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Ɛ-constraint multiobjective approach for optimal network reconfiguration and optimal allocation of DGs in radial distribution systems using the butterfly optimizer

机译:使用蝶形优化器的最佳网络重新配置和径向分布系统中DG的最优配置方法的约束多目标方法

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In recent days, the optimal allocation of distributed generators (DGs) problem in the distribution system caught several reader's attention to improve the system efficiency, to meet the future load growth. In this article, a multiobjective approach is proposed to determine the optimal locations for DGs, optimal DGs sizes, optimal power factors of DGs in the presence of optimal network reconfiguration to maximize the maximum loadability(lambda(max)), minimize the active power loss and maximize the loading margin factor(lambda(v))of the system. Butterfly Optimization (BO) algorithm is implemented to optimize the desired objectives. The epsilon-constraint method is used for multiobjective optimization, spanning tree technique is used for checking the radiality of the system and modified repetitive power flow using radial load flow algorithm is developed for finding the lambda(max),lambda(v).The proposed approach is tested on 33 bus and 69 bus radial distribution test systems. Four scenarios are considered to achieve the desired objectives and each scenario consists of two cases: optimal allocation of DGs in the initial configured network, optimal allocation of DGs in the optimal reconfigured network. Results suggest that optimal allocation of DGs in scenario 4 gives a better improvement in maximum system loadability, loading margin factor, and active power loss reduction of the system. Obtained results compared with the suitable methods and algorithms that are available in the literature.
机译:最近几天,分销系统中分布式发电机(DGS)问题的最佳分配占据了几个读者的注意力,以提高系统效率,以满足未来的负荷增长。在本文中,提出了一种多目标方法来确定DGS的最佳位置,最佳DGS大小,在最佳网络重新配置的情况下,DG的最佳功率因子,以最大限度地提高最大可加载性(Lambda(MAX)),最大限度地减少有源功率损耗并最大化系统的加载边缘因子(Lambda(v))。蝴蝶优化(BO)实现以优化所需目标。 epsilon约束方法用于多目标优化,跨越树技术用于检查系统的辐射性,使用径向载荷算法进行修改的重复功率,用于查找Lambda(MAX),Lambda(V)。该提议在33总线和69母线径向分布测试系统上测试了方法。认为四种情况是实现所需的目标,每种情况都包含两个情况:在初始配置网络中的DGS最佳分配,最佳重新配置网络中的DGS最佳分配。结果表明,方案4中DGS的最佳分配给出了最大系统可加载性,加载边缘因子和系统的有源功率损耗的更好提高。与文献中可用的合适方法和算法相比,获得的结果。

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