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Adaptive Load Control of Microgrids with Non-Dispatchable Generation

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Adaptive Load Control of Microgrids with Non-Dispatchable Generation Adaptive Load Control of Microgrids with Non-dispatchable Generation MASSACHUSETTS INSTfT E by OF TECHNOLOGY Kevin Martin Brokish AUG 0 7 2009 B.S., University of Colorado (2007) LIBRARIES Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering ARCHIVES at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2009 @ Massachusetts Institute of Technology 2009. All rights reserved. A uthor ........ -------.... .. .. .. .. .. .. Department of Electrical Engineering and Computer Science May 8, 2009 Certified by.... Dr. James L. Kirtley, Jr. Professor Thesis Supervisor .._---:;2 /) Accepted by. / Dr. Terry P. Orlando Chairman, Department Committee on Graduate Theses Adaptive Load Control of Microgrids with Non-dispatchable Generation by Kevin Martin Brokish Submitted to the Department of Electrical Engineering and Computer Science on May 8, 2009, in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Abstract Intelligent appliances have a great potential to provide energy storage and load shed- ding for power grids. Microgrids are simulated with high levels of wind energy pene- tration. Frequency-adaptive intelligent appliances are deployed and optimized within the simulation, indicating the usefulness and feasibility of these loads on microgrids. The economic feasibility and implementation of these appliances is also discussed. Thesis Supervisor: Dr. James L. Kirtley, Jr. Title: Professor Acknowledgements I would like to thank Dr. Jim Kirtley for his insight and guidance on this project. Once I had determined Markov Chains were incapable of modeling what I hoped they could, it was Dr. Scott Kennedy's idea that I write about it. The math in Chapter 2 came from brainstorming sessions with Ozan Candogan. Thanks to Dr. Hatem Zeineldin and Dr. Mirjana Marden for their insights on microgrids. Finally, I am deeply grateful to the MIT-Portugal program for its sponsorship of this work. Contents 1 Introduction 1.1 Description of FAPERs .......................... 1.2 Thesis Layout .. ........... ............ 2 Wind Power Modeling 21 2.1 Introduction.................. ............... 2 1 2.2 How Markov Chains Work ......... ............... 2 1 2.2.1 Mathematical Description ..... .............. 2 2 2.2.2 Higher Order Markov Chains . .. .............. 23 2.3 Using Markov Chains to Model Wind .. .............. 23 2.3.1 Markov Chain Creation ...... ............... 24 2.3.2 Wind Speed or Wind Power? . .. .............. 2 5 2.3.3 Appeal of Markovian Wind .... ............... 26 2.4 R esults .................... ............... 28 2.4.1 Autocorrelation ........... ............... 29 2.4.2 Autocorrelation Error ....... ............... 3 1 2.5 Why Markov Wind Models Are Dangerous .............. 33 2.5.1 Underestimated Storage ...... .............. 3 3 2.5.2 Not Accurate for FAPER Simulations . ............. 33 3 Microgrid Model and Simulation 35 3.1 W hat is a M icrogrid? ................... ....... 35 3.2 Simulated M icrogrid ........................... 37 3.2.1 Simulation Description ... .... ... ......... 38 4 FAPER Model 41 4.1 Linear Approximation of Appliance Behavior .. ............ 41 4.2 FAPER-capable Appliances . ...... .. ...... .. 43 4.2.1 Refrigerators and Freezers ...... .............. 43 4.2.2 Air Conditioners and Electric Heaters . .. .. ........ 44 4.2.3 Hot Water Heaters, Clothes Driers, and Dish Washers . ... 44 4.2.4 Pool Heaters ................... ... 45 4.2.5 Shorten Time to Hibernate . .................. 45 4.2.6 Plug-in Hybrid Electric Vehicles ................. 45 4.3 FAPER Simulation Setup ............... .. 46 5 Insights to Optimal FAPER Control 49 5.1 Literature Survey ..... ..... .. ...... ... ..... 49 5.2 Observations and Insights into FAPER Control . ... ....... 51 5.3 Probabilistic Algorithm ....... ... ... .... ....... 53 5.3.1 Optimization of Control Algorithm .. ... .......... 55 5.3.2 Results and Comparison .......... ... .. .. 57 6 Analysis of FAPER Behavior 59 6.1 Semi-Linearity ................ .... ......... 59 6.1.1 Definition of Variables .............. .. .. ... 59 6.1.2 Linear for Slow Frequency Changes ... .. .. .. 60 6.1.3 Nonlinear for Rapid Frequency Changes . .. ... ..... 61 7 Viability and Implementation 63 7.1 Economic Viability ....... ... ........ .. .. 63 7.2 Implementation Via Retrofitting ............. ... .. 64 7.3 Future Research ........ ...... ..... ...... 66 7.4 Conclusion .................. ................. 67 8 A Wind Power Modelling 69 A .1 M atlab Code . 69 A.1.1 Profile Creation .......................... 69 A .2 Prim ary Script .. .. ..... ... .. .. ... .. .. .. 71 A.3 Markov Chain Generator ......................... 