DEVELOPMENT OF A METHODOLOGY AND EVALUATION TOOL FOR ASSESSING THE FEASIBILITY OF IMPLEMENTING RENEWABLE ENERGY SYSTEMS AS AN AIRPORT REVENUE SOURCE
By
DENY DWIANTORO
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2014
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© 2014 Deny Dwiantoro
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To Allah: “The only source of all truths where the mankind innate knowledge inherited from; yet, immeasurable”
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ACKNOWLEDGMENTS
Bismillahirrahmanirrahim, firstly, special gratitude to Dr. Charles J. Kibert, PE for being my mentor and guardian with his unlimited encouragement from the starting point
I joined the University of Florida. As a mentor, he has been greatly guiding me during my hard time until the completion of the program. As a guardian, he has become my role model since he always supports and directs me in how to keep on the right track during the program. Dr. Ian Flood, my co-chair, who has firstly supported me to enter the program by giving the appropriate paths and directions; Dr. James G. Sullivan, the one who always keeps maintaining my courage also asking my progress and Dr. S.A.
Sherif my external committee member from mechanical and aerospace engineering department who gave me “refreshment” in engineering equations at the time of PhD qualifying exam. Mr. John B. Helms II, the Dunnellon Airport manager, your recommendations have helped me out with the methodology improvement and given me lesson learned about the airport conditions. Dean Dr. Christopher Silver, the one who challenged me with GRE test before entering the program, I finally made it at that time. All of you have been performing boundless efforts and patience for my achievement during the program.
Secondly, I thank all my editorial team members, Hadyan S. Ramadhan the next
Indonesian mechanical engineer from UF, Wayne Foster, Msc and Desy Foster family.
You have helped improving my language both written and speaking. My great family in
Gainesville James and Aan Morgan ; Hector and Uli Dones; Carlos Maeztu; Debra
Anderson; Scott Davis; Dustin Stephany; and, the Indonesian architect, Aditia Mulia
Rachman, you all have become my real relatives in the USA. Also, my TN-V and
Mesin-ITB 97 special groups, you guys have helped me staying awake and have given 4 continuous amusing conversations during my dissertation writing. I think this is the advantage of having a 12-hour time difference.
Thirdly, to all my academia advisors from Institut Teknologi Bandung, Dr. Ir.
Aditianto Ramelan; Dr. Ir. Hendrawan; Prof. Dr. Jann Hidajat Tjakaraatmadja, MSIE; and Dwi Larso, Ph.D as well as my industry supervisors from Tripatra Engineers and
Constructors (TPEC), Ir. Rushlie Effendy, IPU; Ir. Raymond N. Rasfuldi, Ir. Dikki S.
Burhan, IPU and Ir. Asoka Pandji Wisnhu from Petronas. It was all started from the recommendation letters; and, all of you have been maintaining my optimism during my stay in the USA.
All in all, my achievement will not be complete without my wife, Shanty Y.
Rachmat; and my little one, Denisha Ken Ayu, my father and mother, Koestomo, Msc and Femmy N Daing, my father and mother in law, Dr. Ir. Rachmat Setiadi and Sri S.
Soetarso; brothers: Tomy A. Koestomo, Irwan Setiadi, Erwin Setiadi, R. Ersan Keswara; sisters: Silvia P. Koestomo; Yanti Susanti and Mia M. Keswara; the littles: Airel, Aysha in Indonesia as well as my special Kayla in the garden of heaven. A three-year effort has been accomplished. However, the experience I have gained during our stay for more than four and half years in the US will be a treasure that I am sure it can never be washed away. Next challenges will require more patient and valor during the implementation of what I have done here. May Allah bless all of us.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS ...... 4
LIST OF TABLES ...... 11
LIST OF FIGURES ...... 16
LIST OF ABBREVIATIONS ...... 22
ABSTRACT ...... 25
CHAPTER
1 INTRODUCTION ...... 27
Background ...... 27 Problem Statement and Justification ...... 28 Research Focus ...... 30 Research Organization ...... 30 Research Objectives ...... 31 Chapter Summary ...... 32
2 LITERATURE REVIEW ...... 35
The Economics of Renewable Energy System in Airport ...... 35 Airport Revenue Diversification ...... 35 Defining the Role of Airport ...... 36 Defining Cost of Electricity ...... 38 Revenue Model Formulation ...... 40 Renewable Energy Incentives ...... 42 Regulatory and Safety Issues in Airport ...... 46 Airports and Sustainability ...... 47 Existing Methodology to Assess Renewable Energy System ...... 48 Types of Renewable Energy System ...... 52 Solar Energy ...... 52 Wind Energy ...... 54 Hydro Energy ...... 55 Ocean Energy ...... 56 Biomass Energy ...... 58 Geothermal Energy ...... 58 Current Status of U.S. Renewable Energy Condition ...... 59 Research Significance to Decision Knowledge ...... 69 Chapter Summary ...... 71
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3 METHODOLOGY DEVELOPMENT...... 74
Research Design ...... 74 Decision Model and Evaluation Tool Validation Defined ...... 75 Hypothetical Argument...... 77 Step 1: Assessment of the Airport Revenue System ...... 77 Existing Financial Structure in Airport ...... 77 Enhancing Revenue System in Airport ...... 79 Step 2: Theoretical Potential Quick Assessment—Rationale of Florida Renewable Energy Resource Potential ...... 80 Step 3: Siting Evaluation Using Specific Safety and Regulatory Constraints ...... 87 Site Location Criteria ...... 87 Technical and Safety Design Assessment ...... 90 Step 4: Determining Ownership Options ...... 91 Step 5: Revenue Evaluation Using Developed Feasibility Assessment Tool ...... 92 Chapter Summary ...... 93
4 SELECTED CASE STUDIES AND DATA PROCESSING ...... 97
Selected Case Studies Overview ...... 97 Jacksonville International Airport ...... 97 Hendry County—Airglades Airport ...... 98 Marion County—Dunnellon Airport ...... 99 Data Sources ...... 100 Step 1: Revenue Assessment ...... 101 JAX Airport Revenue Analysis ...... 101 Airglades Airport Revenue Analysis ...... 106 Dunnellon Airport Revenue Analysis ...... 107 Dunnellon Airport Energy Consumption Data ...... 110 Summary of Selected Case Studies Revenue Assessment ...... 111 Step 2: Solar Energy Resource Potential Assessment ...... 111 Available Potential Areas for RES Installation ...... 111 Summary of Resource Potential Analysis...... 114 Step 3: Airport RES Siting Evaluation ...... 114 Wind Load Evaluation Based on ASCE 7-05...... 115 Safety Check: Incorporating Solar Glare Hazard Analysis Tool (SGHAT) ...... 129 Summary of RES Siting Evaluation ...... 133 Step 4: Ownership Options ...... 134 Chapter Summary ...... 135
5 INTEGRATION WITH DEVELOPED AIRPORT RENEWABLE ENERGY SYSTEM REVENUE ASSESSMENT TOOL ...... 136
Introducing Newly Developed Airport Revenue Assessment Tool for Solar Energy ...... 136 Data Input and Assumptions ...... 137 Financial Parameters ...... 139
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Public and Private Sector Defined ...... 140 Ascertaining the Costs ...... 142 Establishing System Performance ...... 144 Chapter Summary ...... 145
6 RESULTS AND DISCUSSIONS ...... 146
Utility-scale Annual Solar Energy Production Results Comparisons ...... 146 Results for Fixed-tilt System Utility-scale Annual Solar Energy Production .... 146 Results for 1-Axis Tracker System Utility-scale Annual Solar Energy Production ...... 149 Discussions for the Utility-scale Solar Energy Production ...... 151 Review of Net Zero Energy Concept and Applying to Dunnellon Airport ...... 155 NZEB Design Annual Solar Energy Production Results Comparisons ...... 157 Results for Fixed-tilt System NZEB Design Annual Solar Energy Production 157 Discussions for the NZEB Design Solar Energy Production ...... 158 Step 5: Revenue Evaluation Using Developed Feasibility Assessment Tool ...... 159 Dunnellon Airport as System Owner ...... 159 Results for Maximum Energy Scenario ...... 159 Discussions for Maximum Energy Scenario ...... 165 Results for Realistic Design Scenario ...... 167 Discussions for Realistic Design Scenario ...... 173 Results for NZEB Scenario ...... 175 Discussions for NZEB Scenario ...... 182 Private Entity as System Owner ...... 188 Results for Maximum Energy Scenario ...... 191 Discussions for Maximum Energy Scenario ...... 198 Results for Realistic Design Scenario ...... 200 Discussions for Realistic Design Scenario ...... 207 Results for NZEB Scenario ...... 212 Discussions for NZEB Scenario ...... 220 Examining the Hypothesis ...... 221 Non-traditional Revenue Percentage from Dunnellon Airport Ownership ...... 221 Non-traditional Revenue Percentage from Leasing ...... 226 Chapter Summary ...... 227
7 CONCLUSIONS AND FUTURE RESEARCH ...... 229
Review ...... 229 Conclusions ...... 231 Conclusion #1: The Renewable Energy Potentials ...... 231 Conclusion #2: Utility-Scale Maximum Energy Scenario ...... 231 Conclusion #3: Realistic Design Scenario ...... 232 Conclusion #4: NZEB Scenario ...... 233 Conclusion #5: The Role of Incentives ...... 234 Conclusion #6: New Decision Model ...... 235 Conclusion #7: Evaluation Tool for Revenue Assessment ...... 235
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Policy Implications ...... 236 FAA Regulation ...... 236 In Relation with LEED ...... 237 Funding and Incentives ...... 238 Limitations ...... 238 Suggestions for Future Research ...... 239 Methodology—Other Caveats to Solar Installation ...... 240 Revenue Evaluation Tool—Inputs and Assumptions ...... 241 Chapter Summary ...... 242
APPENDIX
A FUNDAMENTALS OF FEASIBILITY ASSESSMENT ...... 244
Overview ...... 244 TLCC ...... 244 LCOE ...... 245 SIR ...... 245 NPV ...... 246 PB ...... 246 Salvage ...... 247 Feed in Tariff ...... 247 Net Metering ...... 248 MACRS Depreciation ...... 249
B TECHNICAL DESIGN ALGORITHMS ...... 250
Overview ...... 250 Evaluation Tool Database Validation ...... 250 PV Array Area and Power ...... 254 Example Calculations ...... 254 Optimum Tilt Angle Calculation ...... 258
C EXAMPLE OF SOLAR GLARE HAZARD TOOL OUTPUT FOR VARIOUS TILTS AND AZIMUTHS ...... 259
Overview ...... 259 Flight Path 1 ...... 259 Flight Path 2 ...... 264 Air Traffic Control ...... 269
D APPLICABLE REFERENCE FIGURES AND TABLES FROM ASCE 7-05 ...... 271
E DEVELOPED REVENUE ASSESSMENT TOOL GRAPHIC USER INTERFACE (GUI) AND GUIDELINES ...... 277
Overview ...... 277 Opening Microsoft Excel ® 2010 or 2013 Containing the Evaluation Tool ...... 277
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Site Location and Technical Interface ...... 278 Block of Section #1 ...... 278 Block of Section #2 ...... 279 Block of Section #3 ...... 279 Block of Section #4 ...... 282 Block of Section #5 ...... 282 Block of Section #6 ...... 282 Block of Section #7 ...... 282 Block of Section #8 ...... 283 Examples of the Result #1 ...... 283 Economic Analysis ...... 285 Block of Section #9 ...... 285 Block of Section #10 ...... 286 Block of Section #11 ...... 286 Block of Section #12 ...... 286 Block of Section #13 ...... 286 Block of Section #14 ...... 287 Block of Section #15 ...... 287 Block of Section #16 ...... 287 Block of Section #17 ...... 287 Block of Section #18 ...... 288 Block of Section #19 ...... 288 Block of Section #20 ...... 288 Block of Section #21 ...... 288 Examples of the Result #2 ...... 289 Example of the Final Result ...... 292
LIST OF REFERENCES ...... 293
BIOGRAPHICAL SKETCH ...... 305
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LIST OF TABLES
Table page
2-1 Levelized total cost for various energy generating system for 2018 ...... 40
2-2 Safety and regulatory constraints ...... 46
2-3 Sales for small scale wind turbine ...... 63
2-4 Natel Energy Inc. Low-head hydropower cost estimation (2009) ...... 66
3-1 State of Florida renewable energy technical potential ...... 81
3-2 Checklist for siting solar energy in airport ...... 87
4-1 Statement for revenue, expense and NI for JAA ...... 102
4-2 Statement for revenue, expense and NI for Airglades airport ...... 106
4-3 Statement for revenue, expense and NI for Dunnellon airport ...... 109
4-4 FY 2012 energy consumption and utility charge for Dunnellon airport ...... 110
4-5 Potential locations for RES data in Dunnellon airport ...... 113
4-6 Net pressure results at 0 degree wind direction (load case A) ...... 120
4-7 Net pressure results at 180 degree wind direction (load case A)...... 120
4-8 Net pressure results at 0 degree wind direction (load case B) ...... 120
4-9 Net pressure results at 180 degree wind direction (load case B)...... 121
4-10 Net pressure results at 0 degree wind direction (load case A) ...... 121
4-11 Net pressure results at 180 degree wind direction (load case A)...... 121
4-12 Net pressure results at 0 degree wind direction (load case B) ...... 122
4-13 Net pressure results at 180 degree wind direction (load case B)...... 122
4-14 Net pressure results at 0 degree wind direction (load case A) ...... 122
4-15 Net pressure results at 180 degree wind direction (load case A)...... 123
4-16 Net pressure results at 0 degree wind direction (load case B) ...... 123
4-17 Net pressure results at 180 degree wind direction (load case B)...... 123
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4-18 Part of net pressure coefficient from Figure 6-18D ASCE 7-05 ...... 125
4-19 Net pressure results at 90 degree wind direction (33 feet) ...... 125
4-20 Net pressure results at 90 degree wind direction (1.5 feet) ...... 125
4-21 Part of net pressure coefficient from Figure 6-18D ASCE 7-05 ...... 125
4-22 Net pressure results at 90 degree wind direction (7 feet) ...... 126
4-23 Net pressure results at 0 and 180 degree wind direction (load case A) ...... 127
4-24 Net pressure results at 0 and 180 degree wind direction (load case B) ...... 127
4-25 Net pressure results at 90 degree wind direction (12 feet) ...... 128
5-1 Summary of solar energy system siting evaluation ...... 138
5-2 Financial parameters input assumptions ...... 139
5-3 Progress Energy rates input assumptions ...... 140
5-4 GRU FIT rates 2013 input assumptions ...... 141
5-5 Possible incentives in Dunnellon airport area ...... 141
5-6 Complete case cost breakdown ...... 142
5-7 Estimated current inverter price including extended warranty ...... 143
5-8 Degradation rate and performance on system lifetime ...... 145
6-1 Fixed-tilt results of annual production at azimuth 135o (crystalline) ...... 146
6-2 Fixed-tilt results of annual production at azimuth 150o (crystalline) ...... 147
6-3 Fixed-tilt results of annual production at azimuth 165o (crystalline) ...... 147
6-4 Fixed-tilt results of annual production at azimuth 195o (crystalline) ...... 147
6-5 Fixed-tilt results of annual production at azimuth 135o (thin film) ...... 148
6-6 Fixed-tilt results of annual production at azimuth 150o (thin film) ...... 148
6-7 Fixed-tilt results of annual production at azimuth 165o (thin film) ...... 148
6-8 Fixed-tilt results of annual production at azimuth 195o (thin film) ...... 148
6-9 1-Axis tracker results of annual production at azimuth 135o (crystalline) ...... 149
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6-10 1-Axis tracker results of annual production at azimuth 150o (crystalline) ...... 149
6-11 1-Axis tracker results of annual production at azimuth 165o (crystalline) ...... 149
6-12 1-Axis tracker results of annual production at azimuth 195o (crystalline) ...... 150
6-13 1-Axis tracker results of annual production at azimuth 135o (thin film) ...... 150
6-14 1-Axis tracker results of annual production at azimuth 150o (thin film) ...... 151
6-15 1-Axis tracker results of annual production at azimuth 165o (thin film) ...... 151
6-16 1-Axis tracker results of annual production at azimuth 195o (thin film) ...... 151
6-17 System size results summary with combined configurations ...... 154
6-18 Fixed-tilt results of annual production at azimuth 135o (crystalline) ...... 157
6-19 Fixed-tilt results of annual production at azimuth 135o (thin film) ...... 158
6-20 System size results summary with combined configurations ...... 158
6-21 General input assumptions for utility scale for public ownership...... 160
6-22 Final net inception cost for each combination (maximum energy) ...... 163
6-23 Financial measures for decision making (25 years for maximum energy) ...... 166
6-24 Input difference with general input assumptions (realistic design) ...... 167
6-25 Final net inception cost for Solar Farm-1 (realistic design) ...... 170
6-26 Final net inception cost for Solar Farm-2 (realistic design) ...... 170
6-27 Final net inception cost for Solar Farm-3 (realistic design) ...... 171
6-28 Financial measures for decision making (25 years for realistic design) ...... 174
6-29 Input difference with general input assumptions (NZEB-FIT) ...... 176
6-30 Final net inception cost for each combination (NZEB- FIT) ...... 177
6-31 Monthly revenue stream for NZEB design ...... 181
6-32 Final net inception cost for each combination (NZEB-NM) ...... 182
6-33 Financial measures for decision making (25 years for NZEB- FIT) ...... 183
6-34 Financial measures for decision making (25 years for NZEB-NM) ...... 184
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6-35 Input assumptions for utility scale under private-owned option ...... 190
6-36 Final net inception cost for each combination (maximum energy) ...... 194
6-37 Financial measures for decision making (25 years for maximum energy) ...... 199
6-38 Input difference with general input assumptions (realistic design) ...... 200
6-39 Final net inception cost for Solar Farm-1 (realistic design) ...... 203
6-40 Final net inception cost for Solar Farm-2 (realistic design) ...... 203
6-41 Final net inception cost for Solar Farm-3 (realistic design) ...... 204
6-42 Financial measures for decision making (25 years for realistic design) ...... 208
6-43 Input difference with general input assumptions (NZEB- FIT) ...... 212
6-44 Final net inception cost for each combination (NZEB- FIT) ...... 215
6-45 Final net inception cost for each combination (NZEB-NM)) ...... 219
6-46 Financial measures for decision making (25 years for NZEB- FIT) ...... 220
6-47 Financial measures for decision making (25 years for NZEB-NM) ...... 221
6-48 Dunnellon airport PW of revenue and expense (2012) for comparison ...... 222
6-49 Dunnellon airport non-traditional revenue ratios (utility-scale) ...... 223
6-50 Dunnellon airport non-traditional revenue ratios (realistic design) ...... 224
6-51 Dunnellon airport non-traditional revenue ratios (NZEB- FIT)...... 225
6-52 Dunnellon airport non-traditional revenue ratios (NZEB-NM) ...... 225
6-53 Dunnellon airport PW of revenue and expense (2012) for comparison ...... 226
6-54 Dunnellon airport non-traditional revenue ratios (leasing option)...... 227
A-1 Financial measures selection criteria ...... 247
A-2 MACRS depreciation multiplier ...... 249
B-1 Database statistical error results ...... 252
B-2 Peak sun hours in Ocala ...... 256
C-1 Results summary of flight path 1 ...... 259
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C-2 Results summary of flight path 2 ...... 264
C-3 Results summary of FAA antennae ...... 269
D-1 Importance factor, I (wind loads)—refer to ASCE 7-05 Table 6-1 ...... 275
D-2 Terrain exposure constants—refer to ASCE 7-05 Table 6-2 ...... 275
D-3 Velocity pressure exposure, Kh and Kz—refer to ASCE 7-05 Table 6-3 ...... 275
D-4 Wind directionality, Kd—refer to ASCE 7-05 Table 6-4 ...... 276
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LIST OF FIGURES
Figure page
2-1 General aviation revenue generation with additional revenue from RES ...... 36
2-2 Factors influencing airport financial services ...... 37
2-3 Level of privatization ...... 38
2-4 Private ownership financial model from the airport standpoint ...... 41
2-5 Airport-owned financial model from the airport standpoint ...... 42
2-6 Modified potential determination based on NREL report ...... 51
2-7 Schematic of small hydropower ...... 56
2-8 U.S. energy consumption by energy source 2011 ...... 59
2-9 Average price of photovoltaic cell and modules during 2002-2011period ...... 60
2-10 Wind turbine price ...... 62
2-11 Hydropower project classification ...... 64
2-12 Share for turbine price 2008 ...... 64
2-13 Share for turbine price 2009 ...... 65
2-14 Wave energy estimate ...... 67
2-15 Tidal energy estimate ...... 67
3-1 On-shore wind classification in range of class 1 to class 2 in Florida ...... 81
3-2 On-shore wind potential in Florida ...... 82
3-3 Off-shore wind potential in Florida ...... 82
3-4 Biomass potential in Florida...... 84
3-5 Global radiation for photovoltaic potential in Florida ...... 85
3-6 Solar radiation for CSP potential in Florida ...... 86
3-7 Imaginary surfaces to protect airspace physical penetration ...... 88
3-8 Airplane approach surface penetration ...... 89
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3-9 Runaway protection zone diagram ...... 90
3-10 Ownership options for RES installation project on airports ...... 91
3-11 Final new methodology for airport RES assessment as a revenue source ...... 94
3-12 Detail evaluation tool block diagram ...... 95
3-13 Detail technical design block diagram ...... 96
4-1 JAX airport diagram ...... 98
4-2 Airglades airport diagram ...... 99
4-3 Dunnellon airport diagram ...... 100
4-4 Net income before capital contribution for JAA ...... 104
4-5 Other revenues of JAA ...... 105
4-6 Utility service charge for JAA ...... 105
4-7 Net income before beginning balance for Airglades airport ...... 107
4-8 Operating expenses charge for Airglades airport ...... 107
4-9 Net income before beginning balance for Dunnellon airport ...... 108
4-10 Operating expenses charge for Dunnellon airport ...... 108
4-11 Non-aviation development proposed for solar farm installation ...... 112
4-12 Existing building area proposed for net-zero energy design ...... 112
4-13 Interpolation results for net pressure coefficient (load case A) ...... 119
4-14 Interpolation results for net pressure coefficient (load case A) ...... 119
4-15 Interpolation results for net pressure coefficient (load case A) ...... 126
4-16 Interpolation results for net pressure coefficient (load case B) ...... 127
4-17 Matrix summary from SGHAT results for Solar Farm-1 (ground-mounted) ...... 130
4-18 Matrix summary from SGHAT results for Solar Farm-2 (ground-mounted) ...... 130
4-19 Matrix summary from SGHAT results for Solar Farm-3 (ground-mounted) ...... 131
4-20 Matrix summary from SGHAT results for Solar Farm-1 (tracking-system) ...... 131
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4-21 Matrix summary from SGHAT results for Solar Farm-2 (tracking-system) ...... 131
4-22 Matrix summary from SGHAT results for Solar Farm-3 (tracking-system) ...... 131
4-23 Matrix summary from SGHAT results for Solar Farm-4 (ground-mounted) ...... 131
4-24 Matrix summary from SGHAT results for Solar Farm-5 (ground-mounted) ...... 132
4-25 Matrix summary from SGHAT results for Solar Farm-6 (ground-mounted) ...... 132
4-26 Matrix summary from SGHAT results for Solar Farm-7 (ground-mounted) ...... 132
4-27 Matrix summary from SGHAT results for Rooftop-1 ...... 132
4-28 Matrix summary from SGHAT results for Rooftop-2 ...... 132
4-29 Matrix summary from SGHAT results for Parking-1 ...... 133
4-30 Matrix summary from SGHAT results for Parking-2 ...... 133
5-1 Main dashboard of airport revenue assessment tool for solar energy ...... 136
5-2 Economic dashboard of airport revenue assessment tool for solar energy ...... 137
5-3 Installed cost breakdown in year 2011 ...... 142
6-1 Sketch drawing snapshot for proposed area and electrical connection ...... 153
6-2 Revenue stream as loan or bond payment over 20 years (fixed-tilt) ...... 161
6-3 Revenue stream as loan or bond payment over 20 years (1-axis tracking) ...... 162
6-4 Cumulative revenue stream goes to Dunnellon airport (fixed-tilt) ...... 164
6-5 Cumulative revenue stream goes to Dunnellon airport (1-axis tracking) ...... 165
6-6 Revenue stream as loan or bond payment over 20 years (fixed-tilt) ...... 168
6-7 Revenue stream as loan or bond payment over 20 years (1-axis tracking) ...... 169
6-8 Cumulative revenue stream goes to Dunnellon airport (realistic design) ...... 172
6-9 Cumulative revenue stream goes to Dunnellon airport (realistic design) ...... 173
6-10 Cumulative revenue stream goes to Dunnellon airport (NZEB- FIT) ...... 178
6-11 Energy production to approach actual monthly use profile ...... 179
6-12 Energy consumed and excessed in Dunnellon airport ...... 180
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6-13 ROI sensitivity analysis under NZEB-NM ...... 185
6-14 TLCC sensitivity analysis under NZEB-NM ...... 185
6-15 LCOE sensitivity analysis under NZEB-NM ...... 186
6-16 SIR sensitivity analysis under NZEB-NM ...... 187
6-17 NPV sensitivity analysis under NZEB-NM ...... 187
6-18 NPV sensitivity analysis under NZEB-NM ...... 188
6-19 Cumulative revenue stream goes to private entity (fixed-tilt) ...... 191
6-20 Cumulative revenue stream goes to private entity (1-axis tracking) ...... 192
6-21 Cumulative net present value (fixed-tilt) ...... 196
6-22 Cumulative net present value (1-axis tracking) ...... 197
6-23 Cumulative revenue stream from leasing (maximum energy)...... 198
6-24 Cumulative revenue stream goes to private entity (fixed-tilt) ...... 201
6-25 Cumulative revenue stream goes to private entity (1-axis tracking) ...... 202
6-26 Cumulative net present value (fixed-tilt) ...... 205
6-27 Cumulative net present value (1-axis tracking) ...... 206
6-28 Cumulative revenue stream from leasing (realistic design) ...... 207
6-29 ROI sensitivity analysis for realistic design ...... 209
6-30 TLCC sensitivity analysis for realistic design ...... 209
6-31 LCOE sensitivity analysis for realistic design...... 210
6-32 SIR sensitivity analysis for realistic design ...... 210
6-33 NPV sensitivity analysis for realistic design ...... 211
6-34 Payback sensitivity analysis for realistic design...... 211
6-35 Cumulative revenue stream goes to private entity (fixed-tilt) ...... 213
6-36 Cumulative revenue stream goes to private entity (crystalline)...... 216
6-37 Cumulative revenue stream goes to private entity (thin film) ...... 216
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6-38 Cumulative revenue stream from leasing (NZEB- FIT) ...... 217
B-1 Sunpower electrical data ...... 255
B-2 Sunpower dimensions ...... 256
C-1 Runway 23 identified as flight path 1 ...... 260
C-2 Glare at point threshold ...... 260
C-3 Glare at point ¼ miles ...... 261
C-4 Glare at point ½ miles ...... 261
C-5 Glare at point ¾ miles ...... 262
C-6 Glare at point 1 mile ...... 262
C-7 Glare at point 1¼ miles ...... 263
C-8 Glare at point 1½ miles ...... 263
C-9 Runway 27 identified as flight path 2 ...... 264
C-10 Glare at point threshold ...... 265
C-11 Glare at point ¼ miles ...... 265
C-12 Glare at point ½ miles ...... 266
C-13 Glare at point ¾ miles ...... 266
C-14 Glare at point 1 mile ...... 267
C-15 Glare at point 1 ¼ miles ...... 267
C-16 Glare at point 1 ½ miles ...... 268
C-17 Glare at point 1 ¾ miles ...... 268
C-18 Glare at point 2 miles ...... 269
C-19 FAA antennae identified as ATC ...... 270
C-20 Glare at FAA antennae ...... 270
D-1 Basic wind speed—eastern gulf of Mexico and Southeastern U.S. hurricane .. 271
D-2 Tracking system assumptions as mono-slope free roofs ...... 272
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D-3 Fixed-tilt assumptions as pitched free roofs-A ...... 273
D-4 Fixed-tilt assumptions as pitched free roofs-B ...... 274
E-1 Welcome menu and evaluation tool identification ...... 277
E-2 GUI for site location and technical specification ...... 278
E-3 GUI tab for tilt variations analysis ...... 280
E-4 GUI tab for economic analysis ...... 281
E-5 GUI tab for results summary ...... 281
E-6 Load design chart ...... 283
E-7 Energy production chart ...... 284
E-8 Offset energy chart ...... 284
E-9 GUI for economic analysis ...... 285
E-10 GUI from economic result presents interest and principal payment ...... 289
E-11 Chart depicts amortization ...... 289
E-12 Chart depicts yearly cost ...... 290
E-13 Chart depicts life-cycle cost ...... 290
E-14 Chart depicts present worth of revenue ...... 290
E-15 Chart depicts present worth of net savings ...... 291
E-16 Chart depicts cumulative net savings or payback ...... 291
E-17 Chart depicts energy production life-cycle ...... 291
E-18 Final result dashboard ...... 292
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LIST OF ABBREVIATIONS
AC Alternating Current
ACRP Airport Cooperative Research Program
AIP Airport Improvement Program
AWEA American Wind Energy Association
AWOS Automated Weather Observing System
BOEM Bureau of Ocean Energy Management
CAES Compressed Air Energy Storage
CAFR Comprehensive Annual Financial Report
CC Capital Contribution
CCS Carbon Capture Sequestration
CSP Concentrating Solar Power
DC Direct Current
DNV Det Norske Veritas
DOE Department of Energy
DOT Department of Transportation
EBN Environmental Building News
EERE Energy Efficiency And Renewable Energy
EGS Enhanced Geothermal Systems
EIA Energy Information Administration
EMEC European Marine Energy Centre
EPA Environmental Protection Agency
EPRI Electric Power Research Institute
FAA Federal Aviation Administration
FAC Florida Airport Council
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FIT Feed in Tariff
FSEC Florida Solar Energy Center
GA General Aviation
GW Giga Watt
GIS Geographical Information Systems
HAWT Horizontal Axis Wind Turbine
HVAC Heating Ventilating And Air Conditioning
IDN Idaho National Laboratory
IEC Iowa Energy Center
IPCC Intergovernmental Panel on Climate Change
ITC Incentive Tax Credit
JAA Jacksonville Airport Authority
JAX Jacksonville International Airport
JAXEX Jacksonville Executive Airport
LCOE Levelized Cost of Electricity
LEC Levelized Energy Cost
LEED Leadership In Energy and Environmental Design
MACRS Modified Accelerated Cost Recovery System
MW Mega Watt
NCTCOG North Central Texas Council of Governments
NEPA National Environmental Policy Act
NI Net Income
NPIAS National Plan of Integrated Airport System
NPV Net Present Value
NREL National Renewable Energy Laboratory
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NSRDB National Solar Radiation Data Base
NTSB National Transportation and Safety Board
NZEB Net Zero Energy Building
OSHA Occupational Safety and Health Association
OTEC Ocean Thermal Energy Conversion
PB Pay Back
PW Present Worth
PPA Power Purchase Agreement
PTC Production Tax Credit
PV Photovoltaic
REC Renewable Energy Credit
RES Renewable Energy Systems
RFP Request For Proposal
ROI Return of Investment
RPS Regional Portfolio Standard
RPZ Runaway Protection Zone
SIR Saving to Investment Ratio
SGHAT Solar Glare Hazard Analysis Tool
TISEC Tidal In-Stream Energy Current
TMY3 Typical Meteorological Year 3
TRB Transportation Research Board
TSC Transpired Solar Collector
USGBC United States Green Building Council
VAWT Vertical Axis Wind Turbine
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
DEVELOPMENT OF A METHODOLOGY AND EVALUATION TOOL FOR ASSESSING THE FEASIBILITY OF IMPLEMENTING RENEWABLE ENERGY SYSTEMS AS AN AIRPORT REVENUE SOURCE
By
Deny Dwiantoro
May 2014
Chair: Charles J. Kibert Cochair: Ian Flood Major: Design, Construction and Planning
There are numerous motivations for implementing renewable energy as widely as possible. Among these motivations are mitigating climate change, minimizing non- renewable energy resource depletion, minimizing the uncertainty of energy supplies from unstable regions of the world, and coping with the rising costs of energy.
Additionally, the opportunities presented by the rapidly falling costs of many types of renewable energy systems can be turned into a profit center for many operations, including airports. Extensive open areas common to airports and the proximity of specific renewable energy resources such as geothermal energy and tidal power can provide airport authorities and management with a potential profit center. However, the decision to implement renewable energy systems in the vicinity of an airport presents a complex and interlocking set of technical, financial and regulatory problems that must be analyzed in order to assess the feasibility of capitalizing on renewable energy. This dissertation has developed a decision making methodology incorporated into an evaluation tool. In the implementation process, the methodology has used the state of
Florida as a test case with Dunnellon Airport as a site-specific case study. Three
25 scenarios to approach the potential of non-traditional revenue in Dunnellon Airport have been investigated. The results confirmed that the utility-scale maximum energy scenario would successfully reach the highest maximum revenue. Under the realistic design scenario, two proposed solar farms, namely Solar Farm-2 and Solar Farm-3 with combined 1-axis tracking and crystalline module, could also generate maximum revenue. In the case of NZEB under feed-in tariff scheme, although Dunnellon Airport is a less energy-intensive airport, the target of covering at least 5% of all expenses could not be reached unless Dunnellon Airport installed crystalline module on all rooftops and parking areas. Meanwhile, the NZEB scenario under net metering scheme failed to meet the target at all locations. However, Dunnellon Airport still can save up to 93.7% of its annual energy cost. Finally, in the case of leasing, the only possible way to meet the target is to lease all 642,218 square-foot of non-aviation development Solar Farm-3 area to private entity.
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CHAPTER 1 INTRODUCTION
Background
Airport authorities have traditionally depended solely on revenue generated by the traditional airport management system, for example, rental space and fuel sales to the airlines. Unstable gas prices have made it even more difficult for the airport to sustain its profitability. The growing sustainability movements, including the push for more renewable energy use, have triggered many actors both individuals and organizations to start considering sustainability in their goals. This mindset-shift creates a domino effect that affects many other fields, such as--in this case – airports.
In the past, airports have investigated the potential for implementing renewable energy systems as an additional income. However, a comprehensive approach that includes an evaluation process for selecting the most appropriate renewable energy system, determining the optimum financial structure, and assessing the impact of regulations has not been developed. Most airports can benefit from often abundant resources.
The growing pace of utility service providers to purchase electricity from renewable energy has also encouraged this implementation. As a matter of fact, the airport authority should experience multiple stages in conducting feasibility study for implementing this opportunity. Different types of renewable source potentials on airport area, current viable technology, available space, financial funding as well as local regulatory issues require a comprehensive decision system to evaluate renewable energy project options for airports. Most importantly, the rapidly falling cost of renewable energy could benefit the installation of renewable energy. There are two questions that can be immediately posed—can an evaluation tool be made to assist
27 airports in identifying and choosing a renewable energy system that is applicable in their areas? How can installed renewable energy system benefit the airports’ long-term goal of adding an additional revenue source?
Problem Statement and Justification
The tendency of airports to implement sustainability programs has triggered researchers and many organizations to provide guidelines for various sustainability programs. In 2012, particularly in airport administration, an assessment tool was developed—Airport Sustainability Assessment Tool (ASAT). This was a collaborative report project submitted to the Transportation Research Board (TRB) for the Airport
Cooperative Research Program (ACRP). The project was conducted by a group that consisted of four companies: Landrum & Brown, Inc., Environmental Consulting Group,
Inc., Primera Engineers, Ltd., and Muller & Muller, Ltd. This report explains a wide variety of applicable sustainability programs including environmental impact assessment for the surrounding area near airport activities. The group has provided steps and methods in doing airport assessment and possible activities to support sustainability implementation and made a spreadsheet-based decision analysis tool that can be used by airport authority. Most principles and steps made are generalization and the development of Leadership in Energy and Environmental Design (LEED) certification system by the United States Green Building Council USGBC). The results of the report present a step-by-step process in identifying and providing information for airport authority to involve for sustainability programs. It started from the airport organization knowledge, readiness and interest in implementing sustainability to the implementation of sustainability by covering social, environmental and economic benefits. The report
28 however does not clearly define sustainability for each specific case, especially the implementation of renewable energy in airports.
As publicly announced towards the end of 2012 in TRB website, the ACRP requested for proposal (RFP) under ACRP 01-24 project to do a research project in developing specific guideline in incorporating renewable energy as additional revenue source for the airport. The primary reference of the ACRP-RFP 01-24 is a technical guidance for solar energy on airports that suggested five further research possibilities for development, based on the Federal Aviation Administration (FAA) recommendations. The FAA has suggested conducting further research improvements that cover the following items:
1. Further research and development of reflectivity and communication systems interference standards for solar projects and related analysis requirements, especially for CSP technology.
2. Development of a 7460 supplemental form to improve Part 77 documentation for solar projects, particularly reflectivity analysis.
3. Design and development of new assessment tools for modeling and evaluating solar projects.
4. Development of a specific NEPA categorical exclusion (Catex) for small solar projects.
5. A cost-benefit analysis of airport solar applications that compares alternative solar technologies (e.g. PV, thermal and parabolic) with site design alternatives (e.g. building vs. ground mounting; tracking vs. fixed mounting) (Plante, Barrett, DeVita & Miller, 2010, p. 70).
From those further research suggestions, it is obvious that the ACRP-RFP -1-24 has referred to point number 3 and 5; yet, to be more specific on evaluating potentials of airport revenue source. The complexity of the environment and the wide variety of methods will significantly influence the results. Nonetheless, the chosen evaluation
29 methods have to be clearly understood and easily used by various audiences. To do this, the case-based investigation is required to make a proper model that is applicable in airport management systems. For instance, the uniqueness of each airport management system, regulatory constraints around the airport location, type of funding and agreement will affect the determination of what kind of renewable energy is suitable in the implementation. Since the goal of this renewable energy use is to gain additional revenue source, one part of the decision systems will be a financial model that applies in the region. As a result, the goal of the study is to develop methodology that can help decision makers in various scenarios such as type of systems, financing options based on ownership and airport sizes.
Research Focus
Mainly, this dissertation focuses on the finding of maximum profit that can be generated from the selected renewable energy source near airports. Hence, this dissertation attempts to develop a new methodology for taking decision under various scenarios to find maximum profit. In addition, this dissertation also includes evaluation of other type renewable energy systems as well as the justification whether the system can suitably be implemented on airports. The specific case study will be based on the airports in Florida.
Research Organization
This research will address three main phases. In Phase I, the research starts with the justification, literature review and ends by developing research operationalization.
The research justification section has covered the reasons of conducting the research and the literature review section will cover theoretical foundation to develop the decision
30 model. The gap identifications cover all pertinent information that will be required to the final decision making process chart.
Phase II covers the more detailed methodology based on three defined factors, i.e. technical, economical and regulatory issues. In this phase, there will be two work parts. First part will cover the assessment of revenue system and the newer revenue system of the airports and present literature study for Florida renewable energy potential assessment as a test case to implement quick assessment for selecting applicable renewable energy system.
Secondly, this research provides the algorithms used to generate the database as well as variables used in the tool development that influence the decision—the selection process of the system that can provide significant revenue outcome. For example, the type of photovoltaic panel will affect the overall system. The final outcome of the first part will be comprehensive evaluation results—based on literature review—of the environment and regulatory, technical as well as economic factor.
The second part will cover the integration of the methodology in a spreadsheet- based evaluation tool. It starts from the data assumptions investigation process, running the tool and comparing the results for the selected case study under several scenarios. The spreadsheet-based tool will display the sensitivity analysis to be compared with the baseline assumptions. Finally, Phase III covers the results and discussions followed by conclusions and recommendations.
Research Objectives
The objectives of this dissertation are to conduct a more detailed analysis in how airport authority chooses renewable energy sources and investigate the reasons behind the process. Airports aim to identify potential renewable energy source as an additional 31 revenue generation. The most important objective of this research is to provide a methodology or tool to decision-making process in evaluating the significance of renewable energy potential in airport improvement program. However, since analyzing the potential renewable energy in each airport depends on various factors, the outcome of this dissertation will allow the airport authority to investigate the step-by-step approach to solve its complexity. The breakdown of the objective can be discussed as follows:
Objective 1. Provide evaluation of renewable energy systems current situation from various literatures and methodologies to assess the potential.
Objective 2. Identify factors from economical—airport revenue system, technical for renewable energy system availability and regulatory issue due to safety reason from the airport’s improvement project especially by aligning its goal of maximizing revenue based on potential renewable energy resource on airport surrounding area.
Objective 3. Seek, identify and set the appropriate criteria for renewable energy potential site selection for installation purpose on airports, constrained by Federal Aviation Administration regulation; and, the criteria for economic measures.
Objective 4. Develop a new methodology and evaluation tool for evaluating applicable renewable energy source potential using the state of Florida as a test case and implement the methodology to a more site-specific case, the airport.
Objective 5. Conduct site visit, interview and small focus group discussion with airport manager or authority in order to refine, validate and obtain inputs as case study lesson learned for the improvement of decision making process.
Objective 6. Conduct feasibility analyses mainly the potential revenue stream for various scenarios to test the hypothesis and assure the stakeholders using appropriate financial parameters as well confirm the results to prior studies.
Chapter Summary
Airports have tried to align their organization’s mission with sustainability. The airport authority is yet to find the best method to assess and conduct feasibility analysis that can cover comprehensive steps. Therefore, it is required to further investigate the
32 steps of doing the assessments for choosing the renewable energy system that is applicable on airport area. This dissertation attempts to accomplish the goals of the
ACRP-RFP requirements as the directive. Finally, by conducting research that aligns with the airport’s needs and FAA suggestions, the expected outcomes are expected to have significant contribution especially in the organization management knowledge, decision making realm and airport authority as well as aviation practitioners.
This dissertation aims to the development of a new methodology and a tool to assist decision makers in evaluating the implementation of renewable energy systems as an additional revenue resource. Since the goal is to propose the cutting edge methodology from the complex process of decision making process that have not been made before—the revenue maximization in airports due to the diversification of non- traditional revenue of implementing renewable energy system on airport; therefore, the division of this dissertation helps the investigation of the step by step methodology development. The organization of this dissertation covers seven chapters and divided by three phases. Phase I of this dissertation consists of the first two chapters, i.e.
Chapter 1 and Chapter 2. Phase II comprises of two parts, namely Part 1 and Part 2.
Part 1 is a thorough process that covers Chapter 3. Part 2 contains Chapter 4 and
Chapter 5. Phase III is the final phase that provides the finalization of this dissertation.
The following paragraphs elaborate each the contents of each chapter.
Chapter 1 provides problem statement and research justification in which determines research objectives, limitations and focus. Chapter 2 comprises a thorough literature review. The literature review elaborates in-depth problem statement, revenue formulation in existing airport financial system, the US renewable energy current
33 conditions and the background that supports the theoretical framework of developing the soundness of this dissertation. Chapter 3 is the methodology development in detail.
The goal is to seek the appropriate of the decision making flowchart presented in step by step progression. This chapter also elaborates the applicable renewable energy types with a test case the state of Florida. Chapter 4 is the continuation of Chapter 3 and contains the rationale of implementing a specific renewable energy type.
Furthermore, after obtaining the appropriate renewable energy type, it is required to further investigate the revenue for specific airports case studies. Chapter 5 is the integration of the methodology using the newly developed evaluation tool. This chapter provides the basis of finding appropriate input assumptions from pertinent literatures.
Chapter 6 presents the results and discussions. Chapter 7 provides the conclusions and recommendations in which elaborates the final results and investigate future research suggestion due to the limitation of the study as well as potential research because of factors that are important; yet, have not been covered in this dissertation.
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CHAPTER 2 LITERATURE REVIEW
The Economics of Renewable Energy System in Airport
As a continuation of Phase I, this chapter organizes literature review by defining the economics of renewable energy system (RES) and current revenue system in airports, definition of renewable energy as well as the sustainability goals of the airport authority. The renewable energy literature review will only be limited to the use of solar, wind, hydro/tidal, geothermal and biomass energy.
There are two main sources of airport revenue, i.e. aeronautical activity and passenger related services. However, the fashion of airport business model has shifted from previously traditional model to a newer system using any other potential income generations that is passenger-independent business model (Kramer, 2010). The below section will explore the current situation of airport revenue generation.
Airport Revenue Diversification
Airport operators nowadays are trying to diversify the income generation using various possible sources. According to Airport Economic Sustainability report in 2011 from North Central Texas Council of Governments’ (NCTCOG), in support of the results of Kramer (2010), the general aviation has newer system for airport management to sustain its operational activities. The old—traditional system—refers to mainly charging the airlines as service providers for passengers. Nevertheless, the newer systems provide some possible revenue diversification. The graphic below presents the summary of NCTOG report aligned with the newer system shift developed by Kramer
(2010).
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Possible New Income Drivers (Non-Traditional Revenue)
Main Revenue Generator Temporary Uses for (Traditional Revenue) Special Events
Land, Terminal and Agriculture Office Leases Mineral Extraction Hangar Leases Total Revenue Utility Services Ramp/Tie-down Fees Renewable Energy Fuel Sales and Fuel Flowage Fees Online Auctions
Figure 2-1. General aviation revenue generation with additional revenue from RES
As shown in Figure 2-1 above, the green highlighted possible new income drivers are the foci of this research. The utility services box refers to the land agreement, which means that the airport authority can be the electricity provider or it can share the ownership with the utility provider to provide electricity to the surroundings, i.e., residential area. The calculation for the actual economic model will incorporate all possible income generation based on the newer sources after including all renewable energy cost estimations.
