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Optimal integration of distributed generation resources in active distribution networks for techno-economic benefits
Energy Reports ( IF 4.7 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.egyr.2020.12.004
Ahmed S. Hassan , ElSaeed A. Othman , Fahmy M. Bendary , Mohamed A. Ebrahim

Abstract In recent years, modern electricity utilities face great challenges regarding the deregulated energy markets, transition toward sustainable smart grids and the increased load demand. These challenges are a part of the reasons that have paved the way toward the rapid progress of spreading the different Distributed Generation (DG) technologies into the modern Distribution Networks (DNs). DGs integration into DNs can be employed as a key solution for tackling the problems, facing the distribution systems, and verifying more technical and economic benefits while considering the systems’ uncertainties and the operational policies of the distribution utilities. This paper introduces the application of Modified Sine Cosine Algorithm (MSCA) for enhancing the DNs performance through the integration of multiple DG technologies in order to optimize the active power losses, the fast voltage stability index and the total costs, considering the DGs penetration level as well as the DG units’ operating power factor constraints. The proposed algorithm has been implemented using MATLAB software and applied on three-benchmark IEEE test systems (30-bus, 33-bus and 300-bus) as different models of electric power networks. The attained results show that the suggested optimization platform especially using MSCA, is more effective and successful in determining and finding better results than existing results.

中文翻译:

分布式发电资源在有源配电网中的优化整合以实现技术经济效益

摘要 近年来,现代电力公司面临着能源市场放松管制、向可持续智能电网过渡以及负荷需求增加等方面的巨大挑战。这些挑战是为将不同的分布式发电 (DG) 技术迅速传播到现代配电网络 (DN) 铺平道路的部分原因。将分布式电源集成到 DN 中可以作为解决问题、面对配电系统、验证更多技术和经济效益的关键解决方案,同时考虑系统的不确定性和配电公用事业的运营政策。本文介绍了改进正弦余弦算法 (MSCA) 的应用,通过集成多种 DG 技术来提高 DNs 性能,以优化有功功率损耗、快速电压稳定指数和总成本,考虑 DGs 渗透水平为以及 DG 机组的运行功率因数限制。所提出的算法已使用MATLAB软件实现,并应用于三个基准IEEE测试系统(30-bus、33-bus和300-bus)作为不同的电力网络模型。获得的结果表明,建议的优化平台,尤其是使用 MSCA,在确定和找到更好的结果方面比现有结果更有效和更成功。考虑到 DG 的渗透水平以及 DG 机组的运行功率因数限制。所提出的算法已使用MATLAB软件实现,并应用于三个基准IEEE测试系统(30-bus、33-bus和300-bus)作为不同的电力网络模型。获得的结果表明,建议的优化平台,尤其是使用 MSCA,在确定和找到更好的结果方面比现有结果更有效和更成功。考虑到 DG 的渗透水平以及 DG 机组的运行功率因数限制。所提出的算法已使用MATLAB软件实现,并应用于三个基准IEEE测试系统(30-bus、33-bus和300-bus)作为不同的电力网络模型。获得的结果表明,建议的优化平台,尤其是使用 MSCA,在确定和找到更好的结果方面比现有结果更有效和更成功。
更新日期:2020-11-01
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