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深度|电力现货市场环境下风电与储能如何联合参与市场并合理分配收益?

2019-10-24 10:42来源:电网技术关键词:电力现货市场电价电力交易收藏点赞

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4.2.2 风险偏好系数对运行策略的影响

为比较不同风险偏好系数的设置对联盟运行策略及收益的影响,图7给出了不同风险偏好系数下联盟整体预期收益及CVaR有效前沿。

img_9.png

图7 CVaR及联盟预期收益的有效前沿Fig. 7 Efficient frontier of CVaR vs expected revenue

可以看出,随着风险偏好系数的增加,联盟预期总收益逐渐较少,CVaR逐渐增加,且当风险偏好系数较小时,联盟预期总收益随CVaR的增加减少缓慢;但当风险偏好系数较大时,CVaR即使增加较小的数值仍会使联盟预期总收益大幅下降。

考虑到本文所提模型中联盟参与现货市场对应的收益不确定风险主要来源于风电不确定性导致的不平衡结算风险,而联盟中抽蓄电站可依据第二阶段的实时情况调整自身的运行状态尽可能减少不平衡电量,因而抽蓄运行模式受联盟风险偏好程度影响较大。图8描绘了不同风险偏好系数下联盟中抽水蓄能机组的申报情况。

img_10.png

图8 不同风险偏好系数下联盟中抽水蓄能电站竞标策略Fig. 8 Offering strategies for pumped-storage stations under different risk preference coefficients

由图可知随着风险偏好系数β的增加,抽蓄机组逐渐改变原有的利用电价差套利的最优运行状态,申报功率趋于保守,逐步变为尽可能保持半蓄水状态以最大限度的对可能的风电实时出力不平衡进行调节,这一结果也再次说明本文所提竞标策略模型能够很好的将第二阶段可能的实时情况考虑在内。

5 结论

针对我国电力现货市场建设初期,缺乏风电等新能源主体参与现货市场的优化运行策略,本文基于合作博弈论,提出了风电-抽水蓄能电站联合参与现货市场的优化运行策略及收益分配模型。考虑风电出力不确定性及在平衡市场中面临的风险,建立了两阶段随机优化模型,能够给出风储联盟的日前优化运行策略,能够分析风电预测精度、多风场出力相关性、市场成员风险态度等因素对联盟参与现货市场运行策略及收益分配的影响。算例验证了所提模型的有效性以及收益分配的合理性。研究成果为新能源、储能等多元市场主体参与现货市场的行为决策提供理论指导。

参考文献

[1]国家发展和改革委员会新能源研究所.中国2050高比例可再生能源发展情景暨路径研究[R/OL].(2015-11-02)[2018-11-02].

[2]国家能源局综合司.关于进一歩推进电力现货市场建设试点工作的意见(征求意见稿)[R/OL].(2019-03-07)[2019-03-10]..

[3]马莉,范孟华,郭磊,等.国外电力市场最新发展动向及其启示[J].电力系统自动化,2014,38(13):1-9.MaLi,FanMenghua,GuoLei,et al.Latest development trends of international electricity markets and their enlightenment[J].Automation of Electric Power Systems,2014,38(13):1-9(in Chinese).

[4]南方能源监管局.广东电力市场运营基本规则(征求意见稿)[R/OL].(2018-08-31)[2018-12-31].http://nfj.nea.gov.cn/ adminContent/initViewContent.do?pk=402881e56579be6301658d7123c2001a.

[5]North American Electric Reliability Corporation (NERC).Accommodating high levels of variable generation,special report[R].Princeton,NJ,special report [R].Princeton,NJ,USA,2009.

[6]梁子鹏,陈皓勇,雷佳,等.考虑风电不确定度的风-火-水-气-核-抽水蓄能多源协同旋转备用优化[J].电网技术,2018,42(7):2111-2119.LiangZipeng,ChenHaoyong,LeiJia,et al.A multi-source coordinated spinning reserve model considering wind power uncertainty[J].Power System Technology,2018,42(7):2111-2119(in Chinese).

[7]Morales JM,Conejo AJ,Pérez-RuizJ.Short-term trading for a wind power producer[J].IEEE Transactions on Power Systems,2010,25(1):554-564.

[8]BaringoL,Conejo AJ.Strategic offering for a wind power producer[J].IEEE Transactions on Power Systems,2013,28(4):4645-4654.

[9]ZugnoM,Morales JM,PinsonP,et al.Pool strategy of a price-maker wind power producer[J].IEEE Transactions on Power Systems,2013,28(3):3440-3450.

[10]BaringoL,Conejo AJ.Offering strategy of wind-power producer: a multi-stage risk-constrained approach[J].IEEE Transactions on Power Systems,2016,31(2):1420-1429.

[11]BanaeiM,Oloomi-BuygiM,Zabetian-Hosseini S M.Strategic gaming of wind power producers joined with thermal units in electricity markets[J].Renewable Energy,2018,115:1067-1074.

