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Optimal deployment of renewable DG and battery storage system in distribution system considering techno-economic, environment and reliability aspects

机译:考虑到技术经济,环境和可靠性方面的问题,可再生能源和电池存储系统在配电系统中的优化部署

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In the present scenario of energy market restructuring and global warming incorporation of Renewable Distributed Generation (DG) in the distribution system is one of the efficient and viable strategies that can be adapted by the Distribution Companies (DISCO) for the overall benefit of the system. In this paper, a Non-Dominant Sorted Genetic Algorithm II (NSGA II) based multi-objective approach is developed for optimal deployment of DG to maximize voltage profile improvement, reliability, economic and environmental benefit. To address the issue of intermittence of renewable DG, Battery Storage System (BSS) is also used in the system. A new formula is proposed to evaluate the failure rate of the distribution line. Reliability indices such as Expected Energy not served (EENS) is used to assess the reliability of the network by the incorporation of DG. New and logical formulas such as Network voltage profile improvement index (NVPII), Benefit Cost Ratio (BCR), and Environmental Benefit Index (EBI) are formulated to assess the overall performance of the device The proposed method is tested on 33-bus radial system for time variant practical load models. The proposed technique when compared with other multi-objective algorithms shows good applicability and superior performance. This analysis facilitates the planning engineers to choose an optimal approach for the overall benefit of the system.
机译:在当前的能源市场重组和全球变暖的情况下,将可再生分布式发电(DG)纳入配电系统是配电公司(DISCO)可以为系统的整体利益而进行调整的一种高效可行的策略之一。本文提出了一种基于非支配排序遗传算法II(NSGA II)的多目标方法,用于DG的最佳部署,以最大程度地提高电压曲线,提高可靠性,经济和环境效益。为了解决可再生DG的间歇性问题,系统中还使用了电池存储系统(BSS)。提出了一种新的公式来评估配电线路的故障率。通过合并DG,使用诸如未提供预期能量(EENS)之类的可靠性指标来评估网络的可靠性。制定了新的逻辑公式,例如网络电压曲线改善指数(NVPII),效益成本比(BCR)和环境效益指数(EBI),以评估设备的整体性能。该方法在33总线径向系统上进行了测试用于时变实际负载模型。与其他多目标算法相比,所提出的技术具有良好的适用性和优越的性能。该分析有助于规划工程师选择最佳方法,以实现系统的整体效益。

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