72 A.4 Monte Carlo Data Generator ....................... 73 B FAPER Simulation 75 B.1 M atlab Code . .. .................... 75 B .1.1 M ain .. .............................. 75 B .1.2 Setup ......... ............ .......... 77 B.2 Simulink Blocks .............................. 78 B.2.1 Appliance Block .......................... 78 B.2.2 Other Simulink Blocks ...................... 81 C EIA Data 83 10 List of Figures 1-1 Sample FAPER Control Algorithm .............. 2-1 A simple first-order Markov chain for wind power modeling . 2-2 Markov Chain Disparities ................... 2-3 Autocorrelation Plot 1 . .................... 2-4 Autocorrelation Plot 2 . .................... 2-5 First Order Autocorrelation Error . .............. 2-6 Second Order Autocorrelation Error . ............. 2-7 Third Order Autocorrelation Error .............. 2-8 Storage Estimates and RMS Error . .............. 3-1 A Simple Microgrid .................... ....... 38 3-2 Simplified Simulation Block Diagram .. ...... .. ...... 39 3-3 Full Simulation Block Diagram .... .... ..... ...... 40 4-1 Household Electricity Consumption Makeup ...... 5-1 Control Function from Homeostatic Utility Control 5-2 Control Function from Stabilization of Grid Frequency Through Dy- namic Demand Control . ................. 5-3 FAPER Instabilities ................... 5-4 FAPER Clusters ..................... 5-5 New FAPER Algorithm ................. 5-6 Pareto Frontier . ..................... 5-7 FAPER Temperatures Over Time . ........... 5-8 Non-Probabilistic FAPER Temperatures .. .. 58 5-9 Pareto Frontier Algorithm Comparison . .. .. .. .. .. 58 6-1 Variables Used in FAPER Analysis for Small mbound . .. .. 60 6-2 Variables Used in FAPER Analysis for Large mbound . .. .. 6 2 B-1 Power Calculation Simulink Block . ... ... .. .. 81 B-2 Droop Generation Simulink Block .. ... .. ... .. 81 B-3 Gain Calculation Simulink Block . .. ... ... .. .. 82 B-4 Grid Frequency Simulink Block . .......... .... ... .. 82 List of Tables C.1 End-Use Consumption of Electricity . .................. 84 14 Chapter 1 Introduction The era of cheap fossil fuel energy is drawing to a close: fuel prices are becoming increasingly volatile as global demand increases, the science behind dire ecological impacts of continued carbon emissions is generally accepted, and national energy security permeates political discussions. Society places great hopes on renewable energy sources such as wind and solar, but these are non-dispatchable: they produce predictable but variable quantities of power. On hourly and daily timescales, non-dispatchable power generation must be bal- anced by other forms of power generation or by energy storage. On windless days in Denmark, for example, energy is imported from neighboring countries, and on windy days, excess energy is exported [36]. Norway, which has the most hydro-powered gen- eration per capita in the world [39], can effectively act as energy storage for Denmark: hydro power generation is relatively easy to start and stop to balance wind, and while it is stopped, energy is stored as water fills reservoirs. Not all countries have such resources at the necessary scale (or neighbors with such resources), and the risk and integration problems that come with high penetration of renewables has stimulated a flurry of research in microgrids. Microgrids are essentially islandable partitions of a large power grid paired with an added layer of intelligence. The two chief benefits of microgrids are the ability to effec- tively integrate micro distributed generation, and the ability to intentionally island. The first is achieved because the microgrid appears like a single producer/consumer to the rest of the power grid. The Consortium for Electric Reliability Technology Solu- tions (CERTS) Microgrid Concept paper claims that "the CERTS MicroGrid concept eliminates dominant existing concerns and the consequent approaches for integrating [distributed energy resources]" [32]. This is partially true in that microgrids effectively delegate protection and coordination issues to the microgrid managers rather than the utilities, and may open the door for more home generation such as photovoltaic roofs. The second benefit of microgrids is the ability to disconnect and function as an island, weathering catastrophic failures on the larger grid. This increases user reliability because without islanding capability, consumers on the microgrid would be dragged into a brownout or blackout along with the rest of the grid. Microgrids, however, do not specifically solve the problem of balancing load and generation. Energy storage and backup generation on large scales are still required in order to balance non-dispatchable sources of energy. In fact, on islanded microgrids (and
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