基于期望效用-熵的风电交易风险控制方法

doi:10.11930/j.issn.1004-9649.201804147

中国电力 ›› 2020, Vol. 53 ›› Issue (2): 29-35,82.DOI: 10.11930/j.issn.1004-9649.201804147

基于期望效用-熵的风电交易风险控制方法

张伟¹, 秦艳辉², 凌静³, 陈宁⁴, 杜习周⁵, 孙谊媊¹, 高丙团³

1. 国网新疆电力有限公司, 新疆乌鲁木齐 830002;
2. 国网新疆电力有限公司电力科学研究院, 新疆乌鲁木齐 830002;
3. 东南大学电气工程学院, 江苏南京 210096;
4. 中国电力科学研究院有限公司新能源与储能运行控制国家重点实验室, 江苏南京 210003;
5. 国网上海市电力公司经济技术研究院, 上海 200120

收稿日期:2018-04-26 修回日期:2019-04-10 出版日期:2020-02-05 发布日期:2020-02-05
作者简介:张伟(1966-),男,高级工程师,从事电力系统自动化和新能源发电方面的研究,E-mail:alml8488@sohu.com
基金资助:
国家电网公司科技项目（NY71-17-008）。

Risk Management for Wind Power Trading Based on Expected Utility-Entropy

ZHANG Wei¹, QIN Yanhui², LING Jing³, CHEN Ning⁴, DU Xizhou⁵, SUN Yiqian¹, GAO Bingtuan³

1. State Grid Xinjiang Electric Power Co. Ltd., Urumqi 830002, China;
2. Electric Power Research Institute, State Grid Xinjiang Electric Power Co. Ltd., Urumqi 830002, China;
3. School of Electrical Engineering, Southeast University, Nanjing 210096, China;
4. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Nanjing 210003, China;
5. Economic Research Institute of State Grid Shanghai Electric Power Company, Shanghai 200120, China

Received:2018-04-26 Revised:2019-04-10 Online:2020-02-05 Published:2020-02-05
Supported by:
This work is supported by the Science and Technology Project of SGCC (No.NY71-17-008)

摘要/Abstract

摘要： 在逐步开放的电力市场环境下，发电商可通过在合约市场以及现货市场等不同类型的市场中合理分配交易电量来规避交易风险。在此背景下，针对溢价机制下风电商在电力市场中的电量分配问题，综合考虑了现货电价的不确定性、风电出力的波动性以及风电商对风险的喜好程度等因素，提出了基于期望效用&-熵的风电商电量分配决策模型。应用该模型，对风电商在年度合约市场、月度合约市场以及现货市场3个市场的总发电量分配进行了计算。计算结果表明：该模型能够反映风电商对市场风险的偏好程度改变时的决策特征，可使风电商在获得其期望收益水平的同时降低交易风险。

关键词: 电力市场, 风险控制, 期望效用-熵, 电量分配策略

Abstract: In a gradually deregulated electricity market, a power producer can minimum risk by reasonably allocating trading electricity in different markets such as the contract market and spot market. In this context, the paper addresses the electricity allocation issue of wind power producers in the power market under the premium mechanism. Considering the uncertainties of the spot electricity prices, the volatility of the wind power output and the risk preference of the wind power producers, an expected utility-entropy-based electricity allocation decision-making model is proposed, based on which, the electricity allocation of the wind power producers is calculated respectively in such three markets as annual contract market, monthly contract market and spot market. The results show that the model can not only reflect the decision-making characteristics of the wind power producers when their preference of the market risk changes, but also reduce the trading risk of wind power producers while obtaining expected return level.

Key words: electricity market, risk management, expected utility-entropy, electricity allocation strategy

张伟, 秦艳辉, 凌静, 陈宁, 杜习周, 孙谊媊, 高丙团. 基于期望效用-熵的风电交易风险控制方法[J]. 中国电力, 2020, 53(2): 29-35,82.

ZHANG Wei, QIN Yanhui, LING Jing, CHEN Ning, DU Xizhou, SUN Yiqian, GAO Bingtuan. Risk Management for Wind Power Trading Based on Expected Utility-Entropy[J]. Electric Power, 2020, 53(2): 29-35,82.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201804147

