519 Other Information Services Office (large/small) 561 Administrative and Support Services Office (large/small) 621 Ambulatory Health Care Services Office (large/small) 624 Social Assistance Office (large/small) 921 Executive, legislative and general government Office (large/small) 923 Administration of human resource programs Office (large/small) 924 Administration of environmental programs Office (large/small) 925 Community and housing program administration Office (large/small) 926 Administration of economic programs Office (large/small) 927 Space research and technology Office (large/small) 928 National security and international affairs Office (large/small)
14 Office is deemed large or small based on Census employee count bin.
CHP POTENTIAL ANALYSIS | E-16 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type14 42511 Business to Business Electronic Markets Office (large/small) 42512 Wholesale Trade Agents and Brokers Office (large/small) 53119 Lessors of Other Real Estate Property Office (large/small) 53121 Office (large/small)s of Real Estate Agents and Brokers Office (large/small) 53131 Real Estate Property Managers Office (large/small) 53132 Office (large/small)s of Real Estate Appraisers Office (large/small) 53139 Other Activities Related to Real Estate Office (large/small) Construction, Transportation, Mining, and Forestry 53241 Machinery and Equipment Rental and Leasing Office (large/small) Office (large/small) Machinery and Equipment Rental and 53242 Leasing Office (large/small) Other Commercial and Industrial Machinery and 53249 Equipment Rental and Leasing Office (large/small) Lessors of Nonfinancial Intangible Assets (except 53311 Copyrighted Works) Office (large/small) Promoters of Performing Arts, Sports, and Similar Events 71131 with Facilities Office (large/small) Promoters of Performing Arts, Sports, and Similar Events 71132 without Facilities Office (large/small) Agents and Managers for Artists, Athletes, Entertainers, 71141 and Other Public Figures Office (large/small) 71151 Independent Artists, Writers, and Performers Office (large/small) 71211 Museums Office (large/small) 71212 Historical Sites Office (large/small) 71329 Other Gambling Industries Office (large/small) 92211 Courts Office (large/small) 92212 Police protection Office (large/small) 92213 Legal counsel and prosecution Office (large/small) 811211 Consumer Electronics Repair and Maintenance Office (large/small) Computer and Office (large/small) Machine Repair and 811212 Maintenance Office (large/small) 811213 Communication Equipment Repair and Maintenance Office (large/small) Other Electronic and Precision Equipment Repair and 811219 Maintenance Office (large/small) 811412 Appliance Repair and Maintenance Office (large/small) 811420 Reupholstery and Furniture Repair Office (large/small) 811430 Footwear and Leather Goods Repair Office (large/small) Other Personal and Household Goods Repair and 811490 Maintenance Office (large/small) 812921 Photofinishing Laboratories (except One-Hour) Office (large/small) 812922 One-Hour Photofinishing Office (large/small)
CHP POTENTIAL ANALYSIS | E-17 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type14 812990 All Other Personal Services Office (large/small) 813110 Religious Organizations Office (large/small) 813211 Grantmaking Foundations Office (large/small) 813212 Voluntary Health Organizations Office (large/small) 813219 Other Grantmaking and Giving Services Office (large/small) 813311 Human Rights Organizations Office (large/small) 813312 Environment, Conservation and Wildlife Organizations Office (large/small) 813319 Other Social Advocacy Organizations Office (large/small) 813410 Civic and Social Organizations Office (large/small) 813910 Business Associations Office (large/small) 813920 Professional Organizations Office (large/small) 813930 Labor Unions and Similar Labor Organizations Office (large/small) 813940 Political Organizations Office (large/small) Other Similar Organizations (except Business, 813990 Professional, Labor, and Political Organizations) Office (large/small) 722 Food Services and Drinking Places Restaurant, Sit-Down 71111 Theater Companies and Dinner Theaters Restaurant, Sit-Down 441 Motor Vehicle and Parts Dealers Retail, Large 442 Furniture and Home Furnishings Stores Retail, Large 443 Electronics and Appliance Stores Retail, Large Building Material and Garden Equipment and Supplies 444 Dealers Retail, Large 446 Health and Personal Care Stores Retail, Large 447 Gasoline Stations Retail, Large 448 Clothing and Clothing Accessories Stores Retail, Large Sporting Goods, Hobby, Musical Instrument, and Book 451 Stores Retail, Large 452 General Merchandise Stores Retail, Large 453 Miscellaneous Store Retailers Retail, Large 454 Nonstore Retailers Retail, Large 53211 Passenger Car Rental and Leasing Retail, Large Truck, Utility Trailer, and RV (Recreational Vehicle) Rental 53212 and Leasing Retail, Large 53221 Consumer Electronics and Appliances Rental Retail, Large 53222 Formal Wear and Costume Rental Retail, Large 53223 Video Tape and Disc Rental Retail, Large 53229 Other Consumer Goods Rental Retail, Large 53231 General Rental Centers Retail, Large
