Community-driven Smart EV charging With Multi-Agent Deep Reinforcement Learning

EV charging optimization with utilization of decentralized renewable energy resources can be seen as a promising tool towards domestic EV fleet decarbonization. However, optimization is usually conducted on an individual household level, and community-driven approaches with shared resources are heavily underexplored. In this work, a community-driven smart EV charging optimization scheme with Multi-Agent Deep Reinforcement Learning is proposed. Compared to existing single-agent approaches, the employment of a multi-agent one allows for concurrent EV charging optimization for each household within a community, deployed in a centralized, shared, energy management system, while utilizing a community-owned solar photovoltaic (PV) panel. Our approach results in reduced cost barriers for domestic EV owners that desire to optimize their charging profile, as it eliminates the investment on an individual PV panel and energy management system. Experimental results on the Pecan Street dataset validate the effectiveness of our approach compared to individual household optimization, resulting in cost savings up to 17.65%, increase in PV power utilization of up to 133.10%, as well as network stress reduction of up to 18.75%.