LAUSR

Abstract In recent years, the importance of electric mobility has increased in response to climate change. The fast-growing deployment of electric vehicles (EVs) worldwide is expected to decrease transportation-related C O 2 emissions, facilitate the integration of renewables, and support the grid through demand–response services. Simultaneously, inadequate EV charging patterns can lead to undesirable effects in grid operation, such as high peak-loads or low self-consumption of solar electricity, thus calling for novel methods of control. This work focuses on applying deep reinforcement learning (RL) to the EV charging control problem with the objectives to increase photovoltaic self-consumption and EV state of charge at departure. Particularly, we propose mathematical formulations of environments with discrete, continuous, and parametrized action spaces and respective deep RL algorithms to resolve them. The benchmarking of the deep RL control against naive, rule-based, deterministic optimization, and model-predictive control demonstrates that the suggested methodology can produce consistent and employable EV charging strategies, while its performance holds a great promise for real-time implementations.