Collaborative forecasting management model for multi-energy microgrid considering load response characterization

Abstract

Multi-energy microgrids (MEMG) have become an effective means of integrated energy management due to their unique advantages, including area independence, diverse supply, flexibility, and efficiency. However, the uncertain deviation of the renewable energy generators (REGs) output and the uncertain deviation of the multiple energy load response cumulatively lead to the deterioration of the MEMG model performance. To address these issues, this article proposes a cooperative forecasting management model for MEMG that considers multiple uncertainties and load response knowledge characterization. The model combines a multi-energy load prediction model with a management model based on deep reinforcement learning. It proposes multiple iterations of data, fits the dynamic environment of MEMG by continuously improving the long short-term memory (LSTM) neural network based on knowledge distillation (KD) architecture, and then optimizes the MEMG state space by considering the knowledge of load response characteristics, Furthermore, it combines multi-agent deep deterministic policy gradient (MADDPG) with horizontal federated (hF) learning to co-train multi-MEMG, addressing the issues of training efficiency during co-training. Finally, the validity of the proposed model is demonstrated by an arithmetic example.