Abstract
Grid-tied renewable energy sources (RES) with battery-behind-meter (BBM) architectures have successfully been used to ensure effective energy cooperation between the grid and RES-based microgrids. Such environments are quite stochastic, thus making power management very challenging. This paper presents the use of an asynchronous Q-learning in performing a power flow management task in a multi-source electric vehicle charging station with the integration of vehicle-to-microgrid technology. The power scheduling problem is first formulated as a Markov decision process. Asynchronous Q-learning is then used to solve it. The algorithm is tested with a typical charging station load profile over a 24-hour period and compared with a simple rule-based algorithm. Simulation results show that the proposed method is able to select a power schedule that reduces the energy cost with a better utilization of both the battery storage system and the vehicle to microgrid energy compared to the rule-based method.
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