基于深度优先搜索和灰狼算法的微电网重构-《郑州大学学报(工学版)》

文章信息/Info

Title:: Optimization Method Based on Depth-first Search and Grey Wolf Algorithms for Reconfiguration of Microgrid

Author(s):: Zhang Qinglin; Xin Xiaonan; Cheng Zhiping; School of Electrical Engineering, Zhengzhou University

Keywords:: Microgrid; refactoring; depth-first search; Gray wolf optimization algorithm; Grid structure; radial structure

摘要:: 针对主网发生非计划故障和微电网并网转孤岛时，微电网对负荷持续供电和系统稳定运行的问题，为了从全局高度对电力网络内设备协调控制，建立了微电网重构模型，提出了一种深度优先搜索和灰狼优化算法相混合的重构方法。该方法以开关状态和可调设备的功率为优化变量，针对重构优化过程中的非辐射状网架结构问题，用深度优先搜索对网架结构进行识别、分析和处理，用前推回代法计算网络潮流分布，以灰狼优化算法为框架，获得重构方案。仿真结果表明，所提出的混合重构方法，其全局搜索能力更强，其重构结果可行并且较优，而且重构策略以开关状态和功率的组合为优化变量，比仅有开关状态或仅有功率的变量，对系统优化调整更具有优势。

Abstract:: Aiming at the problem of continuous power supply to loads and stable operation of the system when the main grid has an unplanned failure and the microgrid is connected to the island, in order to coordinate and control the equipment in the power network from a global perspective, a microgrid reconfiguration model is established. A reconstruction method combining depth-first search and gray wolf optimization algorithm is proposed. In this method, the switch state and the power of the adjustable equipment are used as optimization variables. Aiming at the non-radial grid structure problem in the process of reconstruction optimization, the depth-first search is used to identify, analyze and process the grid structure. The network power flow distribution is calculated by the method, and the reconstruction scheme is obtained by using the gray wolf optimization algorithm as the framework. The simulation results show that the proposed hybrid reconstruction method has stronger global search ability, and its reconstruction results are feasible and better, and the reconstruction strategy takes the combination of switch state and power as the optimization variable, which is better than only the switch state or only Variables with power are more advantageous for system optimization and adjustment.