[12]Guerrero-MestreV,de la Nieta A A S,ContrerasJ,et al.Optimal bidding of a group of wind farms in day-ahead markets through an external agent[J].IEEE Transactions on Power Systems,2016,31(4):2688-2700.

[13]Garcia-GonzalezJ,de la Muela R M R,Santos LM,et al.Stochastic joint optimization of wind generation and pumped-storage units in an electricity market[J].IEEE Transactions on Power Systems,2008,23(2):460-468.

[14]WangJ,Kennedy SW,Kirtley JL.Optimization of forward electricity markets considering wind generation and demand response[J].IEEE Transactions on Smart Grid,2014,5(3):1254-1261.

[15]PapavasiliouA,Oren SS.Large-scale integration of deferrable demand and renewable energy sources[J].IEEE Transactions on Power Systems,2014,29(1):489-499.

[16]张刘冬,殷明慧,卜京,等.基于成本效益分析的风电-抽水蓄能联合运行优化调度模型[J].电网技术,2015,39(12):3386-3392.ZhangLiudong,YinMinghui,BuJing,et al.A joint optimal operation model of wind farms and pumped storage units based on cost-benefit analysis[J].Power System Technology,2015,39(12):3386-3392(in Chinese).

[17]邹金,赖旭,汪宁渤.以减少电网弃风为目标的风电与抽水蓄能协调运行[J].电网技术,2015,39(9):2472-2477.ZouJin,LaiXu,WangNingbo.Mitigation of wind curtailment by coordinating with pumped storage[J].Power System Technology,2015,39(9):2472-2477(in Chinese).

[18]刘斌,陈来军,汪雨辰,等.应对风电出力不确定性的备用成本分摊:联盟博弈方法[J].控制理论与应用,2016,33(4):437-445.LiuBin,ChenLaijun,WangYuchen,et al.Allocating reserve cost for hedging against wind generation uncertainty:a coalitional-game- theoretic approach[J].Control Theory & Applications,2016,33(4):437-445(in Chinese).

[19]何永秀,宋栋,夏天,等.基于合作博弈论的常规能源与新能源发电权置换交易模式研究[J].电网技术,2017,41(8):2485-2490.HeYongxiu,SongDong,XiaTian,et al.Mode of generation right trade between renewable energy and conventional energy based on cooperative game theory[J].Power System Technology,2017,41(8):2485-2490(in Chinese).

[20]谭俊源,黄月婷,潘凯.基于核仁的输电网固定成本分摊对用户的经济激励[J].电网技术,2008,32(S2):226-229.TanJunyuan,HuangYueting,PanKai.Economic stimulation for consumers from nucleolus solution based fixed transmission cost allocation[J].Power System Technology,2008,32(S2):226-229(in Chinese).

[21]马溪原.含风电电力系统的场景分析方法及其在随机优化中的应用[D].武汉:武汉大学,2014.

[22]Chaves-ávila JP,Hakvoort RA,RamosA.The impact of European balancing rules on wind power economics and on short-term bidding strategies[J].Energy Policy,2014,68(5):383-393.

[23]VandezandeL,MeeusL,BelmansR,et al.Well-functioning balancing markets:a prerequisite for wind power integration[J].Energy Policy,2010,38(7):3146-3154.

[24]CarrionM,Philpott AB,Conejo AJ,et al.A stochastic programming approach to electric energy procurement for large consumers[J].IEEE Transactions on Power Systems,2007,22(2):744-754.

[25]Thmas LC.Games-theory and applications[M].Ellis:Horwood Limited,1984:120-150.

[26]Baeyens,Bitar,Khargonekar,et al.Wind energy aggregation:a coalitional game approach[C]//Decision & Control & European Control Conference.IEEE,2011.

[27]伍栋文,于艾清.大规模多源联合外送协调调度中基于核仁理论的利润分配[J].电网技术,2016,40(10):2975-2981.WuDongwen,YuAiqing.Research on nucleolus theory based profit distribution method for joint delivery system of large-scale hybrid power generation[J].Power System Technology,2016,40(10):2975-2981(in Chinese).

[28]BaeyensE,Bitar EY,Khargonekar PP,et al.Coalitional aggregation of wind power[J].IEEE Transactions on Power Systems,2013,28(4):3774-3784.

[29]Gabriel SA,Conejo AJ,Fuller JD,et al.Complementarity modeling in energy markets[M].Springer Science & Business Media,2012.

[30]周明,武昭原,贺宜恒,等.兼顾中长期交易和风电参与的日前市场出清模型[J/OL].中国科学:信息科学..Zhou Ming,Wu Zhaoyuan,He Yiheng,et al.A day-ahead electricity market clearing model considering medium-and long-term transactions and wind producer participation[J/OL].Scientia Sinica Informationis.(in Chinese).


原标题:基于合作博弈论的风储联合参与现货市场优化运行策略
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