Research on Optimal Scheduling of Multi-micro-grid-Shared Energy Storage Power Station Based on Improved Vulture Algorithm

The current operation of user-side distributed energy storage faces several challenges, including limited diversity, high costs, and low utilization. In order to address these issues, this study proposes an optimization method that combines the enhanced vulture algorithm (CAOBES) with the power self-operation and sharing mode of multiple microgrid-shared energy storage joint operation. The focus is on optimizing energy trading in the electricity market environment. This study provides a brief explanation of the operational mode and profit mechanism of shared energy storage power stations while improving upon the traditional vulture search algorithm by incorporating chaos optimization and adaptive opposition-based learning techniques. Furthermore, it discusses its application in a multi-microgrid system for combined heat and power (CHP), which effectively integrates optimal configuration of shared energy storage power stations with efficient operations of multi-microgrid systems. The results demonstrate that CAOBES can reduce users' energy consumption costs, increase new energy consumption rates in multi-microgrid systems to over 95%, decrease total operating costs of multi-microgrid systems by 6.4%, and achieve mutual benefits between users and operators of energy storage stations.