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Stochastic optimization for AC optimal transmission switching with generalized Benders decomposition

机译:具有广义弯曲分解的交流最优传输切换的随机优化

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Optimal transmission switching is proposed in recent years to optimize the power system operational cost in deterministic studies. With the rapid growth of renewable generations, the grid uncertainties have greatly increased, which cannot be ignored in the decision-making of optimal transmission switching problems. This paper proposes a novel two-stage stochastic optimization formulation with a convex relaxation for AC optimal transmission switching problems. A generalized Benders decomposition based algorithm, including an inner loop and an outer loop, is proposed to solve the AC optimal transmission switching problem with grid uncertainties. The optimal switching plan and the expected system cost will be found in the iterative calculation without any sacrifice of accuracy. Numerical studies on the IEEE 118-bus system and the South Carolina 500-bus confirm the effectiveness of the proposed decomposition approach in solving the AC optimal transmission switching problems with grid uncertainties. The scalability analysis shows the proposed approach is efficient in dealing with a large number of scenarios.
机译:近年来提出了最佳传输切换,优化了确定性研究中的电力系统运营成本。随着可再生代的快速增长,网格不确定性大大增加,在最佳传输切换问题的决策中不能忽略。本文提出了一种新型两阶段随机优化配方,具有凸松弛,用于AC最佳传输切换问题。提出了一种基于弯曲的基于分解的算法,包括内环和外环,以解决网格不确定性的AC最佳传输切换问题。在迭代计算中将在迭代计算中找到最佳切换计划和预期的系统成本,而无需准确性。 IEEE 118总线系统的数值研究和南卡罗来纳州500公交车确认了建议分解方法在求解网格不确定性的求解交流热传输切换问题方面的有效性。可伸缩性分析显示所提出的方法在处理大量方案方面是有效的。

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