CHP POTENTIAL ANALYSIS | E-18 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type14 71311 Amusement and Theme Parks Retail, Large 71312 Amusement Arcades Retail, Large 71321 Casinos (except Casino Hotels) Retail, Large 71395 Bowling Centers Retail, Large 71399 All Other Amusement and Recreation Industries Retail, Large 812111 Barber Shops Retail, Large 812112 Beauty Salons Retail, Large 812113 Nail Salons Retail, Large 812191 Diet and Weight Reducing Centers Retail, Large 812199 Other Personal Care Services Retail, Large 812210 Funeral Homes and Funeral Services Retail, Large 812220 Cemeteries and Crematories Retail, Large 812310 Coin-Operated Laundries and Drycleaners Retail, Large 6111 Elementary and Secondary Schools School Business Schools and Computer and Management 6114 Training School 6115 Technical and Trade Schools School 6116 Other Schools and Instruction School 6117 Educational Support Services School 481 Air Transportation Warehouse, Non-Refr 483 Water Transportation Warehouse, Non-Refr 484 Truck Transportation Warehouse, Non-Refr 485 Transit and Ground Passenger Transportation Warehouse, Non-Refr 486 Pipeline Transportation Warehouse, Non-Refr 487 Scenic and Sightseeing Transportation Warehouse, Non-Refr 562 Waste Management and Remediation Services Warehouse, Non-Refr Automobile and Other Motor Vehicle Merchant 42311 Wholesalers Warehouse, Non-Refr Motor Vehicle Supplies and New Parts Merchant 42312 Wholesalers Warehouse, Non-Refr 42313 Tire and Tube Merchant Wholesalers Warehouse, Non-Refr 42314 Motor Vehicle Parts (Used) Merchant Wholesalers Warehouse, Non-Refr 42321 Furniture Merchant Wholesalers Warehouse, Non-Refr 42322 Home Furnishing Merchant Wholesalers Warehouse, Non-Refr Lumber, Plywood, Millwork, and Wood Panel Merchant 42331 Wholesalers Warehouse, Non-Refr Brick, Stone, and Related Construction Material Merchant 42332 Wholesalers Warehouse, Non-Refr 42333 Roofing, Siding, and Insulation Material Merchant Warehouse, Non-Refr
CHP POTENTIAL ANALYSIS | E-19 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type14 Wholesalers 42339 Other Construction Material Merchant Wholesalers Warehouse, Non-Refr Photographic Equipment and Supplies Merchant 42341 Wholesalers Warehouse, Non-Refr 42342 Office (large/small) Equipment Merchant Wholesalers Warehouse, Non-Refr Computer and Computer Peripheral Equipment and 42343 Software Merchant Wholesalers Warehouse, Non-Refr 42344 Other Commercial Equipment Merchant Wholesalers Warehouse, Non-Refr Medical, Dental, and Hospital Equipment and Supplies 42345 Merchant Wholesalers Warehouse, Non-Refr 42346 Ophthalmic Goods Merchant Wholesalers Warehouse, Non-Refr Other Professional Equipment and Supplies Merchant 42349 Wholesalers Warehouse, Non-Refr Metal Service Centers and Other Metal Merchant 42351 Wholesalers Warehouse, Non-Refr 42352 Coal and Other Mineral and Ore Merchant Wholesalers Warehouse, Non-Refr Electrical Apparatus and Equipment, Wiring Supplies, and 42361 Related Equipment Merchant Wholesalers Warehouse, Non-Refr Household Appliances, Electric Housewares, and 42362 Consumer Electronics Merchant Wholesalers Warehouse, Non-Refr Other Electronic Parts and Equipment Merchant 42369 Wholesalers Warehouse, Non-Refr 42371 Hardware Merchant Wholesalers Warehouse, Non-Refr Plumbing and Heating Equipment and Supplies 42372 (Hydronics) Merchant Wholesalers Warehouse, Non-Refr Warm Air Heating and Air-Conditioning Equipment and 42373 Supplies Merchant Wholesalers Warehouse, Non-Refr Refrigeration Equipment and Supplies Merchant 42374 Wholesalers Warehouse, Non-Refr Construction and Mining (except Oil Well) Machinery and 42381 Equipment Merchant Wholesalers Warehouse, Non-Refr Farm and Garden Machinery and Equipment Merchant 42382 Wholesalers Warehouse, Non-Refr Industrial Machinery and Equipment Merchant 42383 Wholesalers Warehouse, Non-Refr 42384 Industrial Supplies Merchant Wholesalers Warehouse, Non-Refr Service Establishment Equipment and Supplies Merchant 42385 Wholesalers Warehouse, Non-Refr Transportation Equipment and Supplies (except Motor 42386 Vehicle) Merchant Wholesalers Warehouse, Non-Refr Sporting and Recreational Goods and Supplies Merchant 42391 Wholesalers Warehouse, Non-Refr Toy and Hobby Goods and Supplies Merchant 42392 Wholesalers Warehouse, Non-Refr 42393 Recyclable Material Merchant Wholesalers Warehouse, Non-Refr
