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研究生: Puspa Endah Widowaty
Puspa - Endah Widowaty
論文名稱: Optimal Integrated Locations of Wind Power Generations by Considering Ancillary Market
Optimal Integrated Locations of Wind Power Generations by Considering Ancillary Market
指導教授: 郭明哲
Ming-Tse Kuo
口試委員: 連國龍
Kuo-Lung Lian
郭政謙
Kuo, Cheng-Chien
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2016
畢業學年度: 104
語文別: 英文
論文頁數: 86
中文關鍵詞: reactivepowerprocurementlocationalmarginalpricing(LMP)totalpaymentfunction(TPF)societaladvantagefunction(SAF)costofopportunitylost(LOC)
外文關鍵詞: reactive power procurement, locational marginal pricing (LMP), total payment function (TPF), societal advantage function (SAF), cost of opportunity lost (LOC)
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  • Wind power is the one of most promising renewable energy sources today, but the rapid development of wind power installations should be balanced with the capability of the power system. In a deregulated power system, the basic responsibility of the Independent System Operator (ISO) is to maintain system reliability and security by providing for ancillary service such as reactive power support. Reactive power management is required to support the real power shipment and supply reactive loads reliably and securely. In this thesis, DFIG turbine is integrated to the IEEE 14-bus test system to analyze the best integrated location to reach optimum ancillary service. There are three methods of reactive power procurements are applied in integrated system to illustrate the best procurement model; expected formula cost (EFC) with locational marginal pricing (LMP) based, modified total payment function (TPF) and marginal benefits cost. The results show that wind integration on bus number 3 has less cost generation and high profit system. Cost generation of wind integration on bus 3 can be reduced up to 21.8%. Profit system on similar location can be increased up to 130%. The minimum ISO support is achieved by marginal benefits cost method. The closure of this thesis is societal advantage function (SAF) optimization of final generator reactive power cost payment after considering bids from each service provider. Maximum payment of reactive power is achieved by wind integration on bus number 3. Generator receive reactive power payment per hour of 4,992.2 $ for 110% load factor and 5,908.9 $ for 120% load factor. The SAF optimization gives the results that would form a basis of contractual agreement for provisional calculation of reactive power procurement.


    Wind power is the one of most promising renewable energy sources today, but the rapid development of wind power installations should be balanced with the capability of the power system. In a deregulated power system, the basic responsibility of the Independent System Operator (ISO) is to maintain system reliability and security by providing for ancillary service such as reactive power support. Reactive power management is required to support the real power shipment and supply reactive loads reliably and securely. In this thesis, DFIG turbine is integrated to the IEEE 14-bus test system to analyze the best integrated location to reach optimum ancillary service. There are three methods of reactive power procurements are applied in integrated system to illustrate the best procurement model; expected formula cost (EFC) with locational marginal pricing (LMP) based, modified total payment function (TPF) and marginal benefits cost. The results show that wind integration on bus number 3 has less cost generation and high profit system. Cost generation of wind integration on bus 3 can be reduced up to 21.8%. Profit system on similar location can be increased up to 130%. The minimum ISO support is achieved by marginal benefits cost method. The closure of this thesis is societal advantage function (SAF) optimization of final generator reactive power cost payment after considering bids from each service provider. Maximum payment of reactive power is achieved by wind integration on bus number 3. Generator receive reactive power payment per hour of 4,992.2 $ for 110% load factor and 5,908.9 $ for 120% load factor. The SAF optimization gives the results that would form a basis of contractual agreement for provisional calculation of reactive power procurement.

    LIST OF FIGURES VIII LIST OF TABLES IX CHAPTER ONE 1 INTRODUCTION 1 1.1 Research Background and Motivation 1 1.2 Research Objective 2 1.3 Thesis structure 2 CHAPTER TWO 4 LITERATURE REVIEW 4 2.1 Power market and Ancillary Service 4 2.2 Location Marginal Price Definition and Calculation 8 2.2.1 Definition 8 2.2.2 Characteristic 9 2.2.3 Calculation 11 2.3 Wind Power Generation and Integration 13 2.3.1 Wind Turbines 13 2.3.2 Wind Turbine Integration 16 2.4 Reactive Power Market 17 2.4.1 Reactive Power Market Policies in International Experiences 17 2.4.2 Reactive Power Payment Mechanism in Power Market 23 2.4.3 Pricing Methods for Reactive Power 24 CHAPTER THREE 26 METHODS, SIMULATION & OPTIMIZATION 26 3.1 Optimal Power Flow in PowerWorld 26 3.2 Locational Marginal Pricing 29 3.2.1 Location Marginal Pricing Formula 29 3.2.2 Application in Power Market 29 3.3 Modeling Method 32 3.3.1 Test Model 32 3.3.2 Research Process Flow Diagram 33 3.4 The Pricing Structure of Reactive Power Support by ISO 38 3.4.1 Expected Formula Cost (EFC) based on LMP 38 3.4.2 Total Payment Function (TPF) based on modified Reactive Power Market Framework 40 3.4.3 Marginal Benefits Function by Considering Constraints 42 3.4.4 Societal Advantage Function (SAF) 46 CHAPTER FOUR 50 RESULT & DISCUSSION 50 4.1 Power Flow Analysis for Integrating Wind Turbine 50 4.1.1 LMP Analysis 50 4.1.2 Hourly Cost Analysis 51 4.1.3 Profit Analysis All Generators 51 4.1.4 Profit Analysis of Wind Turbine 52 4.2 Calculation Total Reactive Payment by ISO of Wind Turbine 53 4.2.1 Expected Formula Cost (EFC) Based on LMP 53 4.2.2 Total Payment Function (TPF) Based on Modified Reactive Market Framework 55 4.2.3 Marginal Benefits Function by Considering Constraints 58 4.3 Comparison of Three Calculation Methods for Minimum Reactive Payment of Wind Generation 64 4.4 Reactive Power Optimization by Societal Advantage Function (SAF) 67 4.5 Result 68 CHAPTER FIVE 71 CONCLUSION & FUTURE WORKS 71 5.1 Conclusion 71 5.2 Future Works 72 REFERENCES 73 APPENDIX A: PARAMETERS TEST SYSTEM 75 APPENDIX B: PARAMETERS OF PROPOSED METHODS 85

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