基于鲁棒后悔度的含风电日前和日内市场两阶段出清优化
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摘要
由于风电的不确定性,含有风电较多的系统将引入日内市场,以减小实时市场的功率偏差,提高系统运行的经济性和稳定性。文中利用区间模型表示风电出力的不确定性,在日前市场中考虑日内市场风电偏差功率的不确定性,建立日前市场和模拟日内市场联合优化模型;在各个日内市场考虑对应实时市场风电偏差功率的不确定性,建立各日内市场和模拟实时市场联合优化模型。在日前市场和日内市场中考虑购电决策者的后悔心理,以鲁棒后悔度最小为目标,实现分阶段不同时间尺度购电出清优化,提高日前市场和日内市场购电方案的经济性和鲁棒性。最后,基于一个具体算例对优化模型和结果进行详细的分析,体现了两阶段优化出清方法的可行性和优越性。
Due to the uncertainty of wind power, the intra-day market is introduced for the system with large-scale wind power integration, to reduce the power deviation in a real-time market and promote the economy and stability of power system operation. By using the interval model to express the uncertainty of wind power output, this paper proposes a joint optimization model of day-ahead and simulative intra-day markets with consideration of uncertainty of wind power deviation of the intra-day day market in the day-ahead market. Similarly, a joint optimization model of intra-day and simulative real-time markets is built with consideration of uncertainty of wind power variation of the related real-time market in each intra-day market. Considering the regret psychology of power purchase decision-makers in day-ahead and intra-day markets, the two-stage clearing schedules for minimizing the robust regret are obtained according to different time scales. Thus, the economy and the robustness of the power purchase scheme in day-ahead and intra-day markets are improved. Finally, a case is studied and analyzed in detail to verify the feasibility and superiority of the proposed model.
Due to the uncertainty of wind power, the intra-day market is introduced for the system with large-scale wind power integration, to reduce the power deviation in a real-time market and promote the economy and stability of power system operation. By using the interval model to express the uncertainty of wind power output, this paper proposes a joint optimization model of day-ahead and simulative intra-day markets with consideration of uncertainty of wind power deviation of the intra-day day market in the day-ahead market. Similarly, a joint optimization model of intra-day and simulative real-time markets is built with consideration of uncertainty of wind power variation of the related real-time market in each intra-day market. Considering the regret psychology of power purchase decision-makers in day-ahead and intra-day markets, the two-stage clearing schedules for minimizing the robust regret are obtained according to different time scales. Thus, the economy and the robustness of the power purchase scheme in day-ahead and intra-day markets are improved. Finally, a case is studied and analyzed in detail to verify the feasibility and superiority of the proposed model.
引文
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[19] WANG Yang,XIA Qing,KANG Chongqing.Unit commitment with volatile node injections by using interval optimization[J].IEEE Transactions on Power Systems,2011,26(3):1705-1713.
[20] LOOMES G,SUGDEN R.Testing for regret and disappointment in choice under uncertainty[J].Economic Journal,1987,97:118-129.
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[22] DONG C,HUANG G H,CAI Y P,et al.An interval-parameter minimax regret programming approach for power management systems planning under uncertainty[J].Applied Energy,2011,88(8):2835-2845.
[23] FAN Lei,WANG Jianhui,JIANG Ruiwei,et al.Min-Max regret bidding strategy for thermal generator considering price uncertainty[J].IEEE Transactions on Power Systems,2014,29(5):2169-2179.
[24] JIANG Ruiwei,WANG Jianhui,ZHANG Muhong,et al.Two-stage minimax regret robust unit commitment[J].IEEE Transactions on Power Systems,2013,28(3):2271-2282.
[25] 王刚,孙文健,李歧强,等.基于鲁棒后悔度的光储微网优化调度[J].电网技术,2017,41(1):106-111.WANG Gang,SUN Wenjian,LI Qiqiang,et al.Robust regret optimal scheduling of microgrid with PV and battery [J].Power System Technology,2017,41(1):106-111.
[2] FARAHMAND H,AIGNER T,DOORMAN G L,et al.Balancing market integration in the northern European continent:a 2030 case study[J].IEEE Transactions on Sustainable Energy,2012,3(4):918-930.
[3] JIANG Yuewen,CHEN Meisen,YOU Shi.A unified trading model based on robust optimization for day-ahead and real-time markets with wind power integration[J].Energies,2017,10(4):554.
[4] REDDY S S,BIJWE P R,ABHYANKAR A R.Optimal posturing in day-ahead market clearing for uncertainties considering anticipated real-time adjustment costs[J].IEEE Systems Journal,2015,9(1):177-190.
[5] 江岳文,温步瀛.结合风电功率超短期预测值偏差的实时市场调度[J].电力自动化设备,2015,35(3):12-17.JIANG Yuewen,WEN Buying.Real-time market dispatch based on ultra-short-term forecast error of wind power[J].Electric Power Automation Equipment,2015,35(3):12-17.
