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An optimal allocation and sizing strategy of distributed energy storage systems to improve performance of distribution networks

机译:分布式储能系统的优化分配和规模调整策略,以提高配电网的性能

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The allocation of grid-scale energy storage systems (ESSs) can play a significant role in solving distribution network issues and improving overall network performance. This paper presents a strategy for optimal allocation and sizing of distributed ESSs through P and Q injection by the ESSs to a distribution network. The investigation is carried out in a renewable-penetrated (wind and solar) medium voltage IEEE-33 bus distribution network for two different scenarios: (1) using a uniform ESS size and (2) using non-uniform ESS sizes. DIgSILENT PowerFactory is used for system modeling and testing, and simulation events are automated using Python scripting. A hybrid meta-heuristic optimization algorithm such as the fitness-scaled chaotic artificial bee colony algorithm is applied to optimize parameters of the objective function. The artificial bee colony algorithm is also applied to justify the results attained from the fitness-scaled chaotic artificial bee colony algorithm. A performance comparison, in relation to proposed PQ injection approach with previously applied P injection technique, is presented. The obtained results suggest that the proposed PQ injection-based ESS placement strategy performs better than the P injection-based approach, which can significantly improve distribution network performance by minimizing voltage deviation, power losses, and line loading.
机译:电网规模的储能系统(ESS)的分配在解决配电网络问题和提高整体网络性能方面可以发挥重要作用。本文提出了通过ESS向配电网注入P和Q来优化分布式ESS的分配和大小调整的策略。该调查是针对两种不同的情况在可再生渗透(风能和太阳能)中压IEEE-33总线配电网络中进行的:(1)使用统一的ESS大小,(2)使用非统一的ESS大小。 DIgSILENT PowerFactory用于系统建模和测试,并且使用Python脚本自动执行模拟事件。提出了一种混合元启发式优化算法,例如适应度尺度混沌人工蜂群算法,以优化目标函数的参数。人工蜂群算法也被用来证明从适应性规模混沌人工蜂群算法获得的结果的合理性。提出了与以前应用的P注入技术相关的PQ注入方法的性能比较。获得的结果表明,所提出的基于PQ注入的ESS放置策略比基于P注入的方法表现更好,它可以通过最小化电压偏差,功率损耗和线路负载来显着提高配电网络性能。

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