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The Impacts of a Decision Making Framework on Distribution Network Reconfiguration

机译：决策框架对分发网络重新配置的影响

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摘要

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.
机译：与传统系统相比，现代化的电力系统面临更复杂的挑战。对这些挑战作出反应的强大技术是网络重新配置，需要适应现代电力系统的“最近”的问题。因此，必须为更好的时间管理确定最佳配置，以便更好地管理每小时系统的决策。本文提出了一种优化算法，命名并行青蛙迁移算法（PFMA），以便快速识别每小时系统拓扑的最佳系统拓扑。所提出的技术的重要性是发展基于模糊的决策单元，以实际评估实施所识别的拓扑的必要性。这将显着降低系统操作员在分析所识别的最佳配置时的计算负担。在包含风力涡轮机和光伏（PV）分布发电单元（DGS）的不平衡136总线分配网络上验证了所提出的PFMA的有效性。结果证明，所示的PFMA能够比一般代数建模系统（Gams）软件的非线性求解器更快地识别接近最佳溶液。更重要的是，验证了开发的决策机制有效利用可再生DGS，并降低了网络重新配置的必要性。

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