Optimizing Charging Time Periods of Electric Vehicles With Reinforcement Learning

The increasing integration of electric vehicles into electrical distribution grids will result in grid congestions, which lead the focus of research to Demand Side Management and thus the control of charging processes. Mainly complex and hardly realizable coordination strategies have been investigated, which often involve high efforts in data measurement, collection and communication. We therefore investigate the optimal design of fixed charging time periods as a counter approach, using our own grid simulation framework. Our method to optimize these charging times is the training of a Reinforcement Learning Agent, who successfully learns how to limit charging processes under minimization of grid constraint violations and mobility limitations.