[6] JIANG Yu,CHEN Xingying,YU Kun,et al.Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm[J].Journal of Modern Power Systems and Clean Energy,2017,5(1):126-133.
[7] KIVILUOMA J,MEIBOM P,TUOHY A,et al.Short-term energy balancing with increasing levels of wind energy[J].IEEE Transactions on Sustainable Energy,2012,3(4):769-776.
[8] MORALES J M,CONEJO A J,PEREZ-RUIZ J.Short-term trading for a wind power producer[J].IEEE Transactions on Power Systems,2010,25(1):554-564.
[9] AYON X,ANGELES MORENO M,USAOLA J.Aggregators’ optimal bidding strategy in sequential day-ahead and intraday electricity spot markets[J].Energies,2017,10(4):450.
[10] KIVILUOMA J,O'MALLEY M,TUOHY A,et al.Impact of wind power on the unit commitment,operating reserves,and market design[C]// IEEE Power & Energy Society General Meeting,July 24-29,2011,Detroit,USA:8p.
[11] MEIBOM P,BARTH R,HASCHE B,et al.Stochastic optimization model to study the operational impacts of high wind penetrations in Ireland[J].IEEE Transactions on Power Systems,2011,26(3):1367-1379.
[12] JAFARI A M,ZAREIPOUR H,SCHELLENBERG A,et al.The value of intra-day markets in power systems with high wind power penetration[J].IEEE Transactions on Power Systems,2014,29(3):1121-1132.
[13] AIGNER T,FARAHMAND H,GJENGEDAL T.Modeling the northern European electricity market[C]// IEEE Power & Energy Society General Meeting,July 22-26,2012,San Diego,USA:8p.
[14] SANCHEZ-MARTIN P,ALBERDI-ALEN A.Day ahead and intraday stochastic decision model for EV charging points[C]// IEEE Grenoble Conference,June 16-20,2013,Grenoble,France:6p.
[15] 艾欣,周志宇,魏妍萍,等.基于自回归积分滑动平均模型的可转移负荷竞价策略[J].电力系统自动化,2017,41(20):26-31.DOI:10.7500/AEPS20170119009.AI Xin,ZHOU Zhiyu,WEI Yanping,et al.Bidding strategy for time-shiftable loads based on autoregressive integrated moving average model[J].Automation of Electric Power Systems,2017,41(20):26-31.DOI:10.7500/AEPS20170119009.
[16] WAN Can,XU Zhao,PINSON P,et al.Optimal prediction intervals of wind power generation[J].IEEE Transactions on Power Systems,2014,29(3):1166-1174.
[17] 丁浩,高峰,刘坤,等.基于鲁棒优化的含风电工业微电网经济调度模型[J].电力系统自动化,2015,39(17):160-167.DING Hao,GAO Feng,LIU Kun,et al.An economic dispatch model in an industrial microgrid with wind power based on robust optimization[J].Automation of Electric Power Systems,2015,39(17):160-167.
[18] 魏韡,刘锋,梅生伟.电力系统鲁棒经济调度:(一)理论基础[J].电力系统自动化,2013,37(17):37-43.WEI Wei,LIU Feng,MEI Shengwei.Robust and economical scheduling methodology for power systems:Part one theoretical foundations[J].Automation of Electric Power Systems,2013,37(17):37-43.
[19] WANG Yang,XIA Qing,KANG Chongqing.Unit commitment with volatile node injections by using interval optimization[J].IEEE Transactions on Power Systems,2011,26(3):1705-1713.
[20] LOOMES G,SUGDEN R.Testing for regret and disappointment in choice under uncertainty[J].Economic Journal,1987,97:118-129.
[21] RITOV I.Probability of regret:anticipation of uncertainty resolution in choice[J].Organizational Behavior & Human Decision Processes,1996,66(2):228-236.
[22] DONG C,HUANG G H,CAI Y P,et al.An interval-parameter minimax regret programming approach for power management systems planning under uncertainty[J].Applied Energy,2011,88(8):2835-2845.
[23] FAN Lei,WANG Jianhui,JIANG Ruiwei,et al.Min-Max regret bidding strategy for thermal generator considering price uncertainty[J].IEEE Transactions on Power Systems,2014,29(5):2169-2179.
[24] JIANG Ruiwei,WANG Jianhui,ZHANG Muhong,et al.Two-stage minimax regret robust unit commitment[J].IEEE Transactions on Power Systems,2013,28(3):2271-2282.
[25] 王刚,孙文健,李歧强,等.基于鲁棒后悔度的光储微网优化调度[J].电网技术,2017,41(1):106-111.WANG Gang,SUN Wenjian,LI Qiqiang,et al.Robust regret optimal scheduling of microgrid with PV and battery [J].Power System Technology,2017,41(1):106-111.