A Hierarchical Distributed Method for Source-Grid-Load-Storage Coordinated Power and Energy Balance in Distribution Networks

The integration of distributed energy resources into modern multi-energy distribution networks introduces complexity, necessitating effective coordinated control to maintain stability. This study proposes a hierarchical distributed control strategy combining distributed autonomy with centralized coordination. A “self-balancing and self-smoothing” tie-line power control method is introduced for small-scale networks, while a coordinated optimization model is established for regional networks, solved using the Evolutionary Particle Swarm Optimization (EPSO) algorithm. Simulation results based on a real-world Zhejiang distribution network demonstrate that the proposed method effectively regulates power fluctuations. Compared to centralized optimization, the hierarchical approach reduced total system operating costs and exhibited improved computational scalability as the number of distributed entities increased. The proposed method achieves economic optimality for various stakeholders and enhances the operational efficiency of “Source-Grid-Load-Storage-Heat” systems, providing a robust solution for energy complementarity in new power systems.