考虑风险规避和需求响应的电力市场可再生能源综合交易决策研究
CSTR:
作者:
作者单位:

(广东电力交易中心,广东 广州 510062)

通讯作者:

陈 灏(1987—),男,硕士,高级工程师,主要从事电力市场研究;E?mail:272507319@qq.com

中图分类号:

TM863

基金项目:

南方电网公司重点科技项目(GDKJXM20201925)


Research on comprehensive trading decision of renewable energy in power market considering the risk aversion and demand response
Author:
Affiliation:

(Guangdong Power Exchange Center, Guangzhou 510062, China)

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    摘要:

    可再生能源在电力市场供给端所占比例逐步扩大,而其供给不确定性将提升电力市场交易风险。在此背景下,考虑日前电价和供给不确定性,提出了一种综合交易策略。首先,可再生能源聚合体采用需求响应来应对电力生产的不确定性,并综合考虑合同结算价格、激活需求响应费用与需求响应运营商签订合同,再根据需求转移功率。需求响应转换到不同时间的成本将由合同功率保证,无需考虑其最终使用情况。然后,运营商通过非合同需求响应参与日前市场交易,进一步提高其收益。最后,通过条件风险价值(CVaR)来评估预期成本波动性,将风险规避纳入决策模型中,避免过度保守的交易方案。在测试系统对所提出的决策方法进行了评估,验证了方法的有效性,结果表明该方法可在降低相关风险的同时增加不同市场主体的预期收益。

    Abstract:

    The proportion of renewable energy in the supply side of the electricity market is gradually expanding, whilst its supply uncertainty increases the risk of electricity market trading. Under the background, an integrated trading strategy is proposed considering the day?ahead electricity price and supply uncertainty. Firstly, renewable energy aggregators utilize the demand response to deal with the uncertainty of electricity production. Then, they make a contract with demand response operators by comprehensively considering the contractual settlement price and activation charge of demand response. After that, the power is transferred according to demand. The cost of demand response switching to different times is decided by the contracted power, no need to consider when and where they will be used. Secondly, the operator participates in day?ahead market transactions through non?contractual demand response and further increases its revenue. Finally, the conditional value?at?risk (CVaR) by assessing expected cost volatility. Thereby, the risk aversion is incorporated into the decision model to avoid overly conservative trading scenarios. In the end, the proposed decision method is evaluated in a test system to validate the effectiveness of the method. It is shown that the proposed method can increase the expected returns of different market participants while reducing the associated risks.