https://www.electricpower.com.cn/CN/Y2020/V53/I2/29

参考文献

[1] 舒印彪, 张智刚, 郭剑波, 等. 新能源消纳关键因素分析及解决措施研究[J]. 中国电机工程学报, 2017, 37(1):1-8 SHU Yinbiao, ZHANG Zhigang, GUO Jianbo, et al. Study on key factors and solution of renewable energy accommodation[J]. Proceedings of the CSEE, 2017, 37(1):1-8
[2] GUAN X H, WU J, GAO F, et al. Optimization-based generation asset allocation for forward and spot markets[J]. IEEE Transactions on Power Systems, 2008, 23(4):1796-1808.
[3] 李莉, 王建军, 李宁, 等. 低碳经济下能效电厂的半方差风险投资组合优化模型[J]. 电网技术, 2011, 35(8):26-29 LI Li, WANG Jianjun, LI Ning, et al. A risk investment portfolio optimization model of energy efficiency power plant based on mean semi-variance theory in low-carbon economy environment[J]. Power System Technology, 2011, 35(8):26-29
[4] 张显, 王锡凡, 王建学, 等. 发电商长期电能分配策略研究[J]. 中国电机工程学报, 2005, 25(1):6-12 ZHANG Xian, WANG Xifan, WANG Jianxue, et al. A Long-term allocating strategy of power generators[J]. Proceedings of the CSEE, 2005, 25(1):6-12
[5] 王壬, 尚金成, 冯旸, 等. 基于CVaR风险计量指标的发电商投标组合策略及模型[J]. 电力系统自动化, 2005, 29(14):5-9 WANG Ren, SHANG Jincheng, FENG Yang, et al. Combined bidding strategy and model for power suppliers based on CVaR risk measurement techniques[J]. Automation of Electric Power Systems, 2005, 29(14):5-9
[6] 余鹏飞, 朱继忠. 计及输电网络时空风险的动态电价模型[J]. 电力系统保护与控制, 2019, 47(9):180-187 YU Pengfei, ZHU Jizhong. Dynamic electricity pricing model considering space-time risk factor in transmission networks[J]. Power System Protection and Control, 2019, 47(9):180-187
[7] CONEJO A J, GARCIA-BERTRAND R, CARRIÓN M, et al. Optimal involvement in futures markets of a power producer[J]. IEEE Trans actions on Power Systems, 2008, 23(2):703-711.
[8] 窦迅, 李扬, 王蓓蓓, 等. 电力供应链的收益风险研究[J]. 电力自动化设备, 2012, 32(4):11-15 DOU Xun, LI Yang, WANG Beibei, et al. Risk distribution of electricity supply chain[J]. Electric Power Automation Equipment, 2012, 32(4):11-15
[9] 周洁, 赵琛, 张少华. 基于鲁棒优化的发电商电量分配策略[J]. 电力建设, 2016, 37(3):58-62 ZHOU Jie, ZHAO Chen, ZHANG Shaohua. Electricity capacity allocation strategies of generation companies based on robust optimization[J]. Electric Power Construction, 2016, 37(3):58-62
[10] 潘俊涛, 彭建春, 孙芊, 等. 基于前景理论的发电商电量分配策略[J]. 电网技术, 2011, 35(4):170-175 PAN Juntao, PENG Jianchun, SUN Qian, et al. A prospect theory-based generation energy allocation strategy for gencos[J]. Power System Technology, 2011, 35(4):170-175
[11] 赵文会, 高姣倩, 于金龙, 等. 计及碳交易和绿色证书交易机制的发电权交易模型[J]. 可再生能源, 2016, 34(8):1129-1137 ZHAO Wenhui, GAO Jiaoqian, YU JInlong, et al. A reserve market bidding model considering carbon emission costs[J]. Renewable Energy Resources, 2016, 34(8):1129-1137
[12] 陈政, 杨甲甲, 金小明, 等. 可再生能源发电电价形成机制与参与电力市场的竞价策略[J]. 华北电力大学学报(自然科学版), 2014, 41(2):89-98, 102 CHEN Zheng, YANG Jiajia, JING Xiaoming, et al. A literature survey for pricing mechanisms and bidding strategies of renewable energy generation[J]. Journal of North China Electric Power University (Natural Science Edition), 2014, 41(2):89-98, 102
[13] GAO B T, WU C, WU Y J, et al. Expected utility and entropy-based decision-making model for large consumers in the smart grid[J]. Entropy, 2015, 17(12):6560-6575.
[14] 谭忠富, 张丽英, 王绵斌, 等. 大用户控制购电成本风险的均值-熵权组合优化模型[J]. 电网技术, 2009, 33(11):65-70 TAN Zhongfu, ZHANG Liying, WANG Mianbin, et al. An expectation-entropy portfolio model to control electricity purchasing cost risk for large consumers direct power purchase[J]. Power System Technology, 2009, 33(11):65-70
[15] VON NEUMANN J, MORGENSTERN O. Theory of games and economic behavior (60th anniversary commemorative edition)[M]. Princeton:Princeton University Press, 2007.
[16] 王智冬, 刘连光, 刘自发, 等. 基于量子粒子群算法的风火打捆容量及直流落点优化配置[J]. 中国电机工程学报, 2014, 34(13):2055-2062 WANG Zhidong, LIU Lianguang, LIU Zifa, et al. Optimal configuration of wind & coal power capacity and DC placement based on quantum PSO algorithm[J]. Proceedings of the CSEE, 2014, 34(13):2055-2062
[17] 孙谊媊, 凌静, 秦艳辉, 等. 考虑绿色证书的可再生能源跨区消纳竞价优化方法[J]. 可再生能源, 2018, 36(6):942-948 SUN Yiqian, LING Jing, QIN Yanhui, et al. A bidding optimization method for renewable energy cross-regional transaction under green certificate trading mechanism[J]. Renewable Energy Resources, 2018, 36(6):942-948
[18] 刘文霞, 凌云頔, 赵天阳. 低碳经济下基于合作博弈的风电容量规划方法[J]. 电力系统自动化, 2015, 39(19):68-74 LIU Wenxia, LING Yundi, ZHAO Tianyang. Cooperative game based capacity planning model for wind power in low-carbon economy[J]. Automation of Electric Power Systems, 2015, 39(19):68-74
[19] 黄大为, 韩学山, 郭志忠. 计及机组爬坡速率约束的发电商竞价策略[J]. 电网技术, 2008, 32(11):79-83 HUANG Dawei, HAN Xueshan, GUO Zhizhong. Analysis on generation companies' bidding strategy taking unit ramp rate constraints into account[J]. Power System Technology, 2008, 32(11):79-83
[20] PJM Energy Market. Markets and operations[EB/OL].[2017-09-30]. http://www.pjm.com/markets-and-operations/energy/day-ahead.aspx

基于期望效用-熵的风电交易风险控制方法

Risk Management for Wind Power Trading Based on Expected Utility-Entropy

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基于期望效用-熵的风电交易风险控制方法

Risk Management for Wind Power Trading Based on Expected Utility-Entropy

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