Optimal Scheduling for Integrated Energy System via Model-Based Reinforcement Learning

The Integrated Energy System (IES) integrates energy consumption, conversion, and storage to increase the utilization of renewable energy sources and fossil fuels. When using heuristic algorithms and mathematical optimization methods for optimal scheduling of IES, these methods often face challenges of high computational cost and insufficient model accuracy, for which Reinforcement Learning (RL) offers a promising solution. However, most studies have used Model-Free Reinforcement Learning (MFRL) for optimal scheduling, which is limited by problems of low sampling efficiency and long training time. Model-Based Reinforcement Learning (MBRL) is an effective alternative to accelerate learning and facilitate RL applications. Therefore, this paper compares the performance gap between MFRL and MBRL in the optimal scheduling problem of IES to determine the superiority of MBRL. Experimental results show that the MBPO algorithm significantly improves sampling efficiency and learning speed at high prediction accuracy, outperforming the Soft Actor-Critic (SAC) algorithm.