汪丽伟, 王昊天, 孙英云. 基于前景理论的电动汽车用户日前调频决策模型[J]. 现代电力. DOI: 10.19725/j.cnki.1007-2322.2022.0282
引用本文: 汪丽伟, 王昊天, 孙英云. 基于前景理论的电动汽车用户日前调频决策模型[J]. 现代电力. DOI: 10.19725/j.cnki.1007-2322.2022.0282
WANG Liwei, WANG Haotian, SUN Yingyun. Decision Model of Day Ahead Frequency Regulation for Electric Vehicle Users Based on Prospect Theory[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2022.0282
Citation: WANG Liwei, WANG Haotian, SUN Yingyun. Decision Model of Day Ahead Frequency Regulation for Electric Vehicle Users Based on Prospect Theory[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2022.0282

基于前景理论的电动汽车用户日前调频决策模型

Decision Model of Day Ahead Frequency Regulation for Electric Vehicle Users Based on Prospect Theory

  • 摘要: 为降低电动汽车(electric vehicle,EV)用户参与日前调频辅助服务市场的难度、提高其参与积极性,将EV完成充电离网时所获得的净收益和电池电量对用户心理的影响引入前景理论的价值函数中,建立了有限理性下的EV用户参与调频辅助服务市场的日前决策模型,该模型考虑了EV参与调频辅助服务的电池老化成本,研究了EV备用容量的优化策略及调频辅助服务市场的报价策略,并给出了3种可行的简化EV调频辅助服务参与模式,供用户选择。通过算例分析验证了基于前景理论的EV用户日前调频决策模型的有效性及可行性,并更符合实际EV用户的决策行为。

     

    Abstract: To reduce the difficulty for electric vehicle (EV) users to participate in the day-ahead frequency regulation ancillary services market and improve their participation enthusiasm, by introducing the net income of EV when charging off the grid and the impact of battery power on user psychology into the value function of prospect theory, a day-ahead decision model for EV users to participate in frequency regulation auxiliary service market under bounded rationality was established. This model considers the battery aging cost of EVs participating in frequency regulation auxiliary service, studies the optimization strategy of EV reserve capacity, and the quotation strategy of the frequency regulation auxiliary service market. In addition, three feasible simplified EV frequency regulation auxiliary service participation modes were provided for users to choose from. The effectiveness and feasibility of EV users' day ahead FM decision model based on prospect theory was verified through the analysis of a numerical example, and it is more consistent with the actual EV users' decision-making behavior.

     

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