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: | reactivepowerprocurement 、locationalmarginalpricing(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) |
相關次數: | 點閱:353 下載:3 |
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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.
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