Reinforcement learning-based profit maximization for battery energy storage systems with electric vehicles and photovoltaic systems

Abstract

With the growing penetration of renewable energy and the increasing adoption of electric vehicles, the reliable and secure operation of the power grid is facing significant challenges. The inherent randomness and uncertainty associated with renewable energy generation and electric vehicle charging are major factors contributing to grid instability. To address this issue, this paper proposes the utilization of energy storage systems for actively regulating active and reactive power to mitigate grid supplydemand imbalances. Reinforcement learning algorithms are employed to schedule the active and reactive power of the energy storage system, and sensitivity and economic analyses are conducted. The results demonstrate that the integration of energy storage systems into the grid can effectively mitigate the uncertainties and randomness associated with electric vehicle charging and renewable energy generation. The real-time scheduling strategy outputted by the reinforcement learning algorithm reduces computation time, while the economic and sensitivity analyses confirm the profitability and robustness of the energy storage system.