Defining the Role of Airport
In defining parameters for the economic model, this research will identify parameters that are suitable to be implemented in airport revenue system. According to the ACRP report Synthesis 1 by Nichol (2007), the airport operating net income is simply defined as the result of total revenues minus operating expenses. This net operating income can be used to pay debt service, allocated to the capital investments and returned to the airlines as revenue sharing (Nichol, 2007). However, the role of airport will influence the financial model. For instance, there will be questions regarding
36 this issue. First, who owns the airport? Second, what is the role of the owner? In investigating the role of airport, the role of ownership also influences the financial model for a specific airport (Figure 2-2).
Figure 2-2. Factors influencing airport financial services. Adapted from: Nichol (2007)
From the figure, it can be seen that stakeholders such as city, county and state government also influence the financial decisions in most U.S airports. In addition, the role of privatization will affect the financial model in the case of using partial or full privatization in operational activities (Nichol, 2007, Figure 2-3). From Figure 2-2, it is obvious that various factors will influence the revenue estimation. Moreover, by referring to Figure 2-3, the new system of developing renewable energy system such as
Power Purchase Agreement (PPA) will also influence the revenue estimation especially the profit sharing among parties. In this particular research, the airport revenue
37 estimation will model the actual revenue that can be obtained after offsetting the sharing percentage from for instance, agreement type.
Figure 2-3. Level of privatization. Adapted from: Nichol (2007)
Defining Cost of Electricity
In defining electricity cost, plenty of researches are using the levelized-cost to investigate cost comparison between non-renewable and renewable energy. As per definition, levelized cost of energy (LCOE) is the energy unit cost that if it is kept constant through the life-cycle of energy generator system, the net present value (NPV) of the revenue will be equal to the NPV cost of that system (Short, Packey & Holt, 1995, p. 47). NPV analysis for renewable energy results has also been conducted by Yue &
Yang (2007), and this analysis will be incorporated aligned with LCOE. Another definition of LCOE is the aggregate discounted costs of an energy generation system that have been standardized over the life-cycle evaluation period by using levelized
38 yearly discount factor incorporating the financial analysis annuity concept (Byrne, Zhou,
Shen & Hughes, 2007). It is also determined as a measure of the comparison of competitiveness of different energy generation technologies and presents the present value of cost per kilowatt-hours for developing and operating the energy generation systems during their life-cycle period. The calculations will include all contributing variables starting from capital cost, operation and maintenance cost, and fuel cost to operate the system to the predetermined utility rates (EIA, 2013). Although the LCOE or levelized energy cost (LEC) can be used as the convenient preliminary assessment to the energy generation project investment by decision makers, numerous factors should be taken for considerations since there will be variation in terms of regional policy and available technology (EIA, 2013).
A study from NREL by Fingersh, Hand & Laxson (2006) about wind LCOE states that it will be difficult to generalize levelized cost—in this case wind turbine—because of the cost of project in wind turbine is dependent on the site. This study did not include the incentives of local or state level. Another study conducted a PV LCOE for three cities i.e., Sacramento, Chicago and Boston and this study included the 30% federal tax for generalization and an 8% state tax (Darling, You, Veselka & Velosa, 2011).
However other incentive such as carbon credit was excluded in the calculation. These two studies also support the need of direct implementation in local or regional based analysis for levelized cost. It reflects an equivalent conclusion that will be applicable to other renewable energy implementations. For example, by seeing Table 2-1, although the average forecasted cost of electricity in 2018—solar photovoltaics (PV)—is high compared to other renewable energy sources, one can take the benefit of further
39 investigating the available financial incentives in each region that will offset the cost of electricity of generating solar PV.
Table 2-1. Levelized total cost for various energy generating system for 2018 Total Levelized Energy Generation System Cost [$/kWh] Fossil-based Conventional Coal 0.100 Advanced Coal 0.123 Advanced Coal with Carbon Capture Sequestration (CCS) 0.136 Natural Gas-fired Conventional Combine Cycle 0.067 Advanced CC 0.066 Advanced CC with CCS 0.093 Conventional Combustion Turbine 0.130 Advanced Combustion Turbine 0.104
Renewable Nuclear 0.108 Geothermal 0.090 Biomass 0.111 Solar PV 0.144 Solar Thermal 0.261 Wind 0.087 Hydro 0.090 Source: EIA (2013)
One study has reviewed more than forty LCOE photovoltaic case studies and suggested the need to carefully determine costs, available financing methods and life- cycle as well as loan term since this caveat would result in an erroneous policy decision
(Branker, Pathak & Pearce, 2011). Therefore, in the specific case of gaining revenue for airports, a rigorous analysis in terms of financing system and incentives will be incorporated in the assessment phase for renewable energy project.
Revenue Model Formulation
After knowing the role of airport for its operational and cost formulation, now the important factor is determining the role of airport in the renewable energy project. As a
40 portion of the source of revenue, renewable energy project in airport has been considered to have different types of agreement. This will have significant influence to the financial system of the airport. There are two types of agreements that can be made from producing electricity near airport that have been implemented (Plante et al.,
2010). The agreement will be based on a system called Power Purchase Agreement
(PPA). This is the long-term and fixed-price contract of electricity between two entities, i.e. buyer and seller that can be described as long term revenue source (Cory, Coughlin
& Coggeshall, 2008a; Plante et al., 2010). This type of agreement usually occurs between private developer and either private or public consumer. One example of this
PPA system is the third-party model (Cory et al. 2008a).
Electricity from renewable resources
Private Developer PPA Airport Renewable Energy System i.e. solar, wind, biomass Leasing the land Grid connected electricity (+) revenue generated Utility REC contract Provider
Figure 2-4. Private ownership financial model from the airport standpoint. Source: Plante et al. (2010)
For instance the airport, as a host or public entity, is obligated to buy electricity.
This is private and airport-owned renewable system. Figure 2-4 shows the dollar addition to the airport in the form of private-owned financial model. Figure 2-4 also indicates the airport will obtain the revenue addition mainly from leasing the land. This
41 model will benefit the private entity that not only can get tax credit but also obtain revenue from buying Renewable Energy Credit (REC) and get offset value from electricity price under power purchase agreement. Therefore under this system, the only revenue addition for the airport is from leasing the land.
From Figure 2-5, the airport will obtain the two revenue additions mainly from the offset price of for instance, usually based on net metering or PPA under feed in tariff.
Another source is from buying REC contracts from the utility provider. This model will benefit the airport even after the contract purchase has finished. The airport will get the free electricity after that contract period just because it self-produces the electricity.
Grid connected electricity
Airport owns: Renewable Energy System i.e. solar, wind, biomass (+) first revenue generated from offset price of electricity cost Utility Provider
(+) second revenue generated from REC purchase
REC contract
Figure 2-5. Airport-owned financial model from the airport standpoint. Source: Plante et al. (2010) Renewable Energy Incentives
Renewable energy incentives are promoted by the government to attract individual, public institutions and private sectors to be actively involved in renewable energy projects. Based on the NREL report by Cory et al. (2008b, p.6), the utility providers have tendency to own wind generator instead of signing PPA. The financial
42 model that attracted the utilities is the type of third-party ownership model. In the actual research implementation, these factors should be considered and crosschecked to the actual systems that have been applied before conducting further step. Cory et al.
(2008a) has supported the need to investigate the role of airport in the renewable energy model since this will influence the financial condition for the airport authority.
Therefore, the investigation in each case of airport basis will be important to further validate the model.
Renewable portfolio standards (RPS). RPS, also known as Renewable
Electricity Standard, is a mandate from the government—the most applicable in state wide policy—for the utility providers either to provide or buy electricity from renewable energy generators (Corry et al., 2008b; Cory, Couture & Kreycik, 2009; Plante et al.,
2010). The regulation provides the opportunity to expand the market of renewable energy development nation-wide. In the case of this research, airport will be best to act as owner of the renewable energy.
Renewable energy credits (REC). REC is part of the RPS program. The format is a certificate that states the renewable energy producer—in most cases private company—is eligible to produce renewable energy and this certificate is issued by an independent organization (Plante et al., 2010). The value of this certificate is usually in dollar per 1MWh per stock of renewable electricity and in the range $3 – 30 per MWh
(Cory et al., 2008b, p.13). This is the type of commodity that is tradable and most companies have just bought to show their support of green power.
Tax credits. This incentive is intended to the private entities that support renewable energy development. No public or government entities are eligible to
43 achieve the tax credits. There are two types of tax credits, i.e investment tax credit
(ITC) and production tax credits (PTC) (Plante et al., 2010). ITC can be obtained directly from the Internal Revenue Service (IRS) in the amount of 30% and this value is tradable. This will attract other private sector to collaborate in developing renewable projects (Plante et al., 2010). PTC has previously supported the expansion of wind turbine generators until its expiration in year 2008. PTC did not contribute to the private developers. However, it was just seen to benefit the utilities only. In addition, most
PTC has been expired in the year 2008 (Cory, et al. 2008b, p.3).
Depreciation. Depreciation with other incentives combination can reduce the investment cost (Barbose, Darghouth, Weaver & Wiser, 2013, p.16; Branker et al., 2011 and Kibert et al., 2010, p.8). Cost allowance using asset depreciation would be a favorable option to attract renewable project for energy developer especially in private sectors. Internal revenue service (IRS) has settled a five-year basis depreciation calculation using Modified Accelerated Cost Recovery System (MACRS). After that period, the developers can take benefit of not paying taxable property, which will result to lower the operating expense of producing electricity. The depreciation values will be counted after subtracting the first cost by other incentives. The first year value based on MACRS depreciation multiplication factor will be the factor to reduce the total first cost.
Grants and rebates. This type of incentives can be obtained before or after the renewable construction project finishes. These are the most favorable incentives from the developers’ standpoint since it offers cash instead of credit (Plante et al., 2010).
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Bonds. One system that has been running using bond incentive is Clean
Renewable Energy Bonds (CREB). This is the way of government to fund the renewable energy project. If the government is the borrowing entity, the payment of the money will be in tax credit form (Plante et al., 2010).
Feed-in tariff (FIT). The FIT system is intended to provide renewable energy project reasonable return by setting reasonable price that is higher than the market price (Cory et al., 2009). This system was firstly applied in California in 2008 (Plante et al., 2010).
This system guarantees the electricity generator to have a long-term fixed price based on the price that is not market-dependent. For instance, during peak sun hours, the system sends electricity to the grid. The contract between the renewable generator and the electricity utility enables the renewable energy generator to sell the electricity at predetermined fixed price.
Net metering (NM). This system is straightforward – the renewable energy generator can fulfill its consumption of electricity based on the installed system. In addition, the overproduction can be exported to the utility-grid. The federal law mandates the utilities to buy back this overproduction. In this situation, the utility has to pay for the amount of its avoided cost due to its operational cost that is some amount of dollars determined by the state and equal to the actual utility’s avoided cost (Hempling,
Elefant, Cory & Porter, 2010). Conversely, if the generator system cannot fulfill its electrical load, it has to pay the electricity in the dollar amount based on agreement. In designing the system, the producer does not need to export as much excessive energy during peak sun hours as the producer needs during other time. The constraint is the
45 excess production from the maximum load at certain month will be unpaid. Therefore, it is recommended to design system that approaching the monthly use curve.
Regulatory and Safety Issues in Airport
This section summarizes the regulatory issue as the site constraints based on the previous report approved by FAA—conducted by Barrett & DeVita (2011) and
Plante et al. (2010). The criteria from the FAA to assess the RES in airports are added in this dissertation and will be the distinguishing feature from other studies.
Table 2-2. Safety and regulatory constraints Disturbed Unit RES Type Impediments Application System [feet] PV Glare Airspace, Height: < 3 Might be installed Communication above ground parallel to Runways, Distance from Taxiways, roof-top Transmitter > 250
CSP Glare Airspace, Height: < 200 Avoid runway and Communication taxiway area
Wind Turbulence Airspace, Buffer zone = Avoid runway and Communication 750 taxiway as well as communication area. Can be installed in terminal area
Generating Vapor plume, Airspace Buffer zone = Apply distance to and thermal 1,000 the aircraft Transmission plume preservation area (CSP, Hydro, for instance hangar Geothermal, Biomass) Source: Barret & DeVita (2011); Plante et al. (2010)
The criteria are designed to ensure the safety of landing and departing aircrafts.
For instance, safety analysis for a solar project is very important as the glare from solar
46 panels may obscure the visions of aircrafts’ pilots. The maximum structure height has to be 200 feet from the ground level. The proximity to the runways and taxiways can be close enough, as long as the structure for installing, for example solar panels, does not exceed 3 feet above the ground. This is why solar panels have been considered to have greater flexibility for siting on airport for electricity generation.
PV panels should be installed far away from the communication transmitters, and this distance varies for different airports. For instances, Oakland airport put a setback of
500 feet from the communication system whereas Bakersfield airport placed a setback of 250 feet. Therefore, the setback from the transmitter system will be designed to have a minimum distance of 250 feet. In the case of CSP, the solar towers have to be installed at a height of 459 feet to comply with the FAA standards. This will affect the airport authority to reinforce flight procedure of no lower than 1,350 feet. Table 2-2 above represents the summary of the safety constraints for several alternatives of RES on airports.
Airports and Sustainability
According to the World Commission for Environment and Development (WCED), from The Bruntland Report definition, which was published nearly three decades ago and is still used until today, sustainable development is defined as “providing for the needs of the present generation without compromising the ability of future generations to meet their needs” (WCED, 1987, p.43). The sustainable development, or shortly known as sustainability, has been used as the foundation for numerous efforts to make sure the future generations have a decent quality of life (Kibert, 2013). As it becomes foundation, sustainability has been used by various organizations to improve their operational activities. For example, organizations such as airports have their own 47 definition of sustainability. Referring to the Airport Cooperative Research Program
(ACRP) Synthesis 10 reported by Berry, Gillhespy & Rogers (2008), airport sustainability is defined as ”a broad terms that encompasses a wide variety of practices applicable to the management of airports.” As an organization, airport authority has come to embrace sustainability as its priority for airport project improvement. Some of the sustainability efforts have been widely implemented in both US and international airports. The synthesis also provides various examples that an airport can perform to support sustainability. For instance, in sustainable energy management, since 2005 the
Aéroports de Paris has been addressing issue in climate change in carbon credit to gain revenue as one factor to the strategic decision making. In addition, Tacoma
International Airport in Seattle has invested $7 million to improve electrical consumption by retrofitting equipment with energy efficient equipment and improving heating, ventilating and air conditioning (HVAC) system and installing a solar hot water system as well as a 20 kW roof-top photovoltaic (PV) system.
Existing Methodology to Assess Renewable Energy System
Many methods for specifically exploring the potential and availability of renewable energy have been developed. The examples of these are studies by
Voivontas, Assimacopoulos, Mourelatos & Corominas (1998); Ramachandra & Shruthi
(2007); Byrne (2007); Cai, Huang, Yang, Lin & Tan (2009) and Angelis-Dimakis et al.
(2011). These studies underline not only the development of identification of renewable energy source potentials and economic analyses of the implementations of the systems, but also the evaluation system to investigate the renewable energy sources based on location. The characteristics of the current renewable energy sources are intermittent and greatly dependent on the location. Therefore, it is adequate to identify the potential 48 of renewable energy by using site location. Some literatures are intended to explore the potential based on state level analyses—or province—such as studies conducted by
Grassi, Chokani & Abhari (2011); Guangxu et al. (2011) and Wiginton, Nguyen &
Pearce (2010). The results from the study by Grassi et al. (2011) explain the method for doing assessment both technically and economically as well as the policy side of using large scale wind turbine in the state of Iowa. Meanwhile, the study conducted by
Guangxu et al. (2011) explored the use potential solar source based on site location characteristic at province level in Jiangshu province, China. This study analyzed the roof-mounted photovoltaic system. In the case study conducted by Wiginton et al.
(2010), the use of the rooftop area location is supported only to satisfy electricity demand. The study calculates the net area that can accommodate the installation of solar panels. From those studies, it is proven that in a bigger boundary analysis, the site location can determine the theoretical and available potential of renewable energy source. The case of airport will only be a minor part of a bigger area, which can be assumed to have the same boundary of a smaller study.
In a smaller case study, a research that implements site location to analyze renewable potential has also been conducted. A study conducted by Chaves & Bahill
(2010) and Son et al. (2010) have modeled the PV construction project using university as the case. In this research, the analysis is limited to the distribution of generation system on the buildings around the University of Arizona campus. It analyzed the available rooftops that can be used to install the PV and calculated the amount of required PV panels to satisfy each building’s consumption. In addition, to tackle the stakeholders’ role, it also investigated the optimization method to control the cost for
49 several years after construction. Some of the smaller case studies have just incorporated rooftop solar panels installation. Meanwhile, the available agricultural land could be seen as potential to support renewable energy assessment by including calculation for the excess energy potential due to additional area aggregation of the excess energy that can be sold back to the grid (O’Brien, Kennedy, Athienitis & Kesik,
2010). In this dissertation, not only rooftop assessment will be investigated but also vacant land near airport will also be incorporated. A wide range of analyses have been conducted to assess the viability and potential of using renewable energy from a state level to a region level. These studies have raised the possibility to conduct further research in a small portion of a region, i.e. airport. Therefore, a step by step analysis using the existing method can help investigate the development of airport assessment tool.
There are terminologies that have relation with economic renewable energy measures. Voivontas et al. (1998) studied about the potential of renewable energy using Geographical Information System (GIS) to explore mainly wind energy. This literature explains and adds some terminologies that are usually used in relation with renewable energy. The first term is the theoretical potential, which is defined as the maximum amount of renewable energy in one region. The second term is the available potential. By borrowing the terminology from Kidner (1996), the available potential is part of technological potential that can be produced after screening the environmental impacts. The third term is the technological potential. It is defined as the energy that can be produced based on the characteristic and capability of the current commercially available renewable technology. This term will have relation with the development of
50 renewable energy efficiency in the market. The last term is economical potential. It is the amount of energy that can be produced by incorporating economically viable installations. Although the study explored the wind case only, this terminology will be used throughout this research to understand the amount of resource with the corresponding location to harvest renewable sources such as studies conducted by
Anders et al. (2005); Biberacher, Gadocha & Zocher (2008) and Angelis-Dimakis et al.
(2011). Figure 2-6 shows modified approach to the potential of renewable energy classification based on report from the US Department of Energy (DOE) in 2006 and updated in 2011 by DOE (Lopez, Roberts, Heimiller, Blair and Porro, 2012) as well as study by Biberacher et al. (2008).
Policy implementation/impacts Regulatory limits MARKET Investor response POTENTIAL Regional competition with other energy
ECONOMIC Projected technology Cost POTENTIAL Projected fuel cost
Topographic AVAILABLE/TECHNICAL Slope constraints POTENTIAL Land-use constraints System efficiencies Insolation RESOURCE/THEORETICAL Wind speed POTENTIAL Land cover
Figure 2-6. Modified potential determination based on NREL report
Most studies for renewable energy assessment do not include market potential in the analyses such as Voivontas (1998); Ramachandra & Shruthi (2007); Biberacher
(2008) and Angelis-Dimakis et al. (2011). Neither does this dissertation focus on the
51 market issue. Yet, regulatory issue will be incorporated such as the issue of incentives in one region, tax credits and rebate.
Types of Renewable Energy System
The term renewable energy covers every abundant resource in the nature that can replenish naturally and align with the rate of human energy consumption as well as freely available. Moreover, as renewable energy is known to produce no carbon in the application; yet, it is the opposite of fossil fuel energy. This dissertation will, specifically, highlight the potential of using solar-derived technology i.e. photovoltaic; however, other renewable energy system will be described in the following narratives.
Solar Energy
A sufficient amount of solar radiation is required in generating electricity. There are two types of renewable energy sources that are associated to the solar technology, i.e. solar electric—photovoltaics and solar thermal. The intermittency characteristic of solar radiation is usually tackled by battery banking system and grid connection to the nearest utility provider. In addition, the extended use of PV, from concentrated solar power (CSP) to heat thermal storage and compressed air energy storage (CAES), is also considered as solution to intermittency behavior (Anders et al., 2005). This research is mainly intended to the grid-connected system to fulfill the airport needs.
Therefore, for the purpose of this research, the stand alone system and the CAES system will not be explored. These types of solar technology will be explained in each sub section of this solar energy section.
Solar electric. The technology that can directly convert solar radiation to electricity through photovoltaic cells is called solar electric technology, or widely known
52 as photovoltaic (Anders et al., 2005). The current technologies available for PV cells and modules are made from silicon (both mono and poly crystalline), gallium arsenide, copper indium selenide and thin-film cadmium telluride (Fthenakis, Mason & Zweibel,
2009). The efficiency that can be reached for photovoltaic is normally between 7 to
17% (Kaundinya, Balachandra & Ravindranath, 2009). However, according to the latest study for the progress of PV technology by Green, Emery, Hishikawa, Warta & Dunlop
(2012), the collector laboratory results efficiency can reach up to roughly 25% efficiency for mono crystalline silicon (Si) technology, almost 21% multi crystalline and around 29
% for gallium arsenic (thin film).
For the purpose of construction and installation system for this airport research, the fixed tilt and single- and double-axis tracking systems will be investigated. There are two main areas of photovoltaic, i.e. roof-top based—mainly uses fixed-tilt system and ground-mounted system that can be installed either using fixed-tilt or tracking system. In the cost analysis, the fixed-tilt system requires less cost compare to single- and double-axis tracking system (Gueymard, 2008). Moreover, the implementation of tracking system could add efficiency up to 12% and gain productivity around 20 to 40% in kWh higher than a fixed-tilt system (Smith, 2011). Therefore, the need of finding option that can maximize revenue during operation will be investigated in this research.
Solar thermal. This technology is used to collect heat from the sun and direct the light to heat the water inside a reservoir—storage. There are two common types of use for this technology. The first type is the concentrating solar power (CSP). The temperature that can be achieved to heat up the storage or pipe will be about 300 degree Celsius and that will be enough to generate electricity for industrial applications
53 and it can reach the system efficiency up to 50% (Ezekwe, 1990; Bakos, 2002). The steam produced can be used to generate power as the steam turbine produces electricity. The second type is the solar thermal technology for domestic hot water system. This is a common system that can prevent building to use electricity from the grid only to heat the water. One other type that can be applicable in airport based on
FAA in 2010 is transpired solar collector (TSC), which is defined as solar collector that can be installed on the south facing side-wall of the airport buildings or hangar to absorb heat and then forced to the inner part of the building by using fan (Plante et al., 2010).
This dissertation does not address CSP and TSC use.
Wind Energy
Wind is a form of renewable energy. Many factors influenced the wind formation starting from the earth’s rotational movement, the temperature difference between the hot and cold regions, to the vegetative cover and most importantly the topography (EIA,
1995; Ramachandra and Shruthi, 2007). According to Barrett & DeVita (2011) in the
ACRP Synthesis 28, the hazards for using wind energy near airport are basically due to the height of the wind turbine and the communication radar issue as well as turbulence created by the wind turbine. The communication issue influences the airport radar system and can interrupt navigation. Some methods to prevent these hazards have been tabularized in the analyses of renewable sources such as wind and solar. For instance, to prevent turbulence, the table shows a buffer area of 750 feet around the wind turbine will be considered of a solution.
There are two types that are commercially available in the market, i.e. horizontal axis and vertical axis wind turbine. The following two sections describe those types.
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Horizontal-axis wind turbine. Horizontal-Axis Wind Turbine (HAWT) is commonly used and commercially available in the market. In the installation, it needs to be aligned with the wind directions—parallel (DNV, 2009; IEC, 2013). The ideal place to obtain enough wind speed will be roughly around 100 feet above the grounds (DOE,
2011). The efficiency and average wind speed calculation should be matched between manufacturer data and theoretical potential.
Vertical-axis wind turbine. Vertical-Axis Wind Turbine (VAWT) basically works based on water wheel theory. It moves based on the air flow and the air spin the wheel in the rotational axis. It has much more disadvantages such as: low efficiency, the mechanism is too complicated and needs a lot of works just to replace the main components; the only advantage of using large system VAWT is the access to the generator and gearbox that are installed on the ground bearing (DNV, 2009; IEC, 2013).
VAWT consists of two models, i.e. Savonius and Darrieus and neither type is broadly implemented in the applications (DOE, 2011).
Building-integrated wind turbine. These types of turbines can be either of
HAWT or VAWT. The building is used as turbine mounting system.
Hydro Energy
The classification of hydropower is mainly based on the power output and operating head. The classification of the project based on the power (P) is defined as small hydropower (1 MW ≤ P≤ 30 MW); low power hydropower is defined as hydro energy potential of lower than 1 MW. More specific classification has been made to distinguish the system since there are a lot of references using different name for micro hydropower. For instance 100 kW ≤ P≤ 1 MW refers to mini hydropower and P ≤ 100 kW refers to either micro or pico hydropower (Kosnik, 2010). The low head limit is 55
below 30 feet. The low head system can be divided into two systems, i.e. low-head
conventional turbine and unconventional system (Hall, 2006). Small hydropower has
been considered to be more cost-effective since it runs only in either by constructing
small dam in “run-of-river” or by using the river flow directly, Figure 2-7 (Paish, 2002;
Kosnik, 2010; Wakeyama & Ehara, 2010). Power
Intake output Pipeline conveyance
River upstream Tail race Power house
River downstream
Figure 2-7. Schematic of small hydropower. Source: Kosnik (2010)
Ocean Energy
Ocean energy is also considered as an option in renewable energy sources.
There are three different categories for ocean energy, tidal—currents, wave and ocean
thermal (Bedard et al., 2007; Soerensen & Weinstein, 2008; DOE, 2009).
Currents. This is the type of energy coming from the tides and flow—current—of
the ocean caused by the gravitational forces from the moon and sun as well as earth
rotation motion. Based on the source, there are two types of this energy, ocean current
and river—in stream current. The methods to harness them are typical. The difference
is the fluid or water composition, i.e. salt water for ocean and fresh water for river. The
56 methodology of converting tidal currents to electricity will be based on Tidal In-Stream
Energy Conversion (TISEC) equipment that will catch the kinetic energy of the flowing water (Bedard et al., 2007). Tidal current also has been called as hydrokinetic technologies. Based on the DOE report (2009), the European Marine Energy Centre
(EMEC) in 2005, has classified hydrokinetic technologies into four main energy converters i.e. horizontal-axis, which is similar to wind turbine, ducted horizontal turbine, vertical and oscillating hydrofoils. However, the products are hard to find at the commercial level in the U.S, although some companies have conducted both partial- and full-scale demonstration (DOE, 2009).
Waves. This energy comes from the wave from the ocean surface. According to
DOE (2009), EMEC has categorized the wave energy converter (WEC) into six systems under development, i.e. point absorber, attenuators oscillating wave surge converters, oscillating water column, overtopping devices and submerged pressure differential devices. Some countries have carried field tests of these WEC. However, in the U.S. there are no full-scale applicable operations for using these systems. The WEC systems are still under development (DOE, 2009). Furthermore, the potential of wave energy will be based on the proximity to the shoreline. The more distance from the shoreline, the power will largely diminish (Defne, Haas & Fritz, 2011). It is possible to install this type of RES on airport that near the shoreline, however the case is still rare just because many airports lay in the middle of the mainland.
Ocean thermal. Usually called Ocean Thermal Energy Conversion (OTEC), this type of RES employs temperature difference from deep sea cold temperature to the surface water warm temperature (DOE, 2009). The requirement for this system to work
57 is based on the minimum temperature different of 20 degree Celsius. That condition will be favorable near the equator area when the surface and deep sea (at minimum 600 meters) temperature can be achieved (DOE, 2009).
Biomass Energy
Biomass is described as biodegradable products, wastes and residues that came from industrial, agricultural and forestry. Also, the biomass farms can be developed to produce energy crop that can be used to fuel equipment to produce electricity (Angleis-
Dimakis et al. 2011). Four classifications of biomass potential are usually defined as woody biomass, agricultural biomass, energy crops and industrial residues. For example, if a power plant is planned to be installed near airport land to produce electricity, the variable that has to be taken into account is transportation cost from the theoretical potential of biomass sources. Cost of growing the biomass supply will depend on the market value.
Geothermal Energy
Geothermal energy is a type of RES that is stored in the inner part of the earth.
The energy from geothermal has been divided into two types, i.e. steam and hot water spring. Normally, there are two basic uses of geothermal energy, i.e. electricity production and direct use (Barbier, 2002). In order to incorporate a geothermal system to generate electricity, it requires temperature of at least in the range of 100 to 150 degree Celsius. This RES also can be harvested directly from the sites by incorporating a heat pump with only 80 degree Celsius for space heating (Barbier, 2002). Ground source heat pump (GSHP) has been world-widely known for simple cooling and heating
(Angelis-Dimakis et al., 2011).
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Current Status of U.S. Renewable Energy Condition
Figure 2-8 shows the energy consumption share percentage by energy source.
The percentage of renewable energy use in the U.S. is still below 10% of the total energy. In 2005, one study from Florida Solar Energy Center (FSEC) by McCluney explained some limitations of renewable energy.
Figure 2-8. U.S. energy consumption by energy source 2011. Adapted from: EIA (based on March 2012 data)
The study addresses several impediments in producing electricity from renewable sources. It started from the land issue and environmental impact to the issue of additional costs that occur during the massive production of renewable equipment.
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Figure 2-9. Average price of photovoltaic cell and modules during 2002-2011period. Adapted from: EIA (based on September 2012 data)
For the latter issue, the study reviews that the production still involves fossil- based fuel which is not sustainable and most importantly produces higher cost in total.
However, it also explains the effectiveness of using renewable energy compared to conventional energy. Fthenakis (2009) has supported the use of solar technology even it still has small share in the progress of renewable energy. It states that currently there is a significant reduction in the solar PV cost which of course supports the more competitiveness to produce electricity from renewable sources compared to non- renewable sources. In the newer report from EIA (2013), solar cell and module have tendency to decline as shown in the Figure 2-9.
Although the share of percentage from Figure 2-8 for solar energy is only 1%; hence, the current trend of cost reduction is favorable. Figure 2-9 shows the promising
60 investment in photovoltaic industry due to cost cell and modules drop. The cost of cell per peak watt was 2.12 dollars in 2002, and it declined to 0.19 dollars per peak watt in
2011. For the solar module, it was 3.74 dollars per module and it declined to 1.59 dollars in 2011. The tendency of price drop will be another benefit to the photovoltaic investment. Another newer study based on the fifth annual report from Berkeley
National Laboratory in the Environmental Building News (EBN), supports this situation that the cost of solar panel construction has decreased because of the panel price itself
(EBN, 2013). These two facts encourage the opportunity of using solar energy mainly
PV. For the airport economic analysis purpose, the declining solar panel price will reduce the panel cost and result in increase in the revenue margin for the airports if they start to incorporate renewable strategy to maintain the operational. Nonetheless, further investigation from the aggregation of all revenue might be needed.
Referring to Figure 2-8, the share of renewable energy percentage from wind is
13%. According to Lawrence Berkeley National Laboratory, in a National Renewable
Energy Laboratory (NREL) study by Wiser, Bolinger & Berkeley (2012), the American
Wind Energy Association (AWEA) reported that the price of wind turbine had declined significantly during the 2008 to 2012 period for about 33% (Figure 2-10). The declining price has been also incorporating the significance drop of installation and operation maintenance cost; the price in 2008 was estimated to be 1,500 dollars per kilowatt (kW) and approximately 900 to 1,270 dollars in 2012 (Wiser et al., 2012). In the case of wind energy projects for the purpose of airport installation, the size of wind turbine installed is typically small. The limit between large and small wind turbines is in the 100 kW thresholds (AWEA, 2012; Wiser et al., 2012).
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Figure 2-10. Wind turbine price Adapted from: Lawrence Berkeley National Laboratory, NREL report (based on first half 2012 data)
For the purpose of government market, light industrial and commercial, the turbine size will be around 11 to 100 k, for a grid-tied connection (Wiser et al., 2012,
AWEA, 2012). From the above circumstances, these studies have indicated the possibility for some institutions to take advantage—especially airports for this dissertation—to gain additional revenue based on the cost offset for renewable project.
In the case of hydropower energy, the share of renewable percentage from Figure 2-8 shows the significant amount of hydropower contribution which is 35% from total renewable energy.
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Table 2-3. Sales for small scale wind turbine Annual Sales of Small Wind Turbines (≤ 100kW) into the U.S.
Year Number of Turbines Capacity Addition Revenue (units) (MegaWatt) $(million) 2005 4,234 3.3 11 million
2006 8,330 8.6 36 million
2007 9,102 9.7 43 million
2008 10,386 17.4 74 million
2009 9,820 20.4 91 million
2010 7,811 25.6 139 million
2011 7,303 19.0 115 million
Source: AWEA (based on 2011 market data)
Hydropower has the lowest operational cost since it requires no fuel cost. The only cost that might occur is due to the operation and maintenance of the dams. For instances, dam can be used for recreational area, irrigation and flood control purpose
(Campbell, 2010). The differentiation of the hydropower project is mainly based on the power output or the head as can be seen in Figure 2-11. The high power will cover the hydropower plant larger than 1 Mega-watt (MW) whereas low power refers to hydropower lower than 1 MW.
In addition, the low head refers to water height of lower than 30 feet and the high head covers the water height of higher than 30 feet (Hall, 2006). Considering the cost, if it is compared to the year 2012 with other competitors—other renewable energy technologies such as solar and wind—there is no current data that estimate the turbine cost.
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Figure 2-11. Hydropower project classification. Adapted from: Hall (2006)
The project cost for hydropower will be mainly cost-intensive on the construction of man-made water conveying system and upgrading low head dams without power.
The cost of turbine is also important, nonetheless, it shows the possibility of reducing the price by installing the newer turbine technology (Schneider, 2009).
Figure 2-12. Share for turbine price 2008. Adapted from: Singal & Saini (2008)
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According to Singal & Saini (2008), see Figure 2-12, the cost of electro- mechanical including the turbine will be roughly around 50% of the capital cost. Another study by Ogayar & Vidal (2009) also supports the cost estimation for the turbine that requires more than 50% of the total capital cost of hydropower project (Figure 2-13).
Figure 2-13. Share for turbine price 2009. Adapted from: Ogayar & Vidal (2009)
Table 2-4 shows the results from the latest study by Natel Energy Inc. (2009) on the cost of installing hydropower. The costs have been obtained after searching some conventional turbine manufacturers’ price using two studies by Singal & Saini (2008) and Ogayar & Vidal (2009). The estimated value for electromechanical devices was
2,800 dollars per 100 kW with the 3 meters operating head. However, Natel Energy Inc. has conducted the total value for feasibility of several electromechanical devices that results to the amount of 4,200 dollars per 100 kW with the same 3 meters operating head. These values in Table 2-4 are obtained based on the assumption of installing 1
MW hydropower project with 10 feet (low-head) operating condition with the same assumption of 1.48 million dollars cost for all non-electromechanical system, with a 10
₵/kWh electricity rate excluding all government incentives. The capacity factor was set
65 to 0.65. The results show the significance of turbine cost for hydropower projects with the newer technology (Schneider, 2009).
Table 2-4. Natel Energy Inc. low-head hydropower cost estimation (2009) Natel Energy Conventional Natel Energy Parameters Estimates at turbine Cost estimates commercial scale Turbine Package/kW $3,000 $1,700 $1,000
Total Installed Cost $4.48 million $3.18 million $2.48 million
LCOE $0.86 /kWh $0.66 /kWh $0.55 /kWh
Payback time 19 years 11 years 7 years
Source: Schneider (2009).
Hydropower holds significant amount of renewable energy shares in the US.
Still, the share of ocean energy is still not clearly stated in the share of renewable energy sources and it is still in the early development phase of technology not only in the U.S but also world-wide (BOEM, 2013; IHS, 2010; Gross, 2004; O’Rourke, Boyle &
Reynolds, 2010). The availability of published data is still relatively new and this type of energy is under water program section in Marine and Hydrokinetic which contains current projects that are mostly in site planning and undeveloped (DOE, 2012).
Based on the report from Electric Power Research Institute (EPRI) in 2007 by
Bedard et al., the total potential of wave energy can achieve 2,100 Terra-watt-hour
(TWh) per year combined with Canada with the total of 1,600 TWh per year (see Figure
2-14).
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Figure 2-14. Wave energy estimate. Adapted from: Bedard, et al. (2007)
Another form of ocean energy—current or tidal—can support 19.6 TWh by including two Canadian passages (Figure 2-15). The cost of renewable energy tends to have significant initial cost of development.
Figure 2-15. Tidal energy estimate. Adapted from: Bedard, et al. (2007)
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Ocean energy—both wave and tidal—has been known to be more expensive than other renewable energy competitors because most of them are still in research and development (Carbon Trust, 2006; IHS, 2010) while some projects have been developed in a very severe tidal environment near ocean and proven not very economical based on the sites (O’Rourke et al., 2010). From Figure 2-8, it is obvious that geothermal shares only 2% of the total renewable energy. Nonetheless, the implementation of geothermal energy has been known to supports the decrease of building energy consumption significantly. The use of geothermal energy in airports has also proven the saving mostly in HVAC system from direct tapping to the building surrounding areas. Another type of the geothermal application is to recover the airport sidewalks from icing formation. With regard to cost, geothermal energy still has higher cost –especially for the newer system called enhanced geothermal systems (EGS)— compared to other mature renewable energy sources such as wind and solar and the source of geothermal is very site specific (EIA, 2011). Although the installation costs are higher, it seems that FAA still funds the geothermal installation.
By referring to Figure 2-8, the total energy from biomass covers the biggest share, 48%, compared to any other renewable energy option. However, only one case study has implemented biomass to the airport in the use of boiler, this will be discussed later in biomass case study section. There are still a lot of pros and contras of using biomass for fueling equipment such as boiler to satisfy the demand for HVAC system and generating electricity due to the emission issue (Carneiro and Ferreira, 2012) and the process in the transportation emission of the energy crops distribution and pesticides management of the farming method (Thornley, 2006). To counter that issue,
68 the well-maintained forest management is required (Faaij & Domac, 2006). The biomass energy crops analyses have been widely conducted such as the studies by
Perpina et al. (2009) and Graham, English, & Noon (2000). The results show the database of how biomass would accommodate the transportation issue that usually becomes one of the inhibitor factors of exploiting it. The methodology chapter investigates the step by step assessment to implement renewable energy system.
Research Significance to Decision Knowledge
As previously mentioned, the transportation research board has publicized the need to explore more in decision making process that could be implemented in the airport current situation to gain non-traditional revenue. To be more specific to the need of research inquiries, there are two main reasons that support the necessity of conducting this dissertation. First, is the methodology development in order to guide the airport authority assesses the renewable energy systems implementation. This request is due to the lack of available literatures and guidelines for specific research in airport revenue assessment. Second is developing an evaluation tool. The TRB has requested specifically as follows:
There is limited guidance to help airports identify, evaluate, select, and successfully implement renewable energy projects for revenue generation. Research is needed to develop a guidebook and evaluation tool to help airports understand the feasibility, opportunities, and challenges of renewable energy projects and their implementation for revenue generation (TRB, 2012, p.1).
With respect to contribution to the decision making knowledge, the newly developed methodology is expected to provide flow of decision making process to help airport authority conducting preliminary assessment to take decision for revenue generation.
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The following point 3 of the ACRP-RFP 01-24 statements fortifies another inquiry necessity to conduct research in decision making process:
The guidebook should include: … 3. A flow chart or process map illustrating key decision-making step… (TRB, 2012, p.1).
Therefore, it is noticeable that this dissertation outcome will benefit to the variety of decision making process knowledge since the renewable energy has been becoming the part of sustainability movement. Although renewable energy systems sometimes related to have limitation—in the upfront costs; yet, have the advantage with respect to their abundant amount and far cleaner energy compared to conventional energy sources (Dincer, 2000). For instance, solar-based energy can be collected everywhere directly (McCluney, 2005), and this can be interpreted especially nearby the place where the producers want to generate electricity. However, the available space or exact location itself needs further investigation due to solar availability in a particular area to achieve the maximum power availability. These statements support the airport authority to gain electricity from renewable source especially from solar but not limited to other renewable resources. Another issue is due to the escalation of labor and operational and maintenance cost. By using renewable energy source, electricity producer will lower the operation, maintenance and labor costs relative to the conventional energy producer (Dincer, 1998; 2000).
Taking benefits of the current situation—the tendency of cost decline—and eliminating contradiction of having higher upfront cost, this research will focus on investigating the benefits of using renewable energy. It seems remarkable to better compare the competitiveness of one type of renewable energy system to the other
70 renewable energy sources. For instances, by including all government incentives and newer agreement systems, it will require further assessment method to the implementation of decision making process. Again, this dissertation has aimed to add a significant contribution to the knowledge of decision making process and creating a new methodology and assessment tool that accommodate the use current information, especially for the airport authority to make decision in maximizing the revenue.
Chapter Summary
The need for an evaluation tool for assessing renewable energy options to become additional revenue source is significance since the current traditional airport revenue model cannot sustain the airport in the operation. A report by Bazargan,
Guzhva & Byers (2005) has shown the average general aviation to have loss in their operation—review of this literature is included in methodology development chapter.
They cannot reach the break-even due to the uncertainty of fossil-based fuel price. In addition, the sustainability movements from individual to the wide array of organization systems have encourage the need to maintain natural resource. The renewable energy options seem to be the best opportunity to utilize since they have the capability of self- replenishing in a short period of time. The implementations have been expanding in many fields including transportation, in this case airport. Therefore, an evaluation tool to assess the potencies of renewable energy in airport is required.
The incorporation of available and unused both building roofs and land have been considered as the initial assets of implementing renewable energy. Hence, a thorough assessment should be conducted in investigating these potentials with complex issues. A step by step analysis should be conducted. For instance, the step
71 should be started from the revenue assessment as a goal of the airport authority to target the revenue; continued by theoretical and available potential determination of the renewable in the certain airport area; as well as determining the financing option to support the revenue maximization. In most references, to conduct the site assessment for the renewable energy potentials, the GIS-based analysis has been taken into account to be the favorable method that can either utilize the mapping of potential renewable energy or impact analysis to the environment based on some land use constraints. However, it will require advance skillsets in doing GIS analysis. Thus, in this research the development of evaluation tool will not require such skillset—the GIS analysis is beyond the scope of this research.
Regulatory and safety from FAA guidelines are the main criteria that make this dissertation different from other studies. Methods for selecting location used for generating power are constrained by the safety and regulation of the airport system.
There are two main issues of the RES generation. The first issue is the interference to the radar and transmitter communication systems. The second issue is the airspace interference due to glare—reflectivity—from solar panel. All constraints will be placed as the criteria to choose the suitable location to place the RES around airport.
In assessing the profit maximization, the determination of the selected RES will be significant. This includes the selection of the current technology application such as the determination of the type of the system, for instances, the selections of thin-film or crystalline system for solar panel and the type of tracking system that could increase the productivity for its entire life-cycle. The first factor that will be included in the economic potential is the funding source. The funding source will influence to the length of
72 payback period of the project as well as reduce the upfront cost. Secondly, the determination of the role of airport should be obvious since this will be one of the financial influences to the rest of the renewable energy implementation. For example, if the airport is considered as federal-owned, the calculation of depreciation will not be applicable. Third, the type of agreement, financing and buying-purchase method as well as investigation of benefit from incentives should be considered to maximize the productivity that can benefit the airport. Finally, the lease option of the decision from the airport authority should follow the market value price and then the present value will be compared to the results of the RES project investment.
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CHAPTER 3 METHODOLOGY DEVELOPMENT
Research Design
This chapter is the first part of Phase II progress. This dissertation proposes a new methodology development to help airport decision makers identify and perform initial evaluation of the most applicable renewable energy system (RES) mainly in
Florida. The final outcome will be the evaluation tool to assess RES as additional revenue that can support the airport authority to sustain with the current economic situation. In order to begin the methodology development, each section will be written in a step-by-step manner.
The main design of this dissertation is an evaluation-based research to provide the decision maker—in this case airport authority—to accelerate the feasibility evaluation process during the complex renewable energy system selection. In addition, the research is designed to provide comprehensive financial information for non- traditional revenue enhancement.
In designing a research methodology, Kumar (2005, p.274)—in his book
Research Methodology—stated that evaluation research has rapidly developed in terms of reputation both in its applications and the way of the methodological developments.
He also mentioned that there are many types and various definitions of evaluation research. Also, he provided several definitions from some authors and one suitable definition that aligns with the design of this dissertation is:
Evaluation is a process of ascertaining the decision areas of concern, selecting appropriate information, and collecting and analyzing information in order to report summary data useful to decision makers in selecting among alternatives (Alkin and Solomon, 1983, p.14).
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Another literature that supports the need to collect more information in evaluation research has been described by Pedersen, Emblemsvag, Bailey, Allen & Mistree
(2000):
When dealing with empirical matters, rational beliefs are arrived at by accumulating relevant evidence; a rational individual will suspend belief until an adequate body of evidence has been accumulated and evaluated (Honderich, 2005, p.745).
Hence, this dissertation is designed to rigorously identify complex problems; provide information; formulate a new methodology and develop an evaluation tool that is useful and can be applied by the decision makers.
Decision Model and Evaluation Tool Validation Defined
This dissertation has tried to both develop a new methodology for revenue assessment in the airport and provide an evaluation tool to help airport authority do initial assessment of revenue generation harvested from renewable energy system. In doing the research process, it is required to determine a series of steps that could influence the accuracy of the conclusions (Kumar, 2005, p.153).
Kumar (2005) also explained about the validation process by incorporating expert judgment in the determination of the measurement procedures, although he also argued that this method may extend to the subjectivity of the expert. Pederson et al. (2000) has made clear definition of validation by contrasting two validation methods i.e., logical empiricist validation and relativist validation. Logical empiricist validation is rigid, too formal and usually based on true or false; yet, relativist validation defines validation as a both semi-formal and more communicative process that emphasizes on the usefulness of the novel knowledge (Pedersen et al., 2000). This literature also suggested to conduct literature reviews in order to form validity of the method; documenting each
75 stage of the process using flowchart as well as providing correct information to make it consistent. Most importantly, to accept the usefulness of a methodology, researchers may choose representative of example problems.
