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

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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. 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)

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