Because of the rapid development of electric vehicles (EVs), the energy management of multimicrogrid (MMG) systems has attracted considerable research attention. The objective of this study is to coordinate scheduling performance for MMG systems under large-scale EV operations. To address the problem that the calculation time increases exponentially with the scale of EVs, a clustering algorithm was proposed to speed up the solving efficiency of the coordinated scheduling of MMG systems. To address the problem of the departure and arrival times of clustering EV crossover among various MGs, the proposed clustering algorithm set the boundary of the parking period of clustering EVs by maximizing their controllable time in various MGs. The coordinated scheduling strategy of MMG systems was executed considering the charging cost of EVs, the optimization of transmission power curves, and the absorption of renewable energy. The simulation results revealed that by fully utilizing the mobile energy storage characteristics of EVs, the performance of MMG systems can be maximized. Meanwhile, the computing efficiency of coordinated scheduling can be considerably improved in the case of large-scale EVs integrated into MMG systems by using the proposed clustering algorithm.

中文翻译：

---

考虑电动汽车移动储能特性的多微电网系统协调调度


由于电动汽车（EV）的快速发展，多微电网（MMG）系统的能量管理引起了广泛的研究关注。本研究的目的是协调大规模电动汽车运营下 MMG 系统的调度性能。针对计算时间随着电动汽车规模呈指数增长的问题，提出了一种聚类算法来加快MMG系统协调调度的求解效率。针对集群电动汽车在各MG之间交叉的出发和到达时间问题，提出的聚类算法通过最大化集群电动汽车在各MG的可控时间来设置集群电动汽车的停车周期边界。综合考虑电动汽车充电成本、输电功率曲线优化以及可再生能源消纳等因素，执行MMG系统协调调度策略。仿真结果表明，通过充分利用电动汽车的移动储能特性，可以最大限度地发挥MMG系统的性能。同时，在大规模电动汽车集成到MMG系统的情况下，使用所提出的聚类算法可以显着提高协调调度的计算效率。