Multi-Agent Reinforcement Learning Based Electric Vehicle Charging Control for Grid-Level Services
Multi-Agent Reinforcement Learning Based Electric Vehicle Charging Control for Grid-Level Services
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DOI：
10.1109/iecon49645.2022.9968587
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Md Golam Dastgir;X. Huo;Mingxi Liu
中科院分区：
文献类型：
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作者：
Md Golam Dastgir;X. Huo;Mingxi Liu
协调大量电动汽车（EV）的充电过程在从需求侧增加电网灵活性方面是有希望的，但需要高度可扩展的控制协议。与传统的基于分散优化的方法（需要近似的配电网络模型）相比，本文将电动汽车充电控制问题框架化为多智能体强化学习（MARL）框架。基于MARL的框架通过行动者-评论家网络进行训练，并采用集中训练和分散执行的结构，部分观察。与基于模型的方法相比，所开发的基于MARL的方法更好地捕捉了配电网的属性，提高了电网级服务性能，实现了更好的网络约束控制，降低了通信负载，实现了更快的响应。在IEEE 13节点试验馈线上的仿真结果验证了该方法的有效性和高效性。
Coordinating the charging process of a large population of electric vehicles (EV) is promising in increasing power grid flexibility from the demand side, yet requires highly scalable control protocols. In contrast to classical decentralized optimization based methods that require approximated distribution network models, this paper frames the EV charging control problem into a multi-agent reinforcement learning (MARL) framework. The MARL-based framework is trained through an actor-critic network and adopts the structure of centralized training and decentralized execution with partial observations. Comparing with model-based approaches, the developed MARL-based approach better captures the attributes of the distribution network, improves grid-level service performance, achieves better network constraints control, reduces the communication load, and achieves a faster response. The efficacy and efficiency of the developed method are verified by simulations on the IEEE 13-bus test feeder.