Flexible Resource Scheduling of Distribution Network Based on Deep Reinforcement Learning

This paper addresses the dispatch problem of distribution networks containing renewable energy by proposing an optimization dispatch strategy based on incentive-based demand response. The strategy comprehensively considers the coordinated optimization of transferable loads, reducible loads, and energy storage systems, aiming to minimize electricity purchase costs, wind curtailment costs, energy storage usage costs, and demand response costs. An optimization dispatch model for distribution networks is established. By introducing an incentive-based demand response mechanism, users are guided to participate in grid dispatch, improving the economic efficiency and reliability of system operation. The TD3 (Twin Delayed Deep Deterministic policy gradient) algorithm from reinforcement learning is employed to solve the model, and its effectiveness is validated through case studies on a modified IEEE 33-node system. Experimental results demonstrate that the proposed strategy can effectively reduce system operating costs and increase the utilization rate of renewable energy.