Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.