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Optimal Allocation of Wind Turbines Considering Different Costs for Interruption Aiming at Power Loss Reduction and Reliability Improvement Using Imperialistic Competitive Algorithm

机译:基于帝国竞争算法的考虑中断成本的风力发电机组优化分配,旨在降低功率损耗并提高可靠性

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摘要

In recent years, utilization of wind-based distributed generation (DG) as one of the most widespread used types of green generation technologies has attracted remarkable consideration. Recently, several contributions have accomplished in case of wind-based generation. In this research, in order to minimize the costs of annual energy losses and Energy Not Supplied (ENS) of the distribution networks, a multi-objective probabilistic based approach is proposed to determine the optimal location and capacity of wind-based units along with providing assurance for a desirable level of voltage profile. An Imperialist Competitive Algorithm (ICA)'is employed to overcome non-convexity and complexity of the mixed integer optimization problem of DG allocation in the radial distribution networks. For this purpose, a Fuzzy C-Means (FCM) clustering is used to categorize historical data of load demands and output power of DG units. Then, given the fuzzy center point of the clusters and their respective probability generated from FCM clustering implementation, uncertainty consideration is involved to the probabilistic calculation of both of the wind-based DG and load demand. Moreover, during the calculation process of ENS, different costs of interruption are considered for various customers of the network. Finally, presented technique is exerted for an IEEE 33-bus standard test system subject to the system constrains under different cases and effectiveness and capability of the method is assessed. Obtained results demonstrate competence of the method to produce a significant reduction in ENS value and annual energy losses.
机译:近年来,基于风的分布式发电(DG)作为最广泛使用的绿色发电技术之一的利用已引起了广泛的关注。近来,在基于风力的发电的情况下已经完成了一些贡献。在这项研究中,为了最小化配电网的年度能源损失和未供应能源(ENS)的成本,提出了一种基于多目标概率的方法来确定风电设备的最佳位置和容量,并提供确保理想的电压曲线水平。为了克服径向分布网络中DG分配的混合整数优化问题的不凸性和复杂性,采用了帝国主义竞争算法(ICA)。为此,使用模糊C均值(FCM)聚类对DG机组的负荷需求和输出功率的历史数据进行分类。然后,给定群集的模糊中心点以及从FCM群集实施中生成的相应概率,将不确定性考虑纳入基于风的DG和负荷需求的概率计算中。此外,在ENS的计算过程中,网络的各个客户都考虑了不同的中断成本。最后,针对在不同情况下受系统约束的IEEE 33总线标准测试系统应用了提出的技术,并评估了该方法的有效性和能力。所得结果表明该方法具有显着降低ENS值和年度能量损失的能力。

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