    参考文献
    [1] 卢操,管霖,陈恒安,等.考虑储能调度的可再生能源独立微电网电源规划[J].电测与仪表,2021,58(4):84?91. LU Cao,GUAN Lin,CHEN Heng’an,et al.Generation planning for renewable energy isolated micro?grid considering energy storage dispatching[J].Electrical Measurement & Instrumentation,2021,58(4):84?91.
    [2] 赵晶晶,应伦杰,屈靖雅.多区域含冷热电联供和储能的综合能源系统运行优化[J].电测与仪表,2022,59(10):16?22. ZHAO Jingjing,YING Lunjie,QU Jingya.Operation optimization of multi?region integrated energy system including cool?heat?electricity cogeneration and energy storage equipment[J].Electrical Measurement & Instrumentation,2022,59(10):16?22.
    [3] 鲁宗相,李昊,乔颖.从灵活性平衡视角的高比例可再生能源电力系统形态演化分析[J].全球能源互联网,2021,4(1):12?18. LU Zongxiang,LI Hao,QIAO Ying.Morphological evolution of power systems with high share of renewable energy generations from the perspective of flexibility balance[J].Journal of Global Energy Interconnection,2021,4(1):12?18.
    [4] 刘源,檀勤良,张兴平.基于交互算法的多代理虚拟电厂调度优化及风险分析[J].电力工程技术,2022,41(6):2?12. LIU Yuan,TAN Qinliang,ZHANG Xingping.Multi?agent VPP coordinated control optimization and risk analysis based on the interactive algorithm[J].Electric Power Engineering Technology,2022,41(6):2?12.
    [5] 范宏,陆骁霄.基于HSS算法的多区域虚拟电厂综合能源调度[J].电测与仪表,2021,58(1):124?130. FAN Hong,LU Xiaoxiao.Integrated energy scheduling of multi?regional virtual power plants basedon HSS algorithm[J].Electrical Measurement & Instrumentation,2021,58(1):124?130.
    [6] 李坚,吴亮红,张红强,等.基于排序交叉优化算法的冷热电联供微电网经济调度[J].电力系统保护与控制,2021,49(18):137?145. LI Jian,WU Lianghong,ZHANG Hongqiang,et al.Microgrid economic dispatch of combined cooling, heating and power based on a rank pair learning crisscross optimization algorithm[J].Power System Protection and Control,2021,49(18):137?145.
    [7] 洪扬,惠思思,宋莹.风电直流外送系统风机高压脱网的风险评估[J].高压电器,2022,58(9):102?111. HONG Yang,HUI Sisi,SONG Ying.Risk assessment of wind turbine high voltage trip?off in wind power DC delivery system[J].High Voltage Apparatus,2022,58(9):102?111.
    [8] 王开艳,梁岩,贾嵘.考虑共享储能的冷热电联供型微网低碳经济调度[J].电网与清洁能源,2022,38(11):155?162. WANG Kaiyan,LIANG Yan,JIA Rong.Low?carbon economical dispatch of the combined cooling,heating and power microgrid considering shared energy storage[J].Power System and Clean Energy,2022,38(11):155?162.
    [9] 杨贤东,袁旭峰,熊炜,等.考虑源荷不确定性的风光火储系统低碳经济调度[J].智慧电力,2022,50(8):22?29. YANG Xiandong,YUAN Xufeng,XIONG Wei,et al.Low?carbon economic dispatch of wind?solar?fired?storage system considering source?load uncertainty[J].Smart Power,2022,50(8):22?29.
    [10] 郑涛,戴则梅,姚家豪,等.综合能源系统控制自由度指标及其对经济调度的影响[J].中国电力,2021,54(4):95?106+118. ZHENG Tao,DAI Zemei,YAO Jiahao,et al.Control freedom index of integrated energy system and its impact on economic dispatch[J].Electric Power,2021,54(4):95?106+118.
    [11] JIANG Y,HOU J,LIN Z,et al.Optimal bidding strategy for a power producer under monthly pre?listing balancing mechanism in actual sequential energy dual?market in China[J].IEEE Access,2019,7:70986?70998.
    [12] YU J,RYU J H,LEE I.A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system[J].Applied Energy,2019,247:212?220.
    [13] VAHEDIPOUR?DAHRAIE M,RASHIDIZADEH? KERMANI H,NAJAFI H R,et al.Stochastic security and risk‐constrained scheduling for an autonomous microgrid with demand response and renewable energy resources[J].IET Renewable Power Generation,2017,11(14):1812?1821.
    [14] DAI X,LI Y,ZHANG K,et al.A robust offering strategy for wind producers considering uncertainties of demand response and wind power[J].Applied Energy,2020,279:115742.
    [15] DAI T,QIAO W.Optimal bidding strategy of a strategic wind power producer in the short?term market[J].IEEE Transactions on Sustainable Energy,2015,6(3):707?719.
    [16] MAHMOUDI N,SAHA T K,EGHBAL M.Modelling demand response aggregator behavior in wind power offering strategies[J].Applied Energy,2014,133:347?355.
    [17] 李鹏,吴迪凡,李雨薇,等.基于综合需求响应和主从博弈的多微网综合能源系统优化调度策略[J].中国电机工程学报,2021,41(4):1307?1321. LI Peng,WU Difan,LI Yuwei,et al.Optimal dispatch of multi?microgrids integrated energy system based on integrated demand response and stackelberg game[J].Proceedings of the CSEE,2021,41(4):1307?1321.
    [18] CONEJO A J,MORALES J M,BARINGO L.Real?time demand response model[J].IEEE Transactions on Smart Grid,2010,1(3):236?242.
    [19] JORDEHI A R.Optimisation of demand response in electric power systems,a review[J].Renewable and Sustainable Energy Reviews,2019,103:308?319.
    [20] LI H,WAN Z,HE H.Real?time residential demand response[J].IEEE Transactions on Smart Grid,2020,11(5):4144?4154.
    [21] KLOBASA M.Analysis of demand response and wind integration in Germany’s electricity market[J].IET Renewable Power Generation,2010,4(1):55?63.
    [22] 游广增,汤翔鹰,胡炎,等.基于典型运行场景聚类的电力系统灵活性评估方法[J].上海交通大学学报,2021,55(7):802?813. YOU Guangzeng,TANG Xiangying,HU Yan,et al.Flexibility evaluation method for power system based on clustering of typical operating scenarios[J].Journal of Shanghai Jiaotong University,2021,55 (7):802?813.
    [23] ROCKAFELLAR R T,URYASEV S.Optimization of conditional value?at?risk[J].Journal of Risk,2000,2:21?42.
    [24] AKBARI T,BINA M T.Linear approximated formulation of AC optimal power flow using binary discretisation[J].IET Generation,Transmission & Distribution,2016,10(5):1117?1123.
    [25] CAO D,HU W,XU X,et al.Bidding strategy for trading wind energy and purchasing reserve of wind power producer?A DRL based approach[J].International Journal of Electrical Power & Energy Systems,2020,117:105648.
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引用本文

陈 灏,田 琳,盛剑胜,等.考虑风险规避和需求响应的电力市场可再生能源综合交易决策研究[J].电力科学与技术学报,2023,38(1):27-34.
CHEN Hao, TIAN Lin, SHENG Jiansheng, et al. Research on comprehensive trading decision of renewable energy in power market considering the risk aversion and demand response[J]. Journal of Electric Power Science and Technology,2023,38(1):27-34.

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  • 在线发布日期: 2023-04-10
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