In case of evaluation tool inspection, the data testing should be conducted
(Qureshi, Harrison & Wegener, 1999). In addition, Kumar (2005) has suggested a reliability check by using error method compared to similar case (refer to the existing tool). The measure is the greater the error, the more unreliable the tool (Kumar, 2005, p.156). The error or consistency check of the data will be further explained in Appendix
B. After constructing the model and running the analysis, in order to measure the performance for both decision model and evaluation tool, Qureshi et al. (1999) suggested the need to conduct the sensitivity analysis after developing a model since it can test the deviation of the predicted assumptions.
To sum up this section, it is necessary to validate the proposed methodology by either conducting literature review, direct interview with experts from two different fields, decision making process and airport authority. The expert in decision making has been chosen to be an academician; and, from the airport authority will be the airport manager. The main reason of choosing these expert representatives is due to the purpose of research direction. According to Pederson et al. (2000), in accepting the methodology, the industry—in this case airport authority—tends to analyze the proposed method based on cost and benefit analysis only. On the contrary, from the scholarly perspective, the addition or the contribution to the development of scientific knowledge will be the factor. This dissertation attempts to accommodate both purposes because the case of airport revenue assessment will be both beneficial to the decision
76 making knowledge and advantageous to the airport authority in terms of providing evaluation for conducting quick assessment of renewable energy system installation at airport.
Hypothetical Argument
This dissertation has identified one literature that provides information from actual situation that occurs at the airports. General airports (GA) current financial situations are at a loss of about 60,000 dollars in average per year. To cover most of the general aviation losses, 4.9 to 11.5% additional revenues are needed to cover costs and a number higher than 11% of current revenue to financially break even (Bazargan et al., 2005, p.16). Break even here means revenues equal to expenses. In order to focus the flow of the research, this dissertation tries to formulate a hypothesis: “by diversifying non-traditional revenue in renewable energy system, there is a chance for the airport to gain additional revenue at least 5% to cover all expenses”. There are many measures in conducting feasibility analysis. In the end, all convenient measures will also be investigated to support the evaluation and preview the revenue stream results. The following sections provide step by step methodology development.
Step 1: Assessment of the Airport Revenue System
To start the methodology development, the existing revenue system of the airport should be evaluated. The literature review chapter has described the existing model of airport financial system. The financial formula for the existing and the newer system— to enhance current revenue—will be described in the following section.
Existing Financial Structure in Airport
The types of revenue and expense cover all operating and non-operating revenue and expense, but exclude, for instance, the revenue and expense of renewable 77 energy operation and maintenance as well as replacement cost due to renewable energy installation. In an FAA project report, Bazargan et al. (2005) summarized the
1999 to 2002 financial data provided by FAA National Plan of Integrated Airport System
(NIPAS). It followed the traditional yearly financial report from most general aviation
(GA) and can be seen in the following equations for the Net Income (NI) generation.
Aeronautical Revenues (AR): Landing fees = (AR1) Terminal rents = (AR2) Fixed-based operator = (AR3) Cargo = (AR4) Other aeronautical fees = (AR5) A ARa (3-1) ai
Non Aeronautical Revenues (NAR): Concessions (NAR1) Parking and rental (NAR2) Other non-aeronautical fees (NAR3)
B NARb (3-2) b1
Operating Expenses (OE): Compensation (OE1) Supplies (OE2) Services (OE3)
Other operating expenses (OE4)
C (3-3) OE c c1
Non-operating Expenses (NOE): Debt Service Payments Net of Capitalized Interest (NOE1) Sum of Capital Expenses (NOE2) Other non-operating expenses (NOE3)
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D NOEd (3-4) d i
n n NI (AR NAR ) (OE NOE ) a b c d (3-5) i 1 i 1
By formulating the financial parameters, the NI can be written to the total revenue less total expense. The formulas were translated from Bazargan et al. (2005, p.18), where index a, b, c and d refer to the type of each component and can be generalized from i equal to 1 up to n number of components. This formula will be used to design the
RES based on revenue target option.
Enhancing Revenue System in Airport
The addition of revenue source will compensate the aggregate revenue to cover the loss. Moreover, the revenue addition from renewable energy can be obtained by claiming carbon offset. Carbon offset can be used by the institution—in this case airport—to claim the offset amount from organization that offer buying carbon credit, which is usually independent organization; this is one of the innovative revenue sources from renewable energy (Landrum and Brown, 2012; Nichol, 2007). This additional revenue source will then be added to the existing financial structure. It will be obvious that the revenue target will have relationship for example to the project size, current technology efficiency and the type of the financial system such as power purchase agreement and FIT of net metering system. Therefore, the combined financial model will also include the possible revenue that can be obtained by the airports.
It is clear that to target the overall revenue increase; the airport authority can use available vacant land to produce its own electricity. For instance, a minimum of 2 acres
79 unobstructed land will be required based on the criteria suggested by Environmental
Protection Agency (EPA, 2011) for renewable energy generation—solar and wind and it will be 3 acres minimum for airport area for leasing option (Plante et al. 2010). This target should also consider the acceptance criteria of the RES project investment such as the present worth—net present value—and the return of investment (ROI). The assessment of current financial revenue system in airport will present the Step 1 of the overall decision model. Step 1 also requires the monthly energy consumption data analysis to assess the need of energy that can supply the airport because in most cases, utility cost is included in the operating expense of the airports.
Step 2: Theoretical Potential Quick Assessment—Rationale of Florida Renewable Energy Resource Potential
The objective of the quick assessment process is to find the best option among
RES competitors. The test case in this dissertation is the state of Florida. The application of the final methodology can be applied to other states. Since the problem of renewable energy prioritization involves many criteria and is complicated, the second step will describe the determination of common criteria that can be applied in airport
RES selection. Among several choices of renewable energy, the renewable potential in
Florida can be summarized in the section below.
The report from Lopez et al. (2012) has summarized the renewable potential of each state in the U.S. Table 3-1 presents the summary of renewable energy technical potential in Florida. According to a complete report by Navigant Consulting (2008) and updated in June 2010 (Putnam, 2011, p.12) the renewable energy potentials that are applicable in Florida include biomass, solar and offshore wind energy. In this case of offshore wind turbine, it will be applicable at a depth of 60 meters from the sea level with
80 class 4 and class 5 wind classifications (Navigant, 2008). The onshore wind energy is not applicable since Florida only has class 1 and class 2 wind speed classifications.
(Figure 3-1 and Figure 3-2).
Table 3-1. State of Florida renewable energy technical potential RES Type Area Count Energy Power [km2] [GW] [GW-h] Solar Urban utility-scale PV 830 40 72,787 Rural utility-scale PV 58,597 2,813 5,137,347 Roof-top PV - 49 63,987 CSP 4 - 359 Wind Onshore < 1 < 1 < 1 Offshore 1,930 10 34,684 Biomass 2 13,358 Hydrothermal < 1 < 1 Geothermal 47 374,161 Hydropower 493 < 1 682 Source: Lopez et al. (2012)
Figure 3-1. On-shore wind classification in range of class 1 to class 2 in Florida. Source: Elliot et al. (1983)
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Figure 3-2. On-shore wind potential in Florida. Source: AWS Truepower (2010)
Figure 3-3. Off-shore wind potential in Florida. Source: NREL (2009)
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A part of U.S. offshore wind for Florida can be seen in Figure 3-3. Although,
Florida has significant potential for offshore wind, the technology still cannot be applied until 2020 in Florida (Navigant, 2008). Furthermore, the main barriers of wind offshore application include the cost, which will be 1.5 times the cost of onshore wind turbine, birds and other marine mammals migration route in the offshore lines, harsh environment, and military protected training area (Powell, Simth, Cocke, Bourassa,
Collier, 2010).
For hydropower energy, future development in Florida is limited because of land condition—topography (Putnam, 2011, p.13). Same condition is also faced by the geothermal energy. Although Florida has constant temperature of 72 Fahrenheit, which reduce the electricity cost during summer and winter, it is not significant to generate power (Putnam, 2011, p.12; Navigant, 2008). Therefore, the geothermal system in
Florida cannot be used as electricity resource. Finally, the renewable energy potentials that can be used to generate electricity are solar energy and biomass.
Biomass can be considered as another potential that is applicable based on the current situation in Florida (Figure 3-4). However, the safety concern due to the wood chip combustion can cause significant issue for operating aircrafts. In fact, there is only one case study worldwide that implements biomass in airport as electricity generator as per case study description below:
United Kingdom—Standsted Airport The airport installed a 2 MW biomass boiler to supply electricity in 2008. The system consumes about 2,300 tons woodchip annually. Rationales to install renewable system in airport: 1) Reduce carbon footprint. 2) To implement renewable energy while doing expansion for the airport facility improvement.
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3) Gain economic benefit by following Feed in Tariff (FIT) and Renewable Heat Incentive (RHI) programs for on-site generation.
Although it is possible to exploit biomass as renewable energy in airport, there are factors that hinder biomass in application due to its supply chain in operational characteristics such as maintaining stock availability, transportation, loading and unloading, and storage management to maintain the moisture of the fuel—woodchip
(Rentizelas, Tolis & Tatsiopoulos, 2009). These factors will lead to the need of additional skilled-labor to maintain the storage and result in a higher operational cost.
Therefore, biomass will not be further considered as renewable energy option in Florida airports.
Figure 3-4. Biomass potential in Florida. Source: DOE (2007a)
From the above assessments, the possible energy that can be harvested near airport in Florida is solar energy. The theoretical potential of solar energy in Florida can
84 be viewed in Figure 3-5. The photovoltaics resource potential during the course of the year is averaged between 5 and 5.5 kWh/m2/day, tilted at latitude.
Figure 3-5. Global radiation for photovoltaic potential in Florida. Adapted from DOE (2007b)
Figure 3-6 presents the concentrating solar power (CSP) potential. Only certain latitude angle provides optimum solar radiation resource. The darker area is estimated to be more suitable for flat plate solar collector instead of concentrating solar power.
For example, in the northern part of Florida, the average solar radiation that is effective for CSP application is in the range of 3.5 – 4.5 kWh/m2/day. In the central part of
Florida, the average solar radiation during the course of the year is in the range of 4.5 –
5 kWh/m2/day. For the purpose of this research, the availability of airport vacant land to install CSP producer will not be investigated since the CSP is not suitable to be used in
85 airport area (DeVault, Belant, Blackwell, Martin, Schmidt, 2011). Moreover, CSP will cause safety issue in airport due to its unique reflectivity characteristic (Ho, Ghanbari &
Diver, 2009; Plante et al., 2010). To be more specific, Florida area is less suitable for the application of CSP as compared to the southwestern desert region such as
California, which has 40 to 50% higher potential (Navigant, 2008, p.55; Kibert et al.,
2010, p.4).
Figure 3-6. Solar radiation for CSP potential in Florida. Adapted from DOE (2007c)
After doing the quick assessment for renewable resource potential in the state of
Florida, the next step is to conduct site evaluation for the selected RES that is applicable. The result of the RES potential in the state of Florida is solar energy. Since the airport has strict safety and regulatory parameters, the siting evaluation should also be designed to emphasize on these parameters.
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Step 3: Siting Evaluation Using Specific Safety and Regulatory Constraints
From the quick assessment, the selected RES in Florida has been determined to be solar energy. There two steps to follow in order to evaluation using Step3. The narratives below present those two steps.
Site Location Criteria
A decision tree from EPA (2011) for site screening process has been modified to an evaluation checklist and presented in Table 3-2.
Table 3-2. Checklist for siting solar energy in airport Evaluation Criteria Sub-criteria Requirement References (Y/N) Environment Land Land > 2 acres Y EPA (2011); requirement or >3 Plante et al. acres for (2010, p.43) leasing
Roof > 30,000 ft2 Y EPA (2011)
Unshaded > 2 acres Y EPA (2011)
Unobstructed > 2 acres Y EPA (2011)
Graded road < 1 mile Y EPA (2011)
Technical Grid Transmission < 0.5 miles Y EPA (2011) availability
Obstacles Perimeter from tree > 10 feet Y EPA (2011)
Rooftop Building stories ≤ 3 Y EPA (2011)
Roof age ≤ 10 years Y EPA (2011)
Roof capability for ≈ 3-6 ft.lbs/ ft2 Y EPA (2011) load
Safety and Imaginary Height if near < 3 feet Y Plante et al. Regulatory surfaces runaway protection (2010, p.36) zone (RPZ)
Structure height < 200 feet Y Plante et al. (2010, p.36)
Transmitter > 250 feet Y Plante et al. (2010, p.42) Source: EPA (2011), Plante et al. (2010)
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As mentioned before, the safety and regulatory issues are factors that make this research different with other previous researches in renewable energy evaluation. This dissertation incorporates an additional specific FAA regulatory and safety constraint
(Table 2-2) to help airport authority evaluate the siting selection. Once the airport authority consider evaluating the site selection with a “no”, the solar project may not be viable. Therefore, this checklist can be used as a preliminary guidance to assess applicable location of siting the project.
Figure 3-7. Imaginary surfaces to protect airspace physical penetration. Adapted from Plante et al. (2010)
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The airport authority should also file “The Form 7460 under Part 77” that should be approved by FAA. This form is mandated by FAA to guarantee safety issue due to reflectivity, radar interference that can disturb imaginary surfaces as shown in Figure 3-
7 and Figure 3-8.
Figure 3-8. Airplane approach surface penetration. Adapted from Plante et al. (2010)
The imaginary surface will be used as in the geometric analysis for glare or reflectivity. In this case, Plante et al. (2010) suggests using airport traffic control (ATC) as reference point. Even though there is one case study that suggests an exception, this research is aimed to find best choices that allow airport authority to safely see the combined results of maximum profit generation while addressing safety and regulatory issues.
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Figure 3-9. Runaway protection zone diagram. Adapted from FAA (2012)
Technical and Safety Design Assessment
Most of the design calculation will follow the existing methodology in selecting the best applicable system for solar energy. However, according to Plante et al. (2010) the need to investigate glare or reflectivity is mandatory near airport area. There are three prerequisites for glare analysis on airport.
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Qualitative assessment by interviewing the pilot, control tower and airport officials
Demonstration test with the control tower
Geometric analysis using incident angle of the sun and predict the safety impact.
One of these requirements should at least be conducted during the project site assessment (Plante et al., 2010, p.39). In addition, this dissertation also includes wind load evaluation based on American Society of Civil Engineers (ASCE) 7-05 Standard.
Step 4: Determining Ownership Options
As mentioned in the literature review chapter, there are two options to finance solar project. In Step 4, the ownership scenarios should be firstly enumerated in order to assess the financial target, i.e. revenue. It is required to determine the ownership of the airport.
Ownership
Public-owned system Private-owned system
Lease the Owned and land, roof, operates the RES park area Funds, owned and Find financier organization operates the RES
Private developer, Contract Private funds, owned and with utility developer, Contract with operates RES provider owned and utility provider operates RES Lease expense
Figure 3-10. Ownership options for RES installation project on airports
For instance, there are two ownership of general aviation in the U.S., namely, government—state owned—airport and privately owned airport. Secondly, the
91 agreement method of the solar project should be investigated at the first time of the project. Figure 3-10 presents the ownership options of the intended solar project. The need to investigate ownership is mandatory since there are different incentives treatments for renewable energy project as well as depreciation benefit if the project is owned by private sector. In this research, the ownership options will be based on
Figure 3-10 as well as the addition of the agreement such as under net-metering or feed-in tariff program.
Step 5: Revenue Evaluation Using Developed Feasibility Assessment Tool
Step 1 through Step 3 require evaluations mainly based on literature study.
However, starting from Step 4, the airport authority can independently determine the best option that can be applied in the real project. The developed evaluation tool will be an integrated spreadsheet model based on Microsoft Excel® using a built-in programming language—Visual Basic Application (VBA). In the financial model analysis, the economic design criteria of acceptance will be based on return on investment (ROI) of the project, but not limited to the net present value (NPV) result comparison, and the revenue target estimation, only if the airport authority is still in loss after several years of operation. Equation 3-6 is the basis of ROI calculation.
( Benefit Cost ) i j i j ROI x100% (3-6) Cost j j
The indexes i and j are the type of each parameter. Expected criterion of acceptance of the ROI will be about 10% by referring to the average current market (Muneer,
Bhattacharya, & Canizares, 2011). This 10% criterion is the generalization; yet, for the
92 improvement this criterion can be adjusted based on the public and private entity perspectives. All other acceptance criteria and equations are presented in Appendix A.
Chapter Summary
After all five steps have been accomplished; the process of evaluating for solar project in airport is complete. The entire process of the methodology, as can be seen in
Figure 3-11, has been implemented in the state of Florida as a test case; moreover, to implement this methodology, selected airports have also been chosen to implement the entire decision making process. The selected case studies will be based on several factors such as the need of additional revenue resources, the existence of sustainability program as its master plan, the eligibility for FAA grants, and the directions of the runway that may result in a prohibited solar project installation due to glare analysis.
The size of the airport will also be considered as the comparison factor for the evaluation. Figure 3-12 provides the block diagram of the evaluation tool development.
Figure 3-13 presents the more detailed technical flowchart. In the end, the acceptance criteria of ROI will also be aligned with the best optimum angle and safety consideration.
From here, there will be a loop to find the best estimates. If the result cannot meet the intended criterion, the evaluation tool can suggest the airport authority to examine other design parameters in both technical and economic criteria. It is noteworthy that the implementation of the final methodology can refer to the states other than Florida.
However, the modification of the evaluation tool is mandatory if in the case that the applicable RES after quick assessment comes up with the result other than solar energy. Hence, Figure 3-13 should be modified based on the applicable RES technical design.
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Begin Evaluation
Assess the Airport Existing Revenue System
YES Satisfying Yearly Stop Net Income
NO Enhance Airport Revenue System
Other Revenue System RES Revenue System
Evaluation of applicable RES
Not Applicable Applicable Biomass Solar Set the Criteria for Solid Ground-mounted PV RES Assessment Landfill Gas Roof-top PV and Analysis Wind Onshore Offshore Geothermal Hydro Evaluation Tool for Assessing the Feasibility of Implementing RES as an Airport Revenue Source
Technical Design Economic Evaluation
Notes: RES = Renewable Energy Systems ROI = Return on Investment NO Expected ROI
YES
End Evaluation See Figure 3-12
Figure 3-11. Final new methodology for airport RES assessment as a revenue source
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Evaluation Tool for Assessing the Feasibility of Implementing RES as an Airport Revenue Source
Technical Design
O&M, Ownership Current Investment Replacement, Private Technologies Cost Earthwork Cost, in the Market Utility Rate Public
NO Satisfy Design Requirements
See Figure 3-13
Economic Evaluation
Discount Rate, Study Type of Incentives Energy Inflation Period Funding Rate, Loan Rate, General Inflation Rate, Depreciation Lease Option Private Public
YES NO (both) Economic YES (public) NPV Lease Measure option > NPV Lease Acceptance owning RES Note: ROI NPV = Net Present Value YES (private) NO (public)
Sensitivity Analysis
Figure 3-12. Detail evaluation tool block diagram
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Technical Design: Solar Photovoltaics System
Fixed-tilt Single-Tracking Double-Tracking
Module type Module type Module type
Thin-film Thin-film Thin-film
Mono-Crystalline Mono-Crystalline Mono-Crystalline
Poly-Crystalline Poly-Crystalline Poly-Crystalline
Monthly Use
Available Area (Criteria from Table 3-2)
Choose System to Obtain Maximum Power
Wind Load Evaluation: ASCE 7-05
Safety Check: Solar Glare Hazard Analysis Tool (SGHAT)
NO Tilt, Azimuth, Height
YES 1. Wiring Connection; Type of System Capacity (peak) 2. Estimated Total Modules 3. Maximum Yearly Power Generated 4. Array Arrangement (Requiring Module Datasheet) Notes: 1.The shaded box indicates the use of existing tool 2. ASCE = American Society of Civil Engineers
Figure 3-13. Detail technical design block diagram
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CHAPTER 4 SELECTED CASE STUDIES AND DATA PROCESSING
Selected Case Studies Overview
This chapter is part II of the Phase II and it will run the step-by-step evaluation for each of the selected case studies. The assessment will follow Step 1 through Step 4 of the final methodology.
This section describes three selected case studies for the other revenue system analysis, i.e. Jacksonville International Airport (JAX), Hendry County Airport (Airglades) and Marion County Airport (Dunnellon). However, Marion County airport will be the selected case to implement the overall model. The airport diagrams can be seen in
Figure 4-1 to Figure 4-3. The selected cases are in the state of Florida and of different characterictics, such as the capacity or the size—international or regional—and the ownership of the airports. In addition, the airports have different orientations for the runway zone. Therefore, it is adequate to select these case studies to implement the methodology that has been described in the previous chapter.
Jacksonville International Airport
JAX is owned by public in the state of Florida under Jacksonville Aviation
Authority (JAA) management and functions as an international airport. The airport is located in Duval county and the latitude and longitude of the JIA are 30.49o North and –
81.69o West at elevation 30 ft. The total land area is 8,480 acres. JAX has installed a roof-mounted system. It is a joint project with Jacksonville Electric Authority (JEA) with a contract from 2003 to 2015. JEA paid one third of the total installation cost. The project was to install 450 photovoltaic panels on the rooftop of parking garage. The solar panel is claimed to generate 7.5% of the airport’s need for customer building. The
97 reason behind the solar project cevelopment was determined by the airport management as the largest PV producer in Jacksonville. Figure 4-1 presents JAX airport diagram. Since JAX has already installed solar photovoltaics, this dissertation does not include this airport for further analysis, however brief revenue analysis has been included.
Figure 4-1. JAX airport diagram. Adapted from FAA (2013a)
Hendry County—Airglades Airport
The Airglades Airport is located in Hendry County in South Florida and its latitude and longitude are 26.44o North and – 81.03o West at elevation of 20 ft (Figure 4-2). The area is approximately 2,560 acres. Airglades Airport is still in ongoing process of privatization pilot project and the entire process plan is to be finished in mid-2013 (FAA, 98
2010). The airport will no longer be owned by City of Clewiston in Hendry County
Florida. This is a unique case study since it will represent the airport as a private- owned airport that in result will have different treatment in the incentives for the renewable energy, especially in the depreciation and tax credit by the government.
However, this dissertation limits the analysis only to Dunnellon airport. Therefore, this case will be interesting for the further research improvement as a case study.
Figure 4-2. Airglades airport diagram. Adapted from FAA (2013b)
Marion County—Dunnellon Airport
The Dunnellon airport (FAA code: X35) is owned by City of Dunnellon in Marion
County. Dunnellon airport positions between latitude 29.34 North and -82.22 longitude
West at elevation of 65 ft.
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Figure 4-3. Dunnellon airport diagram. Adapted from FAA (2013c)
The airport area is 1,706 acres. This airport serves as a public regional airport.
Figure 4-3 shows the airport diagram for Dunnellon airport.
Data Sources
Some of the data sources for the airports are publicly available online. In this case, for the revenue analysis as the preliminary assessment, the newest reports of financial statements during Fiscal Year (FY) 2012 were used and compared to the previous years. In addition, the master plan data for site selection are also available online. Preferable site location assessment will be firstly assumed based on the newest airport master plan and incorporates the checklist for site assessment. Furthermore, the results should be confirmed by the airport authority for the selected case studies.
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Unlike the data revenue and site location data, the availability of the operational data such as total energy consumption per year and monthly energy consumption of each airport are not published. Therefore, the data were obtained by contacting the airport operational officials for Marion County airport for further analysis. To begin the assessment, the sections below follow all steps of the final methodology flowchart, except for the RES selection quick assessment since the applicable RES in airport has been determined—solar energy. Therefore, the step 2 quick assessment begins by determining possible solar energy potential.
Step 1: Revenue Assessment
Most international airports are managed by a larger structure of organization.
For instance, JAX has been directed under Jacksonville Airport Authority (JAA) management. The available data are connected to JAA report. The airports that are managed under JAA include JAX, Cecil airport, Jacksonville Executive at Craig airport
(JAXEX) and Herlong airport.
JAX Airport Revenue Analysis
According to the existing revenue conditions, it is clear that JAA has experienced loss in net income (NI) before adding capital contribution (CC). Among those 4 airports, analyst cannot determine which airport has bigger contribution in profit and which airport is unproductive. However, the reported net asset changes have been entirely positive due to positive capital contribution of some on-going projects and government funds.
Table 4-1 presents 4 combined airport financial reports under JAA management.
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Table 4-1. Statement for revenue, expense and NI for JAA Jacksonville Airport Authority Statements 2004 2005 2006 2007 2008 2009 2010 2011 2012 Operating revenues:
Landing & ramp fees 11,308 13,623 13,943 15,176 12,117 11,464 12,283 12,485 13,272 Lease rentals 12,907 16,042 14,993 14,692 12,769 16,524 15,718 17,131 14,179 Parking 12,278 13,606 14,713 17,058 17,956 15,985 15,406 16,398 16,171 Concessions 11,268 12,844 12,841 14,740 14,788 14,423 13,768 14,134 14,482 Other revenues 4,469 1,686 1,867 2,177 1,716 1,513 2,104 2,334 2,313 TOTAL I ($) 52,230 57,801 58,357 63,843 59,346 59,909 59,279 62,482 60,417
Operating expenses:
Salaries & benefits 14,824 16,598 16,840 16,336 17,405 16,833 16,862 18,390 19,014 Services & supplies 12,056 11,728 11,641 12,000 12,438 11,671 12,332 13,355 13,755 Business training & travel 286 332 377 340 409 254 237 202 275 Promotion, advertisings 774 1,260 477 448 525 563 486 824 880 Utility services 2,088 3,038 3,646 3,571 4,723 5,973 5,259 5,534 5,425 Maintenance 2,878 1,950 1,979 2,454 2,590 2,085 2,200 1,981 1,978 Other expenses 996 1,658 2,245 2,287 2,788 1,833 1,549 1,743 1,797 Depreciation and 19,796 21,726 21,922 23,880 26,273 30,284 30,394 30,753 27,666 amortization TOTAL II ($) 53,698 58,290 59,127 61,316 67,151 69,496 69,319 72,782 70,790 $TOTAL I-II (Loss) (1,468) (489) (770) 2,527 (7,805) (9,587) (10,040) (10,300) (10,373)
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Table 4-1. Continued Jacksonville Airport Authority Statements 2004 2005 2006 2007 2008 2009 2010 2011 2012 Non-operating revenues
Passenger facility charges 10,668 12,060 12,450 13,130 12,398 11,506 11,329 11,195 10,743 Investment income 616 1,784 5,639 10,992 6,037 3,312 1,549 981 1,036 Payments from primary 2 79 60 12 8 31 1 1 0 government Payments from federal & 136 239 233 206 226 245 280 201 243 state agencies Contributions from other 33 63 0 0 0 0 750 0 governments Other revenues 0 0 9 1 22 0 539 516 1,180 TOTAL III ($) 11,455 14,225 18,391 24,341 18,691 15,094 14,448 12,894 13,202
Non-operating expenses
Interest expense 6,316 6,989 8,012 13,569 10,226 10,191 9,369 9,330 8,733 Contribution to other 0 340 0 0 0 10,000 0 0 0 governments Other expenses 123 185 334 226 1,888 1,463 229 166 144 TOTAL IV ($) 6,439 7,514 8,346 13,795 12,114 21,654 9,598 9,496 8,877 $TOTAL III - IV(Loss) 5,016 6,711 10,045 10,546 6,577 (6,560) 4,850 3,398 4,325 $NI before CC (Loss) 3,548 6,222 9,275 13,073 (1,228) (16,147) (5,190) (6,902) (6,048) Source: Comprehensive Annual Financial Report (CAFR) 2004 – 2012. JAA (2013).
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Starting in 2008, the total operating income has declined. It means the airport needs additional income to cover this operating loss.
Net Income
15,000
10,000 TOTAL Operating (Loss) 5,000
0 TOTAL Non-operating (Loss) 2004 2005 2006 2007 2008 2009 2010 2011 2012 (5,000) Net Income before capital (10,000) contributions (Loss)
$amount in thousands thousands in $amount (15,000)
(20,000) Fiscal Year
Figure 4-4. Net income before capital contribution for JAA
Figure 4-4 shows the average net income loss during 2008 to 2012 due to operational activities. Although the financial reports comprised of four different airports under JAA management, it can be inferred that the airports need to have additional revenue from other sources. In order to be more specific; for instance, data samples from 2012 column are taken. The analysis began by targeting the utility service from operating expense. The goal is to enhance the revenue from non-operating activity and aimed at the expense due to electricity consumption. It is clear that starting 2006, JAA has begun diversifying the revenue (Table 4-1 and Figure 4-5). The ratio between total revenues and total expenses is 92.4%. It means JAA needs additional combined revenue as much as 7.6%. This value is lower than the 1999 to 2002 study. The ratio of total revenue and expense from GA airports was ranging between 95.1 and 88.5%
(Bazargan et al., 2005, p.16). Therefore it is obvious that in the recent years JAA has struggled to maintain its financial problems.
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In 2012, the margin between utility expense and other revenue is $4,245,000—or
21.8% of the utility expense. By comparing this to the combined operating and non- operating expenses, the non-operating revenue diversification has contributed for only
1.48%. The increase of non-operating revenue diversification can help the airport authority obtain additional revenue that can cover the loss during these few years.
Other Revenue
1,200 1,180 1,000 800 600 539 516 400 200
0 0 1 22 0 $amount in thousands in $amount
Fiscal Year
Figure 4-5. Other revenues of JAA
Utility services 7,000
5,973 5,534 6,000 5,259 5,425 4,723 5,000 3,646 4,000 3,571 3,038 3,000 2,088 2,000
1,000 $amount in thousands in $amount 0
Fiscal Year
Figure 4-6. Utility service charge for JAA
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In this case, solar energy could be the best potential revenue addition choice since it has some benefits. For instance, the airport can produce its own electricity that brings significant reduction in the utility bills—charges.
Airglades Airport Revenue Analysis
Table 4-2 presents Airglades airport financial statement. From the table, the NI values for this airport were negative during FY 2009 to FY 2012. In 2009, Airglades diversified the revenue; however, it has been discontinued.
Table 4-2. Statement for revenue, expense and NI for Airglades airport Airglades Airport Statements 2009 2010 2011 2012 Operating revenues:
Intergovernmental 380,696 360,948 638,920 491,687 Landing & ramp fees 1,475,627 489,262 1,775,051 830,054 Other revenues 213,977 325,306 123,538 197,388 $TOTAL 2,070,300 1,175,516 2,537,509 1,519,129 Operating (expenses):
Transportation (475,858) (624,027) (857,808) (869,775) $TOTAL (475,858) (624,027) (857,808) (869,775) $TOTAL Operating (Loss) 1,594,442 551,489 1,679,701 649,354 Non-operating revenues:
Other revenues 464,902 0 0 0 $TOTAL 464,902 0 0 0 Non-operating (expenses)
Interest expense 0 0 0 0 General governments (1,633,317) (748,605) (1,758,290) (1,147,908) Other expenses 0 0 0 0 TOTAL (1,633,317) (748,605) (1,758,290) (1,147,908)
$TOTAL Non-operating (Loss) (1,168,415) (748,605) (1,758,290) (1,147,908) $NI (Loss) 426,027 (197,116) (78,589) (498,554)
$Begin balances 831,367 1,257,394 1,060,278 981,689 $End balances 1,257,394 1,060,278 981,689 483,135 Source: Hendry County Annual Financial Report 2009 – 2012. Hendry County (2013).
During the 2010 to 2012 period, there were no additional non-operating revenue sources that help the Airglades airport sustain its losses. The operational expenses
106 increased during 2009 to 2012. In 2012, the ratio between total revenue and expense was 75.2%. Therefore, it can be concluded that this airport needs additional revenue from innovative non-operating revenue diversification to sustain with current condition.
Net Income
2,000,000 1,500,000 1,000,000 500,000 TOTAL Operating (Loss) $ 0 TOTAL Non-operating (Loss) 2009 2010 2011 2012 (500,000) NI (Loss) (1,000,000) (1,500,000) (2,000,000) Fiscal Year
Figure 4-7. Net income before beginning balance for Airglades airport
Operating Expenses
1,000,000 857,808 869,775 900,000 800,000 700,000 624,027 600,000 475,858 $ 500,000 400,000 300,000 200,000 100,000 0
Fiscal Year
Figure 4-8. Operating expenses charge for Airglades airport
Dunnellon Airport Revenue Analysis
The reports for revenue statement of Dunnellon airport are attached in the yearly financial report. Marion County does not provide a single report for Dunnellon airport.
Table 4-3 provided below is based on Marion County financial reports from 2006 to
2012. From the table, it can be seen that there are no revenue diversifications at
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Dunnellon airport. In 2012, the margins of positive net income are slightly above the break even.
In general, during 7 fiscal years period, Dunnellon Airport had slightly positive net income value. However, the trend of two year—2011 and 2012—period show the revenue from operational activity decreased by $68,301, whereas the operating expense increased by more than twofold from the previous year which is $1,172,493.
Net income 2,500,000 2,000,000 1,500,000 1,000,000 Operating expenses 500,000 $ 0 Operating revenues 2005 2006 2007 2008 2009 2010 2011 2012 (500,000) $ NI (Loss) (1,000,000) (1,500,000) (2,000,000) (2,500,000) Fiscal Year
Figure 4-9. Net income before beginning balance for Dunnellon airport
2,500,000 Operating expenses 1,972,968 2,000,000
1,430,480 1,500,000 $ 1,000,000 800,475 707,957 749,209 648,277 613,864 484,511 500,000
0
Fiscal Year
Figure 4-10. Operating expenses charge for Dunnellon airport
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Table 4-3. Statement for revenue, expense and NI for Dunnellon airport Dunnellon Airport Statements 2005 2006 2007 2008 2009 2010 2011 2012 Operating revenues:
Landing & ramp fees 524,953 739,135 673,079 725,969 534,760 558,018 774,356 706,325 Investment income 9,419 19,190 26,496 18,383 21,229 1,056 1,081 1,225 Intergovernmental 0 0 0 38,194 127,743 716,879 119,647 1,305,454 Other revenues 2,634 1,484 1,494 4,279 0 0 7,290 0 $TOTAL 537,006 759,809 701,069 786,825 683,732 1,275,953 902,374 2,013,004
Operating (expenses):
Operating expenses (484,511) (648,277) (613,864) (707,957) (749,209) (1,430,480) (800,475) (1,972,968) $TOTAL 52,495 111,532 87,205 78,868 (65,477) (154,527) 101,899 40,036 $TOTAL Operating (Loss) 484,511 648,277 613,864 707,957 749,209 1,430,480 800,475 1,972,968
Non-operating revenues: Other revenues 0 0 0 0 0 0 0 0 $TOTAL 0 0 0 0 0 0 0 0
Non-operating (expenses) 0 0 0 0 0 0 0 0 Interest expense 0 0 0 0 0 0 0 0 General governments 0 0 0 0 0 0 0 0 Other expenses 0 0 0 0 0 0 0 0 $TOTAL 0 0 0 0 0 0 0 0 $TOTAL Non-operating (Loss) 0 0 0 0 0 0 0 0
$NI (Loss) 52,495 111,532 87,205 78,868 (65,477) (154,527) 101,899 40,036 Begin balances 309,471 361,966 473,498 560,703 639,571 574,094 419,567 521,466 End balances 361,966 473,498 560,703 639,571 574,094 419,567 521,466 561,502 Source: Marion County Annual Financial Report 2005 – 2012. Marion County (2013).
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Dunnellon Airport Energy Consumption Data
This sub section describes mainly the latest actual data for Dunnellon for FY
2012 for further complete analysis. Table 4-4 presents the data from the operational officials of Dunnellon. It can be concluded that in 2012, Dunnellon used 8,924 kilowatt- hours electricity per year. According to Marion County Master Plan (2010), Progress
Energy is the electricity provider for Dunnellon Airport. By using utility retail cost $0.119
/kWh from Progress Energy (2013a) rate, the monthly use of electricity yield to total
$1,098 per year.
Table 4-4. FY 2012 energy consumption and utility charge for Dunnellon airport 2012 2012 Month [ kWh ] [$] January 920 109 February 828 96 March 920 109 April 736 88 May 736 88 June 736 88 July 644 77 August 552 66 September 736 88 October 644 77 November 736 88 December 736 88 Year (FY 2012) 8,924 1,062 Source: Dunnellon airport operational officials
It is obvious that Dunnellon Airport is not a cost-intensive airport that uses much electricity; yet, the authority has planned to upgrade the electricity to support future facilities (Marion County Florida, 2010, p.81). This will be the opportunity to generate power in as a diversification for non-traditional revenue addition. The reason is the
110 larger the available vacant land—with the insignificant amount of electricity use—, the better opportunity to own or deal with various scheme of agreement.
Summary of Selected Case Studies Revenue Assessment
From the three existing case studies, the revenue of each airport tends to decline for the past few years. In case of Dunnellon Airport, the operating expense for FY
2011-2012 increased by more than 100% of the previous year and reduced the net income. From Table 4-3 it can be inferred that Dunnellon Airport has never diversified the non-operating revenue. Therefore, since there is no non-operating revenue, the airport can expand the revenue by for example installing the renewable energy system.
The revenue addition from non-aviation activity can help the airport to increase the overall revenue.
Step 2: Solar Energy Resource Potential Assessment
After investigating the current revenue for three case studies, for this dissertation, the example of further analysis will be limited to and focused on Dunnellon Airport; since this airport has larger area potential while consumes lower electricity use. Also,
Dunnellon Airport has never tried to diversify its revenue for 7 year period. The scenario of analyses can be varied from various aspects such as ownership options as well as developing “net zero energy” application in Dunnellon airport’s buildings.
Available Potential Areas for RES Installation
By using quick estimation using PV Watts assumption (NREL, 2012), the theoretical potential capacity of solar energy production of all airport area covered by solar photovoltaics —1,706 acres—is approximately 1,355 MW ≈ 1.4GW. However, the available resource potential to implement solar energy in Dunnellon includes lesser extent of areas such as office buildings—including terminals, officials parking area,
111 passenger parking areas and vacant land. As per Dunnellon Airport Master Plan, some area has been considered for future development. In this dissertation, the potential areas available to install solar photovoltaic have been identified by finding the areas that have future development for non-aviation development as well as the areas that are still in the airport properties line.
Figure 4-11. Non-aviation development proposed for solar farm installation
Figure 4-12. Existing building area proposed for net-zero energy design
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For preliminary site selection, these areas have been considered to be available potential of Dunnellon Airport that can produce solar energy after screening using the criteria in Table 3-2. Figure 4-11 and Figure 4-12 present the available potential areas and the maps have been generated using Google Earth.
Table 4-5. Potential locations for RES data in Dunnellon airport Proposed Systems Estimated Facilities total areas Applications Mounting Types [S.F.] Vacant land
(Area-1:green) Non- 269,842 Solar Farm-1 Ground-mounted BOS (Fixed /Tracker) aviation development (utility scale)
(Area-2:blue) Non- 243,390 Solar Farm-2 Ground-mounted BOS (Fixed /Tracker) aviation development (utility scale)
(Area-3:red) Non- 642,223 Solar Farm-3 Ground-mounted BOS (Fixed /Tracker) aviation development (utility scale)
(Area-4: purple) Non- 503,004 Solar Farm-4 Ground-mounted BOS (Fixed) aviation development (utility scale)
(Area-5: north orange) 124,127 Solar Farm-5 Ground-mounted BOS (Fixed) Non-aviation (utility scale) development
(Area-6: middle from 95,551 Solar Farm-6 Ground-mounted BOS (Fixed) north orange) Non- (utility scale) aviation development
(Area-7: south orange) 232,216 Solar Farm-7 Ground-mounted BOS (Fixed ) Non-aviation (utility scale) development
Building rooftop
(north red) Rooftop-1 10,447 Net-Zero Roof-mounted BOS (south red) Rooftop-2 5,084 Net-Zero Roof-mounted BOS
Parking area
(north blue) Parking-1 3,245 Net-Zero Structure (south blue) Parking-2 9,602 Net-Zero Structure
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Table 4-5 presents the summary of potential areas to install renewable energy system project, in this case solar photovoltaics.
In case of the building and parking areas, the goal of the study is to maintain the electricity use of Dunnellon Airport to achieve net zero energy. The energy scenarios will be further investigated using the developed feasibility assessment tool in the integration chapter. For the purpose of the analysis, the areas for the building rooftop-1 and rooftop-2 are symbolized using the red color and the parking area-1 and parking area-2 are symbolized using blue color. The northern sides are marked with number “1” and the southern areas are marked with number “2”.
Summary of Resource Potential Analysis
From the theoretical potential evaluation, not all of the Dunnellon airport area can be used to produce solar energy. To determine the available potential, the master plan has supported the screening process of environmental impacts and the implementation of the proposed systems will assume that the site selection checklist table (Table 3-2) has been fulfilled. The total of the area is 2,138,731 square feet and the estimated capacity is 39 MW—it translates only 2.8 % of total theoretical potential. In addition, another screening for safety and more technical analysis should be conducted to estimate the appropriate installation of solar energy. This means the chance of the solar project investment has to be more distinguished if it is compared to other facilities other than airports to satisfy all criteria. The next step is to make sure these areas are met the safety and regulatory issues as per requirement by FAA.
Step 3: Airport RES Siting Evaluation
For the siting process, this dissertation provides two evaluations, i.e. wind load evaluation for the system design and glare safety analysis. In most central and 114 southern US continent, tilt angle variations of ± 15 degrees and the azimuth variations of ± 45 degrees (due south-west to south-east) still can receive a minimum 90% of the yearly solar radiation (Brooks & Dunlop, 2012, p.19). However, to counter many iterative processes in the safety glare analysis phase, this dissertation only analyzes tilt variations ranging ± 3 degree from latitude.
Wind Load Evaluation Based on ASCE 7-05
The methodology used to determine wind loading in Marion County Airport— specifically on PV panel—is referring ASCE 7-05 Chapter 6—this dissertation provides the applicable reference tables from ASCE 7-05 in Appendix D. It is assumed that the configuration of the panel will follow mono-slope roof. Based on the National
Aeronautics and Space Administration (NASA) 2013 data for the specific location—
Ocala Automated Weather Observing System (AWOS)—the average wind speed at 33 feet (10 m) above the ground level is 3.21 m/s (7.2 mph) for 10-year average. By referring to ASCE 7-05 Figure 6-1B basic wind speed, Dunnellon Airport area is between 100 and 110 mph with the 3 second gust at 33 feet (10 m). According to
Marion County Master Plan, Table 1-1 (2010, p.1-3)—based on the size of applicable aircrafts to land and take off on airport—the crosswind speed coverage for Runway 05-
23 is no more than 12 mph while Runway 09-27 is not exceeding 15 mph. Therefore, for the actual solar photovoltaics implementation on the airport, the design should follow the applicable highest wind velocity on the airport area. In this case, the panel installation should sustain the pressure due to the highest wind velocity—110 mph
(161.33 ft/s).
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Determining panel area. Since the wind contact surface is important, the value of the area will follow the following justification. NREL has identified a 2010 study of residential benchmark using panel area of (0.808 m x 1.580 m) ≈ 1.28 m2 and 14.5% efficiency (NREL, 2012, p.9). Meanwhile, for utility application, a 1.96 m2 panel of
14.5% efficiency can be implemented (NREL, 2012, p.13). This dissertation has inputted various values of peak power produced and available panel efficiency in the evaluation tool; yet, the panel size is set to the predetermined panel module area somewhere in the middle using (1.596 m x 1.049 m) ≈ 1.67 m2 –based on Sunpower
Model E20/333 (Sunpower, 2011).
Ground mounted and tracker. By following sections 6.5.13.2 and 6.5.15 of
ASCE 7-05, the approach for wind loading of a panel will use the equation below:
F p A [lb] (4-1) where,
p = net pressure at certain elevation. G = gust-effect factor, Section 6.5.8 ASCE 7-05. CN = net pressure coefficient, Figs. 6-18A, ASCE 7-05. A = normal projected area to the wind direction, ft2
The net pressure due to wind velocity will use the following equations:
p qz G CN [psf] (4-2) where,
qz = velocity pressure at certain elevation z. G = gust-effect factor, Section 6.5.8 ASCE 7-05. CN = net pressure coefficient, Figs. 6-18 ASCE 7-05.
2 qz 0.00256 Kz Kzt Kd V I [psf] (4-3) where,
Kd = directionality factor Table 6-4 ASCE 7-05.
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Kz = velocity pressure exposure, Section 6.5.4.4 Table 6-3 ASCE 7-05. Kzt = topographic factor, Section 6.5.7.2 ASCE 7-05. V = basic wind velocity I = importance factor (Table 6-1 ASCE 7-05, Category I; with > 100 mph).
The panel area in square feet is equal to 15.55 ft2; the maximum allowable height of 3 feet with the centroid of the panel is 1.5 feet; the directionality factor—defined as rectangular cross section structure type—is 0.85; the gust effect factor is assumed to be for a rigid structure, which is 0.85; the value of velocity pressure with category C
(assumed as grassland is 0.85; meanwhile the topographic factor is set to be 1 since there are no conditions met (not an area that is surrounded by hills), and importance factor is 0.77; hence by substituting the values to equation 4-3, the result for the velocity pressure at 33 feet height is:
2 qz 0.00256 (0.85)(1)(0.85)(161.33) (0.77) [psf]
37.068 [psf]
In order to estimate the precise speed at 1.5 (ground mounted, lower than 3 feet installation limit near runway and taxiway) feet elevation, the power velocity equation 4-
4 can be utilized to estimate the actual speed at the height of module installation.
