LSTM-aided Reinforcement Learning for Energy Management in Microgrid with Energy Storage and EV Charging

This work studies an electric vehicles (EVs) loaded microgrid with renewable energy resources, energy storage system (ESS) and external power grid. The microgrid's energy management problem is formulated to maximize its daily average operation revenue and balance the supply and demand based on the system statistical information, i.e., electricity market price, renewable energy arrivals and EVs charging characteristics. For online optimization, we develop a reinforcement learning (RL) based approach to smartly control the microgrid's ESS in real-time by considering future reward of an charging/discharging action. Moreover, to speed up the RL training stage, a prediction model using long short term memory (LSTM) networks is adopted to explore the system input traces for more accurate future reward counting in current learning process. The simulation results validate the superior performance of the proposed algorithm with comparison to the conventional online optimization version.