深度｜售电公司市场运行服务构架及日前电价风险决策方法

图9研究了B(0)对电价的影响,可以看出随着B(0)的不断增加,电价减小程度越来越大,直至接近最低电价限制,这是由于随着潜在收益比例的增大,售电公司制定电价更倾向于低电价以吸引更多用户加入到该售电公司,该结果也同时验证了市场份额模型在限制电价方面的有效性。

图10 不同σB与σρ下的售电效益

图10显示了当B(0)波动较小时,售电公司收益随σρ的增加先增大后减小(σB为0时单调递减);在图示范围内,随着σB的增大,售电公司收益只随σρ增大而增大。这是由σρ与σB对电价的影响相反造成的,根据式(24),σρ的增大会导致售电公司通过提升售电价格减少购入电量,σB的增大会导致售电公司制定更低的售电价格,两者对电价作用相反。记不考虑风险因素时的最优售电价格为ρop,两者同时作用使得电价出现先靠近ρop后远离ρop的情况,致使收益出现先增长后降低的情况。当σB较大时,仅出现增长的情况,这是由σB比重太大,以至于在σρ达到研究范围的最大值时,仍无法使电价远离ρop造成的。

5 结论

本文考虑各种不确定因素引起的收益风险情况下,提出一种基于MAS框架的售电公司日前小时电价决策与风险评估决策方法。该方法适用于配售分离后所形成的“多买多卖”市场格局,同时考虑用户侧智能终端普及且能够同售电公司进行双向互动场景下的售电电价决策问题。根据算例结果可知,在考虑市场风险情况下,售电公司的售电电价与批发市场价格较一致,并相当于与用户共同决策制定形成的类似于实时电价的定价方法,可为实时电价环境下的售电公司定价问题提供一定的决策依据。本方法在用户建模时,由于采用线性模型即电量电价弹性矩阵近似描述负荷量与电价的关系,难以精确反映不同类型的负荷信息,因而决策电价达到的效果存在与实际情况有差异的风险,未来需要进一步研究解决。

参考文献

[1]刘顺桂,孙成龙,孙杰,等.售电侧放开下需求响应资源代理参与机制设计[J].电力系统及其自动化学报,2017,29(5):115-121.LiuShungui,SunChenglong,SunJie,et al.Design of agency and participation mechanism of DRRs with sale-market released[J].Proceedings of the CSU-EPSA,2017,29(5):115-121(in Chinese).

[2]胡晨,杜松怀,苏娟,等.新电改背景下我国售电公司的购售电途径与经营模式探讨[J].电网技术,2016,40(11):3293-3299.HuChen,DuSonghuai,SuJuan,et al.Preliminary research of trading approach and management modes of Chinese electricity retail companies under new electricity market reform[J].Power System Technology,2016,40(11):3293-3299(in Chinese).

[3]任艺,周明,李庚银.考虑用户需求响应的售电公司购售电决策双层模型[J].电力系统自动化,2017,41(14):30-36.RenYi,ZhouMing,LiGengyin.Bi-level model of electricity procurement and sale strategies for electricity retailers considering users demand response[J].Automation of Electric Power Systems,2017,41(14):30-36(in Chinese).

[4]刘连光,潘明明,田世明,等.考虑源网荷多元主体的售电竞争非合作博弈方法[J].中国电机工程学报,2017,37(6):1618-1626.LiuLianguang,PanMingming,TianShiming,et al.A non- cooperative game analysis of an competitive electricity retail considering multiple subjects of source-grid-load[J].Proceedings of the CSEE,2017,37(6):1618-1626(in Chinese).

[5]NojavanS,Mohammadi-IvatlooB,ZareK.Robust optimization based price-taker retailer bidding strategy under pool market price uncertainty[J].International Journal of Electrical Power Energy Systems,2015(73):955-963.

[6]Meng FL,Zeng XJ.An optimal real-time pricing for demand-side management:a Stackelberg game and genetic algorithm approach [C]//International Joint Conference on Neural Networks.Beijing,China,2014.

[7]JiaL,TongL.Dynamic pricing and distributed energy management for demand response[J].IEEE Transactions on Smart Grid,2016,7(2):1128-1136.

