The Study of Scheduling Optimization for Multi-Microgrid Systems Based on an Improved Differential Algorithm

As traditional power grids are unable to meet growing demand, extensive research on multi-microgrid scheduling has begun to address the issues present in conventional power grids. However, existing studies on the scheduling of grid-connected multi-microgrids still lack sufficient focus on system demand-side and interaction-side aspects. At the same time, the uncertainties of renewable energy and demand-side responses further complicate this research. To address this, this paper proposes an operational scheduling strategy based on an improved differential evolution algorithm, aiming to incorporate power interactions between microgrids, demand-side responses, and the uncertainties of renewable energy, thus enhancing the operational reliability and economic efficiency of multi-microgrid systems. The research in this paper is divided into the following steps: (1) constructing a multi-microgrid model primarily based on renewable energy; (2) formulating an optimization model with the objective of minimizing economic costs while ensuring stable system operation and solving it; (3) proposing an improved differential evolution algorithm for optimizing system scheduling; (4) testing and validating the improved differential algorithm; and (5) designing an operational strategy that accounts for the uncertainties of renewable energy and load demand. Through the application of real-world cases, the feasibility and effectiveness of the operational scheduling strategy based on the improved differential evolution algorithm are verified.