V b z V 88 z 33 ref 60 (4-4) where,
Vz = hourly mean wind velocity at a certain elevation 88 = mph to feet per second 60 b = constant from Table 6-2 ASCE 7-05 z = topographic factor, Section 6.5.7.2 ASCE 7-05 = constant from Table 6-2 ASCE 7-05 Vref = basic wind speed (for Dunnellon case is 110 mph)
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From Table 6-2 ASCE 7-05, the value of b and α are 0.65 and 1/6.5 respectively.
Therefore, by substituting the known values to equation 4-4, the value at 1.5 feet height will yield to Vz equal to the following calculations:
1 6.5 V 0.65 1.5 161.33 z 33
65.34 [mph]
At this point, by substituting the wind speed value to equation 4-3, the pressure related to the designated height at 1.5 feet is:
2 qz 0.00256 (0.85)(1)(0.85)(65.34) (0.77) [psf]
6.08 [psf]
For tracker system, the height of the centroid is based on available application height (Genpro, 2011), the middle value for installation has been chosen to be 84” or equal to 7 feet (different heights are provided, although in this case only one height case is presented to show hand calculation example and line up with the SGHAT tool).
By following similar assumptions, the velocity value at 7 feet will yield to , whose value is determined by the following calculations:
1 6.5 V 0.65 7 161.33 z 33
82.71 [mph]
2 qz 0.00256 (0.85)(1)(0.85)(82.71) (0.77) [psf]
9.74 [psf]
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Figure 4-13. Interpolation results for net pressure coefficient (load case A)
Figure 4-14. Interpolation results for net pressure coefficient (load case A)
The values of the net velocity pressure at designated tilt angles should be aligned with the designated tilt angles for the SGHAT analysis. Figure 4-13 and Figure
4-14 present the interpolation calculations as well as the results of the expected values of net pressure coefficient. These values are marked in the shaded rows.
By taking the values for the tilt angles between 26 and 32 degrees, and substituting the known values into equation 4-2, the final net pressure due to angle
119 inclination can be summarized in the following tables. Table 4-6 through Table 4-9 provide results at 33 feet height.
Table 4-6. Net pressure results at 0 degree wind direction (load case A) G qz Angle C C 0.85 37.068 [degree] NW NL p [psf] p [psf] 26 -1.51 -1.55 -47.58 -48.84 27 -1.68 -1.54 -52.93 -48.52 28 -1.72 -1.52 -54.20 -47.90 Lat. (29.2) -1.77 -1.51 -55.77 -47.58 30 -1.8 -1.5 -56.71 -47.26 31 -1.8 -1.5 -56.71 -47.26 32 -1.8 -1.5 -56.71 -47.26 (-) negative sign refers to leaving top surface
Table 4-7. Net pressure results at 180 degree wind direction (load case A) G qz Angle C C 0.85 37.068 [degree] NW NL p [psf] p [psf] 26 1.88 1.94 59.23 61.13 27 1.94 1.98 61.13 62.39 28 1.99 2.02 62.71 63.65 Lat. (29.2) 2.06 2.07 64.91 65.22 30 2.1 2.1 66.17 66.17 31 2.1 2.1 66.17 66.17 32 2.1 2.1 66.17 66.17 (+) positive sign refers to approaching top surface
Table 4-8. Net pressure results at 0 degree wind direction (load case B) G qz Angle C C 0.85 37.068 [degree] NW NL p [psf] p [psf] 26 -2.44 -0.39 -76.88 -12.30 27 -2.46 -0.42 -77.51 -13.23 28 -2.48 -0.44 -78.14 -13.86 Lat. (29.2) -2.49 -0.48 -78.45 -15.12 30 -2.5 -0.5 -78.77 -15.75 31 -2.48 -0.51 -78.14 -16.07 32 -2.47 -0.53 -77.82 -16.70 (-) negative sign refers to leaving top surface
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Table 4-9. Net pressure results at 180 degree wind direction (load case B) G qz Angle C C 0.85 37.068 [degree] NW NL p [psf] p [psf] 26 2.39 0.84 75.30 26.47 27 2.44 0.88 76.88 27.73 28 2.49 0.92 78.45 28.99 Lat. (29.2) 2.56 0.97 80.66 30.57 30 2.6 1 81.92 31.51 31 2.61 1.01 82.24 31.82 32 2.63 1.03 82.87 32.45 (+) positive sign refers to approaching top surface
Table 4-10 through Table 4-13 provide results at 1.5 feet height as per designated ground mounting system.
Table 4-10. Net pressure results at 0 degree wind direction (load case A) G qz Angle C C 0.85 6.08 [degree] NW NL p [psf] p [psf] 26 -1.51 -1.55 -7.80 -8.01 27 -1.68 -1.54 -8.68 -7.96 28 -1.72 -1.52 -8.88 -7.86 Lat. (29.2) -1.77 -1.51 -9.15 -7.80 30 -1.8 -1.5 -9.30 -7.75 31 -1.8 -1.5 -9.30 -7.75 32 -1.8 -1.5 -9.30 -7.75 (-) negative sign refers to leaving top surface
Table 4-11. Net pressure results at 180 degree wind direction (load case A) G qz Angle C C 0.85 6.08 [degree] NW NL p [psf] p [psf] 26 1.88 1.94 9.72 10.026 27 1.94 1.98 10.02 10.236 28 1.99 2.02 10.28 10.44 Lat. (29.2) 2.06 2.07 10.65 10.69 30 2.1 2.1 10.85 10.85 31 2.1 2.1 10.85 10.85 32 2.1 2.1 10.85 10.85 (+) positive sign refers to approaching top surface
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Table 4-12. Net pressure results at 0 degree wind direction (load case B) G qz Angle C C 0.85 6.08 [degree] NW NL p [psf] p [psf] 26 -2.44 -0.39 -12.61 -2.02 27 -2.46 -0.42 -12.71 -2.17 28 -2.48 -0.44 -12.82 -2.28 Lat. (29.2) -2.49 -0.48 -12.87 -2.48 30 -2.5 -0.5 -12.92 -2.58 31 -2.48 -0.51 -12.87 -2.64 32 -2.47 -0.53 -12.77 -2.74 (-) negative sign refers to leaving top surface
Table 4-13. Net pressure results at 180 degree wind direction (load case B) G qz Angle C C 0.85 6.08 [degree] NW NL p [psf] p [psf] 26 2.39 0.84 12.35 4.34 27 2.44 0.88 12.61 4.55 28 2.49 0.92 12.87 4.75 Lat. (29.2) 2.56 0.97 13.23 5.01 30 2.6 1 13.44 5.17 31 2.61 1.01 13.49 5.22 32 2.63 1.03 13.60 5.32 (+) positive sign refer to approaching top surface
Table 4-14 through Table 4-17 provide results at 7 feet height as per designated rack with four panels mounting system.
Table 4-14. Net pressure results at 0 degree wind direction (load case A) G qz Angle C C 0.85 11.48 [degree] NW NL p [psf] p [psf] 26 -1.51 -1.55 -14.73 -15.12 27 -1.68 -1.54 -16.39 -15.03 28 -1.72 -1.52 -16.78 -14.83 Lat. (29.2) -1.77 -1.51 -17.27 -14.73 30 -1.8 -1.5 -17.56 -14.64 31 -1.8 -1.5 -17.56 -14.64 32 -1.8 -1.5 -17.56 -14.64 (-) negative sign refers to leaving top surface
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Table 4-15. Net pressure results at 180 degree wind direction (load case A) G qz Angle C C 0.85 11.48 [degree] NW NL p [psf] p [psf] 26 1.88 1.94 18.35 18.93 27 1.94 1.98 18.93 19.32 28 1.99 2.02 19.42 19.71 Lat. (29.2) 2.06 2.07 20.10 20.20 30 2.1 2.1 20.49 20.49 31 2.1 2.1 20.49 20.49 32 2.1 2.1 20.49 20.49 (+) positive sign refer to approaching top surface
Table 4-16. Net pressure results at 0 degree wind direction (load case B) G qz Angle C C 0.85 11.48 [degree] NW NL p [psf] p [psf] 26 -2.44 -0.39 -23.81 -3.81 27 -2.46 -0.42 -24.00 -4.10 28 -2.48 -0.44 -24.20 -4.29 Lat. (29.2) -2.49 -0.48 -24.30 -4.68 30 -2.5 -0.5 -24.40 -4.88 31 -2.48 -0.51 -24.20 -4.98 32 -2.47 -0.53 -24.10 -5.17 (-) negative sign refers to leaving top surface
Table 4-17. Net pressure results at 180 degree wind direction (load case B) G qz Angle C C 0.85 11.48 [degree] NW NL p [psf] p [psf] 26 2.39 0.84 23.32 8.20 27 2.44 0.88 23.81 8.59 28 2.49 0.92 24.30 8.98 Lat. (29.2) 2.56 0.97 24.98 9.47 30 2.6 1 25.37 9.76 31 2.61 1.01 25.47 9.86 32 2.63 1.03 25.66 10.05 (+) positive sign refer to approaching top surface
From Table the results in table 4-6 to Table 4-17, the absolute net pressure wind speed values are in the range of 2.02 [psf] at a tilt angle 26 degree, with 0 degree wind direction and load B scenario. Meanwhile, the highest pressure is 82.87 [psf]. The
123 variation of angles and the type of load contribute to the degree of velocity pressure.
According to a research project report on comprehensive assessment of a solar energy project in Turkey Lake Service area, the solar panel itself can resist the wind pressure velocity of up to 50 psf. (Kibert, et al., 2010, p.79). The tilt variation will produce the high wind pressure at a certain angle. For instance, at 27-degree tilt, in Table 4-6 at wind angle of zero degree, the pressure value starts increasing under windward direction of 52.93 psf or greater. In addition, the pressure values also increase under leeward direction with wind direction of 180 degrees.
In terms of wind loading, the cross sectional area of one predetermined solar panel will be the multiplication factor of the net wind speed pressure. For instance, using equation 4-1, the allowable load is:
F 50 (15.55) [lb]
777.5 [lb]
In order for the actual load not to exceed the design load for this single panel, for a specific string arrangement, the area should be determined by using the number of panels in a string arrangement area multiplied by the wind pressure. The designated results should not exceed the designated load of 777.5 lb for each panel as well as each string.
For the 90-degree wind direction, Table 4-18 presents part of 90-degree wind direction with a length of 1.596 meters or 5.24 feet, and a height of 1.5 feet; hence, the horizontal distance from the edge of windward is larger than two times the height of the centroid (> 2 h) for ground mounted system.
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Table 4-18. Part of net pressure coefficient from Figure 6-18D ASCE 7-05 Clear Horizontal distance from windward edge Roof angle Load case window flow A -0.3 >2h ≤ 45 o B 0.3
Table 4-19. Net pressure results at 90 degree wind direction (33 feet) Load Case G qz Angle 0.85 37.068 [degree] A B p [psf] p [psf] All angles -0.3 0.3 -9.45 9.45
Table 4-20. Net pressure results at 90 degree wind direction (1.5 feet) Load Case G qz Angle 0.85 6.08 [degree] A B p [psf] p [psf] All angles -0.3 0.3 -1.55 1.55
Table 4-19 and 4-20 present wind net pressure calculation results for both designated heights, i.e. 33 and 1.5 feet.
For tracker system, with four vertically arranged modules, the wind direction length equals to four times the width of 1.049 m, which is equal to 4.196 meters or 13.76 feet.
With the height of 7 feet, the reference for Figure 6-18D from ASCE 7-05, the horizontal distance from the edge of windward is greater than the height of centroid yet less than twice that value (> h, ≤ 2h). Table 4-21 represents part of reference table from ASCE 7-
05. Table 4-22 provides the net pressure result for 7 feet with 90 degree wind direction.
Table 4-21. Part of net pressure coefficient from Figure 6-18D ASCE 7-05 Load Clear Horizontal distance from windward edge Roof angle case window flow A -0.6 >2h, ≤ 2h ≤ 45 o B 0.5
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Table 4-22. Net pressure results at 90 degree wind direction (7 feet) Load Case G qz Angle 0.85 11.48 [degree] A B p [psf] p [psf] All angles -0.6 0.5 -5.85 4.87
Roof mounted. Referring to the master plan of Dunnellon airport, the roof height is approximately 12 feet above the ground. By assuming that the centroid is also 12 feet high, the velocity value at this height will be Vz , whose value can be determined from the following calculations:
1 6.5 V 0.65 12 161.33 z 33
89.82 [mph]
2 qz 0.00256 (0.85)(1)(0.85)(89.82) (0.77) [psf]
13.51 [psf]
The hand calculation will refer to Figure 6-18B from ASCE 7-05. All assumptions remain typical.
Figure 4-15. Interpolation results for net pressure coefficient (load case A)
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Figure 4-16. Interpolation results for net pressure coefficient (load case B)
Figure 4-15 and 4-16 present the interpolation results of net pressure coefficient.
These values will be treated in the same manner using similar formulas.
Table 4-23. Net pressure results at 0 and 180 degree wind direction (load case A) G qz Angle C C 0.85 13.51 [degree] NW NL p [psf] p [psf] 26 1.19 0.19 13.67 2.18 27 1.22 0.22 14.01 2.53 28 1.24 0.24 14.23 2.80 Lat. (29.2) 1.27 0.27 14.60 3.10 30 1.3 0.3 14.93 3.45 31 1.3 0.34 14.93 3.94 32 1.3 0.38 14.93 4.36 (-) negative sign refers to leaving top surface
Table 4-24. Net pressure results at 0 and 180 degree wind direction (load case B) G qz Angle C C 0.85 13.51 [degree] NW NL p [psf] p [psf] 26 -0.1 -0.845 -1.15 -9.70
27 -0.1 -0.858 -1.15 -9.85 28 -0.1 -0.872 -1.15 -10.01
Lat. (29.2) -0.1 -0.887 -1.15 -10.19 30 -0.1 -0.9 -1.15 -10.34
31 -0.113 -0.86 -1.30 -9.88 32 -0.126 -0.82 -1.45 -9.42
(+) positive sign refer to approaching top surface 127
Table 4-25. Net pressure results at 90 degree wind direction (12 feet) Load Case G qz Angle 0.85 13.51 [degree] A B p [psf] p [psf] All angles -0.6 0.5 -6.90 5.74
From Figure 4-12, it is apparent that the horizontal distance from the edge of windward for both parking and building roof is more than twice of the inclination at centroid. Therefore, by referring back to Table 4-18, the values will be used as a reference table for the net pressure coefficient to calculate the wind pressure at an angle of 90 degree.
Concluding these results, at the standard height—33 feet—it is not recommended to install solar PV with an angle of larger than 26 degrees because the module has a maximum design pressure of 50 psf and calculations show that the pressures at these angles exceed the designated pressure. In case of the ground mounted installation with the height of the centroid of 1.5 feet, various tilt angles configurations still result in a pressure of lower than 50 psf. For the tracker application at a height of 7 feet, the wind pressure values are still under the acceptable value. The ground mounted solar farm with a maximum height of 3 feet, ground mounted installation near both runaway and taxiway, and 7-feet high tracker will be further investigated using SGHAT tool. Most airports however tend to avoid installing PV panels for roof application (FAA, 2010; Devault et al., 2012). From Table 4-23, the highest wind pressure is 14.93 psf (32 degree tilt design). The maximum acceptable criterion is 6 ft-lbs/sq.ft ≈ 6 lbs/ft (EPA, 2011). The total length of a module is 5.24 feet.
In order for a module to sustain the load, the maximum allowable load is 31.44 lbs. By using equation 4-1, the load yields to 231.42 pounds for module area equal to 15.5
128 square-feet. From this, the roof should experience 231.42 pounds divided by 5.24 feet which yields to 44.2 lb/ft, which is higher than the allowable. After repeating this method, no acceptable value is retained. All values surpass the allowable load of 31.44 lbs. At tilt angles ranging from 26 to 32 degrees, the roof cannot sustain the load due to the wind pressure per square feet area of a module—it also represents the entire roof if it is covered by panel arrangement. The next step is to do further analysis by incorporating SGHAT tool to check the glare occurrence. If all roof areas are determined to be safe, there could be possibility to install additional structure to strengthen the roof; hence, the existing roof can withstand this load. The treatment will be different in the parking roof. Dunnellon airport may design new structures to sustain the maximum allowable load from the wind. Therefore, it is assumed that there will be no issues to install photovoltaic panels on the top of parking structures.
Safety Check: Incorporating Solar Glare Hazard Analysis Tool (SGHAT)
The detailed safety analysis as required by FAA should be followed by all airports. Sandia National Laboratories has worked with the US DOE to develop a tool for glare hazard analysis. The tool for glare analysis was firstly published in October
2012 in a report format and newly introduced to be used in April 2013 and updated in
July 2013 (DOE, 2013a). In addition, the tool has been approved by FAA for the glare analysis of solar energy installation on airport areas.
SGHAT data input and set up. In the safety check process, the constraints analyzed are the FAA antennae, “T”; flight path number 23, “F1” and flight path number
27, “F2”. All inputs are set to be conservative as per SGHAT default values. In this dissertation, for future purpose, the FAA antennae have been included although the
FAA antennae are considered not to be significant for the glare analysis. Since there is 129 a chance for the airport authority to install solar power system near the runway and taxiway, the analysis has included 3 and 7 feet high installation—utility scale application. The areas designated for only ground mounted installation, by following the
3 feet criterion, are Area-4; Area-5; Area-6 and Area-7. Meanwhile, the areas that are possible for both ground-mounted and tracker mounting systems are Area-1, Area-2 and Area-3. Rooftop and Parking Structure will be analyzed using the 12 feet high centroid of the panel array arrangement. The complete examples of the SGHAT results can be seen in Appendix C.
Data processing and interpretation. The summary of the data processing is from various tilt and azimuth variation matrices. Table 4-30 through 4-43 present the summary of the data processing results for solar energy installation in various degrees of inclination and directions (azimuth)—“N” stands for no glare; meanwhile, “G” stands for predicted glare. The shaded values will be used as the baseline for analysis and the values in rectangle are the recommended values for further investigation.
Figure 4-17. Matrix summary from SGHAT results for Solar Farm-1 (ground-mounted)
Figure 4-18. Matrix summary from SGHAT results for Solar Farm-2 (ground-mounted)
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Figure 4-19. Matrix summary from SGHAT results for Solar Farm-3 (ground-mounted)
Figure 4-20. Matrix summary from SGHAT results for Solar Farm-1 (tracking-system)
Figure 4-21. Matrix summary from SGHAT results for Solar Farm-2 (tracking-system)
Figure 4-22. Matrix summary from SGHAT results for Solar Farm-3 (tracking-system)
Figure 4-23. Matrix summary from SGHAT results for Solar Farm-4 (ground-mounted)
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Figure 4-24. Matrix summary from SGHAT results for Solar Farm-5 (ground-mounted)
Figure 4-25. Matrix summary from SGHAT results for Solar Farm-6 (ground-mounted)
Figure 4-26. Matrix summary from SGHAT results for Solar Farm-7 (ground-mounted)
Figure 4-27. Matrix summary from SGHAT results for Rooftop-1
Figure 4-28. Matrix summary from SGHAT results for Rooftop-2
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Figure 4-29. Matrix summary from SGHAT results for Parking-1
Figure 4-30. Matrix summary from SGHAT results for Parking-2
Summary of RES Siting Evaluation
The SGHAT tool outputs have shown that not all possible available areas can be utilized for solar panel installation. The wind evaluation calculations with 1.5 feet centroid height (maximum height 3 feet) and 7 feet centroid height indicated that there is no problem in net pressure due to wind condition in Florida at standard height of 33 feet.
However, at height of 33 feet, care must be taken for the tilt angles of greater than 27 degrees because the results showed wind pressures that surpass the allowable panel load. Fortunately, for the specific height design lower than 33 feet, i.e. 3 and 7 feet, all calculations showed that the net pressure results are still within the acceptable range.
In terms of rooftop and parking structure wind evaluation results, unexpected load occurs at 12 feet height with tilt angles of 26 to 32 degrees. The calculations yielded values that are greater than the allowable roof load of 3-6 ft.lbs/ft2 (EPA, 2011). For glare analysis results, by referring to Figure 4-24 through Figure 4-26, glare occurs for
133 all designated tilt and azimuth angles. Therefore, these areas are not suitable for solar panel installation. It is obvious that not all areas support the solar panel installation.
Step 4: Ownership Options
In this section, the Dunnellon Airport ownership options will refer to Figure 3-10.
The descriptions below present the stakeholders that will be involved during the RES project scenarios. There are two options of ownership in the RES project as explained below:
Option 1: Dunnellon airport as the owner. Dunnellon is a public-owned airport. Under this option, Dunnellon Airport authority acts as the owner. It has to further finance, maintain and operate the RES generator for its entire life cycle. Since the system is assumed to be grid-connected—utility interactive—Dunnellon should connect the RES generator to the utility provider—in this case Progress Energy. This will bring the RES project under PPA system. Under this system, there will be two options of agreement either under net metering or feed-in tariff.
Option 2: Dunnellon airport to lease the facility—building and land. Under this option, Dunnellon functions as the land lord. Either private developer or private utility provider acts as the financier body, owner and operator. In case of leasing, according to the airport website, the annual leasing rate for aviation activity is minimum
$0.05/S.F; meanwhile, for the non-aviation development is $0.04/S.F (Marion County
Florida, 2013). The role of the private developer is as the system owner, operator as well as the one financing the project. Private developer usually brings another institution to be involved with the project as the financier.
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Chapter Summary
This chapter has described Step 1 through Step 4 applications. The analysis firstly begins by determining case studies that depict the current financial situation in terms of revenue for airports. Subsequently, the analysis should focus on one case study—Dunnellon Airport as a public-owned airport—and further scrutinize the theoretical and available potentials. The theoretical potential to install all Dunnellon
Airport is about 1,355 MW of solar energy system, whereas the available potential is 39
MW. It translates that only 2.8% of the solar energy system can be harvested.
Following this result, further investigations—including siting evaluation based on wind load and glare analysis—have shown that not all area can support installation since airport has a set of constraints, which mainly is glare to that poses safety and operational concerns. Although all 11 predetermined possible areas have indicated distance of more than 250 feet from both runway and taxiway, 3 areas at the middle are not ideal for tracker system installation due to the height constraint. The height of these areas is 3 feet and the tracker system needs a greater height, in this case 7 feet. For the 12-feet height—net zero energy scenario, rooftop installation indicated possible higher load as compared to the allowable constraint. Only the parking area seems suggestible for the application of net zero energy building.
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CHAPTER 5 INTEGRATION WITH DEVELOPED AIRPORT RENEWABLE ENERGY SYSTEM REVENUE ASSESSMENT TOOL
Introducing Newly Developed Airport Revenue Assessment Tool for Solar Energy
This chapter is the second part of Phase II progress. The newly developed tool is a spreadsheet based analysis focuses mainly on the solar energy assessment.
Under VBA environment, the tool has been designed to utilize the data downloaded from PV Watts (NREL, 2012) with 42 stations incorporating the National Solar Radiation
Data Base (NSRDB) during 1991 to 2005—the updated version Typical Meteorological
Year (TMY) 3 database. The database validation using error method is explained in the tool validation in Appendix B. Figure 5-1 shows the main dashboard to begin the analysis.
Figure 5-1. Main dashboard of airport revenue assessment tool for solar energy
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Additionally, the users can input financial variables by referring to the cost estimations that have been documented in the evaluation tool under the help menu— the cost estimation data are based on the updated data from Barbose, et al. (2013).
Since all cost data related to power purchase agreement vary, the analyst can input the values based on the appropriate values in each area of analysis. This will simply allow analyst to refer all costs for further revenue analysis. Figure 5-2 presents the interface of economic evaluation analysis. The complete guideline to operate the tool has been provided in Appendix E.
Figure 5-2. Economic dashboard of airport revenue assessment tool for solar energy
Data Input and Assumptions
From the results of Step 1 to Step 4, out of 11 possible areas, 3 areas are not suitable for the installation of solar panels. Table 5-1 presents the summary of the specific available potential to install solar panel for net zero energy scenario and utility
137 scale solar power generation. The solar energy system are divided into three system applications i.e. ground mounted, tracker and rooftop.
Table 5-1. Summary of solar energy system siting evaluation Design Status Potential Site System Remarks Tilt Azimuth Height Wind Glare [o] [o] [feet] Solar Farm-1 26-32 135 1.5;3 OK OK Ground 26-32 135 7 OK OK Tracker
Solar Farm 2 26-32 135 1.5;3 OK OK Ground 26-32 135 7 OK OK Tracker 26 195 1.5;3 OK OK Ground
Solar Farm 3 26-32 135 1.5;3 OK OK Ground 26-32 135 7 OK OK Tracker 26-32 150 1.5;3 OK OK Ground 26-32 150 7 OK OK Tracker 26 195 1.5;3 OK OK Ground 26 165 7 OK OK Tracker
Solar Farm 4 26-30 135 1.5;3 OK OK Ground
Solar Farm 5 26-32 135-225 1.5;3 OK Not OK Ground not applicable
Solar Farm 6 26-32 135-225 1.5;3 OK Not OK Ground not applicable
Solar Farm 7 26-32 135-225 1.5;3 OK Not OK Ground not applicable
Rooftop-1 26-30 135 12 Not OK OK Roof additional roof structure required
Rooftop-2 26-32 135 12 Not OK OK Roof additional roof structure required
Parking-1 26-32 135 12 OK OK Roof new structure
Parking-2 26-30 135 12 OK OK Roof new structure
The summary of Step 1 to Step 4 will be used as the input for the newly developed revenue assessment tool for solar energy in airport. All values will be compared to the baseline values—tilted at latitude and oriented to the south azimuth. In order to
138 generate the revenue input, the analyst may either use the monthly use of electricity
(Table 4-4) or area for the preliminary input. The type of scenario will also be required since the results produced will be different.
Financial Parameters
The financial parameters used are based on the U.S. current situation (DOE,
2013b; Rushing, Kneifel & Lippiatt, 2013)—the values have been set up as default values for financial parameter in the economic interface input. Table 5-2 presents the values of financial parameters assumptions.
Table 5-2. Financial parameters input assumptions Parameter Public Reference Adjusted Private Reference Adjusted DOE (2013b); DOE (2013b); Study 30 years Rushing et al. - 30 years Rushing et al. - (2013) (2013) Progress Progress 10 and 10 and Energy - Energy - 25 years 25 years (2013d) (2013d)
Loan rate N/A DOE (2013b) 6% 6% DOE (2013b) -
Discount Rushing et al. 3 % - 4.6% DOE (2013b) 5% rate (2013)
Energy Rushing et al. Rushing et al. 1.17% 1% 1.95% 2% inflation rate (2013) (2013)
General 0.9% DOE (2013b) 1% 1.3% DOE (2013b) 2% inflation rate
Federal N/A DOE (2013b) N/A 34% DOE (2013b) 30% income tax
State N/A DOE (2013b) N/A 6.5% DOE (2013b) N/A income tax
Due to the consideration for computation time, some of the values will be adjusted to the more conservative assumptions (no decimal values) for further analysis.
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As can be seen in Table 5-2, the default values in the economic input of the airport revenue feasibility assessment tool will refer to the adjusted values. The results of the calculations will be presented in chapter results and discussions.
Public and Private Sector Defined
With respect to ownership option, the economic dashboard of the assessment tool requires the ownership type. In this case, Dunnellon Airport can choose to either handle the project or lease the available potential to third party. Since the results will come out significantly different, the analyst should define the role of the airport at the beginning before running the tool. As mentioned before, according to the Dunnellon
Airport master plan (2010), the utility provider of the airport is Progress Energy—a privately owned utility provider. According to Progress Energy official website (2013c), there are three Tiers for the interconnection, e.g. Tier 1 (≤10 kW); Tier 2 (>10 kW and ≤
100 kW) and Tier 3 (>100 KW ≤ 2 MW). Table 5-3 presents the summary of rates that should be input to run the scenarios.
Table 5-3. Progress Energy rates input assumptions Residential Commercial Parameter Reference Reference [$/kWh] [$/kWh] Progress Progress Utility rate 0.123 Energy 0.119 Energy (2013b) (2013a)
Avoided Kibert et al. Kibert et al. 0.03 0.03 cost (2010) (2010)
In terms of utility FIT rates, there is no other utility provider in the state of Florida that accept FIT system for renewable energy incentive except Gainesville Regional Unit
(GRU); the rate for FIT system assumption in City of Gainesville (DSIRE, 2013a) will be used in designing solar utility scale in Dunnellon Airport (Table 5-4).
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Table 5-4. GRU FIT rates 2013 input assumptions Roof or Pavement Ground Mounted System size Reference [$/kWh] [$/kWh] ≤10 kW 0.21 0.21 DSIRE (2013b)
>10 kW ; ≤ 300 kW 0.18 N/A DSIRE (2013b)
>10 kW ; ≤ 25kW N/A 0.18 DSIRE (2013b)
>25 kW ; ≤ 1MW N/A 0.15 DSIRE (2013b)
The input under the net metering option will be both utility rate and avoided cost.
However, under feed in tariff, the value of utility rate will be zero and the analyst should fill out the FIT rate instead.
Viable incentives in Florida. According to DESIRE (2013a), there are only two possible incentives in the Dunnellon airport area. Table 5-5 presents the summary of applicable incentives as inputs for tool integration.
Table 5-5. Possible incentives in Dunnellon airport area Incentives Description Public Private Reference Adjusted Reference Capacity Orange County- Max $700 DESIRE - - - Based OCHEEP (2013a)
Solar energy $4/W; max. DESIRE - - - system $20,000 (2013a)
Production Orlando utilities $0.05 DESIRE - - - Based commission /kWh (2013a)
Green Power $0.01 - Kibert et al.
REC /kWh (2010)
Federal N/A 34% DOE 30% DESIRE income tax (2013b) (2013a)
State N/A 6.5% DOE N/A - income tax (2013b)
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Ascertaining the Costs
The cost data will be based on the newest applicable data from Barbose et al.
(2013). Figure 5-3 provides the installed cost breakdown for the U.S. current situation.
Figure 5-3. Installed cost breakdown in year 2011 Adapted from Barbose et al. (2013)
Table 5-6. Complete case cost breakdown Sub-component Unit Cumulative Reference System component [$/Wdc] [$/Wdc] Moore & Post Modules 3.33 (2008) Moore & Post Array field BOS 0.56 (2008) Moore & Post Site preparation 0.1 - (2008) Moore & Post Structure 0.15 (2008) Moore & Post Electrical 0.3 - (2008) Moore & Post AC intertie 0.01 (2008) Moore & Post Inverter/transformer 0.4 (2008) Moore & Post Indirect/overhead 1.11 (2008) Total 5.4
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Table 5-6 presents the complete cost breakdown that has been investigated after five years of operation at utility scale level in Springfield, Arizona (Moore & Post, 2008). By using both Figure 5-3 and Table 5-6, the estimation of the inverter cost should be made.
For instance, in the fourth quarter of 2011, the inverter price was roughly about 10% of the total installed cost; yet when comparing to the results from Table 5-5, the inverter contributes to 7% of the total cost. At this point, when integrating with the tool, the analyst should highlight the repetition of replacement as per vendor requirement. For instance, the estimation based on the 2010 feasibility analysis for Turkey Lake service plaza Florida (Kibert, et al., 2010), the total installed inverter package, including switchgear for a 3.8 MWdc system capacity, could reach $2.7 million over 10 year replacement (under conservative assumption). This translates to an average of $0.71/ watt.
Table 5-7. Estimated current inverter price including extended warranty Specification & Warranty Installed Cost Time 2010 Reference 2013 Reference Xantrex GT-100-480, $64,100 Kibert et al. $45,495-$61,290 Current market 100 kW, 3-ph 480 Vac (2010, p.86)
Xantrex GT 100 Warranty $4,900 Kibert et al. $4,900 Kibert et al. Etension (2010, p.86) (2010, p.86) 5 to 10 years
Xantrex GT-250-480, $102,400 Kibert et al. $80,99-$97,134 Current market 250 kW, 3-ph 480 Vac (2010, p.86)
Xantrex GT-250 Warranty $7,300 Kibert et al. $7,300 Kibert et al. Extension (2010, p.86) (2010, p.86) 5 to 10 years
Table 5-6 presents the estimated installed cost for utility scale grid-tied (GT) series for
Xantrex inverters—a brand from Schneider Electric. From Table 5-7, the costs in 2013 or current market decreased by up to 9.5%. The current market price ranges were obtained by online searching from various vendors, with varying costs for different
143 vendors. In addition, the vendor’s data do not include the extended warranty cost for the retail inverter. Therefore, the same assumption based on the 2010 market will be used. It is assumed that there is no change in the extended warranty cost since the installed inverter costs remains unchanged as well. Thus, the input for the inverter replacement cost can still refer to the estimated cost in Table 5-7 or $0.71/watt and will plummet to $0.63 /watt. Therefore, the values of the inverter input ranged from $0.4
/watt (Moore & Post, 2008) to $0.63/watt. This range represents both replacement and warranty during the predetermined period by the vendor.
In order to input the yearly operation and maintenance (O&M) cost, Moore & Post
(2008) has identified—based on five years of experience—that the value will be around
0.12% of the initial installation cost for both scheduled and unscheduled O&M cost.
Another additional 0.1% for the inverter O&M cost should be added, which yields to
0.13% of the total annual O&M cost. Larger O&M percentage value can be implemented. The values for the fixed and tracker system are 0.17 and 0.35% respectively of the total installation cost (Lissel & Mosey, 2010, p.29). The latter assumption will be used in this dissertation. From Table 5-6, the estimation cost for the earthwork due to wiring can use similar estimated cost of $0.56/Wdc. For other purposes, the complete cost data references have been documented in the solar revenue assessment tool. The other inputs of the cost can refer to the cost data next to the chart from Figure 5-3.
Establishing System Performance
In order to have an accurate estimation, the production rate will be discounted using a factor called degradation rate. Branker et al. (2011) has summarized the assumption for degradation rate over several years of estimation (Table 5-8) although 144
1% estimation could usually be used by vendors for their warranty (Skoczek, Sample &
Dunlop, 2009).
Table 5-8. Degradation rate and performance on system lifetime Degradation rate Lifetime to 80 % Lifetime to 50 % Pmax [years] Pmax [years] 0.2 % 100 250 0.5 % 40 100 0.6 % 33 83 0.7 % 29 71 0.8 % 25 63 1 % 20 50 Source: Branker et al. (2011)
Chapter Summary
The utilization of the “Airport Revenue Assessment Tool for Solar Energy” will help airport authority identify revenue stream based on several scenarios. The tool has been developed using VBA programming language environment with spreadsheet basis. The complete guideline regarding this tool is provided in Appendix E. However, all of the inputs and assumptions have been described and all references have been included in order for the revenue analysis results to reflect the current situation. The inputs and assumptions can be repeatable and used in the state of Florida. The only different when applying the tool is the area of analysis, monthly use of electricity as well as the need to run SGHAT tool in order to obtain the safe area from glare. All values used as inputs are based on the Dunnellon Airport data, SGHAT tool results, and pertinent literatures. Since the goal of this dissertation is to test the hypothesis, consequently the direction of the analysis will attempt to approach the 5% possible additional revenue to cover the expense in Dunnellon Airport.
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CHAPTER 6 RESULTS AND DISCUSSIONS
Utility-scale Annual Solar Energy Production Results Comparisons
This section provides the calculation outputs based on the use of newly developed tool for airport revenue assessment model. Chapter 6 comprises the entire part of Phase III of the dissertation organization and presents the results and discussions based on the use of the newly developed methodology and tool for revenue assessment.
After the rigorous step-by-step assessment for glare analysis as well as the wind calculation at Dunnellon Airport, the results of available annual solar energy output are compared to the baseline point—tilted at latitude and oriented facing the south azimuth.
However, since the dual axis tracker is capable of tracking all angles (0 to 90 degrees), this dissertation does not include the results because the SGHAT tool requires degree- by-degree process that will be time-intensive; yet, the newly developed evaluation in this dissertation is capable to conduct 2-axis tracker calculations.
Results for Fixed-tilt System Utility-scale Annual Solar Energy Production
The calculations using the newly developed tool have been limited to two panel types, which can produce the maximum possible power.
Table 6-1. Fixed-tilt results of annual production at azimuth 135o (crystalline) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 6,737,986 6,077,331 16,036,347 12,559,977 100 26 6,483,113* 5,847,449 15,429,753 12,084,881* 96.21 27 6,481,971 5,846,417 15,427,036 12,082,752 96.20 28 6,475,727 5,840,785 15,412,174 12,071,113 96.10 Lat. 6,467,506 5,833,372 15,392,612 12,055,790 95.98 30 6,462,231 5,828,609 15,380,050 12,045,951 95.90 31 6,447,826 5,815,618 15,345,767 N/A 95.69 32 6,438,524 5,807,230 15,323,632 N/A 95.55
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Table 6-2. Fixed-tilt results of annual production at azimuth 150o (crystalline) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 6,737,986 6,077,331 16,036,347 12,559,977 100 26 N/A N/A 15,775,441 N/A 98.37 27 N/A N/A 15,777,280 N/A 98.38 28 N/A N/A 15,774,244 N/A 98.36 Lat. N/A N/A 15,744,133 N/A 98.17 30 N/A N/A 15,737,245 N/A 98.13 31 N/A N/A 15,710,476 N/A 97.96 32 N/A N/A 15,692,495 N/A 97.85
Table 6-3. Fixed-tilt results of annual production at azimuth 165o (crystalline) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 6,737,986 6,077,331 16,036,347 12,559,977 100 26 N/A 6,057,918* 15,985,125 N/A 99.68
Table 6-4. Fixed-tilt results of annual production at azimuth 195o (crystalline) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 6,737,986 6,077,331 16,036,347 12,559,977 100 26 N/A N/A 15,955,558* N/A 99.49
Table 6-1through Table 6-4 provides the results from southeast 135, 150, 165 and 195 orientations for polycrystalline type and selected degrees that do not produce glare. From those tables, the maximum productions occur as follows: Solar Farm-1 at
135 degree orientation with a tilt angle of 26 degrees; Solar Farm-2 at 165 degree orientation with a tilt angle of 26 degrees; Solar Farm-3 at 195 degree orientation with a tilt angle of 26 degree; and Solar Farm-4 at 135 degree orientation with tilt angle of 26 degrees. The maximum values are indicated with asterisk signs.
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Table 6-5. Fixed-tilt results of annual production at azimuth 135o (thin film) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 1,344,361 1,186,978 3,132,097 2,453,122 100 26 1,266,232* 1,142,080 3,013,623 2,360,329* 96.21 27 1,266,010 1,141,878 3,013,093 2,359,914 96.20 28 1,264,792 1,140,778 3,010,189 2,357,638 96.10 Lat. 1,263,184 1,139,331 3,006,371 2,353,508 95.98 30 1,262,154 1,138,402 3,003,916 2,352,726 95.90 31 1,259,341 1,135,864 2,997,219 N/A 95.69 32 1,257,526 1,134,225 2,992,897 N/A 95.55
Table 6-6. Fixed-tilt results of annual production at azimuth 150o (thin film) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] Area [m2] Area [m2] Area [m2] Area [m2] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 1,344,361 1,186,978 3,132,097 2,453,122 100 26 N/A N/A 3,081,140 N/A 98.37 27 N/A N/A 3,081,499 N/A 98.38 28 N/A N/A 3,080,908 N/A 98.36 Lat. N/A N/A 3,075,025 N/A 98.17 30 N/A N/A 3,073,680 N/A 98.13 31 N/A N/A 3,068,452 N/A 97.96 32 N/A N/A 3,064,940 N/A 97.85
Table 6-7. Fixed-tilt results of annual production at azimuth 165o (thin film) Annual Energy Production [kWh] Array Tilt [o] Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Area [m2] Area [m2] Area [m2] Area [m2] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 1,344,361 1,186,978 3,132,097 2,453,122 100 26 N/A 1,183,187* 3,122,096 N/A 99.68
Table 6-8. Fixed-tilt results of annual production at azimuth 195o (thin film) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] Area [m2] Area [m2] Area [m2] Area [m2] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 1,344,361 1,186,978 3,132,097 2,453,122 100 26 N/A N/A 3,116,321* N/A 99.49
Table 6-5 to Table 6-8 provide the results from similar orientation for thin film type and selected degrees that do not produce glare. From those tables, the asterisk signs are 148 showing the maximum values of each of the tilt variations and orientations, which are similar to Table 6-1 through Table 6-4; nevertheless, they have lesser amount in annual solar energy production due to the lower peak power of thin film type.
Results for 1-Axis Tracker System Utility-scale Annual Solar Energy Production
Similar tilt variations and orientations have been used as a scenario; yet, the system input has used 1-axis tracker system. Table 6-9 through Table 6-12 present the results from similar orientation for polycrystalline type using 1-axis tracker.
Table 6-9. 1-Axis tracker results of annual production at azimuth 135o (crystalline) Annual Energy Production [kWh] Array Tilt [o] Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Area [m2] Area [m2] Area [m2] Area [m2] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 8,497,259 7,664,551 20,224,572 15,840,276 100 26 7,918,405* 7,142,009 18,845,730 14,760,340* 93.18 27 7,912,191 7,136,407 18,830,947 14,748,762 93.10 28 7,903,966 7,128,988 18,811,368 14,733,429 93.01 Lat. 7,894,685 7,120,617 18,789,283 14,717,718 92.91 30 7,892,314 7,118,479 18,783,641 14,711,712 92.87 31 7,878,046 7,105,608 18,749,678 N/A 92.70 32 7,863,541 7,092,525 18,715,158 N/A 92.53
Table 6-10. 1-Axis tracker results of annual production at azimuth 150o (crystalline) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] Area [m2] Area [m2] Area [m2] Area [m2] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 8,497,259 7,664,551 20,224,572 15,840,276 100 26 N/A N/A 19,596,720 N/A 96.89 27 N/A N/A 19,586,494 N/A 96.84 28 N/A N/A 19,571,788 N/A 96.77 Lat. N/A N/A 19,554,130 N/A 96.68 30 N/A N/A 19,541,584 N/A 96.62 31 N/A N/A 19,539,025 N/A 96.61 32 N/A N/A 19,504,587 N/A 96.44
Table 6-11. 1-Axis tracker results of annual production at azimuth 165o (crystalline) Annual Energy Production [kWh]
o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 8,497,259 7,664,551 20,224,572 15,840,276 100 26 N/A 7,607,072* 20,072,902* N/A 99.25 149
Table 6-12. 1-Axis tracker results of annual production at azimuth 195o (crystalline) Annual Energy Production [kWh]
o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 8,497,259 7,664,551 20,224,572 15,840,276 100 26 N/A N/A 20,048,371 N/A 99.12
According to Table 6-9 through Table 6-12, the maximum energy production from each solar farm design is slightly different from the fixed-tilt system. For instance, at
195 degree orientation, the maximum output of Solar Farm-3 is lesser than that oriented at 165 degrees. Therefore, the results of the yearly production comparisons can be summarized as follows. Solar Farm-1 occurs at azimuth of 135 degrees and tilted at 26 degrees. For both Solar Farm-2 and Solar Farm-3, the possible annual maximum energy occurs at azimuth of 165 degrees; tilted at 26 degrees. Solar Farm-4 produces maximum energy at 135 degree orientation and a tilt angle of 26 degrees.
Table 6-13 through Table 6-16 provide the results from similar orientation for thin film type using 1-axis tracker. The asterisk signs indicate the maximum annual energy production that can be achieved.
Table 6-13. 1-Axis tracker results of annual production at azimuth 135o (thin film) Annual Energy Production [kWh]
o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 1,659,716 1,496,983 3,950,111 3,093,803 100 26 1,546,563* 1,394,924 3,680,807 2,882,879* 93.18 27 1,545,350 1,393,829 3,677,919 2,880,618 93.10 28 1,543,743 1,392,379 3,674,097 2,877,623 93.01 Lat. 1,541,929 1,390,747 3,669,783 2,874,244 92.91 30 1,541,469 1,390,327 3,668,681 2,873,380 92.87 31 1,538,680 1,387,815 3,662,047 N/A 92.70 32 1,535,847 1,385,258 3,655,304 N/A 92.53
150
Table 6-14. 1-Axis tracker results of annual production at azimuth 150o (thin film) Annual Energy Production [kWh]
o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 1,659,716 1,496,983 3,950,111 3,093,803 100 26 N/A N/A 3,827,484 N/A 96.89 27 N/A N/A 3,825,488 N/A 96.84 28 N/A N/A 3,822,615 N/A 96.77 Lat. N/A N/A 3,819,469 N/A 96.68 30 N/A N/A 3,816,714 N/A 96.62 31 N/A N/A 3,816,215 N/A 96.61 32 N/A N/A 3,809,489 N/A 96.44
Table 6-15. 1-Axis tracker results of annual production at azimuth 165o (thin film) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] Area [m2] Area [m2] Area [m2] Area [m2] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 1,659,716 1,496,983 3,950,111 3,093,803 100 26 N/A 1,485,756* 3,920,489* N/A 99.25
Table 6-16. 1-Axis tracker results of annual production at azimuth 195o (thin film) Annual Energy Production [kWh] o Solar Farm-1 Solar Farm-2 Solar Farm-3 Solar Farm-4 Array Tilt [ ] Area [m2] Area [m2] Area [m2] Area [m2] % 25,069 22,611 59,664 46,730 Baseline:29.2; 180 1,659,716 1,496,983 3,950,111 3,093,803 100 26 N/A N/A 3,915,696 N/A 99.12
Discussions for the Utility-scale Solar Energy Production
Table 6-1 through Table 6-16 show the comparison for each tilt angle at a certain azimuth position i.e. 135, 150, 165 and 195 degrees. From those combinations, the asterisk signs are the expected results since they produce maximum energy. Ideally, the baseline—tilt and azimuth are at latitude and facing south respectively—is the maximum possible system configuration; however, the fact that glare and wind overload are of concern in Dunnellon Airport made this configuration unacceptable. To further investigate the specific place to maximize energy production, which in return comes up
151 with maximum profit, the wiring analysis should also be included in the selection process.
