Coordinated scheduling of integrated energy microgrid with multi-energy hubs based on MADDPG and two-layer game

Abstract

Considering that traditional centralized scheduling cannot realize energy complementarity and benefit coordination between multi-energy hubs (EHs) and advantages of artificial intelligence technology in the application of integrated energy microgrid (IEM), a coordinated scheduling model and method of IEM with multi-EHs based on multi-agent deep deterministic policy gradient (MADDPG) is proposed. First, an IEM framework of multi-EHs is constructed, and the rationality of multi-EH construction is illustrated. Then, considering operation cost, environmental cost, and benefit of new energy, a two-layer economic optimal scheduling model of IEM is established. Furthermore, distributed deep reinforcement learning based on MADDPG and game theory is introduced, and a coordinated scheduling method of IEM with multi-EHs based on the algorithm is proposed. At the same time, based on the idea of transfer learning, an off-line training and online learning method is proposed, which can improve the training speed of online learning. Finally, a numerical example is constructed to verify the effectiveness of the proposed model and method.