Data‐driven energy sharing for multi‐microgrids with building prosumers: A hybrid learning approach

Abstract

AbstractThe real‐time optimal scheduling of distributed energy resources (DERs) in interconnected multiple microgrids (MMGs) is facing great challenges due to the uncertainty of renewables, non‐linear network constraints, the involvement of multi‐level interest entities etc. Here, a data driven hybrid learning approach is proposed for real‐time hierarchical energy sharing for MMGs with building prosumers. First, a data‐driven XGBoost‐based supervised learning model is established to characterize price‐based demand response behaviours of prosumers for online P2P energy sharing results estimation among prosumers. Moreover, a multi‐agent deep reinforcement learning (MADRL) method is developed for the energy sharing among MMGs and multi‐agent deep deterministic policy gradient (MADDPG) algorithm is adopted to solve the optimization problem through centralized training and decentralized implementation. Particularly, the XGBoost‐based demand response model of prosumers is embedded into the MADRL environment so that a balanced optimization strategy can be learned through the continuous interaction between MMGs agents and the environment. Finally, the effectiveness of the proposed method is demonstrated by a case study simulation with an artificial intelligence experimental platform.