Over the last years, extreme weather events have caused extensive damages in power systems, leaving millions of customers without electricity and therefore highlighting the necessity to enhance power system resilience. This paper proposes a resilience constrained day-ahead unit commitment framework for increasing resiliency of a power system exposed to an extreme weather event. The weather-dependent failure probabilities of the transmission lines are taken into account in order to decide the scheduling of generators that minimizes load shedding in the most efficient way, while respecting operating limits of the system. The problem is formulated as a tri-level optimization problem that is transformed to a bi-level problem using duality theory and linearization techniques. The problem is solved as a two-stage robust optimization problem using a Column & Constraint Generation based decomposition algorithm. The master problem provides the unit commitment and the subproblem identifies the worst damage scenario due to weather event. A Sequential Monte Carlo simulation of a modified IEEE Reliability Test System and IEEE 118-bus System is applied to illustrate and validate the effectiveness of the proposed framework.

中文翻译：

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极端天气事件下抗灾力受限的日前机组承诺

在过去几年中，极端天气事件对电力系统造成了广泛破坏，导致数百万客户断电，因此凸显了提高电力系统弹性的必要性。本文提出了一种弹性受限的日前机组承诺框架，用于提高暴露于极端天气事件的电力系统的弹性。考虑到传输线的天气相关故障概率，以便确定以最有效的方式最小化减载的发电机调度，同时尊重系统的运行限制。该问题被表述为一个三级优化问题，该优化问题使用对偶理论和线性化技术转换为一个二级问题。该问题使用基于列和约束生成的分解算法作为两阶段鲁棒优化问题解决。主问题提供单位承诺，子问题确定由于天气事件造成的最坏破坏情况。应用修改后的 IEEE 可靠性测试系统和 IEEE 118 总线系统的顺序蒙特卡罗模拟来说明和验证所提出框架的有效性。