CHP POTENTIAL ANALYSIS | E-20 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type14 Jewelry, Watch, Precious Stone, and Precious Metal 42394 Merchant Wholesalers Warehouse, Non-Refr Other Miscellaneous Durable Goods Merchant 42399 Wholesalers Warehouse, Non-Refr 42411 Printing and Writing Paper Merchant Wholesalers Warehouse, Non-Refr Stationery and Office (large/small) Supplies Merchant 42412 Wholesalers Warehouse, Non-Refr Industrial and Personal Service Paper Merchant 42413 Wholesalers Warehouse, Non-Refr 42421 Drugs and Druggists' Sundries Merchant Wholesalers Warehouse, Non-Refr Piece Goods, Notions, and Other Dry Goods Merchant 42431 Wholesalers Warehouse, Non-Refr Men's and Boys' Clothing and Furnishings Merchant 42432 Wholesalers Warehouse, Non-Refr Women's, Children's, and Infants' Clothing and 42433 Accessories Merchant Wholesalers Warehouse, Non-Refr 42434 Footwear Merchant Wholesalers Warehouse, Non-Refr 42441 General Line Grocery Merchant Wholesalers Warehouse, Refriger 42442 Packaged Frozen Food Merchant Wholesalers Warehouse, Refriger Dairy Product (except Dried or Canned) Merchant 42443 Wholesalers Warehouse, Refriger 42444 Poultry and Poultry Product Merchant Wholesalers Warehouse, Refriger 42445 Confectionery Merchant Wholesalers Warehouse, Refriger 42446 Fish and Seafood Merchant Wholesalers Warehouse, Refriger 42447 Meat and Meat Product Merchant Wholesalers Warehouse, Refriger 42448 Fresh Fruit and Vegetable Merchant Wholesalers Warehouse, Refriger Other Grocery and Related Products Merchant 42449 Wholesalers Warehouse, Refriger 42451 Grain and Field Bean Merchant Wholesalers Warehouse, Non-Refr 42452 Livestock Merchant Wholesalers Warehouse, Non-Refr 42459 Other Farm Product Raw Material Merchant Wholesalers Warehouse, Non-Refr Plastics Materials and Basic Forms and Shapes Merchant 42461 Wholesalers Warehouse, Non-Refr Other Chemical and Allied Products Merchant 42469 Wholesalers Warehouse, Non-Refr 42471 Petroleum Bulk Stations and Terminals Warehouse, Non-Refr Petroleum and Petroleum Products Merchant 42472 Wholesalers (except Bulk Stations and Terminals) Warehouse, Non-Refr 42481 Beer and Ale Merchant Wholesalers Warehouse, Non-Refr Wine and Distilled Alcoholic Beverage Merchant 42482 Wholesalers Warehouse, Non-Refr 42491 Farm Supplies Merchant Wholesalers Warehouse, Non-Refr
CHP POTENTIAL ANALYSIS | E-21 SGIP Market Transformation Report
NAICS Code NAICS Description SitePro Building Type14 42492 Book, Periodical, and Newspaper Merchant Wholesalers Warehouse, Non-Refr Flower, Nursery Stock, and Florists' Supplies Merchant 42493 Wholesalers Warehouse, Non-Refr 42494 Tobacco and Tobacco Product Merchant Wholesalers Warehouse, Non-Refr 42495 Paint, Varnish, and Supplies Merchant Wholesalers Warehouse, Non-Refr Other Miscellaneous Nondurable Goods Merchant 42499 Wholesalers Warehouse, Non-Refr 49211 Couriers and Express Delivery Services Warehouse, Non-Refr 49221 Local Messengers and Local Delivery Warehouse, Non-Refr 49311 General Warehousing and Storage Warehouse, Non-Refr 49312 Refrigerated Warehousing and Storage Warehouse, Refriger 49313 Farm Product Warehousing and Storage Warehouse, Refriger 49319 Other Warehousing and Storage Warehouse, Non-Refr Lessors of Nonresidential Buildings (except 53112 Miniwarehouses) Warehouse, Non-Refr 53113 Lessors of Miniwarehouses and Self-Storage Units Warehouse, Non-Refr 811111 General Automotive Repair Warehouse, Non-Refr 811112 Automotive Exhaust System Repair Warehouse, Non-Refr 811113 Automotive Transmission Repair Warehouse, Non-Refr Other Automotive Mechanical and Electrical Repair and 811118 Maintenance Warehouse, Non-Refr Automotive Body, Paint, and Interior Repair and 811121 Maintenance Warehouse, Non-Refr 811122 Automotive Glass Replacement Shops Warehouse, Non-Refr 811191 Automotive Oil Change and Lubrication Shops Warehouse, Non-Refr 811198 All Other Automotive Repair and Maintenance Warehouse, Non-Refr Commercial and Industrial Machinery and Equipment 811310 (except Automotive and Electronic) Repair and Warehouse, Non-Refr 811411 Home and Garden Equipment Repair and Maintenance Warehouse, Non-Refr 812910 Pet Care (except Veterinary) Services Warehouse, Non-Refr 812930 Parking Lots and Garages Warehouse, Non-Refr
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