Analysis of the Performance of an Optimization Model for Time-Shiftable Electrical Load Scheduling Under Uncertainty
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Calhoun: The NPS Institutional Archive Theses and Dissertations Thesis and Dissertation Collection 2016-12 Analysis of the performance of an optimization model for time-shiftable electrical load scheduling under uncertainty Olabode, John A. Monterey, California: Naval Postgraduate School http://hdl.handle.net/10945/51591 NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS ANALYSIS OF THE PERFORMANCE OF AN OPTIMIZATION MODEL FOR TIME-SHIFTABLE ELECTRICAL LOAD SCHEDULING UNDER UNCERTAINTY by John A. Olabode December 2016 Thesis Co-Advisors: Susan M. Sanchez Emily M. Craparo Second Reader: W. Matthew Carlyle Approved for public release. Distribution is unlimited. THIS PAGE INTENTIONALLY LEFT BLANK REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704–0188 Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202–4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave Blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED December 2016 Master’s Thesis 01-04-2016 to 09-23-2016 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS ANALYSIS OF THE PERFORMANCE OF AN OPTIMIZATION MODEL FOR TIME- SHIFTABLE ELECTRICAL LOAD SCHEDULING UNDER UNCERTAINTY 6. AUTHOR(S) John A. Olabode 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER Naval Postgraduate School Monterey, CA 93943 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSORING / MONITORING AGENCY REPORT NUMBER N/A 11. SUPPLEMENTARY NOTES The views expressed in this document are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. IRB Protocol Number: N/A. 12a. DISTRIBUTION / AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE Approved for public release. Distribution is unlimited. 13. ABSTRACT (maximum 200 words) To ensure sufficient capacity to handle unexpected demands for electric power, decision makers often over-estimate expeditionary power requirements. Therefore, we often use limited resources inefficiently by purchasing more generators and investing in more renewable energy sources than needed to run power systems on the battlefield. Improvement of the efficiency of expeditionary power units requires better managing of load requirements on the power grids and, where possible, shifting those loads to a more economical time of day. We analyze the performance of a previously developed optimization model for scheduling time-shiftable electrical loads in an expeditionary power grids model in two experiments. One experiment uses model data similar to the original baseline data, in which expected demand and expected renewable production remain constant throughout the day. The second experiment introduces unscheduled demand and realistic fluctuations in the power production and the demand distributions data that more closely reflect actual data. Our major findings show energy grid power production composition affects which uncertain factor(s) influence fuel con- sumption, and uncertainty in the energy grid system does not always increase fuel consumption by a large amount. We also discover that the generators running the most do not always have the best load factor on the grid, even when optimally scheduled. 14. SUBJECT TERMS 15. NUMBER OF expeditionary, energy, optimization, robust optimization, Parameter Uncertainty, deferrable, fuel, mixed integer PAGES 93 linear program, design of experiment, Latin hypercube, fuel consumption, robust designs 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF OF REPORT OF THIS PAGE OF ABSTRACT ABSTRACT Unclassified Unclassified Unclassified UU NSN 7540-01-280-5500 Standard Form 298 (Rev. 2–89) Prescribed by ANSI Std. 239–18 i THIS PAGE INTENTIONALLY LEFT BLANK ii Approved for public release. Distribution is unlimited. ANALYSIS OF THE PERFORMANCE OF AN OPTIMIZATION MODEL FOR TIME-SHIFTABLE ELECTRICAL LOAD SCHEDULING UNDER UNCERTAINTY John A. Olabode Lieutenant Commander, United States Navy B.S., University of Ibadan, Nigeria, 1999 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN OPERATIONS RESEARCH from the NAVAL POSTGRADUATE SCHOOL December 2016 Approved by: Susan M. Sanchez Thesis Co-Advisor Emily M. Craparo Thesis Co-Advisor W. Matthew Carlyle Second Reader Patricia A. Jacobs Chair, Department of Operations Research iii THIS PAGE INTENTIONALLY LEFT BLANK iv ABSTRACT To ensure sufficient capacity to handle unexpected demands for electric power, decision