[8]孔祥玉,曾意,陆宁,等.基于多智能体竞价均衡的微电网优化运行方法[J].中国电机工程学报,2017,37(6):1626-1633.KongXiangyu,ZengYi,LuNing,et al.Optimal bidding management for agent-based microgrid operation[J].Proceedings of the CSEE,2017,37(6):1626-1633(in Chinese).

[9]杨萌,艾欣,唐亮,等.计及风险规避的售电公司平衡市场优化交易策略研究[J].电网技术,2016,40(11):3300-3308.YangMeng,AiXin,TangLiang,et al.Optimal trading strategy in balancing market for electricity retailer considering risk aversion[J].Power System Technology,2016,40(11):3300-3308(in Chinese).

[10]宋艺航,谭忠富,于超,等.需求侧峰谷分时电价对供电公司购售电风险影响分析模型[J].电工技术学报,2010,25(11):183-190.Song Yihang,TanZhongfu,YuChao,et al.Analysis model on the impact of demand-side TOU electricity price on purchasing and selling risk for power supply company[J].Transactions of China Electrotechnical Society,2010,25(11):183-190(in Chinese).

[11]徐辉,陈致宏,鞠立伟,等.多市场分时段下电网公司购售电风险控制优化模型[J].水电能源科学,2014(5):202-206.XuHui,ChenZhihong,JuLiwei,et al.Period-decoupled electricity purchasing and selling risk control optimization mode for power grid corporation under multi-market[J].Water Resources and Power,2014(5):202-206(in Chinese).

[12]朱文昊,谢品杰.基于CVaR的峰谷分时电价对供电公司购电组合策略影响分析[J].电力系统保护与控制,2015,43(14):16-21.ZhuWenhao,XiePinjie.Influence analysis of CVaR model based TOU electricity price on portfolio strategy[J].Power System Protection and Control,2015,43(14):16-21(in Chinese).

[13]张钦,王锡凡,王建学.需求侧实时电价下供电商购售电风险决策[J].电力系统自动化,2010,34(3):22-27.ZhangQin,WangXifan,WangJianxue.Electricity purchasing and selling risk decision for power supplier under real-time pricing[J].Automation of Electric Power Systems,2010,34(3):22-27(in Chinese).

[14]甘宇翔,蒋传文,白宏坤,等.市场环境下园区售电商的最优报价和运行优化[J].电网技术,2018,42(3):707-714.GanYuxiang,JiangChuanwen,BaiHongkun,et al.Optimal bidding strategy and operation of industrial park electricity retailer in electricity market[J].Power System Technology,2018,42(3):707-714(in Chinese).

[15]FabioB,GiovanniC,DominicG.基于JADE的多Agent系统开发[M].程志锋等,译.北京:国防工业出版社,2013.

[16]秦祯芳,岳顺民,余贻鑫,等.零售端电力市场中的电量电价弹性矩阵[J].电力系统自动化,2004,28(5):16-19.QinZhenfang,YueShunmin,YuYixin,et al.Price elasticity matrix of demand in current retail power market[J].Automation of Electric Power Systems,2004,28(5):16-19(in Chinese).

[17]ShenZhao,ZhouMing.Modeling demand response under Time- of-Use pricing[C]//International Conference on Power System Technology.Chengdu,China,2014.

[18]Federal Energy Regulatory Commission.Settlement intervals and shortage pricing in markets operated by regional transmission organizations and independent system operators[R].Docket No.RM15-24-000,2015.

[19]孙云涛,宋依群,姚良忠,等.售电市场环境下电力用户选择售电公司行为研究[J].电网技术,2018,42(4):1124-1131.SunYuntao,SongYiqun,YaoLiangzhong,et al.Study on power consumers’ choices of electricity retailers in electricity selling market[J].Power System Technology,2018,42(4):1124-1131(in Chinese).

[20]康重庆,庄彦,胡江溢,等.售电市场中售电量的风险分析[J].中国电机工程学报,2009,29(19):78-84.KangChongqing,ZhuangYan,HuJiangyi,et al.Risk analysis on electricity sales in power sale market[J].Proceedings of the CSEE,2009,29(19):78-84(in Chinese).

[21]WollO.Mean-risk hedging strategies in electricity markets with limited liquidity[R].Mannheim:Centre for European Economic Research,2015.

[22]LiuB,WangL,Jin YH,et al.Improved particle swarm optimization combined with chaos[J].Chaos Solitons Fractals,2005,25(5):1261-1271.