Wiring connection to utility provider—the impediments. The upfront cost is definitely a factor that influences the success of a project; however, the limit of the applicable system also thwarts the exploitation of possible solar energy generation. For instance, the utility provider, in this case Progress Energy, only allows a 2 MW maximum power generation and is recognized as Tier 3 (Progress Energy, 2013c).
In Dunnellon Airport, there are three electrical regulators within one vault that covers those regulators; the regulators that supply the power comprise of a 7.5 kW and two 4 kW regulators. The location of the electrical vault is marked as “orange star” in
Figure 6-1. The principal lines are located in the main entrance on SW 110th Street and
SW 147th Court; incorporating overhead electric service powered by Progress Energy
(Marion County Florida, 2010, p.1-14). This is a factor that should be considered in wiring connection, because distance—if it is related to construction activity—will not only influences the cost of earthwork but also disturbs airport activities in terms of underground wiring connection works. Therefore, due to distance issue to the electrical vault, the proposed area for Solar Farm-4 is eliminated.
152
Figure 6-1. Sketch drawing snapshot for proposed area and electrical connection Adapted from Marion County Florida (2010)
153
Table 6-17. System size results summary with combined configurations Combined System Type Solar Farm-1 Solar Farm-2 Solar Farm-3 [1] [2] [3] [4] Fixed-tilt, crystalline; Tilt = 26;Size [MWdc] 4.92 4.43 11.7 Azimuth 135 165 195 Estimated Module 15,380 13,872 36,604 Annual Output [kWh] 6,483,113 6,057,918 15,955,558
Fixed-tilt, thin film; Tilt = 26;Size [MWdc] 0.96 0.87 2.28 Azimuth 135 165 195 Estimated Module 7,690 6,936 18,302 Annual Output [kWh] 1,266,232 1,183,187 3,116,321
1-axis, crystalline; Tilt = 26;Size [MWdc] 4.92 4.43 11.7 Azimuth 135 165 165 Estimated Module 15,380 13,872 36,604 Annual Output [kWh] 7,918,405 7,607,072 20,072,902
1-axis, thin film; Tilt = 26;Size [MWdc] 0.96 0.87 2.28 Azimuth 135 165 165 Estimated Module 7,690 6,936 18,302 Annual Output [kWh] 1,546,563 1,485,756 3,920,489
In order to compare the final combinations, a system that can produces maximum power generator, Table 6-17 sums up the final results of maximum annual solar energy as well as system size. The estimation used the highest peak power of each module type i.e., polycrystalline and thin film. These results will be used at the end of economic analysis (utilizing the final investigation: Step 5 of the methodology).
From these results, the total possible area to install utility scale power generations have decreased to 21.05 MW for the crystalline type and only 4.11 MW for the thin film type.
The available potential in Dunnellon Airport has been previously determined to be 39
MW. The results of the evaluation tool also comprise the total estimated module as well as array arrangement. For the latter option, the need to collect panel datasheet is mandatory since the input requires both voltage and current design in which the values can be determined if only the panel datasheet exists. Since the developed evaluation tool aims to provide quick comprehensive report to airport authority, the array
154 arrangement calculations have also been provided; even though further economic evaluation will not necessarily need these values. However, an example of panel arrangement—string and inverter size—is presented in technical algorithm in Appendix
B. With respect to the Tier 3 limit, Dunnellon Airport should notice that, the regulation enacted by Progress Energy will somehow impede the area utilization. In the meantime, the maximum total area, which can be exploited for solar energy generation, can achieve 21.05 MW. 2 MW translates only 9.5% out of the total possible solar panel installations. However, by using those three connections as mentioned before, it is possible for Dunnellon Airport to expand the connection up to 6 MW utility-scale interconnected systems. Another impediment rises; the FIT rate provided by GRU has been limited up to 1 MW utility-scale system. For the purpose of simulation of the methodology, this dissertation has proposed the assumption of using a 3 acre ≈ 12,140 m2 utility-scale system (realistic design); therefore, the Dunnellon Airport case study revenue can be compared to the leasing option, which requires a minimum 3 acre leasing requirement (Plante et al., 2010, p.43).
Review of Net Zero Energy Concept and Applying to Dunnellon Airport
There is one literature that has described the emerging policy that may be applied in the airport. This study mainly investigated possible improvement concerning energy efficiency in airports. The results of this study have challenged the airport direction towards net zero energy building (NZEB). Since the airport has been known to be energy-intensive; hence, NZEB strategy will be an ideal option for airport managers to tackle the increasing costs of energy (Lau, Stromgren & Green, 2010, p.41). As previously mentioned, Dunnellon Airport is not an energy-intensive airport. This will be a suitable case study to achieve the NZEB target, because it does not require a large 155 system size. When discussing about net zero energy, the main conception is to limit the boundary and the scope of analysis because the net zero conception itself has several definitions i.e., net zero site energy, net zero source energy, net zero energy costs and net zero energy emission (Torcellini, Pless, Deru & Crawley, 2006; Kibert, 2012).
Usually, the limit will be based on the building footprint because of the general assumption: there is connection between the building and the grid that allows the energy flows (Kibert, 2012). The paragraphs below will review the net zero conceptions based on the definitions from both Torcellini et al. (2006) and Kibert (2012) and the energy is accounted in annual basis.
“Net zero annual site energy”, is defined as the total annual energy exported from the building site equals to the total annual energy imported from the electricity or gas imported from the utility provider (source) and the boundary is limited to the building footprint only. “Net zero annual source energy” is described as the total annual amount of energy that is needed by utility provider to generate and send to the recipient building; the amount should be equal to the annual generation from the recipient building. “Net zero annual energy cost” is the total annual revenue of the building owners as the compensation due to the exported energy to the utility provider’s grid and the annual revenue amount should be equal to the annual use of combined utility bills of electricity and gas. “Net zero annual energy emission” is the total emission produced by the utility as the emission producer, which should be the same with the total non- emission energy producer (building with renewable energy system). All net zero energy conceptions have identified the concept by assuming the recipient building to own its power generation such as solar or wind generator. Since the boundary analysis is
156 important, therefore, four areas, i.e. two rooftops as well as two parking structures scenarios have been used near airport building footprint. In this dissertation analysis, the net zero conception will be limited to net zero annual energy assumption.
NZEB Design Annual Solar Energy Production Results Comparisons
The treatment of the net zero energy analysis will be slightly different from the solar farm utility scale analysis. In utility-scale analysis, since monthly energy use is far below the designated system capacity, the favorable will be the analysis under the FIT.
Under the FIT system, a bigger system translates to larger energy production, thus larger profit. Meanwhile, in net zero design, under the net metering system, the system capacity design should approach the actual monthly use curve. However, to compare the end results, this dissertation also tries to simulate the maximum area in NZEB scenario. For the mountings and panel types, the analysis will include the same system type combinations; but the tracker system is not applicable.
Results for Fixed-tilt System NZEB Design Annual Solar Energy Production
The Table 6-18 and Table 6-19 below present the results of annual solar energy production. The asterisk signs show the maximum energy that can be generated. The tilt angle is 26 degrees with fixed system.
Table 6-18. Fixed-tilt results of annual production at azimuth 135o (crystalline) Annual Energy Production [kWh]
o Rooftop-1 Rooftop-2 Parking-1 Parking-2 Array Tilt [ ] 2 2 2 2 Area [m ] Area [m ] Area [m ] Area [m ] % 971 473 302 892 Baseline:29.2; 180 260,983 127,132 81,171 239,749 100 26 251,111* 122,323* 78,102* 230,681* 96.21 27 251,066 122,301 78,087 230,642 96.20 28 250,824 122,183 78,012 230,419 96.10 Lat. 250,509 122,026 77,914 230,124 95.98 30 250,301 121,929 77,849 229,938 95.90 31 N/A 121,657 77,675 229,425 95.69 32 N/A 121,482 77,564 N/A 95.55
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Table 6-19. Fixed-tilt results of annual production at azimuth 135o (thin film) Annual Energy Production [kWh] o Rooftop-1 Rooftop-2 Parking-1 Parking-2 Array Tilt [ ] Area [m2] Area [m2] Area [m2] Area [m2] % 971 473 302 892 Baseline:29.2; 180 50,974 24,830 15,853 46,826 100 26 49,046* 23,890* 15,254* 45,056* 96.21 27 49,037 23,887 15,252 45,047 96.20 28 48,989 23,864 15,237 45,003 96.10 Lat. 48,925 23,834 15,217 44,947 95.98 30 48,888 23,813 15,205 44,909 95.90 31 48,777 23,762 15,171 44,810 95.69 32 N/A 23,728 15,150 N/A 95.55
Discussions for the NZEB Design Solar Energy Production
Table 6-20 presents the maximum energy production at 26 degree angle for both module types with fixed-tilt mounting. These values will be used for further analysis. In order to align the actual monthly used data, these values will be adjusted based on the actual monthly use curve. The design for NZEB will be based on peak energy use in
Dunnellon Airport.
Table 6-20. System size results summary with combined configurations Combined System Type Rooftop-1 Rooftop-2 Parking-1 Parking-2 [1] [2] [3] [4] [5] Fixed-tilt, crystalline; Tilt = 26;Size [kWdc] 190.72 92.8 59.2 175 Azimuth 135 135 135 135 Estimated Module 596 290 185 547 Annual Output [kWh] 251,111 122,323 78,102 230,681
Fixed-tilt, thin film; Tilt = 26;Size [kWdc] 37.25 18.12 11.62 34.25 Azimuth 135 135 135 135 Estimated Module 298 145 92 274 Annual Output [kWh] 49,046 23,890 15,254 45,056
Wiring connection. The connection to utility provider refers to Figure 6-1. All the proposed areas for NZEB design, fortunately, are close enough to the electricity connection to utility provider. Therefore, the combinations for both rooftop and parking structure will be used for economic analysis. The design for NZEB will accommodate
158 net metering scheme. In the economic analysis, both FIT and net metering will be investigated.
Step 5: Revenue Evaluation Using Developed Feasibility Assessment Tool
The economic assessment plays an important role in a project. Specifically, this dissertation highlights the potential revenue streams from the exploitation of a renewable energy system, in this case solar energy. Several scenarios have been used to run the evaluation assessment tool. For instances, in case Dunnellon Airport wants to handle the entire the project and operate the system, the financial option will be treated differently compared to the case when Dunnellon Airport just decides to lease the land for power generation.
Dunnellon Airport as System Owner
The results below were run using the evaluation tool by integrating the defined assumptions in the previous chapter; then, continued by expanding the ownership options by three branch scenarios—utility-scale maximum energy or maximum energy, realistic design and net zero energy building scenarios. In determining maximum energy scenario, all possible areas will be calculated by integrating the highest panel efficiency (Kibert et al., 2010). The realistic design will follow similar assumption; yet, there will be area adjustment due to impediment by utility load limitation (2 MW) per block meter. For net zero energy, the analysis will be highlighted to only rooftop and parking structure near the building footprint.
Results for Maximum Energy Scenario
The following narrative shows the output from evaluation tool for maximum energy scenario. The design study period has been set to 25 years (Progress Energy,
2013d) and 20 years of the revenue (derived from yearly energy saving under FIT
159 scheme) will be treated as the bond/loan payment (Kibert et al., 2010). Table 6-21 provides the summary of inputs for the revenue assessment tool.
Table 6-21. General input assumptions for utility scale for public ownership Variables Value Unit Remarks Tilt angle 26 [ o ] Azimuth; direction 135,165,195 [ o ] Polycrystalline DC wattage 320 [ watt ] Efficiency 20.4 [ % ] Not used for predetermined area Thin film DC wattage 125 [ watt ] Efficiency 18.3 [ % ] Not used for predetermined area Degradation rate 0.8 [ % ] 25 years Utility Rate 0.119 [ $/kWh ] Utility FIT 0.15 [ $/kWh ] Utility-scale ground mounted Installation Fixed-tit Crystalline 3.3 [ $/watt ] Thin film 3.2 [ $/watt ] 1-Axis tracking Crystalline 3.6 [ $/watt ] Thin film - [ $/watt ] O&M 0.35 % of total installed cost Earthwork 0.56 [ $/watt ] Inverter 0.4 [ $/watt ] 10 years replacement Solar Farm-1 (crystalline) $1,968,640 / 10 years Solar Farm-2 (crystalline) $1,775,616 / 10 years Solar Farm-3 (crystalline) $4,685,312 / 10 years Solar Farm-1 (thin film) $384,500 / 10 years Solar Farm-2 (thin film) $346,800 / 10 years Solar Farm-3 (thin film) $915,100 / 10 years Down Payment 10 % of new initial cost Term of Loan 20 [ years ] Depreciation 5 [ years ] Study Period 25 [ years ] Loan rate (LR) 6 [ % ] Baseline Discount rate (DR) 3 [ % ] Baseline Energy inflation rate (EIR) 1 [ % ] Baseline General inflation rate (GIR) 1 [ % ] Baseline Grant 1 [ $/kW ] Maximum $700 REC 4 [ $/W ] Maximum $20,000 Grant 0.06 [ $/kWh ]
All references and assumptions for the inputs have been previously determined in
Chapter 5, ascertaining the costs section. The values are distinguished based on the system combinations, ownership option as well as the type of agreement such as net metering and feed in tariff. For the maximum energy analysis, the emphasis will be on
160 the use of all available area that could reach maximum energy generation for solar energy installation on airport. All solar farm application choices refer to Table 6-17, i.e.,
Solar Farm-1, Solar Farm-2 and Solar Farm-3.
Bond or loan payment. This program contributes significantly for the airport because it can reduce the upfront cost. After running the evaluation tool, the total bond payment—up to 20 years to maturity—for the three fixed-tilt system of the maximum energy case are:
Solar Farm-1; 4.92 MW; Fixed-tilt; Crystalline = $14,731,251 Solar Farm-2; 4.43 MW; Fixed-tilt; Crystalline = $13,764,827 Solar Farm-3; 11.7 MW; Fixed-tilt; Crystalline = $36,261,134 Solar Farm-1; 0.96 MW; Fixed-tilt; Thin-film = $2,873,822 Solar Farm-2; 0.87 MW; Fixed-tilt; Thin-film = $2,685,069 Solar Farm-3; 2.28 MW; Fixed-tilt; Thin-film = $7,078,882
Revenue Stream Present Worth over 20 Years $2,500,000.00
$2,000,000.00
$1,500,000.00
$1,000,000.00
Discounted Revenue Discounted $500,000.00
$- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Term of Loan [years]
Solar Farm-1, Fixed-tilt, Crystalline, Tilt = 26, Az = 135 Solar Farm-1, Fixed-tilt, Thin Film, Tilt = 26, Az=135 Solar Farm-2, Fixed-tilt, Crystalline, Tilt = 26, Az=165 Solar Farm-2, Fixed-tilt, Thin Film, Tilt=26, Az=165 Solar Farm-3, Fixed-tilt, Crystalline, Tilt = 26, Az=195 Solar Farm-3, Fixed-tilt, Thin Film, Tilt=26, Az=195
Figure 6-2. Revenue stream as loan or bond payment over 20 years (fixed-tilt)
Figure 6-2 presents the lifecycle of the discounted revenue over 20 years to maturity. The revenue stream for this period is not considered solely belongs to
161
Dunnellon Airport. Nonetheless, the amount of the revenue should cover the loan and should be paid to the financial institution. There is a 5 year period that, in fact, can be accounted as the revenue for Dunnellon Airport. The value will be investigated in the
Dunnellon Airport potential revenue stream section.
Since there are no specific references that estimate the combination of thin-film module and tracking system; therefore, no further calculation for this combination is available.
The calculations include only the combined system 1-axis tracking and crystalline. The following details represent the total loan payment of that combination:
Solar Farm-1; 4.92 MW; 1-Axis; Crystalline = $17,993,520 Solar Farm-2; 4.43 MW; 1-Axis; Crystalline = $17,285,893 Solar Farm-3; 11.7 MW; 1-Axis; Crystalline = $45,619,430
Figure 6-3 presents the revenue stream for 1-axis system using crystalline module type.
Revenue Stream Present Worth over 20 Years
$3,500,000.00
$3,000,000.00
$2,500,000.00
$2,000,000.00
$1,500,000.00
$1,000,000.00
Discounted Revenue Discounted $500,000.00
$- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Term of Loan [years] Solar Farm-1, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 135 Solar Farm-2, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165 Solar Farm-3 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165
Figure 6-3. Revenue stream as loan or bond payment over 20 years (1-axis tracking)
REC and grant. The types of renewable energy credit as well as grant type and assumptions have been reviewed in Chapter 5, viable incentives in Florida, specifically
162 for Dunnellon Airport. The total incentives that can be obtained will also reduce the total investment cost. This will be summed together with bond payment and categorized as funding; therefore, the net inception cost will further be offset with the funding total values. The results for each combination can be seen in Table 6-22. The sum of bond and grant can be seen in the following table by referring to column [7] and column [8].
These values are factors that contribute to lower the net inception cost (column [9]).
Table 6-22. Final net inception cost for each combination (maximum energy) Yearly Funding Net Size Total Cost Production Saving Inception Scenario Type Bond Grant Cost [MW] [ $] [kWh] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] SF-1 Fixed-tilt Max. Crystalline 4.92 18,997,376 6,483,113 972,190 14,731,251 24,607 4,241,517 energy Cost/Watt = 0.86 Cost/kWh = 0.65 Thin film 0.96 3,614,300 1,266,232 189,658 2,873,822 4,806 735,672 Cost/Watt = 0.76 Cost/kWh = 0.58 1-axis Crystalline 4.92 20,473,856 7,918,405 1,187,4811 17,993,520 24,607 2,455,727 Cost/Watt = 0.49 Cost/kWh = 0.31 SF-2 Fixed-tilt Max. Crystalline 4.43 17,134,694 6,057,918 908,411 13,764,827 22,195 3,347,673 energy Cost/Watt = 0.68 Cost/kWh = 0.55 Thin film 0.87 3,259,920 1,183,187 177,201 2,685,069 4,335 570,516 Cost/Watt 0.65 Cost/kWh = 0.48 1-axis Crystalline 4.43 18,466,406 7,607,009 1,140,784 17,285,893 22,195 1,158,318 Cost/Watt = 0.26 Cost/kWh = 0.15 SF-3 Fixed-tilt Maxi. Crystalline 11.7 45,213,260 15,955,558 2,393,057 36,261,134 58,566 8,893,560 energy Cost/Watt = 0.76 Cost/kWh = 0.55 Thin film 2.28 8,601,940 3,116,321 467,172 7,078,882 11,438 1,511,619 Cost/Watt = 0.66 Cost/kWh = 0.48 1-axis Crystalline 11.7 48,727,245 20,072,902 3,010,659 45,619,430 58,566 3,049,249 Cost/Watt = 0.26 Cost/kWh = 0.15
163
Dunnellon Airport potential revenue. The cumulative revenue stream for each combination—that can be accounted as revenue stream that goes to Dunnellon
Airport—will be investigated in Figure 6-4 and Figure 6-5.
Cumulative Revenue Stream
$7,000,000.00
$6,000,000.00
$5,000,000.00
$4,000,000.00
$3,000,000.00
$2,000,000.00 Cumulative Revenue Cumulative $1,000,000.00
$- 21 22 23 24 25 The Rest of Study Period [years]
Solar Farm-1, Fixed-tilt, Crystalline, Tilt = 26, Az = 135 Solar Farm-1, Fixed-tilt, Thin Film, Tilt = 26, Az=135 Solar Farm-2, Fixed-tilt, Crystalline, Tilt = 26, Az=165 Solar Farm-2, Fixed-tilt, Thin Film, Tilt=26, Az=165 Solar Farm-3, Fixed-tilt, Crystalline, Tilt = 26, Az=195 Solar Farm-3, Fixed-tilt, Thin Filmm Tilt = 26, Az=195
Figure 6-4. Cumulative revenue stream goes to Dunnellon airport (fixed-tilt)
The values for the possible revenues after finishing the loan payment can be recapped in Figure 6-4 and summarized as follows:
Solar Farm-1; 4.92 MW; Fixed-tilt; Crystalline = $2,576,066 Solar Farm-2; 4.43 MW; Fixed-tilt; Crystalline = $2,407,066 Solar Farm-3; 11.7 MW; Fixed-tilt; Crystalline = $6,341,014 Solar Farm-1; 0.96 MW; Fixed-tilt; Thin-film = $502,548 Solar Farm-2; 0.87 MW; Fixed-tilt; Thin-film = $469,640 Solar Farm-3; 2.28 MW; Fixed-tilt; Thin-film = $1,237,889
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For the cumulative present worth values that portrays the revenue stream goes to
Dunnellon Airport using combined 1-axis tracking and crystalline can be reviewed as follows:
Solar Farm-1; 4.92 MW; 1-Axis; Crystalline = $3,146,541 Solar Farm-2; 4.43 MW; 1-Axis; Crystalline = $3,022,797 Solar Farm-3; 11.7 MW; 1-Axis; Crystalline = $7,977,506
Cumulative Revenue Stream
$9,000,000.00
$8,000,000.00
$7,000,000.00 $6,000,000.00 $5,000,000.00 $4,000,000.00 $3,000,000.00
$2,000,000.00 Discounted Revenue Discounted $1,000,000.00 $- 21 22 23 24 25 The Rest of Study Period [years]
Solar Farm-1, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 135 Solar Farm-2, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165 Solar Farm-3 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165
Figure 6-5. Cumulative revenue stream goes to Dunnellon airport (1-axis tracking)
Discussions for Maximum Energy Scenario
The revenue that can be claimed by Dunnellon Airport is after 20 years of loan payment period. From Table 6-21, the value of net initial cost is lower when Dunnellon
Airport decides to choose a 1-axis tracking with crystalline type. Compared to other two combinations, the cost per kWh for 1-axis tracking system is the lowest; even after subtracting the total cost from all possible funding, the lowest net inception cost ends up with system with 1-axis tracking using crystalline module.
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With respect to the revenue, the maximum possible revenue is using 1-axis tracker with crystalline module since it can produce revenue up to $7,977,506 for a 25 year study period.
Table 6-23. Financial measures for decision making (25 years for maximum energy) Size ROI TLCC LCOE SIR NPV PB Salvage Scenario Type [MW] [%] [$] [$/kWh] [$] [yrs] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] Solar Fixed-tilt
Farm-1 Maximum Crystalline 4.92 81.4 9,571,779 0.092 1.81 7,694,812 0 602,893 energy Thin film 0.96 120.6 1,745,832 0.086 2.21 1,622,789 0 117,752
1-axis
Crystalline 4.92 457.2 7,411,672 0.058 5.57 13,684,499 0 602,893
Solar Fixed-tilt
Farm-2 Maximum Crystalline 4.43 142.2 8,028,900 0.083 2.42 8,106,260 0 543,779 energy Thin film 0.87 196.3 1,457,029 0.078 2.96 1,690,592 0 106,207
1-axis
Crystalline 4.43 1188.1 5,348,895 0.044 12.88 14,920,209 0 543,779
Solar Fixed-tilt
Farm-3 Maximum Crystalline 11.7 138.9 21,261,786 0.083 2.39 21,243,437 0 1,434,869 energy Thin film 2.28 194.1 3,852,498 0.077 2.94 4.445.833 0 280,247
1-axis
Crystalline 11.7 1191.7 14,105,003 0.044 12.92 39,387,474 0 1,434,869
Table 6-23 presents the value of financial measures that can be used by the airport authority to make the decision. The financial measures acceptance criteria can be summarized as follows:
ROI the percentage should yield to at least 10% Total lifecycle cost (TLCC) should be the lowest is more favorable Similar to TLCC, lifecycle cost of electricity (LCOE) should come up with the lowest value
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Saving to investment ratio (SIR) should be larger than 1 Net present value (NPV) the highest is favorable Payback period, the shortest period is preferred
By referring to those criteria, it is apparent that with the same system, the final decision should be pointed towards the 1-axis tracking with crystalline module, because with regard to the total lifecycle cost, compared to the same capacity, it will supposedly be larger than its competitor—thin film—yet, due to its capability to generate more kWh, in the end under the same financing option, the value of the cost will be lower.
Results for Realistic Design Scenario
The realistic design scenario emphasizes the analysis to 3 acres of area or
12,140 square meters. This area is plausible to implement since the available potential area for each solar farm (SF) i.e., SF1, SF2 and SF3 has to be larger than 3 acres; therefore, for the area analysis, there will be some other combination in terms of mounting and type of panel that will support any potentials in Dunnellon Airport. From the total available area—the sum of three solar farm areas is equal to 107,334 square meters. The achievable design yields if only one area installed yields to 11.3% of the available potential in Dunnellon Airport. Table 6-24 presents the summary of inputs for the evaluation that are different with the general input assumptions. All values other than the values listed in Table 6-24 remain unchanged.
Table 6-24. Input difference with general input assumptions (realistic design) Variables Value Unit Remarks Inverter 0.4 [ $/watt ] 10 years replacement Realistic Design (crystalline) $953,320 / 10 years Realistic Design (thin film) $186,400 / 10 years
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The narrative below presents the results of potential solar energy that fulfill the
FAA requirements for leasing as well as satisfy EPA criterion for the un-shaded and unobstructed area, should be at least 2 acres; and, 3 acres for leasing.
Loan payment. There are two possible system sizes, i.e., 2.38 MW with total estimated module of 7,448 each; and, 466 kW with total estimated module of 3,724.
The bond payment over 20 year term of loan for each combined solar farm (SF) system can be summarized as follows:
Realistic design-SF1; 2.38 MW; Fixed-tilt; Crystalline = $7,131,645 Realistic design-SF2; 2.38 MW; Fixed-tilt, Crystalline = $7,388,491 Realistic design-SF3; 2.38 MW; Fixed-tilt, Crystalline= $7,374,818 Realistic design-SF1; 466 kW; Fixed-tilt; Thin-film = $1,389,525 Realistic design-SF2; 466 kW; Fixed-tilt; Thin-film = $1,439,688 Realistic design-SF3; 466 kW; Fixed-tilt; Thin-film = $1,437,027
Revenue Stream Present Worth over 20 Years
$500,000.00 $450,000.00
$400,000.00 $350,000.00 $300,000.00 $250,000.00 $200,000.00 $150,000.00
Discounted Revenue Discounted $100,000.00 $50,000.00 $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Term of Loan [years]
Realistic Design-SF1, Fixed-tilt, Crystalline, Tilt=26, Az=135 Realistic Design-SF1, Fixed-tilt, Thin Film, Tilt=26, Az=135 Realistic Design-SF2, Fixed-tilt, Crystalline, Tilt=26, Az=165 Realistic Design-SF2, Fixed-tilt, Thin Film, Tilt=26, Az=165 Realistic Design-SF3, Fixed-tilt, Crystalline, Tilt=26, Az=195 Realistic Design-SF3, Fixed-tilt, Thin Film, Tilt=26, Az=195
Figure 6-6. Revenue stream as loan or bond payment over 20 years (fixed-tilt)
Figure 6-6 provides the revenue stream present worth over 20 years. The figure shows the combined fixed-tilt and crystalline will allow the higher revenue. Although,
168 there is a minor discrepancy within each group, i.e., crystalline and thin film, the highest revenue available is from SF2 with the azimuth orientation 165 degree from north.
In terms of 1-axis tracking system, the summary of revenue for loan payment are presented as follows:
Realistic design-SF1; 2.38 MW; 1-Axis Tracking; Crystalline = $8,711,442 Realistic design-SF2; 2.38 MW; 1-Axis Tracking; Crystalline = $9,278,973 Realistic design-SF3; 2.38 MW; 1-Axis Tracking; Crystalline = $9,278,973
The bond values over 20 years for both SF1 and SF2 are the same as well as larger than SF1 installation area as can be seen in Figure 6-7.
Revenue Stream Present Worth over 20 Years
$700,000.00
$600,000.00
$500,000.00
$400,000.00
$300,000.00
$200,000.00
Discounted Revenue Discounted $100,000.00
$- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Term of Loan [years]
Realistic Design-SF1, 1-Axis Tracking, Crystalline, Tilt=26, Az=135 Realistic Design-SF2, 1-Axis Tracking, Crystalline, Tilt=26, Az=165 Realistic Design-SF3, 1-Axis Tracking, Crystalline, Tilt=26, Az=195
Figure 6-7. Revenue stream as loan or bond payment over 20 years (1-axis tracking)
For this purpose, Dunnellon Airport may consider to install on the area SF2 oriented at
165 degree azimuth and SF3 oriented at 195 degree azimuth.
Incentives. The funding summary can be seen in Table 6-25 through 6-27 column [7] and column [8]. The role of bond payment and incentive availability are factors that reduce the total cost significantly.
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Table 6-25. Final net inception cost for Solar Farm-1 (realistic design) Yearly Funding Net System Size Total Cost Production Saving Inception Scenario Bond Grant Cost [MW] [$] [kWh] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9]
Realistic Fixed-tilt Design SF1 Crystalline 2.38 9,199,770 3,139,535 470,654 7,131,645 11,916 2,056,208 Cost/Watt = 0.86 Cost/kWh = 0.65
Thin film 0.47 1,750,280 613,190 91,702 1,389,525 2,327 358,427 Cost/Watt = 0.58 Cost/kWh 0.76
1-axis
Crystalline 2.38 9,914,778 3,834,594 574,912 8,711,442 11,916 1,191,419 Cost/Watt = 0.50 Cost/kWh = 0.31
Table 6-26. Final net inception cost for Solar Farm-2 (realistic design) Yearly Funding Net System Size Total Cost Production Saving Inception Scenario Bond Grant Type Cost [MW] [$] [kWh] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9]
Realistic Fixed-tilt Design SF2 Crystalline 2.38 9,199,770 3,252,539 487,604 7,388,492 11,916 1,799,361 Cost/Watt = 0.75 Cost/kWh = 0.55
Thin film 0.47 1,750,280 635,260 95,012 1,439,688 2,327 308,264 Cost/Watt = 0.65 Cost/kWh = 0.48
1-axis
Crystalline 2.38 9,914,778 4,084,289 612,367 9,278,973 11,916 623,888 Cost/Watt = 0.26 Cost/kWh = 0.15
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Table 6-27. Final net inception cost for Solar Farm-3 (realistic design) Yearly Funding Net System Size Total Cost Production Saving Inception Scenario Bond Grant Type Cost [MW] [$] [kWh] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9]
Realistic Fixed-tilt Design SF3 Crystalline 2.38 9,199,770 3,246,523 486,702 7,374,818 11,916 1,813,035 Cost/Watt = 0.76 Cost/kWh = 0.55
Thin film 0.47 1,750,280 634,089 94,837 1,437,027 2,327 310,926 Cost/Watt = 0.66 Cost/kWh = 0.49
1-axis
Crystalline 2.38 9,914,778 4,084,289 612,367 9,278,973 11,916 623,888 Cost/Watt = 0.26 Cost/kWh = 0.15
Potential revenue. By using the typical assumptions, Dunnellon Airport starts producing revenue for the rest of 5 years of study period. Figure 6-7 shows the chart of the cumulative revenue that goes to Dunnellon Airport. The system consisting 1-axis tracking with crystalline module will increase sharply compared to the fixed-tilt system with crystalline module; although, they start at almost the same point.
Figure 6-8 shows the chart of the cumulative revenue that goes to Dunnellon
Airport. The system consisting fixed-tilt with crystalline module will increase sharply compared to the fixed-tilt system with thin film module; although, they start at almost the same point.
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Cumulative Revenue Stream
$1,400,000.00
$1,200,000.00
$1,000,000.00
$800,000.00
$600,000.00
$400,000.00 Cumulative Revenue Cumulative $200,000.00
$- 21 22 23 24 25 The Rest of Study Period [years] Realistic Design-SF1, Fixed-tilt, Crystalline, Tilt=26, Az=135 Realistic Design-SF1, Fixed-tilt, Thin Film, Tilt=26, Az=135 Realistic Design-SF2, Fixed-tilt, Crystalline, Tilt=26, Az=165 Realistic Design-SF2, Fixed-tilt, Thin Film, Tilt=26, Az=165 Realistic Design-SF3, Fixed-tilt, Crystalline, Tilt=26, Az=195 Realistic Design-SF3, Fixed-tilt, Thin Film, Tilt=26, Az=195
Figure 6-8. Cumulative revenue stream goes to Dunnellon Airport (realistic design)
The total of last five year revenue values can be summarized in the following details:
Realistic design-SF1; 2.38 MW; Fixed-tilt; Crystalline = $1,247,117 Realistic design-SF2; 2.38 MW; Fixed-tilt; Crystalline = $1,292,031 Realistic design-SF3; 2.38 MW; Fixed-tilt; Crystalline = $1,289,640 Realistic design-SF1; 0.96 MW; Fixed-tilt; Thin-film = $242,987 Realistic design-SF2; 0.96 MW; Fixed-tilt; Thin-film = $251,759 Realistic design-SF3; 0.96 MW; Fixed-tilt; Thin-film = $251,294
With regard to 1-axis tracking system, the SF2 and SF3 are showing the same results.
Figure 6-9 presents the chart for cumulative revenue over the rest of study period for 1- axis tracking system.
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Cumulative Revenue Stream
$1,800,000.00
$1,600,000.00 $1,400,000.00 $1,200,000.00 $1,000,000.00 $800,000.00 $600,000.00 $400,000.00 Cumulativee Revenue Cumulativee $200,000.00 $- 21 22 23 24 25 The Rest of Study Period [years]
Realistic Design-SF1,1-Axis Tracking, Crystalline, Tilt=26, Az=135 Realistic Design-SF2,1-Axis Tracking, Crystalline, Tilt=26, Az=165 Realistic Design-SF3,1-Axis Tracking, Crystalline, Tilt=26, Az=165
Figure 6-9. Cumulative revenue stream goes to Dunnellon airport (realistic design)
The revenue stream results over the last five years of study period can be seen as follows:
Realistic design-SF1; 2.38 MW; 1-Axis Tracking; Crystalline = $1,523,377 Realistic design-SF2; 2.38 MW; 1-Axis Tracking; Crystalline = $1,622,621 Realistic design-SF3; 2.38 MW; 1-Axis Tracking; Crystalline = $1,622,621
Discussions for Realistic Design Scenario Unlike the maximum energy scenario, in realistic design, the emphasis is on the area that could support the actual renewable energy system installation. For instance, the incorporation of the predetermined criteria has helped focusing solely on the actual area for installation. Furthermore, the two main hurdles present are the maximum allowable power generation under feed in tariff, which is lower than 1MW (DESIRE,
2013b), and limitation posed by Progress Energy for maximum of 2 MW installation per block meter from the utility provider (Progress Energy, 2013c). With respect to the analysis, the role of simulation has succeeded to model the situation by neglecting the
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GRU constraint due to the 1 MW constraint of using FIT. In the case of Dunnellon
Airport, the suggestion to install both systems either by implementing two areas of installations using fixed-tilt with crystalline or combined fixed-tilt with crystalline as well as fixed-tilt with crystalline and 1-axis system with crystalline only yield to 5.76 MW system size—which is still below the 6 MW comprised of the combined three connections. Table 6-28 presents the comparison of realistic design using the same input assumptions as well as total area of 3 acres. Since the evaluation tool has allowed the variable input adjustment, the realistic design could be further modified based on the more accurate area analysis.
Table 6-28. Financial measures for decision making (25 years for realistic design) Size ROI TLCC LCOE SIR NPV PB Salvage Scenario Type [MW] [%] [$] [$/kWh] [$] [yrs] [$] [1] [2] [3] [5] [6] [6] [7] [8] [9] [10] Realistic Fixed-tilt
Design-SF1 Crystalline 2.38 80.9 4,638,011 0.093 1.81 3,721,028 0 291,953 Thin film 0.47 117,7 848,485 0.087 2.18 780,275 0 57,085
1-axis
Crystalline 2.38 455.8 3,591,955 0.059 5.56 6,621,609 0 291,953
Realistic Fixed-tilt
Design-SF2 Crystalline 2.38 141,62 4,313,238 0.083 2.42 4,347,563 0 291,952 Thin film 0.47 192.8 785,056 0.077 2.93 902,639 0 57,085
1-axis
Crystalline 2.38 1183.2 2,874,323 0.044 12.83 8,006,007 0 291,952
Realistic Fixed-tilt
Design-SF3 Crystalline 2.38 137,9 4,330,528 0.084 2.38 4,314,208 0 291,952 Thin film 0.47 188.2 788,422 0.078 2.88 896,147 0 57,085
1-axis
Crystalline 2.38 1183.2 2,874,323 0.044 12.83 8,006,007 0 291,952
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However, care should be maintained to the three areas of solar farm development. The designated solar farm—Solar Farm-1, Solar Farm-2 and Solar Farm-3—should be used as the area for future development because those areas have passed the wind load design as well as glare analysis. Moreover, there are specific azimuth directions that will influence the glare. The results of the glare calculations have shown angles other than 135, 165, and 195 degrees could produce glare.
Results for NZEB Scenario
In this dissertation, specifically for Dunnellon Airport case study, the NZEB scenario has been investigated separately since the airport is not energy-intensive.
However, all the steps are still strictly used to the newly developed methodology and tool from this dissertation. In order to estimate the available area near the buildings, there are two types of financing, i.e. feed in tariff and net metering that should be analyzed. For feed in tariff, all possible areas near buildings will be investigated using the original area for each case. Meanwhile, for the net metering, only the area that satisfies the peak load of monthly use will be analyzed. All analyses are based on tilt angle of 26 degrees and azimuth orientation of 135 degrees southeast. The results below present the phase of NZEB analysis using feed in tariff option.
Loan payment under feed in tariff. Revenue for over 20 years covers the payment of the initial cost. The input assumptions of the module’s power are still similar, using the highest power for each type. However, there are differences in the additional structure cost as well as the FIT rate. Table 6-29 provides the review of input summary for analyzing NZEB scenario under the feed in tariff option. The other values and assumptions remain the same with Table 6-21.
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Table 6-29. Input difference with general input assumptions (NZEB-FIT) Variables Value Unit Remarks Tilt angle 26 [ o ] Azimuth; direction 135 [ o ] Utility FIT 0.18 [ $/kWh ] Roof/pavement > 10 kW; ≤ 300 kW Installation Size DC ≤ 10 kW 5.3 [ $/watt ] 10 < Size DC ≤ 100 kW 4.9 [ $/watt ] Size DC > 100 kW 4.6 [ $/watt ] Earthwork 0.25 [ $/watt ] Site preparation + structure Inverter 0.4 [ $/watt ] 10 years replacement Rooftop-1 (crystalline) $76,280 / 10 years Rooftop-2 (crystalline) $37,120 / 10 years Parking-1 (crystalline) $23,680 / 10 years Parking-2 (crystalline) $70,000 / 10 years Rooftop-1 (thin film) $14,900 / 10 years Rooftop-2 (thin film) $7,248 / 10 years Parking-1 (thin film) $4,648 / 10 years Parking-2 (thin film) $13,700 / 10 years
From Figure 6-10, the highest revenue streams are the Rooftop-1 and Parking-2 areas with combined fixed-tilt system and crystalline module
Revenue Strem Present Worth over 20 Years $50,000.00
$45,000.00
$40,000.00 $35,000.00 $30,000.00 $25,000.00 $20,000.00 $15,000.00
Cumulative Revenue Cumulative $10,000.00 $5,000.00 $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Term of Loan [years] Rooftop-1, Fixed-tilt, Crystalline Rooftop-1, Fixed-tilt, Thin Film Rooftop-2, Fixed-tilt, Crystalline Rooftop-2, Fixed-tilt, Thin Film Parking-1, Fixed-tilt, Crystalline Parking-1, Fixed-tilt, Thin Film Parking-2, Fixed-tilt, Crystalline Parking-2, Fixed-tilt, Thin Film
Figure 6-10. Revenue stream as loan or bond payment over 20 years (NZEB-FIT)
The total revenue over 20 year period for loan payment can be summarized as follows:
Rooftop-1; 190.7 kW; Fixed-tilt; Crystalline = $676,651
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Rooftop-2; 92.8 kW; Fixed-tilt; Crystalline = $324,683 Parking-1; 59.2 kW; Fixed-tilt; Crystalline = $204,058 Parking-2; 175 kW; Fixed-tilt; Crystalline = $620,928 Rooftop-1; 37.25 kW; Fixed-tilt; Thin-film = $125,523 Rooftop-2; 18.12 kW; Fixed-tilt; Thin-film = $56,774 Parking-1; 11.62 kW; Fixed-tilt; Thin-film = $33,222 Parking-2; 34.25 kW; Fixed-tilt; Thin-film = $114,812
Incentives under feed in tariff. As previously mentioned, incentive is the most important factor that supports the decrease of the initial cost.
Table 6-30. Final net inception cost for each combination (NZEB-FIT) Yearly Funding Net Total System Size Production Saving Inception Scenario Cost Bond Grant Type Cost [kW] [$] [kWh] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9]
Rooftop-1 Fixed-tilt NZEB- 190.7 877,312 251,111 44,656 676,651 954 199,708 Crystalline FIT Cost/Watt = 1.04 Cost/kWh = 0.79 Thin film 37.25 182,525 49,046 8,284 125,523 186 56,816 Cost/Watt = 1.52 Cost/kWh = 1.15 Rooftop-2 Fixed-tilt NZEB- 92.8 454,720 122,066 21,428 324,683 464 129,573 Crystalline FIT Cost/Watt = 1.39 Cost/kWh = 1.06 Thin film 18.12 88,788 23,840 3,747 56,774 97 31,923 Cost/Watt = 1.76 Cost/kWh = 1.33 Parking-1 Fixed-tilt NZEB- 59.2 304,880 77,840 13,467 204,058 296 100,525 Crystalline FIT Cost/Watt = 1.69 Cost/kWh = 1.29 Thin film 11.62 59,225 15,205 2,193 33,280 58 25,945 Cost/Watt = 1.70 Cost/kWh = 2.23 Parking-2 Fixed-tilt NZEB- 175 848,944 230,681 40,978 620,928 875 227,141 Crystalline FIT Cost/Watt = 1.29 Cost/kWh = 0.98 Thin film 34.25 176,388 45,056 7,566 114,812 171 61,576 Cost/Watt = 1.79 Cost/kWh = 1.36
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Combined with the payment, the innovative financing option could considerably result in a RES project at no cost. Table 6-30 presents the results of the net initial cost calculations as a result of total cost minus funding.
Potential revenue under feed in tariff. The revenues that can be harvested from the NZB areas are not as large as the solar farm. Moreover, if the system is under net metering, no excess energy will be credited by utility provider. Figure 6-10 is the cumulative revenue over the rest of five years of study period.
Cumulative Revenue Stream
$140,000.00
$120,000.00
$100,000.00
$80,000.00
$60,000.00
$40,000.00 Cumulative Revenue Cumulative $20,000.00
$- 21 22 23 24 25 Rest of Study Period [years]
Rooftop-1, Fixed-tilt, Crystalline Rooftop-1, Fixed-tilt, Thin Film Rooftop-2, Fixed-tilt, Crystalline Rooftop-2, Fixed-tilt, Thin Film Parking-1, Fixed-tilt, Crystalline Parking-1, Fixed-tilt, Thin Film Parking-2, Fixed-tilt, Crystalline Parking-2, Fixed-tilt, Thin Film
Figure 6-10. Cumulative revenue stream goes to Dunnellon airport (NZEB-FIT)
The total revenues after year 20 to the end of study are summarized as follows:
Rooftop-1; 190.7 kW; Fixed-tilt; Crystalline = $118,326 Rooftop-2; 92.8 kW; Fixed-tilt; Crystalline = $56,778 Parking-1; 59.2 kW; Fixed-tilt; Crystalline = $35,684 Parking-2; 175 kW; Fixed-tilt; Crystalline = $108,582 Rooftop-1; 37.25 kW; Fixed-tilt; Thin-film = $21,950 Rooftop-2; 18.12 kW; Fixed-tilt; Thin-film = $9,928 178
Parking-1; 11.62 kW; Fixed-tilt; Thin-film = $5,810 Parking-2; 34.25 kW; Fixed-tilt; Thin-film = $20,047
NZEB design under net metering. The results presented under this section will be slightly different since the system size as well area analysis will be designed to approach the actual monthly use curve. The design will be based on the peak load analysis. However, in the developed evaluation tool, there are two load analyses, i.e. average and peak. Figure 6-11 provides the load design to achieve net zero energy.
Energy Production
1200
1000
800
600 [kWh]
400
200
0 1 2 3 4 5 6 7 8 9 10 11 12 Monthly Use 920 828 920 736 736 736 644 552 736 644 736 736 Net Zero Design 656 694 675 829 979 829 931 844 921 718 622 608 Average Design 528 559 544 668 788 668 750 680 742 579 501 489 [monthly use and loads design]
Monthly Use Net Zero Design Average Design
Figure 6-11. Energy production to approach actual monthly use profile
In the NZEB design under net metering (NM), the closest approach to the actual monthly used is preferred; because, the utility provider will not compensate more for the excess energy from the installed RES system. The offset energy from the actual use can be summarized in Figure 6-12. By using the input of $0.119 /kWh and $0.03/kWh for retail and avoided rate from Progress Energy, the offset values are multiplied to
179 obtain the monthly revenue stream for one year operation. The revenue stream can be reviewed in Table 6-31. From the table, it can be inferred that the peak load design— net zero design—has provided higher positive offset values from April to October.
Meanwhile, under average design, the values are less. Thus, the net zero design is capable of satisfying both energy consumption and gaining additional revenue.
