A Lagrange Multiplier Based State Enumeration Reliability Assessment for Power Systems With Multiple Types of Loads and Renewable Generations

Zeyu Liu, K. Hou, H. Jia, Junbo Zhao, Dan Wang, Yunfei Mu, Lewei Zhu
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Abstract

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impactincrement method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.
基于拉格朗日乘子的多负荷可再生发电系统状态枚举可靠性评估
随着多类型负荷和可再生发电的整合,系统状态数显著增加。因此,运行最优潮流(OPF)来分析无数个系统状态是一项挑战,这严重制约了状态枚举方法的效率。为了解决这个问题,本文提出了一种基于拉格朗日乘子的状态枚举(LMSE)方法来加速分析而不损失准确性。其核心思想是通过基于拉格朗日乘子的函数直接获得偶然性状态的最优减载,而不是耗时的OPF算法。该方法还可以方便地与影响增量法和聚类技术相结合,进一步提高效率。案例研究在考虑多种类型负载、光伏(pv)和风力涡轮机(WTs)的RTS-79和IEEE 118总线系统上进行。结果表明,该方法可以在不影响计算精度的前提下显著缩短计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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