DEDSAC: Centralized microgrid dispatch via dual exploration mechanism enhanced diffusion soft actor-critic.

Abstract

The rapid development of renewable energy technologies and the proliferation of microgrids have led to increasingly complex power optimization and scheduling challenges in microgrid clusters. These challenges primarily stem from the heterogeneity and scale of the internal units within these clusters. To address these critical optimization issues effectively, we propose the Dual Exploration Mechanism Enhanced Diffusion Soft Actor-Critic (DEDSAC), a novel centralized, diffusion-based Soft Actor-Critic (SAC) framework. This framework features dual probabilistic exploration mechanisms, consisting of diffusion-based latent action generation and probability-triggered selective noise injection, together with a SAC-style squashed Gaussian reparameterization for actor optimization. It is designed to generate globally coordinated, high-quality decisions through direct modeling of the multi-peaked joint action space. The diffusion model serves as a robust policy network, enabling effective exploration of the action space. Extensive experiments across diverse conditions demonstrate that, upon convergence, DEDSAC effectively explores and converges to higher-quality scheduling strategies compared to standard SAC and other existing baseline algorithms. Based on the final converged evaluation reward, DEDSAC improves over SAC by 29.5% and 13.2% in small-scale and large-scale microgrids, respectively. Furthermore, DEDSAC approaches the Oracle Model Predictive Control (MPC) upper bound, achieving final evaluation rewards of -0.79 and -10.75 compared to Oracle MPC rewards of -0.34 and -8.55 in small-scale and large-scale scenarios, respectively. Results confirm DEDSAC's efficacy for optimizing power scheduling in large-scale microgrids, enabling efficient and sustainable energy management amid increasing system complexity and operational uncertainty.