Decentralized Dispatch Strategy for Island Microgrid Clusters Based on Historical Similarity and Offline Training

To address the issues of efficiency and real-time performance in power mutual assistance among island microgrid clusters, a two-stage decentralized dispatching optimization method combining similarity measurement with offline training and online optimization is proposed. Firstly, the power architecture of island microgrid clusters is studied, and the mathematical model of island microgrid units is established. Secondly, a centralized offline training model for island microgrid clusters with economic cost and carbon emissions as indicators is constructed, and the output states of adjustable units under different natural resource conditions are stored. Then, in actual operation, each microgrid selects the most similar historical data through similarity measurement and uses the output of controllable units as the initial dispatching value for online optimization. Finally, the effectiveness of the proposed method is verified through a practical example, reducing the dependence of island microgrid clusters on long-distance communication between islands and the mainland.