文章摘要
陈吕鹏,潘振宁,余涛,等.基于动态非合作博弈的大规模电动汽车实时优化调度[J].电力系统自动化. DOI: 10.7500/AEPS20181116007.
CHEN Lüpeng,PAN Zhenning,YU Tao, et al.Real-time Optimal Dispatch for Large-scale Electric Vehicles Based on Dynamic Noncooperative Game Theory[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20181116007.
基于动态非合作博弈的大规模电动汽车实时优化调度
Real-time Optimal Dispatch for Large-scale Electric Vehicles Based on Dynamic Noncooperative Game Theory
DOI:10.7500/AEPS20181116007
关键词: 动态非合作博弈  大规模电动汽车  实时优化调度  分布式优化  
KeyWords: Dynamic non-cooperative game  Large-scale electric vehicles  Real-time optimization  Distributed optimization
上网日期:2019-07-12
基金项目:国家自然科学基金
作者单位E-mail
陈吕鹏 华南理工大学电力学院 chenlvpeng123@163.com 
潘振宁 华南理工大学电力学院 scutpanzn@163.com 
余涛 华南理工大学电力学院 taoyu1@scut.edu.cn 
王克英 华南理工大学电力学院 epkywang@scut.edu.cn 
摘要:
      针对大规模电动汽车(Electric vehicle,EV)接入电网后,各充电聚合商(Electric vehicle aggregator, EVA)的独立优化目标存在冲突而导致优化调度存在困难的问题。提出了考虑多个EVA各方利益的基于动态非合作博弈的大规模EV实时调度模型。首先构建了大规模EV的集群等效模型并分析了动态电价下各EVA的利益关系,接着利用完全势博弈理论证明了博弈模型存在唯一的纳什均衡解并推导出求解方法,最后提出基于交替方向乘子法的实时分布式算法实现各EVA实时策略的求解。通过算例仿真验证了所提模型可有效实现削峰填谷、降低EVA充电成本。同时,在优化结果、计算时间、保护用户隐私方面更适用于大规模电动汽车实时充电优化调度。
Abstract:
      In order to solve the problem of optimal scheduling caused by conflicts of independent optimized scheduling targets between each electric vehicle aggregator (EVA) after the large-scale electric vehicle (EV) connected to the grid, a large-scale EV real-time scheduling model based on dynamic non-cooperative game considering the interests of EVA is proposed. Firstly, the cluster equivalent model of large-scale EV is constructed and the interest relationship of each EVA under dynamic electricity price mechanism is analyzed. Then the complete potential game theory is used to prove the existence of the unique nash equilibrium solution of the game model and the solution is derived. Finally, a real-time distributed algorithm based on the alternating direction multiplier method is proposed to solve the real-time decision problem of each EVA. The simulation results show that the model can effectively perform peak load shifting and reduce the charging cost of EVAs. At the same time, it is proved that the proposed model is more suitable for real-time large-scale electric vehicle charging optimal scheduling from the optimization results, computing time and protection of user privacy.
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