Traditional scheduling methods predominantly rely on “centralized optimization,” overlooking the potential for energy complementarity between nodes, which leads to local overloading or resource idleness. This study constructs a spatial energy interaction matrix and a time-rolling optimization framework to achieve coordinated energy complementarity between nodes and dynamic adjustments across multiple timescales. A priority-weighted scenario switching mechanism is designed to dynamically adjust scheduling objectives based on real-time status, thereby meeting diverse requirements such as peak shaving, frequency regulation, and emergency reserve. To address the model's high-dimensional nonlinearity, we enhance the standard Particle Swarm Optimization (PSO) algorithm by proposing an Improved PSO (IPSO) algorithm. This incorporates dynamic inertia weights and local search strategies to boost computational efficiency. Simulation results demonstrate that compared to methods without DESS and traditional dispatch approaches, the proposed method effectively reduces system operating costs, increases renewable energy integration rates, improves voltage quality, and significantly enhances the system's adaptability to fluctuations in wind and solar power output and extreme scenarios.
Mao et al. (Sun,) studied this question.