Privacy-Preserving Load Scheduling in Residential Microgrids Using Multi-Agent Reinforcement Learning

This paper investigates the load scheduling problem within a residential microgrid, where the microgrid operator (MO) is regarded as a trusted third-party that provides a limited information exchange for all residential customers. We consider both power exchange between the microgrid and the utility grid and local energy trading between customers, where a pricing model based on the inclining block rate (IBR) is applied to generate the local trading prices (LTPs). A privacy-preserving load scheduling method based on multi-agent deep reinforcement learning (MADRL) is proposed, which can simultaneously reduce the energy cost of the customers and the peak load of the microgrid. To preserve privacy, the proposed method adopts the centralized training with decentralized execution (CTDE) technique so that the trained agents of each customer can schedule their energy consumption only based on the local information. Finally, case studies based on real-world residential load data are presented, and the results show that the proposed method can efficiently reduce the electricity cost of customers and the difference between daily peak and valley load of the microgrid.