Optimization Method Based on Depth-first Search and Grey Wolf Algorithms for Reconfiguration of Microgrid
[1]Zhang Qinglin,Xin Xiaonan,Cheng Zhiping.Optimization Method Based on Depth-first Search and Grey Wolf Algorithms for Reconfiguration of Microgrid[J].Journal of Zhengzhou University (Engineering Science),2020,41(02):73-79.[doi:10.13705/j.issn.1671-6833.2020.03.003]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41
Number of periods:
2020 02
Page number:
73-79
Column:
Public date:
2020-05-31
- Title:
- Optimization Method Based on Depth-first Search and Grey Wolf Algorithms for Reconfiguration of Microgrid
- Keywords:
- Microgrid; refactoring; depth-first search; Gray wolf optimization algorithm; Grid structure; radial structure
- CLC:
- -
- 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.
- References:
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Last Update:
2020-05-30
