黄正伟, 鲍奕辰, 刘璐. 基于非合作博弈的EV换电站容量优化配置与削峰方法研究[J]. 现代电力. DOI: 10.19725/j.cnki.1007-2322.2023.0083
引用本文: 黄正伟, 鲍奕辰, 刘璐. 基于非合作博弈的EV换电站容量优化配置与削峰方法研究[J]. 现代电力. DOI: 10.19725/j.cnki.1007-2322.2023.0083
HUANG Zhengwei, BAO Yichen, LIU Lu. Research on Optimal Capacity Allocation and Peak Shaving Method of EV Battery-swap Stations Based on Non-cooperative Game[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2023.0083
Citation: HUANG Zhengwei, BAO Yichen, LIU Lu. Research on Optimal Capacity Allocation and Peak Shaving Method of EV Battery-swap Stations Based on Non-cooperative Game[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2023.0083

基于非合作博弈的EV换电站容量优化配置与削峰方法研究

Research on Optimal Capacity Allocation and Peak Shaving Method of EV Battery-swap Stations Based on Non-cooperative Game

  • 摘要: 针对中大型换电站的高峰时段和用电重载问题,考虑换电站的削峰需求和用户换电体验,提出基于非合作博弈的换电站容量优化配置方法。首先,根据各时段用户换电需求和换电站削峰任务,提出换电站的服务能力约束和削峰任务约束。其次,将换电站和用户作为博弈参与者,分别以换电站日综合收益最大和用户换电满意度最高为优化目标,提出基于非合作博弈的换电站容量优化配置方法。然后,考虑各博弈参与者的利益需求,利用粒子群算法对该非合作博弈模型进行求解,并确定二者利益最大化的纳什均衡点。最后,以国内某换电站为算例进行仿真分析,结果表明该模型能够在用户换电体验最佳的前提下使换电站的收益达到最大,验证了所提方法的有效性。

     

    Abstract: Aiming at issues of peak period and heavy load in medium and large-scale battery swapping stations, a capacity optimization configuration method of battery swapping stations based on non-cooperative game is proposed considering the peak shaving demand of battery swapping stations and the user’s battery swapping experience. Firstly, the service capacity constraint and the peak shaving task constraint of the power station are proposed according to the demand of users in each period and the peak shaving task of the power station. Secondly, a non-cooperative game-based battery swapping station capacity optimization allocation method is proposed with the battery swapping station and the user taken as the game participants. The maximum daily comprehensive income of the battery swapping station and the highest satisfaction degree of the user are selected as the optimization objectives respectively. Considering the interest needs of each game participant, the particle swarm optimization algorithm is then employed to solve the non-cooperative game model, and the Nash equilibrium point that maximizes both interests is determined. Finally, a domestic battery swapping station is taken as an example for simulation analysis. The results demonstrate that the model can maximize the revenue of the battery swapping station under the premise of the best user experience, thereby verifying the effectiveness of the proposed method.

     

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