Abstract
To minimize total operation cost considering carbon emission, the energy management problem of a micro-grid is formulated by scheduling conventional generators (CGs), energy storage systems (ESSs) and power trading with the main grid. It is proposed in this paper to convert the energy management problem to a Markov Decision Process (MDP) and solve it with soft actor-critic (SAC), a cutting-edge deep reinforcement learning (DRL) algorithm. The agent is able to capture uncertainty of load and renewable generation (RG) from large volumes of historical data in training and able to provide scheduling plan from real-time measurement of uncertainty once training is completed. Simulation results demonstrate the effectiveness of the proposed method and its advantage over deep deterministic policy gradient (DDPG) in both operation cost and constraint violation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