Offset Energy - Consumption/Production 400 300 200 100
0
-100
[kWh] -200 -300 -400 -500 1 2 3 4 5 6 7 8 9 10 11 12 Net Zero Design -264 -134 -245 93 243 93 287 292 185 74 -114 -128 Average Load -392 -269 -376 -68 52 -68 106 128 6 -65 -235 -247
[monthly offset] Net Zero Design Average Load
Figure 6-12. Energy consumed and excessed in Dunnellon Airport
The area of the system was calculated using the evaluation tool to estimate the possible area to achieve NZEB design. The estimated area is 36 square meters. The area was estimated using the actual monthly use data as well as by incorporating the maximum module power rating in the market—the panel peak power equals to 320 watt and the efficiency equals to 20.4%. The inverter yields to $2,816 / 10 year for a 7.04 kW system. In addition, since the available cost data for the system lower than 10 kW indicates the similar thin film cost, i.e. $5.3/watt (Barbose et al. 2013), the analysis using thin film is determined to yield to a lower value. 180
Table 6-31. Monthly revenue stream for NZEB design Dunnellon System Consumed Excess Expense Revenue Month Energy Use 7.04 kW Energy Energy Output [kWh] [kWh] [kWh] [kWh] [$] [$] January 920 656 264 31.4 February 828 694 134 15.9 March 920 675 245 29.2 April 736 829 93 3.3 May 736 979 243 8.5 June 736 829 93 3.3 July 644 931 287 10.1 August 552 844 292 10.2 September 736 921 185 6.5 October 644 718 74 2.6 November 736 622 114 13.6 December 736 808 128 15.2 Yearly 8,924 9,310 105.3 44.5
This is plausible since the wattage produced to generate energy output is lower than crystalline. Hence, no further analysis is required. Nonetheless, the analysis of using structure will be slightly different in structure cost. The input additional structural cost, i.e. $0.25/watt, has been added to see whether there are discrepancies between rooftop and parking structure.
Loan payment and revenue. The results below show the 20 year bond payment of the rooftop and parking structure. The revenue remains the same since both systems have typical size and module types. The value for the payment over 20 years is $15,071, and the value for the revenue goes to Dunnellon Airport for the rest of study period is $2,636.
Incentives. The upfront cost will be reduced by availability of incentives. For this analysis, the system size is the same and will result the final values in Table 6-32.
The only difference is the total cost; this is just due to the additional work for the structure in the parking area.
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Table 6-32. Final net inception cost for each combination (NZEB-NM) Total Yearly Funding Net Inception System Size Production Scenario Cost Saving Bond Grant Cost Type [kW] [$] [kWh] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9]
Rooftop Fixed-tilt NZEB-NM Crystalline 7.04 37,312 9,310 995 17,701 570 21,670 Cost/Watt = 3.18 Cost/kWh =2.40
Parking Fixed-tilt NZEB-NM Crystalline 7.04 39,072 9,310 995 15,071 570 23,430 Cost/Watt = 3.33 Cost/kWh = 2.51
Discussions for NZEB Scenario
Under feed in tariff, Dunnellon Airport can exploit the available area near the building since the larger the installed system, the larger the profit that can be reached.
Table 6-33 and Table 6-34 present the measures for decision making purpose. The measures are based on 25 years of study period neglecting the 20 years revenue that should be placed as bond payment.
However, the measures are convincing for airport authority to make decision since they represent the value of the project for its entire life. From table 6-33, the projects that can reach positive ROI larger than 10% are the system with crystalline type with more than 100 kW size. According to Barbose, et al. (2013, p.1)—these values have been documented in the evaluation tool cost reference; also can be referred to Figure 5-
3—the system divisions for both residential and commercial based on the system size in
2012 projects are as follows:
System size ≤ 10 kW equals to $5.3/watt 10 kW < system size ≤ 100 kW equals to $4.9/watt System size > 100 kW equals to $4.6/watt
182
Table 6-33. Financial measures for decision making (25 years for NZEB-FIT) Size ROI TLCC LCOE SIR NPV PB Salvage Scenario Type [kW] [%] [$] [$/kWh] [$] [yrs] [$] [1] [2] [3] [5] [6] [6] [7] [8] [9] [10] Rooftop-1 Fixed-tilt 368,13 NZEB-FIT Crystalline 190.7 84.34 424,963 0.106 1.84 0 23,361 3
Thin film 37.25 -28.6 106,265 0.136 0.72 40,818 1 4,563
Rooftop-2 Fixed-tilt 130,88 NZEB-FIT Crystalline 92.8 1.02 249,5970 0.128 1.01 1 11,367 8
Thin film 18.12 -70.55 57,110 0.150 0.29 9,402 5 2,220
Parking-1 Fixed-tilt NZEB-FIT Crystalline 59.2 -44.01 182,801 0.147 0.56 56,287 2 7,251
Thin film 11.62 -118.47 43,696 0.181 -0.18 -4,791 25 1,423
Parking-2 Fixed-tilt 279,32 NZEB-FIT Crystalline 175 22.98 448,364 0.122 1.23 1 21,437 7
Thin film 34.25 -60.65 110,079 0.154 0.39 24,230 2 4,195
Whereas, the installed cost for utility scale can be summarized as follows (Barbose, et al., 2013, p.3):
Fixed-tilt, crystalline module equals to $3.3/watt Tracking, crystalline module equals to $3.6/watt Fixed-tilt, thin film equals to $3.2/watt
It is convincing that a larger system will result in the lower cost of investment. Based on
Table 6-34, although at the beginning Parking-2 has higher investment cost compared to Rooftop-1; in the end Parking-2 has lower TLCC. However, in analyzing net metering scheme, the system shall be designed based on the actual monthly use. This hurdles the RES installation because it cannot allow the authority to install as large system as possible—in purpose of generating larger revenue. Unlike the feed in tariff analysis, the system under net metering should be treated differently since the power purchase
183 agreement will not allow the payment of the excess energy more than the peak power needed from the actual monthly use.
Table 6-34. Financial measures for decision making (25 years for NZEB-NM) Size ROI TLCC LCOE SIR NPV PB Salvage Scenario Type [kW] [%] [$] [$/kWh] [$] [yrs] [$] [1] [2] [3] [5] [6] [6] [7] [8] [9] [10] Rooftop Fixed-tilt NZEB-NM Crystalline 7.04 -176 32,287 0.23 -0.76 -14,660 25 862
Parking Fixed-tilt NZEB-NM Crystalline 7.04 -180 34,629 0.25 -0.80 -17,006 25 862
Table 6-34 presents the financial project measures under net metering scheme. No values are determined favorable. This should be further investigated using sensitivity analysis for various financial parameter rates or compared to set up under FIT scheme.
Sensitivity analysis. The sensitivity analysis was conducted by using the results of parking structure assumptions. For the baseline case, using 6% loan rate
(LR), 3% discount rate (DR), 1% energy inflation rate (EIR) and 1% general inflation rate (GIR) the values for the economic measures are unfavorable. The sensitivity analysis is the way the analyst can predict the influence of the financial variable changes. Since the rate can be different in the assumptions, for instance, due to the ownership option; therefore, the influence of parameter change to the results can be further investigated by assuming all other parameters constant. For each acceptance criteria, the influence of financial parameters can be depicted in graphical forms in order to make simpler influence between input and output. The shaded values in each figure below present the baseline case for comparisons.
184
ROI Sensitivity Analysis
600.0%
400.0%
200.0%
0.0%
Percent -200.0%
-400.0%
-600.0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LR -137.8% -145.4% -153.6% -162.1% -171.0% -180.3% -189.9% -199.9% -210.2% -220.8% -231.7% -242.8% -254.2% -265.7% -277.5% DR -173.5% -178.1% -180.3% -180.9% -180.4% -179.1% -177.3% -175.3% -173.1% -170.8% -168.5% -166.2% -164.0% -161.9% -159.9% EIR -180.3% -162.8% -142.7% -119.6% -93.0% -62.4% -27.2% 13.6% 60.8% 115.4% 178.8% 252.3% 337.7% 437.0% 552.5% GIR -180.3% -184.5% -189.5% -195.3% -202.0% -209.9% -219.1% -229.8% -242.4% -257.1% -274.4% -294.6% -318.2% -345.9% -378.3% Rate [%] LR DR EIR GIR
Figure 6-13. ROI sensitivity analysis under NZEB-NM
Since the size was determined to be different in structure cost, the two cases, i.e. rooftop and parking, the cases are assumed typical for sensitivity analysis. The evaluation tool has provided two variations with 15% rates and 30% rates with 1% increment. The results below are only based on 15% rates.
TLCC Sensitivity Analysis
70000 60000
50000 40000 30000 20000 10000 0
-10000 Dollar Amount Dollar -20000 -30000 -40000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LR 24663.1 26464.2 28365 30362.1 32451.7 34629.5 36890.9 39231 41644.8 44127.3 46673.4 49278.2 51937 54645 57398 DR 37707 36248.6 34629.5 32950.5 31276.5 29648.5 28091 26617.3 25233.5 23940.8 22737.4 21619.7 20582.8 19621.4 18730 EIR 34629.5 32708.6 30542.7 28097.7 25335.1 22210.7 18674.3 14668.4 10127.9 4978.61 -863.91 -7495.7 -15026 -23578 -33293 GIR 34629.5 35098.8 35591.8 36101.9 36618.9 37128.1 37609.2 38034.5 38367.1 38558.2 38544.2 38243 37549.4 36329.7 34414.9 Rate [%] LR DR EIR GIR
Figure 6-14. TLCC sensitivity analysis under NZEB-NM
185
LCOE Sensitivity Analysis
0.600 0.500 0.400
0.300 0.200 0.100 [$/kWh] 0.000 -0.100 -0.200 -0.300 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LR 0.17874 0.19090 0.20374 0.21723 0.23134 0.24605 0.26132 0.27712 0.29342 0.31018 0.32738 0.34497 0.36292 0.38121 0.39980 DR 0.21268 0.23011 0.24605 0.26061 0.27393 0.28613 0.29730 0.30756 0.31699 0.32566 0.33367 0.34107 0.34794 0.35432 0.36027 EIR 0.24605 0.23307 0.21845 0.20194 0.18328 0.16218 0.13830 0.11125 0.08058 0.04581 0.00635 -0.0384 -0.0892 -0.1470 -0.2126 GIR 0.24605 0.25262 0.26026 0.26914 0.27948 0.29153 0.30558 0.32196 0.34108 0.36340 0.38946 0.41990 0.45544 0.49694 0.54541 Rate [%]
LR DR EIR GIR
Figure 6-15. LCOE sensitivity analysis under NZEB-NM
After conducting sensitivity analysis from the base line, the NZEB net metering project will be favorable only if the energy inflation rate jumps to 8% or more. The values of
ROI will rise to 13.6% which is favorable based on the acceptance criterion. However, in terms of TLCC (Figure 6-14), the total cost for the entire life-cycle (25 years) will reduce to about zero at 11% energy inflation rate. The values of EIR higher than 11% will yield the positive cost which is favorable.
According to EIA (2013), referring back to Table 2-1, the value of LCOE forecast until 2018 for solar PV is estimated at $0.144 /kWh; yet, the lower LCOE is preferred.
The results from Figure 6-15 indicate that range of lower LCOE can be achieved at 8%
EIR, whereas the other values of financial parameters tend to escalate LCOE values.
186
SIR Sensitivity Analysis
8.00
6.00
4.00
2.00
0.00
-2.00
-4.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LR -0.38 -0.45 -0.54 -0.62 -0.71 -0.80 -0.90 -1.00 -1.10 -1.21 -1.32 -1.43 -1.54 -1.66 -1.77 DR -0.73 -0.78 -0.80 -0.81 -0.80 -0.79 -0.77 -0.75 -0.73 -0.71 -0.68 -0.66 -0.64 -0.62 -0.60 EIR -0.80 -0.63 -0.43 -0.20 0.07 0.38 0.73 1.14 1.61 2.15 2.79 3.52 4.38 5.37 6.53 GIR -0.80 -0.85 -0.90 -0.95 -1.02 -1.10 -1.19 -1.30 -1.42 -1.57 -1.74 -1.95 -2.18 -2.46 -2.78 Rate [%] LR DR EIR GIR
Figure 6-16. SIR sensitivity analysis under NZEB-NM
The value of SIR is preferred when it is greater or equal to 1. From Figure 6-16, it is obvious that, again, 8% EIR or higher will yield the favorable SIR.
NPV Sensitivity Analysis
200000
150000
100000
50000
Dollar Amount Dollar 0
-50000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LR -7039.7 -8840.8 -10742 -12739 -14828 -17006 -19268 -21608 -24021 -26504 -29050 -31655 -34314 -37022 -39775 DR -15405 -16483 -17006 -17145 -17023 -16725 -16315 -15835 -15316 -14780 -14242 -13711 -13195 -12697 -12220 EIR -17006 -12897 -8188.1 -2781 3438.34 10603.7 18871.5 28424.6 39477.1 52279.1 67123.2 84351.4 104364 127627 154687 GIR -17006 -17499 -18021 -18569 -19133 -19701 -20257 -20775 -21223 -21559 -21724 -21646 -21229 -20351 -18858 Rate [%]
LR DR EIR GIR
Figure 6-17. NPV sensitivity analysis under NZEB-NM
187
Unlike the other parameters, the preferred value of NPV is the value whenever it has a positive value. From Figure 6-17, the positive NPV will be started at 5% EIR.
Payback Sensitivity Analysis 30 25
20 15 Years 10 5
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LR 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 DR 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 EIR 25 25 25 25 23 15 6 2 0 0 0 0 0 0 0 GIR 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 Rate [%]
LR DR EIR GIR
Figure 6-18. NPV sensitivity analysis under NZEB-NM
Payback period will be favorable if the investment can have return directly in the beginning year. At this point, the increment towards zero years will start at 5% EIR
(Figure 6-18). The other parameters will take no effect to the payback period of the investment. In the end, although the financial measures present different acceptance criteria, the financial parameters, i.e., LR, DR, and GIR will be factors that influence the preliminary assumption for valuing the project assessment. Most importantly, the determination of EIR will significantly affect most financial measures to all the acceptance criteria.
Private Entity as System Owner
All descriptions in this section are the results of evaluation tool using private- owned option. In this case, the private entity will assumed to be an integrated entity with the financier body. Therefore, there is no loan payment—from revenue—in the
188 calculation processes. In lieu of treating loan payment as revenue, there are two main benefits that can reduce the total installation cost. The first is the incentive from tax reduction, 30% of the total installation cost, in which the value can be taken as cash grant (Kibert et al., 2010, p.97).
The second is the sum of available REC and other rebates that are enacted in the case study area, in this case Dunnellon Airport. Third, after subtracting the installed cost with the sum of tax credit and other incentives; there will be another 20% first year depreciation that can help private entity to reduce the installed cost. This value can be obtained by subtracting the total installed cost with the sum of 30% tax incentives and other incentives; then, multiplying the value based on the final value as a result of the tax credit subtraction. This will become another source of funding that can reduce the installation cost.
In the economic evaluation tool calculation processes, under private-owned option, there is another expense by the private entity that will go as the revenue for airport authority (Figure 3-10); and, this expense is considered as another yearly cost for private entity. However, this will be treated as revenue to the airport. Table 6-35 provides the input assumptions for the private-owned option mainly for solar farm application. The inverter input can be investigated by multiplying the system size with the $0.4/watt assumption.
189
Table 6-35. Input assumptions for utility scale under private-owned option Variables Value Unit Remarks Tilt angle 26 [ o ] Azimuth; direction 135, 165, 195 [ o ] Polycrystalline DC wattage 320 [ watt ] Efficiency 20.4 [ % ] Thin film DC wattage 125 [ watt ] Efficiency 18.3 [ % ] Degradation rate 0.8 [ % ] 25 years Utility Rate 0.119 [ $/kWh ] Utility FIT 0.15 [ $/kWh ] Installation Fixed-tit Crystalline 3.3 [ $/watt ] Thin film 3.2 [ $/watt ] 1-Axis tracking Crystalline 3.6 [ $/watt ] Thin film - [ $/watt ] O&M 0.35 % of total installed cost Earthwork 0.56 [ $/watt ] Inverter 0.4 [ $/watt ] 10 years replacement Solar Farm-1 (crystalline) $1,968,640 / 10 years Solar Farm-2 (crystalline) $1,775,616 / 10 years Solar Farm-3 (crystalline) $4,685,312 / 10 years Solar Farm-1 (thin film) $384,500 / 10 years Solar Farm-2 (thin film) $346,800 / 10 years Solar Farm-3 (thin film) $915,100 / 10 years Down Payment - % of new initial cost - Term of Loan - [ years ] Assumed no loan for private-owned Depreciation 5 [ years ] Study Period 25 [ years ] Loan rate (LR) 6 [ % ] Not used Discount rate (DR) 5 [ % ] Baseline Energy inflation rate (EIR) 2 [ % ] Baseline General inflation rate (GIR) 2 [ % ] Baseline Grant 1 [ $/kW ] Maximum $700 REC 4 [ $/W ] Maximum $20,000 Grant 0.06 [ $/kWh ] Tax Credit 30 % of total installed cost Land Lease 0.04 [ $/S.F] Solar Farm-1 $10,794 / year Solar Farm-2 $9,735 / year Solar Farm-3 $25,689 / year
190
Results for Maximum Energy Scenario
This value will require input from the airport authority. For instance in Dunnellon
Airport, the value for non-aviation land lease is measured at $0.04 per square foot
(Marion County Florida, 2013). Under this scenario, to achieve maximum energy, the three solar farm system variations and the expense that should be paid by private- owned as a result of leasing expense will be investigated. This leasing expense will be treated as revenue for the airport and will be presented separately. The results below show the process that will use similar steps compared to Dunnellon Airport as the owner of the RES.
Cumulative Revenue Stream over 25 Years
$45,000,000.00
$40,000,000.00
$35,000,000.00 $30,000,000.00 $25,000,000.00 $20,000,000.00 $15,000,000.00
$10,000,000.00 Cumulative Revenue Cumulative $5,000,000.00 $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Study Period [years]
Solar Farm-1, Fixed-tilt, Crystalline, Tilt = 26, Az = 135 Solar Farm-1, Fixed-tilt, Thin Film, Tilt = 26, Az=135 Solar Farm-2, Fixed-tilt, Crystalline, Tilt = 26, Az=165 Solar Farm-2, Fixed-tilt, Thin Film, Tilt=26, Az=165 Solar Farm-3, Fixed-tilt, Crystalline, Tilt = 26, Az=195 Solar Farm-3, Fixed-tilt, Thin Film, Tilt=26, Az=195
Figure 6-19. Cumulative revenue stream goes to private entity (fixed-tilt)
The cumulative revenue over 25 years (Figure 6-19) can be assumed to be the private entity revenue. The values can be summarized as follows:
Solar Farm-1; 4.92 MW; Fixed-tilt; Crystalline = $15,561,447 Solar Farm-2; 4.43 MW; Fixed-tilt; Crystalline = $14,540,559 Solar Farm-3; 11.7 MW; Fixed-tilt; Crystalline = $38,304,672 Solar Farm-1; 0.96 MW; Fixed-tilt; Thin-film = $3,035,779
191
Solar Farm-2; 0.87 MW; Fixed-tilt; Thin-film = $2,836,389 Solar Farm-3; 2.28 MW; Fixed-tilt; Thin-film = $7,477,821
It is clear that Solar Farm-3 has the maximum values because it has both the largest and highest energy produced using crystalline panel. However, since these values represent the revenue, the other factors such as installed cost, operation and maintenance cost, inverter replacement and leasing cost should be taken into account to obtain the net present value of the project entire life cycle.
Figure 6-20 presents the cumulative revenue stream for 1-axis tracking system.
Since the area is the only factor that influences the system, regardless module types, system with the larger area yields to larger cumulative revenue.
Cumulative Revenue Stream over 25 Years
$60,000,000.00
$50,000,000.00
$40,000,000.00
$30,000,000.00
$20,000,000.00
$10,000,000.00 Cumulative Revenue Cumulative $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Study Period [years]
Solar Farm-1, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 135 Solar Farm-2, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165 Solar Farm-3 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165
Figure 6-20. Cumulative revenue stream goes to private entity (1-axis tracking)
The final revenue values over 25 years study period for 1-axis tracking system with crystalline module are listed as follows:
Solar Farm-1; 4.92 MW; 1-Axis; Crystalline = $19,007,565 Solar Farm-2; 4.43 MW; 1-Axis; Crystalline = $18,260,059 Solar Farm-3; 11.7 MW; 1-Axis; Crystalline = $48,190,364
192
Tax incentives, grant and 1-year depreciation. The incentives for private- owned system are slightly different with the system under public ownership. As previously mentioned, there are three possible incentives that can reduce the total installed cost, see column [7]; [8] and [9]. The detailed values of the net inception cost can be seen on column [10] in Table 6-36. Unlike the public-owned option, the values of 1-year depreciation and tax credit are factors that give a significance reduction in the total cost. Similar to previous analyses, in column [2], there are cost per watt analysis and cost per energy production per hour. The value for cost per watt can be obtained by dividing the results of new inception cost with the system size. Whereas, the value for cost per energy production can be acquired by dividing net inception cost with the energy production for each different system. Since the option for private entity in this dissertation based on the assumption that both private entity as system owner is the same body with the financier; hence, no loan obligation to pay. Therefore, in the end the sum of every yearly production can be assumed to be the owner revenue during a certain study period. Although this dissertation does not provide the analysis for separate financier body and private entity, for further analysis enrichment, private entity can model the same option assumption using the private entity and financier body are separate entity; thus, the private entity can be benefited by another incentive using bond calculation during certain study period. This will lower the new inception cost.
However, during the payment of the bond using the production, the revenues generated will not be counted as private entity revenue.
193
Table 6-36. Final net inception cost for each combination (maximum energy) Funding Net Size Total Cost Production Yearly 1-year Inception Scenario Type Tax Credit Grant Saving depreciation Cost [MW] [$] [kWh] [$] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
Solar Farm-1 Fixed-tilt Maximum Crystalline 4.92 18,997,376 6,483,113 972,190 2,654,711 5,699,213 24,607 10,618,844 Energy Cost/Watt 2.15 Cost/kWh 1.63
Thin film 0.96 3,614,300 1,266,232 189,658 505,041 1,084,290 4,806 2,020,163 Cost/Watt 2.10 Cost/kWh 1.59
1-axis Crystalline 4.92 20,473,856 7,918,405 1,187,484 2,861,418 6,142,157 24,607 11,445,673 Cost/Watt 2.32 Cost/kWh 1.44
Solar Farm-2 Fixed-tilt Maximum Crystalline 4.43 17,134,694 6,057,918 908,411 2,394,418 5,140,408 22,195 9,577,673 energy Cost/Watt 2.16 Cost/kWh 1.58
Thin film 0.87 3,259,920 1,183,187 177,201 455,522 977,976 4,335 1,822,087 Cost/Watt 2.09 Cost/kWh 0.64
1-axis Crystalline 4.43 18,466,406 7,607,072 1,140,784 2,580,858 5,539,922 22,195 10,323,431 Cost/Watt 2.33 Cost/kWh 1.35
194
Table 6-36. Continued Funding Net Size Total Cost Production Yearly 1-year Inception Scenario Type Tax Credit Grant Saving depreciation Cost [MW] [$] [kWh] [$] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
Solar Farm-3 Fixed-tilt Maximum Crystalline 11.7 45,213,261 15,955,558 2,393,057 6,318,143 13,563,978 58,566 25,272,573 energy Cost/Watt 2.16 Cost/kWh 1.58
Thin film 2.28 8,601,940 3,116,321 467,171 1,201,984 2,580,582 11,439 4,807,935 Cost/Watt 2.10 Cost/kWh 1.54
1-axis Crystalline 11.7 48,727,245 20,072,902 3,010,659 6,810,101 14,618,173 58,566 27,240,404 Cost/Watt 2.32 Cost/kWh 1.35
195
Figure 6-21 shows the cumulative net present value. This value represents the discounted annual revenue subtracted by the final values of yearly costs and expenses over the study period. The cumulative of the net present value may also determine the certain period that could reach the break-even point.
Cumulative Net Present Value over 25 Years $10,000,000.00
$5,000,000.00
$0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ($5,000,000.00)
($10,000,000.00)
($15,000,000.00) Cumulative NPV Cumulative ($20,000,000.00)
($25,000,000.00) Study Period [years] Solar Farm-1, Fixed-tilt, Crystalline, Tilt = 26, Az = 135 Solar Farm-1, Fixed-tilt, Thin Film, Tilt = 26, Az=135 Solar Farm-2, Fixed-tilt, Crystalline, Tilt = 26, Az=165 Solar Farm-2, Fixed-tilt, Thin Film, Tilt=26, Az=165 Solar Farm-3, Fixed-tilt, Crystalline, Tilt = 26, Az=195 Solar Farm-3, Fixed-tilt, Thin Film, Tilt=26, Az=195
Figure 6-21. Cumulative net present value (fixed-tilt)
The values of net present values and payback period can be summarized as follows:
Solar Farm-1; 4.92 MW; Fixed-tilt; Crystalline ; NPV = $1,518,074; PB = 19 years Solar Farm-2; 4.43 MW; Fixed-tilt; Crystalline ; NPV = $1,874,142; PB = 17 years Solar Farm-3; 11.7 MW; Fixed-tilt; Crystalline ; NPV = $4,881,817; PB = 17 years Solar Farm-1; 0.96 MW; Fixed-tilt; Thin-film ; NPV = $198,019; PB = 22 years Solar Farm-2; 0.87 MW; Fixed-tilt; Thin-film ; NPV = $546,449; PB = 16 years Solar Farm-3; 2.28 MW; Fixed-tilt; Thin-film ; NPV = $724,032; PB = 19 years
The range of payback periods are around 17 to 22 years. Although it has a longer payback time compared to public-owned airport, the property (RES) on the airport belongs to private entity. Therefore, the greater the net present value, the better the project.
196
Cumulative Net Present Value over 25 Years $15,000,000.00
$10,000,000.00
$5,000,000.00
$0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ($5,000,000.00)
($10,000,000.00)
($15,000,000.00) Cumulative NPV Cumulative ($20,000,000.00)
($25,000,000.00)
($30,000,000.00) Study Period [years] Solar Farm-1, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 135 Solar Farm-2, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165 Solar Farm-3 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165
Figure 6-22. Cumulative net present value (1-axis tracking)
The cumulative NPV and the payback period are summarized as follows:
Solar Farm-1; 4.92 MW; Tracking; Crystalline ; NPV = $4,086,158; PB = 14 years Solar Farm-2; 4.43 MW; Tracking; Crystalline ; NPV = $4,801,698; PB = 13 years Solar Farm-3; 11.7 MW; Tracking; Crystalline ; NPV = $12,677,811; PB = 13 years
From the results in Figure 6-22, the range of payback period is between 13 to 14 years.
The highest NPV came to the system with combined tracking and crystalline module.
Leasing expense goes as revenue to Dunnellon Airport. The value that will be accounted to the revenue of Dunnellon Airport is based on the leasing payment from the private entity. The comparison of owning the system with leasing option will be the foundation to decide for Dunnellon Airport regardless the system size, mounting and module type. The result below shows the cumulative present worth of leasing benefit, this value is treated as expense for private entity, for a 25 year study period.
197
Cumulative Revenue Stream from Leasing over 25 Years
$500,000.00 $450,000.00
$400,000.00 $350,000.00 $300,000.00 $250,000.00 $200,000.00 $150,000.00
Cumulative Revenue Cumulative $100,000.00 $50,000.00 $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Study Period [years]
Solar Farm-1 (Area=269,841 S.F.) Solar Farm-2 (Area=243,383 S.F.) Solar Farm-3 Area (Area=642,218 S.F.)
Figure 6-23. Cumulative revenue stream from leasing (maximum energy)
The values of the cumulative present worth as leasing revenue that go to Dunnellon
Airport can be summarized as follows:
Solar Farm-1; 4.92 MW; Area 1; NPV = $189,196 Solar Farm-2; 4.43 MW; Area 2; NPV = $170,632 Solar Farm-3; 11.7 MW; Area 3; NPV = $450,275
Discussions for Maximum Energy Scenario
Under private ownership, there are two possibilities or options of ownership
(Figure 3-10). The first option is for the private entity to own the funding. The second option is for the private entity cooperates with the financier body to fund the project. In this case, the private entity is united with the financier body, so that it acts as the same body. Some other financial measures for decision making is presented in Table 6-37.
The values indicate ROI below 0% (column [4]). However, since the system ownership belongs to private sector, it is convincing that as long as the net present value achieves positive value, the project will be beneficial. Since the focus of this dissertation is the
198 revenue assessment for airport authority, the case of revenue assessment that goes to
Dunnellon Airport will be compared in the chapter summary section for each scenario case. Although the private-owned system seems not favorable to the private entity, the private entity in fact owns and operates the system.
Table 6-37. Financial measures for decision making (25 years for maximum energy) Size ROI TLCC LCOE SIR NPV PB Salvage Scenario Type [MW] [%] [$] [$/kWh] [$] [yrs] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] Solar Fixed-tilt Farm-1 Maximum Crystalline 4.92 -85.7 14,005,930 0.165 0.14 1,518,074 19 476,879 energy Thin film 0.96 -90.2 2,826,402 0.172 0.10 198,019 22 93,140
1-axis
Crystalline 4.92 -64.3 14,881,461 0.144 0.36 4,086,158 14 476,879
Solar Fixed-tilt Farm-2 Maximum Crystalline 4.43 -80.4 12,632,646 0.160 0.20 1,874,142 17 430,121 energy Thin film 0.87 -70.1 2,549,264 0.152 0.30 546,449 16 84,008
1-axis
Crystalline 4.43 -53.5 13,422,331 0.136 0.47 4,801,698 13 430,121
Solar Fixed-tilt Farm-3 Maximum Crystalline 11.7 -80.7 33,333,743 0.161 0.19 4,881,817 17 1,134,959 energy Thin film 2.28 -84.9 6,726,756 0.166 0.15 724,032 19 221,672
1-axis
Crystalline 11.7 -53.5 35,417,483 0.136 0.47 12,677,811 13 1,134,959
The payback period is in the range between 13 and 22 years. In addition, the salvage value after the 25 year contract will be in range of $84,008 to $1,134,959. Salvage value is the residual value that could benefit the system owner, in this case, this occurs because of the time of replacement period. Residual values will subtract the TLCC and 199 added the cash flow (NPV). The evaluation tool has incorporated both calculations.
The salvage value cannot be used to decide the feasibility of a project; however, the residual value can help the decision maker whether they want to sell or keep the system until the last study period.
Results for Realistic Design Scenario
The underneath descriptions present the realistic design as opposed to the maximum energy scenario. This scenario allows Dunnellon Airport to install RES based on the actual size and safety consideration. Table 6-38 presents the input differences for the evaluation tool. The other values from Table 6-35 remain the same. Under realistic design scenario, it is assumed that the area used will be the applicable area that Dunnellon Airport should consider. The maximum energy can be used to estimate the theoretical potential especially for solar farm application. The available potential from this amount is the realistic design scenario.
Table 6-38. Input difference with general input assumptions (realistic design) Variables Value Unit Remarks Inverter 0.4 [ $/watt ] 10 years replacement Realistic Design (crystalline) $953,320 / 10 years Realistic Design (thin film) $186,400 / 10 years Land Lease 0.04 [ $/S.F. ] Realistic Design 5,227 / year
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Cumulative Revenue Stream over 25 Years
$9,000,000.00
$8,000,000.00 $7,000,000.00 $6,000,000.00 $5,000,000.00 $4,000,000.00 $3,000,000.00 $2,000,000.00
Cumulative Revenue Cumulative $1,000,000.00 $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Study Period [years]
Solar Farm-1, Fixed-tilt, Crystalline, Tilt = 26, Az = 135 Solar Farm-1, Fixed-tilt, Thin Film, Tilt = 26, Az=135 Solar Farm-2, Fixed-tilt, Crystalline, Tilt = 26, Az=165 Solar Farm-2, Fixed-tilt, Thin Film, Tilt=26, Az=165 Solar Farm-3, Fixed-tilt, Crystalline, Tilt = 26, Az=195 Solar Farm-3, Fixed-tilt, Thin Film, Tilt=26, Az=195
Figure 6-24. Cumulative revenue stream goes to private entity (fixed-tilt)
The results for cumulative revenue that go to private entity are separated because of the panel type regardless of the area (it has the same input 12,140 square meters).
The type of panel and the azimuth direction can influence the system production. The cumulative revenue results (Figure 6-24) can be summarized as follows:
Solar Farm-1; 2.38 MW; Fixed-tilt; Crystalline = $7,533,557 Solar Farm-2; 2.38 MW; Fixed-tilt; Crystalline = $7,804,878 Solar Farm-3; 2.38 MW; Fixed-tilt; Crystalline = $7,790,434 Solar Farm-1; 466 kW; Fixed-tilt; Thin-film = $1,467,834 Solar Farm-2; 466 kW; Fixed-tilt; Thin-film = $1,520,823 Solar Farm-3; 466 kW; Fixed-tilt; Thin-film = $1,518,012
Similar to the maximum energy scenario, the results for Solar Farm-2 and Solar Farm-3 under 1-axis tracking system are unchanged since they have both have typical tilt and azimuth angles. The tilt equals to 26 degrees and the azimuth direction is at 165 degrees from north. Figure 6-25 provides the chart of cumulative revenue for 1-axis tracking realistic design.
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Cumulative Revenue Stream over 25 Years
$12,000,000.00
$10,000,000.00
$8,000,000.00
$6,000,000.00
$4,000,000.00
Cumulative Revenue Cumulative $2,000,000.00
$- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Study Period [years]
Solar Farm-1, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 135 Solar Farm-2, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165 Solar Farm-3 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165
Figure 6-25. Cumulative revenue stream goes to private entity (1-axis tracking)
Solar Farm-1; 2.38 MW; 1-Axis tracking; Crystalline = $9,202,385 Solar Farm-2; 2.38 MW; 1-Axis tracking; Crystalline = $9,801,900 Solar Farm-3; 2.38 MW; 1-Axis tracking; Crystalline = $9,801,900
Incentives. The incentives compositions are the same with the solar farm under maximum energy scenario. In realistic design scenario under private ownership, the net inception costs are higher than the public ownership since the system owned by the private entity since the beginning. Table 6-39 through Table 6-41, column [7] through
[9], provide the incentive breakdown under private-owned system. The tables were purposely separated in three different areas in order to allow Dunnellon Airport to investigate each case.
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Table 6-39. Final net inception cost for Solar Farm-1 (realistic design) Net System Yearly Funding Size Total Cost Production Inception Scenario Saving Type 1-year depreciation Tax Credit Grant Cost [MW] [$] [kWh] [$] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] Realistic Fixed-tilt
Design Crystalline 2.38 9,199,770 3,139,535 470,654 1,285,584 2,759,931 11,916 5,142,338 SF1 Cost/Watt = 2.16
Cost/kWh = 1.58
Thin film 0.47 1,750,280 613,190 91,702 244,573 525,084 2,327 978,295 Cost/Watt =2.08
Cost/kWh = 1.53
1-axis
Crystalline 2.38 9,914,778 3,834,594 574,912 1,385,686 2,974,433 11,916 5,542,742 Cost/Watt = 2.32
Cost/kWh = 1.35
Table 6-40. Final net inception cost for Solar Farm-2 (realistic design) Net System Yearly Funding Size Total Cost Production Inception Scenario Saving Type 1-year depreciation Tax Credit Grant Cost [MW] [$] [kWh] [$] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] Realistic Fixed-tilt Design Crystalline 2.38 9,199,770 3,252,539 487,604 1,285,584 2,759,931 11,916 5,142,338 SF2 Cost/Watt = 2.16
Cost/kWh =1.58
Thin film 0.47 1,750,280 635,260 95,012 244,573 525,084 2,327 978,295 Cost/Watt = 2.08
Cost/kWh = 1.53
1-axis
Crystalline 2.38 9,914,778 4,084,289 612,367 1,385,686 2,974,433 11,916 5,542,742 Cost/Watt = 2.32
Cost/kWh = 1.35
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Table 6-41. Final net inception cost for Solar Farm-3 (realistic design) Net System Yearly Funding Size Total Cost Production Inception Scenario Saving Type 1-year depreciation Tax Credit Grant Cost [MW] [$] [kWh] [$] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] Realistic Fixed-tilt
Design Crystalline 2.38 9,199,770 3,246,523 486,702 1,285,584 2,759,931 11,916 5,142,338 SF3 Cost/Watt = 2.16
Cost/kWh = 1.58
Thin film 0.47 1,750,280 634,089 94,837 244,573 525,084 2,327 978,295 Cost/Watt = 2.08
Cost/kWh = 1.53
1-axis
Crystalline 2.38 9,914,778 4,084,289 612,367 1,385,686 2,974,433 11,916 5,542,742 Cost/Watt = 2.32
Cost/kWh = 1.35
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If it is compared to public ownership (Table 6-25 through Table 6-27), the only difference is the treatment of the loan as revenue. In private ownership, since the calculations of net initial cost have excluded the revenue as loan payment; thus, all values remain the same for the entire Table 6-39 through Table 6-41.
Cumulative Net Present Value over 25 Years
$2,000,000.00
$1,000,000.00
$0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ($1,000,000.00)
($2,000,000.00)
($3,000,000.00) Cumulative NPV Cumulative
($4,000,000.00)
($5,000,000.00) Study Period [years]
Solar Farm-1, Fixed-tilt, Crystalline, Tilt = 26, Az = 135 Solar Farm-1, Fixed-tilt, Thin Film, Tilt = 26, Az=135 Solar Farm-2, Fixed-tilt, Crystalline, Tilt = 26, Az=165 Solar Farm-2, Fixed-tilt, Thin Film, Tilt=26, Az=165 Solar Farm-3, Fixed-tilt, Crystalline, Tilt = 26, Az=195 Solar Farm-3, Fixed-tilt, Thin Film, Tilt=26, Az=195
Figure 6-26. Cumulative net present value (fixed-tilt)
From chart in Figure 6-26, the net present value for each case can be summarized as follows:
Solar Farm-1; 2.38 MW; Fixed-tilt; Crystalline; NPV = $732,873; PB = 19 years Solar Farm-2; 2.38 MW; Fixed-tilt; Crystalline; NPV = $1,004,195; PB = 17 years Solar Farm-3; 2.38 MW; Fixed-tilt; Crystalline ; NPV = $989,750; PB = 17 years Solar Farm-1; 0.47 MW; Fixed-tilt; Thin-film; NPV = $93,346; PB = 22 years Solar Farm-2; 0.47 MW; Fixed-tilt; Thin-film; NPV = $146,335; PB = 19 years Solar Farm-3; 0.47 MW; Fixed-tilt; Thin-film; NPV = $143,524; PB = 19 years
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Cumulative Net Present Value over 25 Years
$4,000,000.00 $3,000,000.00
$2,000,000.00
$1,000,000.00 $0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ($1,000,000.00) ($2,000,000.00)
($3,000,000.00) Cumulative NPV Cumulative ($4,000,000.00) ($5,000,000.00) ($6,000,000.00) Study Period [years] Solar Farm-1, 1-Axis Tracking, Crystalline, Tilt = 26, Az = 135 Solar Farm-2 or Solar Farm-3 , 1-Axis Tracking, Crystalline, Tilt = 26, Az = 165
Figure 6-27. Cumulative net present value (1-axis tracking)
From Figure 6-27, the cumulative NPV as well as the payback period can be seen as follows:
Solar Farm-1; 2.38 MW; Tracking; Crystalline; NPV = $1,976,500; PB = 14 years Solar Farm-2; 2.38 MW; Tracking; Crystalline; NPV = $2,576,015; PB = 13 years Solar Farm-3; 2.38 MW; Tracking; Crystalline; NPV = $2,576,014; PB = 13 years
Revenue from leasing. This revenue actually comes from the expense of private entity of renting the vacant land or roof. The results in the realistic design will be similar for the three cases of solar farm since they are designed to have similar area of
12,140 square meters. Figure 6-28 below shows the net present worth of leasing expense from private entity that benefits Dunnellon Airport over 25 year study period.
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Cumulative Revenue Stream from Leasing over 25 Years
$100,000.00
$90,000.00
$80,000.00 $70,000.00 $60,000.00 $50,000.00 $40,000.00 $30,000.00
$20,000.00 Cumulative Revenue Cumulative $10,000.00 $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Study Period [years]
Solar Farm (Area = 130,673 S.F.)
Figure 6-28. Cumulative revenue stream from leasing (realistic design)
The value of revenue from leasing that goes to Dunnellon Airport, which is an expense for the private entity, is $91,619 over 25 years.
Discussions for Realistic Design Scenario
The realistic design scenario allows for the real application to install solar farm in a three different areas since it has also been considered as the minimum acreage to lease. For decision making purpose, Table 6-42 provides the summary of economic measures for decision making under realistic design scenario. According to Table 6-42 as the results of the calculations, the payback period is in the range of 13 to 18 years.
The ROI values are still lower than the expected 10%. The results are convincing since the installed RES will be owned by the private sector, which is assumed to own- financing the project without any obligation to pay the loan.
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Table 6-42. Financial measures for decision making (25 years for realistic design) Size ROI TLCC LCOE SIR NPV PB Salvage Scenario Type [MW] [%] [$] [$/kWh] [$] [yrs] [$] [1] [2] [3] [5] [6] [6] [7] [8] [9] [10] Realistic Fixed-tilt Design- Crystalline 2.38 -85.7 6,782,552 0.166 0.14 732,873 19 230,930 SF1 Thin film 0.47 -90.5 1,368,987 0.172 0.10 93,346 22 45,153
1-axis
Crystalline 2.38 -64.3 7,206,541 0.144 0.36 1,976,500 14 230,930
Realistic Fixed-tilt Design- Crystalline 2.38 -80.7 6,782,552 0.160 0.20 1,004,195 17 230,930 SF2 Thin film 0.47 -85.0 1,368,987 0.166 0.154 146,336 19 45,153
1-axis
Crystalline 2.38 -53.5 7,206,541 0.136 0.46 2,576,014 13 230,930
Realistic Fixed-tilt Design- Crystalline 2.38 -80.8 6,782,552 0.161 0.19 989,750 19 230,930 SF3 Thin film 0.47 -85.3 1,368,987 0.166 0.15 143,524 19 45,153
1-axis
Crystalline 2.38 -53.5 7,206,541 0.136 0.46 2,576,014 13 230,930
However, in order to investigate in depth the feasibility of the project, a sensitivity analysis should be conducted. In terms of the revenue that goes to Dunnellon Airport, the airport authority could benefit by receiving the revenue from leasing option.
Sensitivity Analysis. The use of sensitivity analysis in this scenario is to find the best parameter estimate, in terms of financial rate assumptions that represent the financial result improvement. The results below present the output of evaluation tool up to 15% maximum rate with 1% increment. In order to run the sensitivity analysis, the case calculated is based on a fixed-tilt system with crystalline module type (Realistic
208
Design-SF1). Unlike the public-owned scenario, the private entity uses 0% rate due to no loan option, 5% discount rate, 2% both energy and general inflation rate.
ROI Sensitivity Analysis
700.0% 600.0% 500.0%
400.0% 300.0% 200.0% 100.0% 0.0% Percent -100.0% -200.0% -300.0% -400.0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DR -28.5% -46.8% -62.2% -75.0% -85.7% -94.8% -102.6% -109.1% -114.7% -119.6% -123.7% -127.3% -130.3% -133.0% -135.4% EIR -100.6% -85.7% -68.6% -48.8% -25.8% 0.9% 32.0% 68.2% 110.4% 159.8% 217.6% 285.3% 364.7% 457.9% 567.2% GIR -81.3% -85.7% -91.0% -97.0% -104.0% -112.2% -121.6% -132.7% -145.5% -160.5% -177.9% -198.3% -222.0% -249.8% -282.1% Rate [%]
DR EIR GIR
Figure 6-29. ROI sensitivity analysis for realistic design
TLCC Sensitivity Analysis
18000000 16000000
14000000 12000000 10000000 8000000 6000000
Dollar Amount Dollar 4000000 2000000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DR 8322613 7846274 7438411 7087064 6782552 6517012 6284038 6078388 5895762 5732621 5586038 5453589 5333254 5223350 5122463 EIR 6782552 6782552 6782552 6782552 6782552 6782552 6782552 6782552 6782552 6782552 6782552 6782552 6782552 6782552 6782552 GIR 6555267 6782552 7045349 7349560 7702084 8110976 8585640 9137052 9778008 105234211139065712399926135747311494237716534572 Rate [%]
DR EIR GIR
Figure 6-30. TLCC sensitivity analysis for realistic design
The shaded areas symbolize the values for baseline assumptions. From Figure 6-29 the ROI will increase to an acceptable criterion (more than 10%) at 7% EIR which yields to 32% ROI. The other parameters will tend to decrease the value of ROI. At a 10%
209
EIR, the TLCC value will remain unchanged due to the similar rates of EIR and GIR, which are 2%.
LCOE Sensitivity Analysis 0.450 0.400 0.350
0.300 0.250 0.200 [$/kWh] 0.150 0.100 0.050
0.000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DR 0.13284 0.14070 0.14895 0.15758 0.16654 0.17580 0.18531 0.19504 0.20496 0.21502 0.22520 0.23546 0.24578 0.25613 0.26649 EIR 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 0.16654 GIR 0.16096 0.16654 0.17299 0.18046 0.18912 0.19916 0.21082 0.22436 0.24009 0.25840 0.27969 0.30448 0.33332 0.36691 0.40600 Rate [%] DR EIR GIR
Figure 6-31. LCOE sensitivity analysis for realistic design
In the LCOE results from Figure 6-31, the values will be preferable if the DR is at a 2% discount rate, which yields LCOE to $0.1407/ kWh.
