Cloud-Edge Collaborative Optimization Model of Multi-Energy System for Energy Storage Aggregation Agent

Under the goal of "dual carbon", the regional integrated energy system is the core carrier for integrating intermittent renewable energy and multi-type loads, and distributed energy storage is a key resource to calm the fluctuation of new energy.This study constructs a cloud-edge collaborative optimization model for multi-energy systems. The cloud side focuses on the global energy balance of the cluster of regional integrated energy systems (RIES) to minimize the cost of purchased energy, while the edge side optimizes the internal operation of individual RIES, balancing cost control and the enhancement of power adjustable capacity. The Alternating Direction Method of Multipliers (ADMM) is employed to coordinate the decision variables of the cloud and edge sides, thereby avoiding data transmission delays.The simulation comparison of the three regional integrated energy systems shows that the adjustable margin of each energy subsystem is more balanced under cloud-edge collaborative optimization, which achieves the smallest external energy demand and the best internal operating cost of the cluster, which verifies the effectiveness of the model and improves the flexibility and autonomy of the system.