Modern distribution power systems face more complex challenges compared to the conventional systems. A powerful technique to react to such challenges is network reconfiguration, which needs to be adapted with “recent” concerns of modern power systems. Hence, it is essential to determine optimal configurations “rapidly” for better time management of hour-ahead system's decision making. This paper proposes an optimization algorithm, named parallel frog migrating algorithm (PFMA), to rapidly identify the optimal system topology for hour-ahead systems operation. The significance of the proposed technique is the development of a fuzzy-based decision making unit to realistically evaluate the necessity of implementing the identified topologies. This will noticeably decrease the computational burden of system operator in analyzing the identified optimal configurations. The effectiveness of the proposed PFMA is validated on an unbalanced 136-bus distribution network containing wind turbine and photovoltaic (PV) distributed generation units (DGs). It is demonstrated that the presented PFMA is able to identify close to optimal solutions faster than the nonlinear solver of General Algebraic Modeling System (GAMS) software. More importantly, it is verified that the developed decision making mechanism effectively takes advantage of renewable DGs and reduces the necessity of network reconfiguration.

中文翻译：

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决策框架对配电网络重构的影响

与常规系统相比，现代配电电源系统面临更复杂的挑战。应对此类挑战的一项强大技术是网络重新配置，它需要适应现代电力系统的“最新”关注。因此，至关重要的是“迅速”确定最佳配置，以便更好地管理提前小时系统的决策时间。本文提出了一种优化算法，称为并行青蛙迁移算法（PFMA），可以快速识别提前小时系统运行的最佳系统拓扑。所提出的技术的重要性在于开发了一个基于模糊的决策单元，以现实地评估实施已识别拓扑的必要性。这将显着减少分析确定的最佳配置时系统操作员的计算负担。PFMA的有效性在不平衡的136总线配电网络上得到了验证，该配电网络包含风力涡轮机和光伏（PV）分布式发电装置（DG）。结果表明，与通用代数建模系统（GAMS）软件的非线性求解器相比，所提出的PFMA能够更快地识别出接近最优的解决方案。更重要的是，可以证明所开发的决策机制有效地利用了可再生DG的优势，并减少了网络重新配置的必要性。结果表明，与通用代数建模系统（GAMS）软件的非线性求解器相比，所提出的PFMA能够更快地识别出接近最优的解决方案。更重要的是，可以证明所开发的决策机制有效地利用了可再生DG的优势，并减少了网络重新配置的必要性。结果表明，与通用代数建模系统（GAMS）软件的非线性求解器相比，所提出的PFMA能够更快地识别出接近最优的解决方案。更重要的是，可以证明所开发的决策机制有效地利用了可再生DG的优势，并减少了网络重新配置的必要性。