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Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty

机译：考虑负荷不确定性的Big Bang-Big Crunch算法在配电网络中的多目标最优重配置和DG（分布式发电）功率分配

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In this paper, a multi-objective framework is proposed for simultaneous network reconfiguration and power allocation of DGs (Distributed Generations) in distribution networks. The optimization problem has objective functions of minimizing power losses, operation cost, and pollutant gas emissions as well as maximizing the voltage stability index subject to different power system constraints. The uncertainty of loads is modeled using the TFN (Triangular Fuzzy Number) technique. A novel solution method called MOHBB-BC (Multi-objective Hybrid Big Bang-Big Crunch) is implemented to solve the optimization problem. The MOHBB-BC derives a set of non-dominated Pareto solutions and accumulates them in a retention called Archive. The diversity of Pareto solutions conserved by applying a crowding distance operator and afterwards, the 'best compromised' Pareto solution is selected using a fuzzy decision maker. The proposed method is tested on two test systems of 33-bus and 25-bus in different cases including unbalanced three-phase loads. Results obtained from test cases elaborate that the MOHBB-BC results in more diversified Pareto solutions implying a better exploration capability even with a higher fitness. In addition, considering load uncertainty leads to a more realistic solution than deterministic loads but with higher level of power losses. (C) 2016 Elsevier Ltd. All rights reserved.
机译：在本文中，提出了一个多目标框架，用于分布式网络中分布式发电的同时网络重新配置和功率分配。优化问题的目标功能是最大程度地降低功率损耗，运营成本和污染物气体排放，并使受不同电力系统约束的电压稳定性指标最大化。使用TFN（三角模糊数）技术对负载的不确定性进行建模。为了解决该优化问题，提出了一种新颖的求解方法，称为MOHBB-BC（多目标混​​合Big Bang-Big Crunch）。 MOHBB-BC派生了一组非支配的Pareto解决方案，并将它们累积在称为存档的保留项中。通过应用拥挤距离算子来保存帕累托解决方案的多样性，然后使用模糊决策者选择“最佳折衷”帕累托解决方案。所提出的方法在包括不平衡三相负载在内的不同情况下的33总线和25总线的两个测试系统上进行了测试。从测试案例中获得的结果详细说明了MOHBB-BC导致更多样化的Pareto解决方案，这意味着即使具有更高的适用性，也具有更好的勘探能力。此外，考虑负载不确定性会导致比确定性负载更切合实际的解决方案，但功耗会更高。 （C）2016 Elsevier Ltd.保留所有权利。

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