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Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Cost of Operation Risk

机译:考虑运营成本的配电系统中分布式发电机的最优选址和选型

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With the penetration of distributed generators (DGs), operation planning studies are essential in maintaining and operating a reliable and secure power system. Appropriate siting and sizing of DGs could lead to many positive effects forthe distribution system concerned, such as the reduced total costs associated with DGs, reduced network losses, and improved voltage profiles and enhanced power-supply reliability. In this paper, expected load interruption cost is used as the assessment of operation risk in distribution systems, which is assessed by the point estimate method (PEM). In light with the costs of system operation planning, a novel mathematical model of chance constrained programming (CCP) framework for optimal siting and sizing of DGs in distribution systems is proposed considering the uncertainties of DGs. And then, a hybrid genetic algorithm (HGA), which combines the GA with traditional optimization methods, is employed to solve the proposed CCP model. Finally,the feasibility and effectiveness of the proposed CCP model are verified by the modified IEEE 30-bus system, and the test results have demonstrated that this proposed CCP model is more reasonable to determine the siting and sizing of DGs compared with traditional CCP model.
机译:随着分布式发电机(DG)的普及,运行计划研究对于维护和运行可靠且安全的电力系统至关重要。 DG的正确选址和大小调整可能会给相关的配电系统带来许多积极影响,例如与DG相关的总成本的降低,网络损耗的降低,电压曲线的改善以及电源可靠性的提高。本文将预期的负荷中断成本用作配电系统中操作风险的评估,该评估通过点估计法(PEM)进行评估。鉴于系统运营计划的成本,考虑分布式发电系统的不确定性,提出了一种新的机会分配规划(CCP)数学模型,用于分布式系统中分布式发电系统的最佳选址和规模确定。然后,将遗传算法与传统的优化方法相结合的混合遗传算法(HGA)用于求解所提出的CCP模型。最后,通过改进的IEEE 30-bus系统验证了该CCP模型的可行性和有效性,测试结果表明,与传统的CCP模型相比,该CCP模型在确定DG的选址和选型方面更为合理。

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