SIR Sensitivity Analysis
8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 -1.00 -2.00 -3.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DR 0.72 0.53 0.38 0.25 0.14 0.05 -0.03 -0.09 -0.15 -0.20 -0.24 -0.27 -0.30 -0.33 -0.35 EIR -0.01 0.14 0.31 0.51 0.74 1.01 1.32 1.68 2.10 2.60 3.18 3.85 4.65 5.58 6.67 GIR 0.19 0.14 0.09 0.03 -0.04 -0.12 -0.22 -0.33 -0.45 -0.60 -0.78 -0.98 -1.22 -1.50 -1.82 Rate [%]
DR EIR GIR
Figure 6-32. SIR sensitivity analysis for realistic design
210
The results from Figure 6-32 show the SIR will be favorable at 6% EIR. The other parameters will just lower the SIR.
NPV Sensitivity Analysis
40000000 35000000
30000000 25000000 20000000 15000000 10000000 5000000
0 Dollar Amount Dollar -5000000 -10000000 -15000000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DR 3678617 2733779 1946358 1287156 732873.3 264836.2 -131982 -469727 -758254 -1005595 -1218321 -1401832 -1560584 -1698265 -1817945 EIR -31451.3 732873.3 1614759 2634674 3816795 5189674 6787033 8648692 10821665 13361439 16333488 19815037 23897135 28687080 34311254 GIR 964116.1 732873.3 465067.9 154533.7 -205953 -624853 -1112068 -1679185 -2339754 -3109609 -4007247 -5054255 -6275815 -7701279 -9364837 Rate [%]
DR EIR GIR
Figure 6-33. NPV sensitivity analysis for realistic design
By referring to Figure 6-33, the NPV value will be positive and get larger starting at 2%
EIR; meanwhile the NPV will decline to negative at 5% GIR and 7% DR.
Payback Sensitivity Analysis
30 25
20 15
Years 10 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 DR 14 14 15 16 19 23 25 25 25 25 25 25 25 25 25 EIR 25 19 16 14 13 12 11 11 10 10 8 8 8 7 7 GIR 17 19 21 24 25 25 25 25 25 25 25 25 25 25 25 Rate [%]
DR EIR GIR
Figure 6-34. Payback sensitivity analysis for realistic design
211
From Figure 6-35 the payback period will be faster than the baseline at 3% EIR and 1%
GIR. However, it will be getting larger starting at 6% DR. In the meantime, the values of EIR will influence the overall financial measures. The lower limit will be the baseline,
2%. A larger the EIR will result to more favorable financial measures. In terms of determining the best project based on NPV, for discount rate, the minimum favorable assumption will be in the range of lower than 7% discount rate. Meanwhile for GIR, percent rate of lower than 6% will result in a positive NPV.
Results for NZEB Scenario
Similar to the public-owned option, Dunnellon Airport owns the system, the analysis will emphasize to the potential near the buildings that consume energy. The input values referred to Table 6-29 and Table 6-43; the difference would be the scenario, which is the private-entity option in the evaluation tool.
Table 6-43. Input difference with general input assumptions (NZEB-FIT) Variables Value Unit Remarks Land lease 0.04 [ $/S.F. ] Rooftop-1 $418 / year Rooftop-2 $203 / year Parking-1 $130 / year Parking-2 $384 / year
Similar to previous results from public ownership, all iterations were based on 26 degree inclination and 135 degrees (southeast) azimuth direction. In addition, no tracking systems were taken into account in the rooftop and parking structure. The revenues that belong to private entity are accumulated for a 25 year study period. Meanwhile, the revenue stream that belongs to airport will solely be the leasing expense only from the private entity.
212
Cumulative Revenue Stream over 25 Years
$800,000.00
$700,000.00 $600,000.00 $500,000.00 $400,000.00 $300,000.00 $200,000.00
Cumulative Revenue Cumulative $100,000.00 $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Study Period [years]
Rooftop-1, Fixed-tilt, Crystalline Rooftop-1, Fixed-tilt, Thin Film Rooftop-2, Fixed-tilt, Crystalline Rooftop-2, Fixed-tilt, Thin Film Parking-1, Fixed-tilt, Crystalline Parking-1, Fixed-tilt, Thin Film Parking-2, Fixed-tilt, Crystalline Parking-2, Fixed-tilt, Thin Film
Figure 6-35. Cumulative revenue stream goes to private entity (fixed-tilt)
The values shown from Figure 6-35 belong to the private entity and can be summarized as follows:
Rooftop-1; 190.7 kW; Fixed-tilt; Crystalline = $714,784 Rooftop-2; 92.8 kW; Fixed-tilt; Crystalline = $342,981 Parking-1; 59.2 kW; Fixed-tilt; Crystalline = $215,558 Parking-2; 175 kW; Fixed-tilt; Crystalline = $655,921 Rooftop-1; 37.25 kW; Fixed-tilt; Thin-film = $132,597 Rooftop-2; 18.12 kW; Fixed-tilt; Thin-film = $59,974 Parking-1; 11.62 kW; Fixed-tilt; Thin-film = $35,095 Parking-2; 34.25 kW; Fixed-tilt; Thin-film = $121,101
Incentives. The differences between public and private ownership have been mentioned before (Table 6-36, p.194). No bond/loan incentives have been calculated due to the assumption that financier body as well as private developer are in one integrated body. In the newly developed evaluation tool option, there is a tab that can support the option choice and will accommodate either public ownership calculation without loan incentive or private entity with the loan/bond as incentive. This has been previously mentioned to enrich the scenario development which has not been
213 investigated in this dissertation. In Appendix E section, the user’s guideline will further explore all tab functions. The users can use the system and can vary the scenario development.
Table 6-44 presents the incentives break down of NZEB-FIT scenario. From
Table 6-44, the lowest cost per watt is the rooftop 1 scenario. The system with 190.7 kW can reduce initial cost because it has the average lower cost value for the system larger than 100 kW. Moreover, the advantage of using the larger system also it can achieve larger yearly production that yields to lower life cycle cost of electricity. Also, from Table 6-44, the value with the unfavorable cost per watt is installing photovoltaic system with thin film in Parking-2 area. The cost per watt is larger compared to other scenarios because using the area as nearly as large with the Rooftop-1 scenario as well as another additional cost for constructing the structure. Overall, for all systems the cost per energy production are typical since it has the same inclination degree of tilt angle as well as the same azimuth direction that will produce theoretically the same amount of energy production.
214
Table 6-44. Final net inception cost for each combination (NZEB-FIT) Funding Net Total Yearly System Size Production Inception Scenario Cost Saving 1-year depreciation Tax Credit Grant Type Cost [kW] [$] [kWh] [$] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] Rooftop-1 Fixed-tilt NZEB-FIT Crystalline 190.7 877,312 251,111 44,656 122,633 263,194 954 490,532 Cost/Watt =2.57 Cost/kWh = 1.95 Thin film 37.25 182,525 49,046 8,284 25,526 54,758 186 102,065 Cost/Watt = 2.74 Cost/kWh = 2.08 Rooftop-2 Fixed-tilt NZEB-FIT Crystalline 92.8 454,720 122,066 21,428 63,568 136,416 464 254,272 Cost/Watt = 2.74 Cost/kWh = 2.08 Thin film 18.12 88,788 23,840 3,747 12,412 26,636 97 49,649 Cost/Watt = 2.74 Cost/kWh = 2.08 Parking-1 Fixed-tilt NZEB-FIT Crystalline 59.2 304,880 77,840 13,467 40,552 87,024 296 162,208 Cost/Watt = 2.74 Cost/kWh = 2.08 Thin film 11.62 59,225 15,205 2,193 8,280 17,767 58 33,120 Cost/Watt =2.85 Cost/kWh = 2.17 Parking-2 Fixed-tilt NZEB-FIT Crystalline 175 848,944 230,681 40,978 118,677 254,683 875 474,708 Cost/Watt =2.71 Cost/kWh = 2.05 Thin film 34.25 176,388 45,056 7,566 24,660 52,916 171 98,640 Cost/Watt = 2.88 Cost/kWh = 2.18
215
In order to directly investigate the project benefit, the NPV of each case should be assessed. Since the evaluation tool has provided several financial measures, the method of analyzing the RES project will have to consider the flow of expenses from the private entity (private entity pays all the project costs without bond/loan incentives).
Cumulative Net Present Value over 25 Years
$200,000.00
$100,000.00
$0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ($100,000.00)
($200,000.00)
($300,000.00) Cumulative NPV Cumulative ($400,000.00)
($500,000.00) Study Period [years] Rooftop-1, Fixed-tilt, Crystalline Rooftop-2, Fixed-tilt, Crystalline Parking-1, Fixed-tilt, Crystalline Figure 6-36. Cumulative revenue stream goes to private entity (crystalline)
Cumulative Net Present Value over 25 Years
$0.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
($20,000.00)
($40,000.00)
($60,000.00)
($80,000.00) Cumulative NPV Cumulative
($100,000.00) Study Period [years]
Rooftop-1, Fixed-tilt, Thin Film Rooftop-2, Fixed-tilt, Thin Film Parking-1, Fixed-tilt, Thin Film Parking-2, Fixed-tilt, Thin Film
Figure 6-37. Cumulative revenue stream goes to private entity (thin film)
The NPV method can best explain the net saving due to the offset values between revenues and costs. In addition, NPV may be used to assess the project feasibility as a secondary measure to confirm the validity of whatever primary financial measure used
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(Short, Packey & Holt, 1995, p.39). Figure 6-36 shows the cumulative net present value of the possible RES installation over 25 years for crystalline type, whereas, Figure 6-37 is for thin film type. From the figures above, it is apparent that the thin-film type module cannot satisfy the positive NPV results. The payback periods for the thin film are more than 25 years. The value for each case i.e., crystalline and thin film, for 25 years study period can be summarized as follows:
Rooftop-1; 190.7 kW; Fixed-tilt; Crystalline; NPV = $86,667; PB = 17 years Rooftop-2; 92.8 kW; Fixed-tilt; Crystalline; NPV = $20,780; PB = 22 years Parking-1; 59.2 kW; Fixed-tilt; Crystalline; NPV = $10,020; PB = 22 years Parking-2; 175 kW; Fixed-tilt; Crystalline; NPV = $53,426; PB = 21 years Rooftop-1; 37.25 kW; Fixed-tilt; Thin-film; NPV = $- 2,619; PB = 25 years Rooftop-2; 18.12 kW; Fixed-tilt; Thin-film; NPV= $- 5,795; PB = 25 years Parking-1; 11.62 kW; Fixed-tilt; Thin-film; NPV = $- 8,439; PB = 25 years Parking-2; 34.25 kW; Fixed-tilt; Thin-film; NPV = $- 8,310; PB = 25 years
From the above results, only the 190.7 kW system with crystalline type module, shows both largest positive cumulative NPV and shortest payback period.
Leasing expense goes as revenue to Dunnellon Airport. The revenue assessment that goes to Dunnellon Airport can be perceived as follows:
Cumulative Revenue Stream from Leasing over 25 Years
$8,000.00
$7,000.00 $6,000.00 $5,000.00 $4,000.00 $3,000.00 $2,000.00
Cumulative Revenue Cumulative $1,000.00 $- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Study Period [years] Rooftop-1 (Area=10,452 S.F.) Rooftop-2 (Area=5,081 S.F.) Parking-1 (Area=3,240 S.F.) Parking-2 (Area=9,601 S.F.)
Figure 6-38. Cumulative revenue stream from leasing (NZEB-FIT)
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The cumulative leasing revenues at the end of study period also can be summarized as follows.
Rooftop-1; 190.7 kW; NPV = $7,326 Rooftop-2; 92.8 kW; NPV = $3,557 Parking-1; 59.2 kW; NPV = $2,279 Parking-2; 175 kW; NPV = $6,730
NZEB design under net metering. The input and other option for net metering are typical (section NZEB design under net metering for Dunnellon Airport ownership, p.
169). The only difference will be the ownership option from the evaluation tool input.
Hence, the results will be slightly different in terms of incentives.
Revenue. The revenue that goes to private entity (both for rooftop and parking provide the same results) is calculated based on 25 and yields to $15,921. However, the revenue present worth from leasing that goes to Dunnellon Airport is only $272 with a $15.5 at the beginning value per year. This value is considered small and it is believed cannot cover the operational expense.
Incentives. Under the private entity option, the incentives and the leasing expense that distinguish can reduce the inception cost. Table 6-45 provides the values of initial cost for the private entity scheme. The values are typical each other; yet, both cost per watt as well as cost per energy production are different. The reasons are because, first, the cost per watt depends on the parking structure cost that will add the new inception cost compare to the rooftop. Second, since the installation costs are different, even though energy productions are the same, the result of cost per energy production yields to become different value.
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Table 6-45. Final net inception cost for each combination (NZEB-NM)) Funding Net Total Yearly System Size Production 1-year Tax Inception Scenario Cost Saving Grant Type depreciation Credit Cost [MW] [$] [kWh] [$] [$] [$] [$] [$] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
Rooftop Fixed-tilt NZEB-NM Crystalline 7.04 37.312 9,310 995 5,110 11,194 570 20,438 Cost/Watt = 2.9 Cost/kWh = 2.19
Parking Fixed-tilt NZEB-NM Crystalline 7.04 39.072 9,310 995 5,356 11,721 570 21,424 Cost/Watt = 3.04 Cost/kWh = 2.3
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Discussions for NZEB Scenario
Under private entity ownership option, most of the financial measures do not meet the acceptance criteria. However, under this option, the private sector could still obtain benefits in terms of net present value. The positive of NPV for feed in tariff can be translated as the gain achieved by the private sector over 25 years. Table 6-46 summarized the entire financial measures that can be used for further decision making process by private entity.
Table 6-46. Financial measures for decision making (25 years for NZEB-FIT) Size ROI TLCC LCOE SIR NPV PB Salvage Scenario Type [kW] [%] [$] [$/kWh] [$] [yrs] [$] [1] [2] [3] [5] [6] [6] [7] [8] [9] [10] Rooftop-1 Fixed-tilt NZEB-FIT Crystalline 190.7 -82.3 626,427 0.192 0.18 86,667 17 18,478
Thin film 37.25 -102.6 134,704 0.217 -0.03 - 2,619 25 3,609
Rooftop-2 Fixed-tilt NZEB-FIT Crystalline 92.8 -91.8 321,312 0.203 0.08 20,800 22 8,992
Thin film 18.12 -111.7 65,520 0.212 -0.12 - 5,795 25 1,756
Parking-1 Fixed-tilt NZEB-FIT Crystalline 59.2 -93.8 204,984 0.203 0.06 10,020 22 5,736
Thin film 11.62 -125,5 43,370 0.219 -0.25 - 8,439 25 1,126
Parking-2 Fixed-tilt NZEB-FIT Crystalline 175 -88.75 600,869 0.201 0.11 53,426 21 16,957
Thin film 34.25 -108.4 128,927 0.221 -0.08 - 8,311 25 3,319
From Table 6-46, it is clear that private entity can take benefit from Rooftop-1 scenario since it has shorter and higher ROI and LCOE, shorter payback period as well as higher NPV compared to other options. Table 6-47 provides the results for further decision making process of NZEB under net metering. Under the net metering for net zero energy building scheme, it seems the private entity cannot benefit since all financial measures yield to unfavorable results.
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Table 6-47. Financial measures for decision making (25 years for NZEB-NM) Size ROI TLCC LCOE SIR NPV PB Salvage Scenario Type [kW] [%] [$] [$/kWh] [$] [yrs] [$] [1] [2] [3] [5] [6] [6] [7] [8] [9] [10] Rooftop Fixed-tilt NZEB-NM Crystalline 7.04 -147.9 23,329 0.212 -0.48 - 7,479 25 682
Parking Fixed-tilt NZEB-NM Crystalline 7.04 -150.6 24,373 0.221 -0.53 - 8,525 25 682
Examining the Hypothesis
After conducting a rigorous process, generally, the results and discussions chapter have provided complete information that can be used by the stake holders, mainly airports authority, to assess the feasibility of solar project installation at their operational areas. This dissertation has the goal of developing a methodology and evaluation tool; and, has been implemented to Dunnellon Airport for thorough analysis.
As previously mentioned before, this dissertation tries to maintain the direction of the evaluation by developing a hypothesis. Therefore, as finalization of the results and discussions chapter, this section will test the hypothesis. However, the hypothesis test will be based on the Dunnellon Airport ownership, public-owned option. In terms of private entity option, the results from the revenue that come from leasing will be used to the analysis of 25 years study period.
Non-traditional Revenue Percentage from Dunnellon Airport Ownership
In this sub section, the results will be provided in revenues percentage based on the ratio from actual revenue and expense in Dunnellon Airport. The revenue from RES installation is the non-traditional revenue that will be compared to the existing 2012 revenue and expense. According to Table 4-3 (p.109), the total revenue of Dunnellon
Airport in 2012 was $2,013,004; operating is $1,972,968. Table 6-48 provides the results of both revenue and expense for percentage analysis. Under the public-owned
221 option, only final years revenue—in the last five years of study period—will go to
Dunnellon Airport, therefore the revenue that will be compared are based on those values that have been treated the same way with regard to the inflated (column [2] and
[3]); and, discounted—see column [4] and [5]) values for both revenue and expense over 25 years study period. The present worth (PW) of revenue and expense have used the baseline financial parameters GIR = 1% inflated over 25 years and DR = 3% discounted over 25 years. In terms of revenue from leasing, the total of the last cumulative five years present worth will still be used as the comparison factor.
Table 6-48. Dunnellon airport PW of revenue and expense (2012) for comparison Revenue 2012 Expense (2012) PW Revenue PW Expense Years [$] [$] [$] [$] 2,013,004 1,972,968
[1] [2] [3] [4] [5] 1 2,033,134 1,992,698 1,973,917 1,934,658 2 2,053,465 2,012,625 1,916,424 1,897,092 3 2,074,000 2,032,751 1,860,606 1,860,255 4 2,094,740 2,053,078 1,806,413 1,824,134 5 2,115,687 2,073,609 1,753,799 1,788,714 6 2,136,844 2,094,345 1,702,718 1,753,981 7 2,158,213 2,115,289 1,653,124 1,719,923 8 2,179,795 2,136,442 1,604,975 1,686,527 9 2,201,593 2,157,806 1,558,228 1,653,779 10 2,223,609 2,179,384 1,512,843 1,621,666 11 2,245,845 2,201,178 1,468,779 1,590,178 12 2,268,303 2,223,190 1,425,999 1,559,301 13 2,290,986 2,245,422 1,384,465 1,529,023 14 2,313,896 2,267,876 1,344,141 1,499,333 15 2,337,035 2,290,555 1,304,991 1,470,220 16 2,360,406 2,313,460 1,266,982 1,441,672 17 2,384,010 2,336,595 1,230,080 1,413,678 18 2,407,850 2,359,961 1,194,252 1,386,228 19 2,431,928 2,383,560 1,159,468 1,359,311 20 2,456,247 2,407,396 1,125,697 1,332,917 21 2,480,810 2,431,470 1,092,910 1,307,035 22 2,505,618 2,455,785 1,061,077 1,281,656 23 2,530,674 2,480,342 1,030,172 1,256,769 24 2,555,981 2,505,146 1,000,167 1,232,366 25 2,581,541 2,530,197 971,036 1,208,436 Cumulative of the last five years PW $5,155,363 $6,286,262
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Utility-scale maximum energy scenario. Under the scheme of utility purpose installation, Dunnellon Airport can install RES from solar into three different areas i.e., solar farm 1, solar farm 2 and solar farm 3 and various combination of mounting and module type as well as incorporating maximum power of each module that can be generated. Table 6-49 provides the ratio of the revenue that Dunnellon Airport can gain for the RES installation at airport.
Table 6-49. Dunnellon airport non-traditional revenue ratios (utility-scale) Proposed Area Size System Non- Ratio to 2012 Ratio to 2012 Utility-scale traditional Revenue Expense Revenue [S.F.] [MW] Mounting Module [$] [%] [%] [1] [2] [3] [4] [5] [6] [7] [8] SF-1 269,840 4.92 Fixed-tilt Crystalline 2,576,066 49.9 40.9 0.96 Fixed-tilt Thin film 502,548 9.7 7.9 4.92 1-axis Crystalline 3,146,541 61.0 50.0
SF-2 243,383 4.43 Fixed-tilt Crystalline 2,407,066 46.6 38.3 0.87 Fixed-tilt Thin film 469,640 9.1 7.5 4.43 1-axis Crystalline 3,022,797 58.6 48.0
SF-3 642,218 11.7 Fixed-tilt Crystalline 6,341,014 122.9 100.0 2.28 Fixed-tilt Thin film 1,237,889 24.0 19.7 11.7 1-axis Crystalline 7,977,506 154.7 126.9
The results are convincing since there is no single value lower than 5% (column [8]). In addition, even though, Dunnellon Airport authority decides to choose the lower ratio, system with combined fixed-tilt and thin film module type, the result is still favorable.
The net initial costs after subtracting all incentives have yielded to the lowest installation cost compared to other system sizes and combinations (Table 6-22 column [9], p.163).
Therefore, the results of installing utility-scale RES can benefit the airport with the revenue ratio, compared to expense, of at least 7.5% to cover the yearly Dunnellon
Airport’s operational expense.
Realistic design scenario. The realistic design scenario provides conceivable minimum 3 acres ≈ 130,674 S.F. of leasing area requirement. Therefore, for quick
223 implementation as well as tackling the maximum Tier 3 constraint from Progress
Energy, the results of realistic design will be a more applicable and doable RES option to install at Dunnellon Airport. Table 6-50 provides the summary of non-traditional potential revenue ratio to the existing Dunnellon Airport 2012 revenue and expense.
Table 6-50. Dunnellon airport non-traditional revenue ratios (realistic design) Proposed Area Size System Non- Ratio to 2012 Ratio to 2012 Utility-scale traditional Revenue Expense Revenue [S.F.] [MW] Mounting Module [$] [%] [%] [1] [2] [3] [4] [5] [6] [7] [8] Realistic 130,674 2.38 Fixed-tilt Crystalline 1,247,117 24.2 19.8 design 0.96 Fixed-tilt Thin film 242,987 4.7 3.8 SF-1 4.92 1-axis Crystalline 1,523,377 29.5 24.2
Realistic 130,674 2.38 Fixed-tilt Crystalline 1,292,031 25.1 20.6 design 0.96 Fixed-tilt Thin film 251,759 4.8 4.0 SF-2 2.38 1-axis Crystalline 1,622,621 31.4 25.8
Realistic 130,674 2.38 Fixed-tilt Crystalline 1,289,640 25.0 20.5 design 0.96 Fixed-tilt Thin film 251,294 4.8 3.9 SF-3 2.38 1-axis Crystalline 1,622,621 31.4 25.8
Under this scenario, the RES installation using combined fixed-tilt and thin film module non-traditional revenue percentage vary slightly lower than 5% target expense coverage. However, if Dunnellon Airport decides to install crystalline type panel in those three areas, the chance to cover expenses is almost four times larger than the predetermined hypothesis.
NZEB scenario. The NZEB scenario provides different areas that are aimed to be in the border of building footprint only. In the Dunnellon Airport case, the airport has been shown not to be an energy-intensive airport since it consumes only 8,924 kWh
(2012 data) of energy per year. In addition, since there are two power purchase agreement systems i.e FIT and net metering, the analyses have been expanded into two different schemes. First is NZEB under FIT, which allows the airport to use as large area as possible to benefit from the RES installation. Second is the net metered or buy 224 back system that only emphasizes the peak load analysis. The excess electricity production will not be paid at the agreement rate; but, using the avoided cost rate, which makes is not very beneficial to install a larger system. Therefore, the correct area estimation as well as aligning with the actual load curve shall be conducted in the RES photovoltaic sizing. Table 6-51 represents the potential non-traditional revenue ratios under FIT system; whereas, Table 6-52 provides the potential non-traditional revenue ratios under net metering agreement.
Table 6-51. Dunnellon airport non-traditional revenue ratios (NZEB-FIT) Proposed Area Size System Non- Ratio to 2012 Ratio to 2012 NZEB-FIT traditional Revenue Expense Revenue [S.F.] [kW] Mounting Module [$] [%] [%] [1] [2] [3] [4] [5] [6] [7] [8] Rooftop-1 10,452 190.7 Fixed-tilt Crystalline 118,326 2.3 1.9 37.25 Fixed-tilt Thin film 21,950 0.42 0.34
Rooftop-2 5,081 92.8 Fixed-tilt Crystalline 56,778 1.1 0.90 18.12 Fixed-tilt Thin film 9,928 0.19 0.16
Parking-1 3,240 59.2 Fixed-tilt Crystalline 35,684 0.69 0.57 11.62 Fixed-tilt Thin film 5,810 0.11 0.09
Parking-2 9,601 175 Fixed-tilt Crystalline 108,582 2.1 1.7 34.25 Fixed-tilt Thin film 20,047 0.38 0.31
Combined-1 28,374 517.7 Fixed-tilt Crystalline 319,370 6.2 5.1 Combined-2 28,374 101.2 Fixed-tilt Thin film 57,735 1.1 0.91
Table 6-52. Dunnellon airport non-traditional revenue ratios (NZEB-NM) Proposed Area Size System Non- Ratio to 2012 Ratio to 2012 NZEB-NM traditional Revenue Expense Revenue [S.F.] [kW] Mounting Module [$] [%] [%] [1] [2] [3] [4] [5] [6] [7] [8] Rooftop 376 7.04 Fixed-tilt Crystalline 2,636 0.051 0.042
Parking 376 7.04 Fixed-tilt Crystalline 2,636 0.051 0.042
From Table 6-51, the non-traditional revenue values are lower than the expected 5% expense coverage. However, since the FIT contract allows the host, in this case
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Dunnellon Airport, to design the system without being concerned of the excess production, the combined options can be applicable except for Combined-2 system which consists of all thin film module type. By using the Combined-1, fixed-tilt and crystalline type module, it is convincing that the airport can sustain with the 5% expense coverage. In case of Table 6-52 (NZEB under the net metered agreement), none of the results can help the airport authority to achieve at least 5% expense coverage.
Non-traditional Revenue Percentage from Leasing
In this sub section, the non-traditional revenue was calculated based on the square-foot area times the non-aviation lease rate of Dunnellon Airport.
Table 6-53. Dunnellon airport PW of revenue and expense (2012) for comparison Revenue (2012) Expense (2012) PW Revenue PW Expense Years [$] [$] [$] [$] 2,013,004 1,972,968
[1] [2] [3] [4] [5] 1 2,053,264 2,012,427 1,955,490 1,916,597 2 2,094,329 2,052,676 1,862,371 1,861,838 3 2,136,216 2,093,729 1,773,687 1,808,642 4 2,178,940 2,135,604 1,689,225 1,756,967 5 2,222,519 2,178,316 1,608,786 1,706,768 6 2,266,969 2,221,882 1,532,177 1,658,003 7 2,312,309 2,266,320 1,459,216 1,610,631 8 2,358,555 2,311,646 1,389,730 1,564,613 9 2,405,726 2,357,879 1,323,552 1,519,910 10 2,453,841 2,405,037 1,260,526 1,476,484 11 2,502,917 2,453,138 1,200,501 1,434,299 12 2,552,976 2,502,200 1,143,334 1,393,319 13 2,604,035 2,552,244 1,088,890 1,353,510 14 2,656,116 2,603,289 1,037,038 1,314,838 15 2,709,238 2,655,355 987,655 1,277,271 16 2,763,423 2,708,462 940,624 1,240,778 17 2,818,692 2,762,632 895,832 1,205,327 18 2,875,065 2,817,884 853,174 1,170,889 19 2,932,567 2,874,242 812,546 1,137,435 20 2,991,218 2,931,727 773,854 1,104,937 21 3,051,042 2,990,361 737,003 1,073,367 22 3,112,063 3,050,168 701,908 1,042,700 23 3,174,305 3,111,172 668,484 1,012,908 24 3,237,791 3,173,395 636,651 983,968 25 3,302,546 3,236,863 606,335 955,855 Cumulative of the last five years PW $3,350,381 $5,068,798
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The difference is that the GIR is set to 2% rate and the DR is set to 5% rate. Therefore, both existing 2012 revenues and expenses yielded to the values as shown in Table 6-
53. In the meantime, the only favorable choice is to let the private entity develop, finance and operate the RES for utility-scale purpose for a very large area, Solar Farm-
3. Table 6-54 presents the values of leasing revenue that goes to Dunnellon Airport.
Table 6-54. Dunnellon airport non-traditional revenue ratios (leasing option) Proposed land/roof Area Rate Agreement Non- Ratio to 2012 Ratio to 2012 for lease Type traditional Revenue Expense Revenue [S.F.] [$/year] [$] [%] [%] [1] [2] [3] [4] [5] [6] [7] Solar Farm-1 269,840 10,794 Feed in Tariff 189,196 5.6 3.7 Solar Farm-2 243,383 9,735 Feed in Tariff 170,632 5.1 3.3 Solar Farm-3 642,218 25,689 Feed in Tariff 450,275 13.4 8.8
Realistic Design 130,674 5,227 Feed in Tariff 91,619 2.7 1.8
Rooftop-1 10,452 418 Feed in Tariff 7,326 0.21 0.14 Rooftop-2 5,081 203 Feed in Tariff 3,557 0.10 0.07 Parking-1 3,240 130 Feed in Tariff 2,279 0.07 0.04 Parking-2 9,601 384 Feed in Tariff 6,730 0.20 0.13
Rooftop or Parking 376 15.5 Net Metering 272 0.008 0.005
Chapter Summary
This chapter has presented the results of the last step of the new methodology implementation as well as integrating the evaluation tool in specifically one case study.
The results showed that out of 39 MW available potential, only 21.05 MW that could be used for crystalline system and 4.11 MW for thin film type. This dissertation has separated the installation scenarios mainly based on utility-scale maximum energy system, realistic design and NZEB concept. Meanwhile, those three scenarios have been evaluated under two main ownership options, i.e., Dunnellon Airport, public-owned and private-owned.
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The main direction of this research was investigating the revenue source that could benefit airport authority by finding the best solution among alternatives— maximum profit—assessment. For instances, to emphasize the analysis, not all revenue could be calculated in the public-owned system since the loan or bond incentives would use a payment over a certain period of maturity, for instance 20 year term of loan.
From the results of hypothesis testing, the chance for the airport authority to cover at least 5% of its expense existed if the Dunnellon Airport planned to own the renewable energy system. The utility-scale maximum energy scenario provided at least
7.5% ratio may cover the airport expense over 25 years study period. However to target maximum profit, Dunnellon Airport could feasibly reach up to 154% revenue increase. Under realistic design scenario, a reasonable 19.8% gain could be obtained to exceed 5% of the expense over 25 years study period. This scenario allowed revenue maximization of up to 31.4% increase. In case of NZEB concept, the FIT system using all rooftops and parking areas covered with crystalline system could reach
5.1% revenue that can cover expense; in addition, 6.2% revenue increase could also be achieved. Unfortunately, although there was a $995 yearly saving under the net metering system, it failed to cover expense and fulfill the 5% of all Dunnellon Airport expenses over 25 year; yet, it translated to 93.7% yearly energy cost savings. This case also happened to all leasing options; but, the only scenario that satisfied the hypothesis was to lease all 642,218 square-foot area over 25 years which yielded to
8.8% revenue increase to cover expense and a 13.4% revenue gain over 25 years study period.
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CHAPTER 7 CONCLUSIONS AND FUTURE RESEARCH
Review
This dissertation has been initiated by the following two questions:
Can an evaluation tool be made to assist airports in identifying and choosing a renewable energy system that is applicable in their areas? How can installed renewable energy system benefit the airport’s long-term goal of adding an additional revenue source?
In order to emphasize the assessment on the revenue, this dissertation has identified the role of the contributing parties that will involve in the renewable project energy development in the airport. Hence, there are two other questions that should be addressed while conducting the evaluation process as follows:
Who owns the airport? What is the role of the owner?
The focus of the dissertation was to find maximum profit that can be generated as the result of renewable energy system installation on the airport’s surrounding. The dissertation was directed by developing a hypothesis as applied to revenue source investigation. The main dissertation research area of analysis was in the state of
Florida, specifically, airports in Florida. The review of literatures has shown that the best renewable energy system that is suitable for airports is solar photovoltaic technology.
To answer the first question, a new methodology has been developed using a combination of the existing methodology and more specific analysis mainly due to glare impact of solar panel installation. The new methodology has been provided using block diagram—flowchart. The operationalization of the methodology is using the step by step analysis. This methodology has been approved by both decision process expert
229 and airport management. Before the judgment process, a rigorous literature review process has been conducted to collect appropriate information and suitable assumptions. In addition, this dissertation also attempts to develop an evaluation tool that is expected to be beneficial for the airport authorities in doing initial assessment of photovoltaic installation on their airport territory. Therefore, the answer of the first question is “yes” because the outcomes of the newly methodology and evaluation tool can be implemented to assist and help airport authority conduct renewable energy systems feasibility analysis, mainly, in solar energy application.
After a step-by-step assessment, in order to answer the second question, this dissertation has conducted the case study base analysis and developed a hypothesis to direct the flow of analysis in order to focus on revenue assessment. According to the hypothesis examinations, the NZEB concept under net metering was the only scenario that failed to justify the 5% ratio requirement for the airport authority to cover the expense. The other scenario suggests that the airport owns the system; because, the net present value of leasing option cannot be higher than the net present value of owning the renewable energy system. Thus, in terms of fulfilling long-term revenue addition, a 25 year study period has presented the results that airport generally can cover expense by implementing renewable energy system with utility-scale size.
The third and fourth questions have simultaneously helped each stage of the evaluation process. For instance, at the beginning of the evaluation stage, this dissertation has determined three selected case studies for revenue assessment.
Airglades Airport is on underway of the privatization process. In this case, although, at the inception Airglades is publicly owned, when it comes to the time of planning
230 renewable energy system development, Airglades Airport should assume itself as a private entity airport; because there are some differences in the assumption, mainly in the incentives. Hence, by concluding the second answer to answer the third and fourth questions, the role of the owner is a factor that will influence the feasibility analysis.
Conclusions
This dissertation has succeeded to perform renewable energy assessment in the airport. Since there is no significant research that has conducted the revenue analysis, the development of the hypothesis has helped directing the research to a thorough analysis and implementing both the methodology and evaluation tool to Dunnellon
Airport as a case study.
Conclusion #1: The Renewable Energy Potentials
After conducting a comprehensive analysis, out of the total theoretical potential of
1,355 MW solar energy systems—for all 1,706 acres Dunnellon Airport property, the available potential is only 39 MW. This value corresponds to a 2.8% of the total theoretical potential in Dunnellon Airport.
Conclusion #2: Utility-Scale Maximum Energy Scenario
In terms of public ownership, the utility-scale maximum energy scenario had shown the most likely significant results to implement on the airports. Both non- traditional revenue ratios had shown convincing result. For instance, if Dunnellon
Airport considered installing the utility-scale option, the maximum revenue that could be generated was $7,977,506 with 11.7MW system using combined 1-axis tracking and crystalline module type. This corresponded to 154.7% higher than 2012 revenue; and could exceed 126.9% of 2012 expense. The solar farm should be in the third area
(Solar Farm-3) with 26 degree inclination and orientation of 165 degrees from north. 231
Both inclination and azimuth direction had been tested to be free of glare to the flight paths at Dunnellon Airport. From Dunnellon Airport perspective, the leasing option could not satisfy the hypothesis unless renting all 642,218 square-foot on area 3.
In the private entity option, the private entity could reach the maximum net present value of $12,677,811 with the payback period of 13 years using 1-axis tracking system with crystalline module type. The suggested installation tilt was 26 degree inclination at 165 degree azimuth. The possible leasing expense that went to Dunnellon
Airport was $450,275 over 25 years and this yielded to 8.8% to cover the 2012 expense
(for renting area 3).
Conclusion #3: Realistic Design Scenario
From the Dunnellon Airport perspective, the maximum potential revenue over 25 years was $1,622,621 using 2.38 MW system size. This system could be installed on both Solar Farm-2 and Solar Farm-3 area with 26 tilt degree inclination and 165 degree of azimuth direction. This value is for 2.38 MW system size. In terms of fulfilling all maximum 6 MW maximum Tier 3 limit from Progress Energy, Dunnellon Airport could obtain twofold of the 2.38 MW revenues over 25 years (for 5.76 MW system size). The revenue value translated to 31.4% of 2012 revenue while exceeding as much as 25.8% of 2012 expense.
From the private entity side, the private-entity could benefit up to $2,576,014
NPV with the payback of 13 years using the same system, tilt and orientation with
Dunnellon Airport. The leasing expense that would go to Dunnellon Airport is $91,619 over 25 years which corresponds to 1.8% of the expense of the airport in 2012.
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Conclusion #4: NZEB Scenario
In NZEB scenario, all calculations had referred to the peak load demand from
Dunnellon Airport yearly use. Under feed in tariff scheme, the larger area could benefit the airport as much as $119,326 over 25 years with yearly cost savings up to $44,656.
In this case, Dunnellon Airport should install 190 kW system size with fixed-tilt mounting with crystalline type and 26 tilt as well as 135 degree azimuth from north.
However, in case of the net metering scheme, the case would not be favorable.
Although, there was a yearly saving of $995 and the revenue over 25 years was $2,636
(only cover 0.042% of the 2012 expense); yet, the ROI has shown -176%, which could be translated to be unfavorable under the predetermined financial parameters. There are three reasons following the results. First, is due to the design of NZEB for the less energy-intensive building has come up with the lower system size; therefore, it translates to the higher rate of installation, which is $5.3/watt for the system size lower than 10 kW (Barbose et al., 2013, p.1). This has been keeping the upfront cost high.
Second, the avoided cost rate cannot benefit the system owner since it will not give enough compensation to cover other costs such as inverter cost replacement that would roughly be estimated as $2,816 for every 10 year. One percent energy inflation rate and 6% loan rate were used as assumptions. These results had been followed by sensitivity analysis. Furthermore, the results of sensitivity analysis showed the improvement of higher 13.6% ROI at a rate of 8% of energy inflation while keeping other financial parameters constant. This ROI value was favorable since the acceptance criteria of ROI is at least 10% if it is calculated in average current market situation
(Muneer et al., 2011).
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From the private entity perspective, by installing the same system, 190.7 kW, private entity could have maximum benefit of up to $714,784 revenue and $86,677 net present value over 25 years with payback period of 17 years under feed in tariff. The leasing expense that went to Dunnellon Airport was $7,326 for renting Rooftop-1 area from Dunnellon Airport. Under net metering scheme, the case was similar, private entity could not benefit since the ROI was -147.9% for Rooftop and -150.6% or Parking. The expense to rent the rooftop or parking space would be only $272; which was only 0.005
% of the Dunnellon Airport 2012 expense.
Conclusion #5: The Role of Incentives
The role of ownership of the solar project in airport is cautious; because, the incentives that support the owner will be treated differently since it can reduce the upfront significantly. For instance, in case of Dunnellon Airport’s plans to install a utility- scale Solar Farm-3 with 1-axis tracking and crystalline module type, the new inception cost has decreased to $3,049,249 from $48,727,245. The total funding was the sum from $45,619,430 bond/loan incentives and $58,566 from other incentives. The reduction could reach up to $45,677,996 that corresponds to 93.7% of the total initial cost without any incentive. This value can help airport authority realize the project.
With respect to private ownership option, the private entity could benefit from bonus first year depreciation from MACRS 5 years calculation. For example, using the same system of 11.7 MW with combined 1-axis tracking and crystalline module, the net inception cost was $27,240,404. The incentive from 30% tax reduction was
$14,628,173. The 1-year MACRS depreciation after reducing initial cost with tax was
$6,810,101. The same other renewable energy credits were $58,566. The incentive
234 covered a total of 44.1% of the total up-front cost. Although the initial cost was still large, this option assumed that the developer and the financier organization were the same private entity that they owned money to borrow. Therefore, there was no bond/loan incentive. The more incentives result in the lower new inception cost. In the end, this dissertation has supported the previous study to prove the possibility of having solar project at a lower or even at no cost (Cory et al. 2008a; Lissel & Mosey, 2010;
Kibert et al. 2010; NREL, 2012).
Conclusion #6: New Decision Model
By performing the five steps as the stage of methodology implementation, this dissertation has shown a thorough process; yet unsophisticated to follow. Thus, the airport authority and private entity can simply do the initial assessment of the solar project. The new decision model can practically be used since most of the methodology stages are common to follow in doing solar energy assessment. The incorporation of
SGHAT and the suggestion to develop matrices of analysis will accommodate both the airport authority and private entity to eliminate the inappropriate areas without problems.
Conclusion #7: Evaluation Tool for Revenue Assessment
The evaluation tool has been developed using a spreadsheet basis that is portable. The manual for use is available in Appendix E. All calculations of the database have been tested and confirmed to PV Watts database. The error is lower than 5%, which is favorable to implement for the calculation (Appendix B). The revenue evaluation tool has also provided sensitivity analysis ability to see the effect of different input variation in terms of financial parameters. This evaluation has been completed to
235 have a create report menu that could practically be used as a standardized report for airport authority to check the bidder proposal in terms of financial analysis.
Policy Implications
This dissertation has tried to investigate the policy implications based on the current airport conditions. Bazargan et al. (2005) has identified about the 4.9 to 11% revenues increase to cover the expense to sustain with current economic conditions.
By borrowing the survey results from Bazargan et al. (2005), there are few suggestions from 359 general airport managers from all around US that will support the policy and have relations with this dissertation. The survey was intended to obtain answers about how to diversify income to sustain with current economic conditions. In order to address the issues from the airport managers and to align with the results of this dissertation, the following sub sections will describe the policies that will, in return, influence the airport’s operation system.
FAA Regulation
This dissertation has incorporated the process from SGHAT tool to analyze glare on the flight paths. Some of the airport manager’s suggestions have come to ask for runway extension in their territories. It seems FAA has rigidly enforced the process of runway extensions. The development of runway will imply the possibility of bigger size airplanes to land or take off which in return will allow the airport to add additional revenue concerning fuel gas sale or other revenue from passenger’s ticketing.
According to Bazargan et al. (2005) study, airport with runway larger than 4,000 feet has better financial situation compared to airport with lower runway length. This dissertation has identified several tilt angles as well as azimuth directions. However,
236 not all combinations satisfied the SGHAT test. Moreover, the length of the tested runway on Dunnellon Airport Runway 05-27 is 4,875 feet and the length of the Runway
05-23 is 4,941 feet (Marion County, 2010, p.1-5). If the tests were conducted into the shorter runway, it is believed to have chance of smaller area that can be used for solar installation. It will of course worsen the current financial airport with shorter runway than
4,000 feet length, because the chance to add resource from non-traditional revenue will be harder. Therefore, streamlining the process of runway extension in FAA regulation will help airports sustain with current condition with various possibility of additional revenue source.
In Relation with LEED
Some of the suggestions from the survey were to build more hangars and terminals or office buildings. The terminal buildings and hangars development can support the LEED program. For instances, nowadays, airports have been keeping aligned with sustainability movement. The LEED certification will allow airport reach its sustainability goal. By installing solar project, airport will support the sustainability.
Some of the managers have suggested to not only improving the buildings or terminal buildings, but also doing improvements such as: developing business parks, commercial and industrial on the unused land; encouraging more economic activities or even acquitting the land on the airport surrounding; and, allowing private developments as well as electricity improvements. From these suggestions, the decision makers can contribute to the LEED enforcement as well. For instances, the installation of PV can help every new building achieve LEED energy and atmosphere credit for green power especially for the new building in the airport. Moreover, if the installation complies with
237 the NZEB concept, no green-field will be acquitted. This would mean another LEED sustainable site credit for site selection. The tendency to install PV near the terminal building has been identified to be a factor for noise abatement since the PV can reduce the noise to the passenger terminal building (Rüther, Braun & Zomer, 2006; Elgun &
Shahrabi, 2008). This will be accounted as an additional LEED credits in innovation and design. In case of counting energy from the source, the parcels of the community near the airport can utilize the benefit from the solar farm. For example, in order to support the net zero energy house, the residential occupants can utilize the use of electricity from airport solar farm. This will be beneficial too, since the electricity loss from the utility provider can be eliminated, it has shorter distance to the electricity customers (Elgun & Shahrabi, 2008). These examples help the energy policy makers to fully support PV installation on the airport areas.
Funding and Incentives
Survey suggested that airport managers favor the reduction of the long and exhaustive bureaucracy of the airport improvement program (AIP) process. As the results of this dissertation, it is proven that incentives play an important role of reducing the upfront cost to roughly from 44.1% under private ownership and 93.7% for public ownership scenario. Moreover, most general airports have been depending on both federal and state funding to do the airport improvements (Bazargan et al., 2005).
Therefore, the AIP funding will help airports reduce the cost of installing PV.
Limitations
This dissertation has developed a methodology based on the potential in the state of Florida. Therefore, the potential will be limited to solar energy. The literatures and quick assessment have come up to solar energy system as the priority that is 238 applicable to the airports in the state of Florida. In the end, the evaluation tool for feasibility analysis will be based on solar energy. The evaluation tool is mainly developed for the airport authority to perform initial assessment of renewable energy system installation on airports; and, ended by revenue analysis. Due to that reason, the contractor selection method and political constraint are not covered in this dissertation.
All values were considered as conservative assumptions; for instance, since the cost of control, motor and lubrication are insignificant compared to the production
(Campbell, Blunden, Smeloff & Aschenbrenner, 2009); this dissertation has assumed the maintenance cost for both fixed-tilt and tracking were typical.
