基于增强精英保留遗传算法的虚拟微网群动态划分及能量局域自治

As one of the effective forms of renewable energy utilization, the multi-microgrid system has attracted much attention with the advancement of “carbon peaking and carbon Neutrality”. The purpose of this paper is to study the optimization method towards massive controllable equipment, so as to promote the local consumption of renewable energy and reduce the operation cost of the multi-microgrid system. However, the global control of massive controllable equipment will face the challenge of “curse of dimensionality”. In the existing research, the partitioning based on geographical distribution could achieve such control target via dimensional reduction. However, the fluctuation of renewable energy output, as well as the real-time power change and the migration in space of load will make it difficult for the fixed-boundary partitioning method to be applied in the dynamical energy management under the changing situation of multi-microgrid systems. In view of the above problems, this paper first establishes the system dynamic model for each individual microgrid. Then, the considered multi-microgrid system is divided into multiple virtual microgrid groups whose boundaries can be dynamically adjusted through the strengthen elitist genetic algorithm (SEGA), and the local autonomous energy optimization is implemented. Finally, a modified IEEE-123 node simulation model is built. The simulation results show that for one hour the operation cost of the virtual microgrid groups with dynamic boundary is 13.6% lower than that of the virtual microgrid with fixed boundary, and the time to obtain the solution via SEGA is 7.09% less than that via a conventional genetic algorithm.