摘要
为应对大规模电动汽车无序快充给电力交通耦合网络带来的巨大挑战,首先建立了包含车-站-路-网的多目标优化模型,提出了基于请求驱动的快速充电站推荐模式,利用图强化学习算法实现了不规则环境信息的提取及快速充电引导策略的学习,最后基于MATLAB-SUMO-Python联合仿真平台进行了实验。结果表明,所提算法能够在保证路-网指标优化的同时,有效降低电动汽车充电前耗时并提高充电站的服务均衡度,从而保证耦合网络的长期健康稳定运行,所提方法具有良好的优化效果及实时响应能力。
The unguided fast charging of the large-scale electric vehicles has brought great challenges to the coupled power-transportation networks.In this paper,a multi-objective optimization model including the electric vehicles,the charging stations,the transportation network,and the power grid is firstly established.Then,a request-driven recommendation model is proposed,and the irregular environment information extraction and fast charging guidance strategy learning are realized based on the graph reinforcement learning algorithm.Finally,some experiments are carried out on MATLAB-SUMO-Python joint simulation platform.The results show that the proposed algorithm can effectively reduce the time cost before charging of EVs and improve the service balance of charging stations,which ensures the optimization of transportation network and power grid and the long-term healthy and stable operation of the coupled networks.The proposed method has good optimization effect and real-time response capability.
作者
袁红霞
张俊
许沛东
方舟
YUAN Hongxia;ZHANG Jun;XU Peidong;FANG Zhou(School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,Hubei Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2021年第3期979-986,共8页
Power System Technology
基金
国家重点研发计划项目(2018AAA0101504)
国家电网公司总部科技项目“人在回路的大电网调控混合增强智能基础理论”。
关键词
电力交通耦合网络
图强化学习
电动汽车
快速充电站推荐
coupled power-transportation networks
graph reinforcement learning
electric vehicle
fast charging station recommendation