Suggestions for Future Research
This dissertation has tried to investigate complex and interlocking problems in the determination of which renewable energy systems are suitable in airports. Most literatures suggest using solar panel on airports (Rüther et al., 2006; Plante et al., 2010;
DeVault et al., 2012; Elgun & Shahrabi, 2008). Concerning the methodology, the case has been focused to solar energy installation on airports. The literature studies have suggested that solar energy will be best suited in the state of Florida airports. The result of this dissertation is suitable in the state of Florida. However, there will be other option to install other renewable energy system types, in lieu of solar energy, for other different geographical area. Therefore, the methodology should be improved mainly in the evaluation tool development for the decision making process, because for Florida as a test case will only be limited to solar energy. If in case, after the quick assessment for renewable energy to another test case has come up with different renewable energy system type, such as: geothermal, biomass, wind and so forth, the need to develop new
239 evaluation tool complete with the newer steps, calculation and financial modeling in a spreadsheet basis is compulsory.
Methodology—Other Caveats to Solar Installation
This dissertation has supported other studies suggestion about the utilization of solar photovoltaics on airports. The incorporation of SGHAT tool has been used to investigate the glare due to solar installation. However, there are some other research opportunities that may rise, which have not been covered in this dissertation such as the correlation of the runway length extension to the total possible area that could increase the possibility of available solar potential on airport.
With regard to solar farm installation, since there is no formal research, a study by De Vault et al. (2012) has suggested to further investigate the impact of solar panel installation as attractant to certain animal species. This dissertation has not covered the impact of solar farm to the existing vegetation as well as being attractant to animals that may add some other safety issues on airports. If these are factors, there will be additional stages in the methodology that should be included in the diagram block; and the procedures should be further investigated.
In terms of both theoretical and available potential estimations, all values were referred to a flat and plain area. The solar installation potentials from a perpendicular area such as in the building wall or façade, has not been included. This will be a good practice to implement on the airport properties since airports also consider using PV as noise barriers, if it is installed near the terminal buildings. For further improvements, all areas that potentially contribute to the advantage of installing solar photovoltaics should be estimated. Therefore, the revenue analysis will be more complete and there are more options for installation sites. 240
Revenue Evaluation Tool—Inputs and Assumptions
The revenue evaluation tool has provided databases and assumptions. The assumptions of financial parameters, i.e., loan, discount, energy inflation as well as general inflation rate have been the factors that influence the revenue results.
Therefore, although, the tool has provided option to adjust the financial rate values, care should be taken with respect to identifying the rate based on the year of analysis as well as the rate in each state.
For Dunnellon Airport case study, this dissertation has not considered the calculation of carbon credit, since there was no up to date reference that suggest the incentive from carbon mitigation for Dunnellon Airport area. However, for further research in different Florida places, there is one option that can place the value of carbon mitigation value. This value will help decreasing the first cost; therefore, the solar project at zero cost can be doable.
Concerning the total cost, this dissertation has used data from prior study based on July 2013 data (Barbose et al., 2013, p.1); however, the inevitable rapidly falling cost of the module has made each case dynamically changing within days; yet, the balance of system nearly remains constant. Therefore, the default values in the evaluation tool have been set up for the users to be able to input the newer values. It will require some other calculations by running the tool and setting up mainly on the module cost per watt.
In investigating the earthwork cost, the value will be different if the tool is utilized for different state. However, the value used in the case study had been determined based on five years of project experience in Arizona (Moore & Post, 2008). For a more meticulous analysis, the value of ground or earth work per watt should be further investigated. For instance due to the distance to the electricity connection, Solar Farm- 241
4 area was not analyzed. The corresponding earth work cost per watt due to distance should be further investigated since the earth work cost in the Arizona plant was determined in the area when the installation occurred. This value has a relation with airport operational hours as well as the distance to the electricity central connection. If in case the airport has to reduce hours of operation due to ground work, this will be another factor that should be calculated.
The database was derived from the database from PV Watts, mainly, for 42 stations in Florida area. To extend the use of the revenue evaluation tool, a little modification in the database could enrich the database; hence, it can be applied to other states other than Florida.
This dissertation has shown some charts that could compare various systems with various angles. However, in the revenue evaluation tool utilization, only one area can be analyzed. The user of the tool should calculate manually using the spreadsheet to analyze the results. Therefore, for further improvement, a simultaneous several areas could be done by modification in the programming language.
Chapter Summary
Most literatures have suggested that the best suited renewable energy systems for airports utilize solar energy, mainly photovoltaics. There is no specific guideline or methodology that emphasizes the feasibility analysis on the airports. The results of this dissertation observe the step-by-step methodology process and incorporate the use of new revenue assessment tool using spreadsheet basis on the airport. The safety issue due to glare has been tackled by incorporating the SGHAT tool approved by FAA. This dissertation has applied both the methodology and revenue assessment tool in a case
242 study—Dunnellon Airport. Moreover, this dissertation has concluded that the utility- scale solar farm could have maximum revenue for Dunnellon Airport. Under utility-scale maximum energy scenario, Dunnellon Airport could tackle expense of about 126.9% higher compared to its 2012 expense. For this purpose, the area of installation should be in Solar Farm-3, with combined 1-axis tracking and crystalline module. The system should be installed with 26-degree tilt and oriented at 165-degree azimuth to avoid hazardous glare. Under realistic design scenario, both Solar Farm-2 and Solar Farm-3 with combined 1-axis tracking and crystalline module showed similar results that could make Dunnellon Airport obtain the maximum revenue of as much as 25.8% higher than its 2012 expense. For these results, the airport should install similar systems of 1-axis tracking and crystalline with the same tilt and azimuth direction. In the NZEB scenario, although Dunnellon Airport is not an energy-intensive airport, the target of covering 5% expense from non-traditional revenue could not be reached. The only possible way to reach 5% revenue higher to cover expense can be done by combining all rooftops and parking areas with crystalline panel, using feed in tariff incentives. These combinations can reach up to 5.1% non-traditional revenue higher than 2012 expense. The installation should be tilted at 26 degrees and oriented at 135-degree azimuth to avoid glare. There is no suggestion for Dunnellon Airport if its goal is to reach at least 5% revenue addition to apply NZEB scenario under the net metering scheme. However,
Dunnellon Airport can save the energy cost up to 93.7% annually. In terms of leasing, the only possible way to gain revenue more than 5% of its expense is to rent all 642,218 square-foot area to private entity. This leasing option will generate 8.8% non-traditional revenue increase of the 2012 expense.
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APPENDIX A FUNDAMENTALS OF FEASIBILITY ASSESSMENT
Overview
The economic measures formulas presented in this appendix are based on the interpretation on a report by Short, Packey & Holt (1995), A Manual for the Economic
Evaluation of Energy Efficiency and Renewable Energy Technologies. In addition, some of the informations are based on ASTM Standards on Building Economics, Third
Edition (1994). Several financial measures can be combined to analyze an investment project for decision analysis for different situations (Short et al., 1998, p.39). The measures from Short et al. (1995) have been used to the economic algorithm calculations and translated into programming codes using Microsoft Visual Basic
Application ® (VBA) for Microsoft Excel ® 2010 and 2013 spreadsheet. The reference for financial modeling can be in-depth learned from Walkenbach (2007); Day (2007);
Sengupta (2004); and Walkenbach (2002); however, the actual implementation of the programming codes are independent from this dissertation needs.
TLCC
Total life-cycle cost (TLCC) is the summation of all costs that have been inflated for a certain rate, for instances, all non-fuel related cost, general inflation rate for equipment and all cost related with loan will be inflated with a loan rate in a certain study period; however, this will be treated in a separate loan payment period. Each year, the value then will then be discounted at a certain discount rate up to the entire life cycle the system (the study period). The less TLCC will be preferable.
N C TLCC n (A-1) n0 (1 d)n
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where,
TLCC = total life cycle cost Cn = total cost in year n N = study period D = discount rate
LCOE
Life cycle cost of electricity (LCOE) is TLCC divided by the total energy generated after a certain study period. In this dissertation, the value of energy output had been inflated with the degradation rate. Therefore, the LCOE value can be obtained by using the following equation. The more efficient $/kWh should be taken into consideration.
N E n LCOE TLCC / (A-2) n n 0 (1 d) where,
LCOE = net present value TLCC = total life-cycle cost En = energy output after yearly degraded N = study period d = discount rate
SIR
Saving to Investment Ratio (SIR) is the present worth of the net saving (NPV if there is depreciation) divided by the first investment value. In this dissertation, the value of investment refers to the newer investment value (new inception cost). If SIR is used to compare project investment, the higher SIR is preferred. Specifically, if the value of
SIR is greater or equal to 1.0; then, the project will be cost effective (ASTM, 1994, p.41).
PW (NS) SIR (A-3) I
245 where,
PW (NS) = present worth of net saving I = first cost or new inception cost
NPV
Net present value (NPV) or net present worth is the offset of revenue and all costs and depreciation (if applicable). The calculation is based on the following formula.
In investigating NPV, the higher the NPV the more favorable the project investment; therefore, the decision makers should consider the higher NPV.
N F NPV n (A-4) n n 0 (1 d) where,
NPV = net present value Fn = net cash flow in year n N = study period d = discount rate
PB
Payback (PB) is the accrued value of NPV that will show the position the saving will recover the cost. The calculation is based on the following formula; the shorter the project period the more preferable the project.
Δ In Δ NSn n n n n (A-5) (1 d) (1 d) where,
ΔIn = inflated cost with certain general inflation rate in year n ΔNSn = inflated net saving with certain general inflation rate in year n N = study period n = year d = discount rate
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Salvage
Salvage or residual value is the remaining value at the end of study period. This value may not be considered to decision making process; however, salvage value will reduce the TLCC however will be added to the net saving. In this this dissertation, the salvage value will refer to the inverter replacement cost.
n (I (1 g) x ((n r)/r) Salvage (A-6) n (1 d) where,
I = inverter replacement cost r = replacement time n = study period d = discount rate g = general inflation rate
Table A-1. Financial measures selection criteria Investment Decisions TLCC LCOE SIR NPV PB Accept/Reject for an investment N
Selection from mutually exclusive C N N R N alternatives
Ranking due to limited budget R R N
Risk Assessment R Notes: R= Recommended; N= Not recommended; C= Commonly used; Empty cell= acceptable to use Source: Short et al. (1995)
Feed in Tariff
In calculating power purchase agreement under feed in tariff, all renewable energy system producers may produce electricity as large as possible.
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The retail price will be fixed during a certain study period. Under this scheme, the utility provider will buy all excess energy with a fixed feed in tariff (FIT) rate. The formula to calculate the monthly feed in tariff can be seen as follows:
Feed in tariff F x P R x C (A-7) where,
F = contract feed in tariff rate [$/kWh] P = energy produced per month [kWh] R = retail rate [$/kWh] C = total energy consumed per month [kWh]
Net Metering
For net metering, there will be avoided cost from the utility provider. Although calculating net metering is straight forward, the fact that the utility provider will not pay the excessive energy produced by the renewable energy system owner will be a factor for sizing the system. However, under this scheme, the sizing will have to approach the actual monthly used. The following formula has been used to calculate the offset energy.
If P > C then,
Net Metering P C x A (A-8)
If P < C then,
Net Metering P C x R (A-9)
Where
A = avoided cost [$/kWh] P = energy produced per month [kWh] R = retail rate [$/kWh] C = total energy consumed per month [kWh]
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In this dissertation, the formula to calculate net metering uses the offset value of the energy used and produced. The excess energy will be compensated with a small amount of dollars. This is the reason why sizing the system is critical if the owner has limited budget and there is no feed in tariff incentive within one region.
MACRS Depreciation
The private entity can benefit incentive from the first year depreciation. The calculation will follow the MACRS constants. The total installation cost should be firstly be subtracted by all other incentives before start calculating 1 year depreciation. Table
A-2 presents the constants as multiplier factor to the total cost after subtracted by other incentives.
Table A-2. MACRS depreciation multiplier Year MACRS-5 Years depreciation rate 0 - 1 0.2 2 0.32 3 0.192 4 0.115 5 0.115 6 0.058
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APPENDIX B TECHNICAL DESIGN ALGORITHMS
Overview
The main database for solar radiation, assumptions and technical calculations are based on PV Watts (NREL, 2012) and Photovoltaic Systems second edition
(Dunlop, 2011). All formula and the steps used also have been converted to Microsoft
Visual Basic Application ® (VBA) programming language for Microsoft Excel ® 2010 and
2013 spreadsheet.
Evaluation Tool Database Validation
T-test statistic has been used in many study applications to validate database.
Two studies in investigating database for solar radiation have incorporated statistical method for validation (Lave & Kleissl, 2011; Armstrong & Hurley, 2010). The equations below should be used to do the statistical database validation.
1 n MBE d (B-1) n i i where,
MBE = mean bias error n = amount of data i = value of ith data at n station d = difference between predicted data case and compared data (reference)
1 n RMSE (d )2 (B-2) i n i where,
RMSE = root mean square error n = amount of data i = value of ith data at n station d = difference between predicted data case and compared data (reference)
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In the end, the t-test should be using the following equation:
(n 1) MBE 2 t (B-3) 2 2 RMSE MBE
The following table and calculations are based on the comparison between the results of revenue evaluation tool (EvR) database and the results of running PV Watts; at latitude inclination and 180 facing south azimuth angle.
The system was set at 4 kW systems as applied to PV Watts default system size. In order to conduct the validation, the goal of the calculation process was to reject null hypothesis, there is significant difference between the two compared data. Therefore, if there was no significance difference, the data from the evaluation tool can be accepted and used. From table B-1, the cumulative MBE and RMSE have been summarized.
From the two-tailed test at 5% significance level, the t yields to 2.021 (approached by using 40 degree of freedom). As a result of the calculation using equation B-3, the t value is 3.87. Since the t value larger than that on the t table, the null hypothesis was rejected. It translated to there was no significant difference between the tested revenue evaluation tool (EvR) database and the reference data (PV Watts). This means the evaluation database can be used for further calculations.
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Table B-1. Database statistical error results Iterated AC Energy Iterated AC Energy Difference to Difference to (Fixed) (1-Axis) Reference Reference Data Station Name EvR PV Watts EvR PV Watts Fixed 1-Axis Fixed 1-Axis [kWh] [kWh] [kWh] [kWh] MBE RMSE (MBE)2 (RMSE)2 [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] 1 CRESTVIEW BOB SIKES AP 4,642 4,566 5,714 5,701 76 13 5,776 169 2 DAYTONA BEACH INTL AP 5,402 5,397 6,723 6,798 5 (75) 25 5,625 3 FORT LAUDERDALE EXECUTIVE 4,899 4,868 6,009 6,050 31 (41) 961 1,681 4 FORT LAUDERDALE HOLLYWOOD INT’L 5,404 5,388 6,694 6,743 16 (49) 256 2,401 5 FORT MYERS PAGE FIELD 5,319 5,224 6,549 6,494 95 55 9,025 3,025 6 GAINESVILLE REGIONAL AP 5,042 5,034 6,239 6,305 8 (66) 64 4,356 7 HOMESTEAD AFB 5,541 5,477 6,823 6,803 64 20 4,096 400 8 JACKSONVILLE/CRAIG 4,725 4,731 5,752 5,827 (6) (75) 36 5,625 9 JACKSONVILLE INTL ARPT 5,079 5,050 6,289 6,333 29 (44) 841 1,936 10 JACKSONVILLE NAS 5,091 5,080 6,223 6,278 11 (55) 121 3,025 11 KEY WEST INTL ARPT 5,696 5,680 7,110 7,172 16 (62) 256 3,844 12 KEY WEST NAS 5,608 5,565 6,963 6,972 43 (9) 1,849 81 13 LAKELAND LINDER RGN 5,122 5,084 6,260 6,271 38 (11) 1,444 121 14 MACDILL AFB 5,447 5,410 6,664 6,684 37 (20) 1,369 400 15 MARATHON AIRPORT 5,316 5,186 6,572 6,508 130 64 16,900 4,096 16 MAYPORT NS 5,208 5,245 6,390 6,535 (37) (145) 1,369 21,025 17 MELBOURNE REGIONAL AP 5,253 5,259 6,481 6,602 (6) (121) 36 14,641 18 MIAMI/KENDALL-TAMIA 5,196 5,172 6,438 6,486 24 (48) 576 2,304 19 MIAMI/OPA LOCKA 5,444 5,407 6,759 6,785 37 (26) 1,369 676 20 MIAMI INTL AP 5,269 5,248 6,452 6,493 21 (41) 441 1,681 21 NAPLES MUNICIPAL 5,741 5,711 7,158 7,191 30 (33) 900 1,089 22 NASA SHUTTLE FCLTY 5,246 5,225 6,532 6,585 21 (53) 441 2,809 23 ORLANDO EXECUTIVE AP 5,182 5,132 6,392 6,403 50 (11) 2,500 121 24 ORLANDO INTL ARPT 4,983 4,953 6,115 6,147 30 (32) 900 1,024 25 OCALA MUNI (AWOS) 5,607 5,523 7,070 7,032 84 38 7,056 1,444 26 ORLANDO SANFORD AIRPORT 5,633 5,588 7,085 7,113 45 (28) 2,025 784
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Table B-1. Continued Iterated AC Energy Iterated AC Energy Difference to Difference to (Fixed) (1-Axis) Reference Reference Data Station Name EvR PV Watts EvR PV Watts Fixed 1-Axis Fixed 1-Axis [kWh] [kWh] [kWh] [kWh] MBE RMSE (MBE)2 (RMSE)2 [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] 27 PANAMA CITY BAY CO 5,314 5,305 6,579 6,646 9 (67) 81 4,489 28 PENSACOLA FOREST SHERMAN NAS 5,464 5,482 6,782 6,896 (18) (114) 324 12,996 29 PENSACOLA REGIONAL AP 4,965 4,952 6,070 6,137 13 (67) 169 4,489 30 SARASOTA BRADENTON 5,389 5,376 6,743 6,803 13 (60) 169 3,600 31 SOUTHWEST FLORIDA I 5,138 5,074 6,271 6,259 64 12 4,096 144 32 ST LUCIE CO INTL 4,800 4,779 5,875 5,912 21 (37) 441 1,369 33 ST PETERSBURG ALBERT WHITTED 5,518 5,481 6,878 6,906 37 (28) 1,369 784 34 ST PETERSBURG CLEAR 5,474 5,467 6,812 6,883 7 (71) 49 5,041 35 TALLAHASSEE REGIONAL AP [ISIS] 5,127 5,092 6,338 6,365 35 (27) 1,225 729 36 TAMPA INTERNATIONAL AP 5,531 5,483 6,937 6,962 48 (25) 2,304 625 37 TYNDALL AFB 5,249 5,325 6,404 6,578 (76) (174) 5,776 30,276 38 VALPARAISO ELGIN AFB 5,361 5,429 6,579 6,758 (68) (179) 4,624 32,041 39 VALPARAISO HURLBURT 5,409 5,452 6,665 6,810 (43) (145) 1,849 21,025 40 VERO BEACH MUNICIPAL ARPT 4,901 4,891 5,961 6,015 10 (54) 100 2,916 41 WEST PALM BEACH INTL ARPT 5,377 5,372 6,686 6,775 5 (89) 25 7,921 42 WHITING FIELD NAAS 4,903 4,880 6,046 6,101 23 (55) 529 3,025 Total 23.14 (48.45) 44.66 71.69
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PV Array Area and Power
The algorithms used are maintained with the intended output. For instance, in order to estimate area of the panel array arrangement, the calculations used is as follows:
Pmp A (B-4) (R x η ) m where,
A = estimated area of array arrangement [m2] R = solar radiation [kW/m2] Pmp = maximum power [kWdc] ɳm = module efficiency [%]
The formula that is used to calculate power can be determined as follows:
P V x I (B-5) where,
P = power [kW] V = voltage [volt] I = maximum power [ampere]
This equation can be used to estimate the area whenever the required power data are available. For instance, in order to calculate array arrangement, the monthly use data should be available. The solar radiation has been calculated in a monthly basis and finally calculated in a yearly output.
Example Calculations
The calculations for PV sizing of utility interactive-grid connected systems can follow the steps described in the following sections. The monthly data are used as the
254 input. The results will be in the array arrangement. The following examples apply to other calculations as the reference of other technical calculations in this dissertation.
Area Requirement and Total Panel Preliminary Design
From the preliminary available data, the determination of the area and total modules (panels) as well as array lay-out required can be done by using calculations as seen in the following steps for (135 degree azimuth and 26 degree of inclination) using
Ocala station weather data, and Sunpower -E20 Module (Figure B-1 and Figure B-2).
Figure B-1. Sunpower electrical data. Adapted from Sunpower (2011)
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Figure B-2. Sunpower dimensions. Adapted from Sunpower (2011)
Table B-2 provides the values for the calculations. Column [5] is the data for Dunnellon
Airport area monthly use. The calculations below will refer to the average load sizing calculations. If the value in column [2] is simply multiplied by 1 kW/m2 the result can be interpreted as solar radiation that reaches the earth per square meter per day.
Table B-2. Peak sun hours in Ocala Monthly Month PSH Days Hours Use [kWh] [[1] [2] [3] [4] [5] January 4.4 31 139.5 920 February 5.14 28 126 828 March 4.54 31 139.5 920 April 5.76 30 135 736 May 6.56 31 139.5 736 June 5.74 30 135 736 July 6.21 31 139.5 644 August 5.65 31 139.5 552 September 6.39 30 135 736 October 4.81 31 139.5 644 November 4.28 30 135 736 December 4.05 31 139.5 736 Total Peak Sun Hours (PSH) 5.30 365 1642.5 8,924
PV System Load Requirements = 8,924 [kWh] / 1642.5 [h] = 5.43 kW ac. Direct current (DC) conversion with derating factor (DF), 0.77, = 7.05 kW dc. This is the preliminary theoretical wattage that can support the zero energy system. In order to
256 calculate the area based on the average load, the following steps may be followed. The equations used below are basically the manipulation of equation B-4.
Step 1, determining DC energy daily output Total DC energy daily output = 5.30 [kWh/m2/day] x 20.4[%] = 1.081 [kWh/m2]
Step 2, determining AC energy daily output Total AC energy daily output = 1.081 [kWh/m2] x 0.77 = 0.83 [kWh/m2]
Step 3, determining required array area using average actual yearly AC load The area required to produce 8,924 kWh AC energy should be calculated as follows:
8,924 kWh days 2 A 365 29.45 m kWh 1 0.83 m2 day
Step 4, determining total modules required The designated total modules amount, from the specifications of each module with length of 1,596 mm and width of 1,049 mm (Figure B-2) are as follows: 2 29.45 [m ] Total modules 19 modules 2 1.596 x 1.049 [m ]
In the case when the required voltage is between 150 VDC to 600VDC; therefore, in order to produce VDC output, the design voltage for the strings arrangement will use the open circuit voltage from the panel specification which is VOC = 65.3 V (Figure B-1).
Step 5a, the string arrangements for maximum voltage: 600 / 65.3 = 9.18 10 strings each of 2 series modules (estimated 20 panels) Using the maximum power point (from Figure B-1), the voltage (rated) and current (rated) at maximum power point rated will be Vmpp = 54.7 V and Impp = 6.09 A respectively. Therefore, the actual design power will fulfill the amount of required power using equation B-5:
Power (refer to equation B-2) = V (Strings) x I (Series) = 547 x 12 = 6.5 kW dc
Step 5b, the string arrangements for minimum voltage: 150 / 65.3 = 2.29 3 strings each of 6 series modules (estimated 20 panels)
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Using the maximum power point (from Figure B-1), typical voltage (rated) and current (rated) used, Vmpp = 54.7 V and Impp = 6.09 A. Therefore, the actual design yields to:
Power (refer to equation B-2) = V (Strings) x I (Series) = 164 x 12 = 5.9 kW dc
Optimum Tilt Angle Calculation
The calculation for tilt angle is based on the study by Agha & Sbita (2000) and supported by Armstrong & Hurley (2010), although the calculation is not further explored; however, for further research purpose, the need to analyze the time of use agreement can be investigated. Both of these studies suggest the investigation of optimum tilt angle by using load profile analysis using ambient temperature base. The reason is to simplify the exhaustive complex programming calculation in finding optimum point based on load demand (Agha and Sbita, 2000). Therefore, the use of
18.3 degree Celsius as a base temperature for heating and cooling degree day in the
US is plausible to approach the relations with estimated heating and cooling temperature profiles load. The evaluation tool has been equipped with the same methodology (Agha & Sbita, 2000; Armstrong & Hurley, 2010) in finding the optimum point. The equations below have been converted to the algorithm in finding optimum point based on the same assumptions.
M F tilt (B-6) opt SD (β )
Where
Fopt = tilt optimum factor 2 Mtilt = monthly average solar radiation at certain tilt angle [kW/m /month] SD(β) = standard deviation of the difference between normalized curve of solar radiation and normalized curve of the load assumptions curve at certain β inclination angle
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APPENDIX C EXAMPLE OF SOLAR GLARE HAZARD TOOL OUTPUT FOR VARIOUS TILTS AND AZIMUTHS
Overview
The SGHAT matrix results were arranged to simply eliminate the case whether glare or no glare occurred over two flight paths and one control tower. The matrix form can help users analyzing results of the iterative procedures. In the SGHAT tool input, there are three main points of analysis i.e. flight path 1, flight path 2 and ATC (air traffic control). These points should be analyzed for solar projects due to reflectivity and communications interference (Plante et al., 2010, p.70). The assumptions are based on the suggestion of default values from SGHT; however, array parameters are based on specified azimuth, tilt height and actual ground elevation of Dunnellon Airport. Brief explanations of the SGHAT results are presented in the following sections.
Flight Path 1
Flight path 1 in this dissertation refers to Runway 05-23 at Dunnellon Airport.
Table C-1 presents the results of the SGHAT tool. The results presented here are for
180 degree azimuth at tilt angle of 29.2 (latitude) as the possible maximum point for solar energy generated. The area for this case was for Solar Farm-1 at 1.5 heights for ground mounted purpose. Figure C-1 provides the flight path 1 from SGHT tool.
Table C-1. Results summary of flight path 1 Ground Height above Latitude Longitude Status Flight Path 1 Elevation ground [o] [o] [ft] [ft] G Threshold 29.0591707943 -82.3820436001 63.06 50.0 G ¼ miles 29.056655944 -82.38501592 62.86 119.37 G ½ miles 29.0541410936 -82.3879882398 64.16 187.26 G ¾ miles 29.0516262432 -82.3909605597 62.14 258.45 G 1 mile 29.0491113928 -82.3939328796 62.71 327.05 G 1 ¼ miles 29.0465965425 -82.3969051995 61.72 397.24 G 1 ½ miles 29.0440816921 -82.3998775194 66.18 461.94 G 1 ¾ miles 29.0415668417 -82.4028498392 57.61 539.71 N 2 miles 29.0390519913 -82.4058221591 61.43 605.05 N
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Figure C-1. Runway 23 identified as flight path 1
Although, the glares have not occurred at all points, in the matrix summary, this position, 180 degree azimuth direction as well as 29.2 tilt angle, should be categorized as “G” (Table 4-29). Figure C-1 through Figure C-8 present example results of SGHAT tool for flight path 1.
Figure C-2. Glare at point threshold
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Figure C-3. Glare at point ¼ miles
Figure C-4. Glare at point ½ miles
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Figure C-5. Glare at point ¾ miles
Figure C-6. Glare at point 1 mile
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Figure C-7. Glare at point 1¼ miles
Figure C-8. Glare at point 1½ miles
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At these points of flight path1, the glares did not appear at 1 ¾ miles and 2 miles point.
The legends below the charts represent the glare intensity to eye damage.
Flight Path 2
Flight path 2 refers to Runway 09-27 at Dunnellon Airport. Table C-2 provides the results of the SGHAT tool. The same assumptions for the SGHAT tool inputs have been applied to Solar Farm-1. Figure C-9 presents the flight path 2 from SGHAT tool result.
Table C-2. Results summary of flight path 2 Ground Height above Latitude Longitude Status Flight Path 2 Elevation ground [o] [o] [ft] [ft] G Threshold 29.0594146292 -82.3840606213 63.74 50.0 G ¼ miles 29.0594619316 -82.3881992697 61.12 121.79 G ½ miles 29.0595092341 -82.3923379181 63.82 188.28 G ¾ miles 29.0595565365 -82.3964765665 64.33 256.94 G 1 mile 29.059603839 -82.4006152149 61.79 328.65 G 1 ¼ miles 29.0596511414 -82.4047538633 60.96 398.67 G 1 ½ miles 29.0596984439 -82.4088925117 61.84 466.97 G 1 ¾ miles 29.0597457463 -82.4130311602 64.13 533.87 G 2 miles 29.0597930487 -82.4171698086 59.58 607.59 G
Figure C-9. Runway 27 identified as flight path 2
From the SGHAT tool output, glares were predicted at all points. Figure C-10 through
Figure C-18 provides the results of predicted glares.
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Figure C-10. Glare at point threshold
Figure C-11. Glare at point ¼ miles
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Figure C-12. Glare at point ½ miles
Figure C-13. Glare at point ¾ miles
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Figure C-14. Glare at point 1 mile
Figure C-15. Glare at point 1 ¼ miles
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Figure C-16. Glare at point 1 ½ miles
Figure C-17. Glare at point 1 ¾ miles
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Figure C-18. Glare at point 2 miles
Air Traffic Control
Dunnellon Airport is not equipped with air traffic control (ATC); yet, it uses FAA antennae as communication system. Since solar energy may result in an interference to communication too (Plante et al., 2010); hence, the analysis for control antennae was included. Table C-3 presents the summary of FAA antennae assumed as control tower.
Table C-3. Results summary of FAA antennae Ground Height above Latitude Longitude Status ATC Elevation ground [o] [o] [ft] [ft] G
FAA Antennae 29.0628071653 -82.3691542477 65.18 69.0 G
At this point, glare happens continuously during April to September period at approximately 7 am to 9 am. The glare predicted is at second level of the hazard.
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Figure C-19 provides the position of Solar Farm-1relative to FAA tower; and, Figure C-
20 presents the glare occurrence at FAA antennae.
Figure C-19. FAA antennae identified as ATC
Figure C-20. Glare at FAA antennae
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APPENDIX D APPLICABLE REFERENCE FIGURES AND TABLES FROM ASCE 7-05
All reference pictures and tables provided in this appendix sections are based on
ASCE 7-05. The values existed are used for the wind load calculations in Chapter 4
Selected Case Studies and Data Processing, section Step 3: Airport RES Siting
Evaluation, subsection Wind Load Evaluation Based on ASCE 7-05.
Figure D-1. Basic wind speed—eastern Gulf of Mexico and Southeastern U.S. hurricane. Adapted from: ASCE 7-05 Figure 6-1B (2005, p.35)
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Figure D-2. Tracking system assumptions as mono-slope free roofs. Adapted from: ASCE 7-05 Figure 6-18A (2005, p.66)
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Figure D-3. Fixed-tilt assumptions as pitched free roofs-A. Adapted from: ASCE 7-05 Figure 6-18B (2005, p.67)
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Figure D-4. Fixed-tilt assumptions as pitched free roofs-B. Adapted from: ASCE 7-05 Figure 6-18B (2005, p.69)
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Table D-1. Importance factor, I (wind loads)—refer to ASCE 7-05 Table 6-1 Non-Hurricane Prone Regions and Hurricane Hurricane Prone Regions Category Prone Regions with V = 85- with V > 100 mph 100 mph and Alaska I 0.87 0.77 II 1.00 1.00 III 1.15 1.15 IV 1.15 1.15 Note: Category I represent a low hazard buildings or structures to human life
Table D-2. Terrain exposure constants—refer to ASCE 7-05 Table 6-2 Exposure Zg (ft) aˆ bˆ b c l (ft) Zmin(ft)
B 7.0 1200 1/7 0.84 1/4.0 0.45 0.30 320 1/3.0 30
C 9.5 900 1/9.5 1.00 1/6.5 0.65 0.20 500 1/5.0 15
D 11.5 700 1/11.5 1.07 1/9.0 0.80 0.15 650 1/8.0 7 zmin = minimum height used to ensure that the equivalent height z is greater of 0.6 h or zmin For buildings with h ≤ zmin; shall be taken as zmin
Table D-3. Velocity pressure exposure, Kh and Kz—refer to ASCE 7-05 Table 6-3 Height above Exposure ground level, z B C D ft (m) Case 1 Case 2 Case 1 &2 Case 1 &2 0-15 (0-4.6) 0.70 0.57 0.85 1.03
20 (6.1) 0.70 0.62 0.90 1.08
25 (7.6) 0.70 0.66 0.94 1.12
30 (9.1) 0.70 0.70 0.98 1.16
40 (12.2) 0.76 0.76 1.04 1.22
50 (15.2) 0.81 0.81 1.09 1.27
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Table D-4. Wind directionality, kd—refer to asce 7-05 Table 6-4 Structure Type Directionality Factor Kd Buildings Main Wind Force Resisting System 0.85 Components and Cladding 0.85
Arched Roofs 0.85
Chimneys, Tanks and Similar Structures Square 0.90 Hexagonal 0.95 Round 0.95
Solid Signs 0.85
Open Signs and Lattice Framework 0.85
Trussed Towers Triangular, square, rectangular 0.85 All other cross sections 0.95
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APPENDIX E DEVELOPED REVENUE ASSESSMENT TOOL GRAPHIC USER INTERFACE (GUI) AND GUIDELINES
Overview
The “Airport Revenue Assessment Tool for Solar Energy” is comprised of contiguous graphical user interface (GUI) that may allow users to follow step by initial revenue assessment. The following sections function as guideline that will allow users to follow step by step inputs used based on this dissertation, mainly, using the available panel module data.
Opening Microsoft Excel ® 2010 or 2013 Containing the Evaluation Tool
In order to open the evaluation tool, make sure the users do not open other spreadsheet files. If the users unintendedly open other spreadsheet files, this action will cause the codes that contain programming language refer to the different spreadsheet and fail executing the evaluation tool.
Figure E-1. Welcome menu and evaluation tool identification
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Figure E-1 presents the welcome menu and evaluation tool identifications. If this menu does not appear, simply restart the program; otherwise, close the evaluation tool identification by hitting the red-cross button on the top right corner. The next step is to press the “calculate” tab with orange color below the standard Excel spreadsheet tab.
The position should appear on the most bottom left corner. After pressing this button, another GUI will pop up. This will be the site location and technical input interface.
Site Location and Technical Interface
1 5
2 6
7
3
8
4
Figure E-2. GUI for site location and technical specification
Block of Section #1
This section provides box of the meteorological station code based on TMY 3 database. Database specification can be obtained from the bottom of the excel
278 spreadsheet, with the blue color. The dropdown list on the station name can interactively be changed based on the 42 stations in Florida.
Block of Section #2
This box is the main tab menu. There are four main tabs; the first two tabs from the left are the calculation inputs. Meanwhile, the last two tabs are the display of the results. The monthly use analysis is the standard tab that will frequently be used in the analysis. The tilt variation analysis is the estimation for the optimum tilt angle that can be implemented in the “time of use” power purchase agreement. This dissertation does not address this issue; yet, for further improvement with a little modification in the programming language, the tilt variations can be connected to the financial model to achieve maximum profit under different scenario.
Block of Section #3
This box provides the screen shot after pressing the main menu tab. There are four different GUIs that will appear. The GUI for the first two tabs in the main menu will have both independent and different results. However, for the last two tabs, it will depend on the monthly use analysis tab and tilt variations analysis.
The monthly use analysis. This tab is the input of the first iteration for determining the both technical results and economic analysis. The menu can be seen in Figure E-2. There are four main boxes. The first box is input for the actual monthly use of the case study. The second box is the designated orientation of solar photovoltaics. This box contains dropdown list button that can be used to choose the mounting system or the orientation of the azimuth angle. The third box contains the calculation method. The calculation method algorithms refer to Appendix B. The fourth box provides the main execution buttons. When users press the calculation method
279 button, either the “estimate” or the “run” button will be grayed out. This action indicates which button can be used to do further iteration. Inside the fourth box, there are two other buttons i.e. copy loads and reset loads. “Copy loads” button functions to copy the entered loads to tilt variation analysis menu; meanwhile, “reset loads” button will erase the previous entered values of the energy use.
Tilt variations analysis. This tab is the input of the first iteration for determining optimum tilt angle. The menu can be seen in Figure E-3.
Figure E-3. GUI tab for tilt variations analysis
In this menu tab, there are three main buttons i.e. normalize, optimize and reset loads.
Both “Normalize” and “Optimize Tilt” button will do the normalization and optimization based on the algorithm in Appendix B. The other buttons are typical with the monthly use analysis menu tab. The profile normalization box provides two options for estimation based on actual profile or ambient temperature. All calculations algorithm refer to Appendix B.
Economic analysis. This tab contains the economic input button. The actual economic input can appear after the technical calculations have been conducted.
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However, users can see the example of the input for economic calculation independently. Figure E-4 represents the input for economic analysis.
Figure E-4. GUI tab for economic analysis
The results summary. This tab can be used after all designated iterations have been conducted; otherwise no results can be displayed. Figure E-5 provides the menu inside the results summary tab.
Figure E-5. GUI tab for results summary
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Block of Section #4
The block section number 4 presents the primary buttons that can navigate users up to the final report. There are four main buttons i.e. create report, display all data, reset workbook and about. The “create report” button will show the final report that can be printed out after all calculations have been conducted. The “display all data” button is the button that will require password. The password can be obtained from the Powell
Center for Construction and Environment at the University of Florida. This button is intended for codes programming developer with intention to further research improvement. “About” button presents the evaluation tool identity.
Block of Section #5
Section 5 provides the types of photovoltaics module. There are three basic types i.e. crystalline which consist of poly- and mono-crystalline module type and thin- film module type.
Block of Section #6
This block contains the option to choose the type of analysis. The results of this calculation will be typical. However, the different will be the input option. For instance, under “custom” option, the users are expected to get the datasheet for solar module on hand, since it will require the further input of section 8. In order to conduct initial assessment, the “default” button may be the best way to investigate the project.
Block of Section #7
Section 7 provides the default parameter of each module type specification.
Each input can refer to the “?” button (help). However, users can still adjust the values based on the current market in order to obtain the accurate results. The provided values can be used as conservative assumptions to assess the solar photovoltaics
282 project. For the “area” option, the help button provides the criteria that can has to be fulfilled on the airport site selection.
Block of Section #8
Section 8 presents the input for custom parameters. There will help button for each input; however, it is best to use datasheet on hand to do the custom analysis.
Similar to all help buttons, the values provided can be used to estimate based on conservative assumptions.
Examples of the Result #1
Once the inputs have been entered, the “run” button can be pressed to see the results. The results of the evaluation tool will appear as depicted in Figure E-6.
Figure E-6. Load design chart
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It can be investigated from Figure E-6 that there are several buttons that navigate users to find out the different charts (the “chart” button is blue-colored); to either back to previous input (the “back” button is orange-colored) or to continue to further analysis
(the “continue” button is green-colored). This result GUI also provides other two types of charts; and, Figure E-7 and Figure E-8 show the chart option under similar interface after clicking chart “previous” and “next” button.
Figure E-7. Energy production chart
Figure E-8. Offset energy chart
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By continuing to the further process, the economic GUI will appear and ready to
execute further economic feasibility analysis. The main input of feasibility analysis will
be described in the following sections.
Economic Analysis
Figure E-9 provides the input of revenue feasibility analysis. All assumptions are
built-in the programming codes. All values for financial parameters have been input as
default values. However, user may adjust the values based on the current economic
conditions. 14 13
9
10
11 17
18 12 19
20
21
16 15
Figure E-9. GUI for economic analysis
Block of Section #9
Section 9 comprises of simulation types based on previous calculations. There
are two options i.e. average and net zero analysis. The average load is used whenever
285 users try to investigate the case based on the average monthly use of the case study.
Meanwhile, the net zero case is intended for the users who want to investigate net zero analysis. The values are based on the peak load during a year period of the case study.
Block of Section #10
Section 10 provides the ownership option. Users can investigate the case study based on ownership i.e. public- and private-owned systems. The option should be chosen correctly since it will influence the entire economic analysis.
Block of Section #11
The block section 11 is the type of power purchase agreement. This option will treat different electricity prices. FIT and net metering option will also influence the entire calculations. The calculations fundamentals for FIT and net metering have been presented in Appendix A. Figure E-9 is one example of net metering calculation type.
The value of FIT rate may be left empty since it will not affect the calculation if the net metering option is chosen.
Block of Section #12
Section 12 presents the results of previous technical calculations. For instance, there are two options for the analyses i.e. average load and net zero analysis. By the time the users click, for example net-zero option, the results of average load will not be counted for further analysis. Therefore, the values will refer to net zero load analysis.
Block of Section #13
Section 13 is the most critical assumptions for the costs data. Although users can use the default values, yet, the users may be suggested to find current utility cost
286 rates as well as current cost per watt rate for the system. The help menu has provided data based on a report by Barbose et al. (2013) in July. If the users still use these values assumptions, it will be reasonable to use since the values tend to decrease. The results will be assumed to be more conservative.
Block of Section #14
Block section 14 presents the loan type. The values should be treated differently between public and private sector. Because sometime private sector can be the same organization with the financier body, in which, no loan needed during the project execution.
Block of Section #15
Block section 15 provides the depreciation as incentives. The depreciation fundamentals can refer to Appendix A. The depreciation will be automatically available to choose if the private ownership option has been chosen. Meanwhile, under public- owned renewable energy system, the depreciation option will be grayed out.
Block of Section #16
Section 16 provides the default values of public ownership. However, under private ownership, the financial parameters values will be different. In this case users may also input based on the current and state situations.
Block of Section #17
Section 17 represents all incentive inputs. The users may refer to available incentives in their territories. However, care should be taken in converting the units.
For instance, the carbon credit calculations may also be conducted and entered to the
“other” incentive box; however, the default values should be adjusted with the units
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($/kW or $/kWh) of each column. The maximum values prevent the over-estimated incentives. For instance, in Dunnellon Airport case studies, the values that are enacted in Orlando area are maximum $700 for rebate of each kW system, and $20,000 per
0.04 kW system based on installed capacity.
Block of Section #18
Section 18 provides the lease option. This option will be grayed out during public ownership analysis. It will be active during the private sector analysis. The leasing calculation will be treated as expense for private entity and revenue for public entity—airport.
Block of Section #19
Section 19 shows the panel degradation rate of the module. Most vendors suggest 1% degradation each year, which will be inversely discounted during a certain study period (Skoczek, Sample & Dunlop, 2009).
Block of Section #20
In block section 20, there are two buttons i.e. “refresh” and “reset” buttons. The users need to press “refresh” button every time they try to repeat the calculations. The refresh button will erase previous calculations. The reset button will empty all values for the economic analysis; yet, the simulation results will remain the same. To use the new values, the simulation results should be refreshed.
Block of Section #21
Section 21 provides two standard buttons i.e. the “back” and “run”. The back button will bring users back to the site location and technical GUI. The “run” button will allow user to continue to next stage of analysis. After pressing the “run” button, another
288 stage of results will appear. In this GUI results, there are various charts that can be used for further analysis. The chart will also be available at the end of the analysis—by pressing the “create report” button; all calculations and charts will be presented in a standardized format.
Examples of the Result #2
The results of second iteration can be viewed in Figure E-10. There are several charts available for the analysis. The users can investigate the values in the data table below each chart that can be useful for the analysis.
Figure E-10. GUI from economic result presents interest and principal payment
The chart back and previous buttons allow the users to see the entire charts.
Figure E-11 through Figure E-17 represents the example charts for users’ analysis.
Figure E-11. Chart depicts amortization 289
Figure E-12. Chart depicts yearly cost
Figure E-13. Chart depicts life-cycle cost
Figure E-14. Chart depicts present worth of revenue
290
Figure E-15. Chart depicts present worth of net savings
Figure E-16. Chart depicts cumulative net savings or payback
Figure E-17. Chart depicts energy production life-cycle
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In the meantime, after finish investigating the charts for analysis, the users may continue the analysis to come up with the end of results dashboard. The values are the final results of the economic analysis.
Example of the Final Result
The formulas for calculating the final result refer to Appendix A. All values in
Figure E-18 refer to the feasibility of a project investment; in addition, these values are the economic financial measures that can be considered for further decision making process based on selection criteria provided in Appendix A.
Figure E-18. Final result dashboard
The economic measures have been provided in the final result dashboard. If the results do not meet the criteria, the users may either try to recalculate or further investigate the sensitivity analysis. There are two options of financial parameters limit.
The increment will be 1% for “what if analysis” yet, the limits are 15% and 30 % maximum. The sensitivity analysis results can be reviewed in the results and discussions chapter.
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BIOGRAPHICAL SKETCH
Deny Dwiantoro finished defending his dissertation on February 12, 2014; and, had the commencement on April 25, 2014 completing his Doctor of Philosophy in construction management from M.E. Rinker, Sr. School of Construction Management.
He joined the Powell Center for Construction and Environment in spring of 2011. He had already finished a research project for Building America focusing on tankless water heater retrofit funded by the U.S. Department of Energy. His research as his dissertation work was renewable energy assessment in Florida Airports, supported by
Dr. Charles J. Kibert, PE. Deny holds a B.Sc. in mechanical engineering and MBA degree in management of technology, both from Bandung Institute of Technology (ITB)
Indonesia. He began his professional career as an engineer in training for a power plant equipment manufacturer and contractor in 2003, and continued to work as a project leader in a commercial contracting firm. In addition, he had worked as a piping construction engineer and lead engineer piping in PT. Tripatra Engineers &
Constructors (TPEC-Indonesia)—an engineering, procurement and construction (EPC) company. Deny had also contributed in the field project for the development of oil treatment facilities and liquefied petroleum gas facilities, as well as existing offshore platforms modifications. Deny is a licensed mechanical engineer and a member of
Indonesian Professional Engineer Association (PIPI). His current interests are in construction management and sustainable energy alternatives. He plans to go back to the industry for several years to settle his economy and ready to apply his knowledge and passion in construction by teaching until his retirement.
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