Multi-Agent Deep Reinforcement Learning Approach for Temporally Coordinated Demand Response in Microgrids

Price-based and incentive-based demand response (DR) are both recognized as promising solutions to address the increasing uncertainties of renewable energy sources (RES) in microgrids. However, since the temporally optimization horizons of price-based and incentive-based DR are different, few existing methods consider their coordination. In this paper, a multi-agent deep reinforcement learning (MA-DRL) approach is proposed for the temporally coordinated DR in microgrids. The proposed method enhances micrigrid operation revenue by coordinating day-ahead price-based demand response (PBDR) and hourly direct load control (DLC). The operation at different time scales is decided by different DRL agents, and optimized by a multi-agent deep deterministic policy gradient (MA-DDPG) using a shared critic to guide agents to attain a global objective. The effectiveness of the proposed approach is validated on a modified IEEE 33-bus distribution system and a modified heavily loaded 69-bus distribution system.