摘要
为解决多微网集成聚合控制与运行管理问题,提出一种多微网柔性直流互联方案及多目标优化调度方法。首先介绍了多微网柔性直流互联结构和电压源型变流器(VSC)模型。然后,以微网中电压波动最小和系统网损最小为优化目标,综合考虑交流系统、直流系统和VSC的运行约束,提出了基于柔性直流互联多微网的多目标优化模型。最后,采用改进非劣排序多目标遗传算法(NSGA-Ⅱ)和交直流混合潮流算法对多目标优化模型进行求解,得到帕累托(Pareto)最优解集,针对Pareto最优解集中解的数量大的问题,提出采用模糊聚类法对Pareto最优解集进行筛选,得到最终优化调度方案。对算例进行了仿真分析,结果表明所提模型和算法能够有效抑制微网电压的波动,进而提高可再生能源的渗透率,同时保证系统的经济运行。
In order to solve the problem of multi-microgrids integration and aggregation in controlling and operation management, a multi-microgrids flexible DC interconnect scheme and a multiobjective optimization scheduling method were proposed. Firstly, the structure of multi-microgrids flexible DC interconnection and the voltage source converter (VSC) model were introduced. Then, took the minimum voltage fluctuation and the minimum network loss as optimization objectives, a multiobjective optimization model based on flexible DC interconnection was proposed considering the operation constraints of the AC system, the DC system and the VSC. Finally, the multi-objective optimization model was solved by NSGA-II and AC-DC hybrid power flow algorithm to get the Pareto optimal solution set. In view of the Pareto optimal solution set is large, a method based on fuzzy clustering was used to get the final optimal scheduling solution. The simulation results show that the proposed model and algorithm can effectively reduce the voltage fluctuation of multi-microgrids and improve the permeability of renewable energy while ensuring the economic operation of the system.
作者
张释中
裴玮
杨艳红
肖浩
孔力
Zhang Shizhong;Pei Wei;Yang Yanhong;Xiao Hao;Kong Li(Institute of Electrical Engineering,Chinese Academy of Sciences,Beijing 100190 China;School of Electronic, Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2019年第5期1025-1037,共13页
Transactions of China Electrotechnical Society
基金
国家科技部重点研发专项(2017YFB0903300)
国家自然科学基金青年科学基金(51507164)项目资助
关键词
多微网
柔性直流
多目标优化
NSGA-Ⅱ
模糊聚类
Multi-microgrids
flexible DC
multi-objective optimization
NSGA-Ⅱ
fuzzy clustering