makers often over-estimate expeditionary power requirements. Therefore, we often use limited resources inefficiently by purchasing more generators and investing in more renewable energy sources than needed to run power systems on the battlefield. Improvement of the efficiency of expeditionary power units requires better manag- ing of load requirements on the power grids and, where possible, shifting those loads to a more economical time of day. We analyze the performance of a previously developed optimization model for scheduling time-shiftable electrical loads in an expeditionary power grids model in two experiments. One experiment uses model data similar to the original baseline data, in which expected demand and expected renewable production remain constant throughout the day. The second experiment introduces unscheduled demand and realistic fluctuations in the power production and the demand distributions data that more closely reflect actual data. Our major findings show energy grid power production composition affects which uncertain factor(s) influence fuel consumption, and uncertainty in the energy grid system does not always increase fuel consumption by a large amount. We also discover that the generators running the most do not always have the best load factor on the grid, even when optimally scheduled. v THIS PAGE INTENTIONALLY LEFT BLANK vi Table of Contents 1 Introduction 1 1.1 Department of Defense and Energy.................1 1.2 Cost of Environmental Temperature Control..............4 1.3 Energy Technology Initiatives and Opportunities............7 1.4 Research Objectives....................... 10 2 Literature Review 13 2.1 Expeditionary Energy Power System................ 13 2.2 Energy Efficiency Trade-offs and Power Generation........... 13 2.3 Distribution Systems....................... 19 2.4 Design of Experiments...................... 21 3 Methodology 23 3.1 Sprague’s Optimization Model................... 23 3.2 Baseline Scenario........................ 25 3.3 Experiment I: Baseline Scenario.................. 28 3.4 Experiment II: Time-Varying Production and Demand.......... 30 3.5 Summary........................... 33 4 Analysis of Results 35 4.1 Model Results......................... 35 4.2 DOE Results.......................... 36 4.3 Analysis Summary........................ 59 5 Conclusions and Future Work 61 5.1 Conclusion........................... 61 5.2 Future Work Recommendations.................. 62 5.3 Final Thoughts......................... 63 vii List of References 65 Initial Distribution List 69 viii List of Figures Figure 1.1 U.S. Government Energy Consumption by Agency (Defense vs. Non- Defense) . 2 Figure 1.2 Operational Energy Use for Fiscal Year 2014 . 3 Figure 1.3 Cost Growth Approaching the Tactical Edge . 6 Figure 1.4 U.S. Fatalities in Iraq by IED . 7 Figure 1.5 U.S. Forces Afghanistan Camp Electrical Demand over a 96-Hour Period . 9 Figure 1.6 U.S. Military TQGs Fuel Efficiency as a Function of Load Factor and Generator Size . 10 Figure 2.1 Expeditionary Energy Power System Set-Up . 14 Figure 2.2 Peak Load Shifting . 15 Figure 2.3 REDUCE . 17 Figure 2.4 Solar Powered ECU . 17 Figure 2.5 Team MEP Organization Chart . 18 Figure 2.6 Energy Storage Module . 19 Figure 2.7 Typical Distribution System . 20 Figure 2.8 Intelligent Power Distribution System . 21 Figure 3.1 Southwest Afghanistan United States Marine Corps (USMC) Patrol Base Showing Equipment Inventory . 26 Figure 3.2 Experiment II Unscheduled Demand Pattern . 30 Figure 3.3 Experiment II Renewable Energy Pattern . 31 Figure 3.4 Experiment II Unscheduled Demand Illustration . 32 ix Figure 3.5 Experiment II Renewable Energy Illustration . 33 Figure 4.1 Experiment I Time Step 300 Relative Efficiency Distribution with Outlier . 38 Figure 4.2 Experiment II Time Step 300 Relative Efficiency Distribution with Outlier . 38 Figure 4.3 Experiment I Time Step 300 Relative Efficiency Distribution without Outlier . 38 Figure 4.4 Experiment II Time Step 300 Relative Efficiency Distribution with- out Outlier . 39 Figure 4.5 Experiment I Factors’ Influence on Cumulative Total Fuel Partition Tree, RH-PFK . 40 Figure 4.6 Experiment I Factors’ Influence on Cumulative Total Fuel Partition Tree, RH-U . 41 Figure 4.7 Experiment I Factors’ Influence on Cumulative Total Fuel Stepwise Regression, RH-PFK. 42 Figure 4.8 Experiment I Factors’ Influence on Cumulative Total Fuel Stepwise Linear Regression, RH-U . 43 Figure 4.9 Experiment I Distribution of Generators’