调度电力生产单元以减轻恶劣天气影响：一种高效的计算实现
Scheduling Electricity Production Units to Mitigate Severe Weather Impact: An Efficient Computational Implementation
AI总结：
本文提出一种框架，通过预先安排发电单元的投入以适应可能发生的恶劣天气后的传输拓扑，以最小化负载削减。研究改进了物理定律的表示并开发了高效的求解算法。
摘要：
电力系统中的极端天气事件可能导致线路跳闸或输电基础设施的物理损坏，进而导致大规模负载削减。为减轻此风险，本文提出一个框架，通过战略性地提前安排发电单元（特别是慢启动单元）的投入，以适应可能发生的恶劣天气后的传输拓扑。目标是在最坏条件下最小化负载削减。本文的两个主要贡献是：首先，提供了比先前研究更准确的底层物理定律表示；其次，开发了一种高效求解算法，优于现有最先进的求解器。所提出的框架被建模为一个两阶段鲁棒优化模型。第一阶段安排发电单元以应对中断，第二阶段优化功率调度决策以在最坏的传输拓扑下最小化负载削减。为确保系统可靠性和安全性，我们整合了凸化的交流功率流约束。所得到的模型是一个三阶段混合整数非线性优化问题。为解决其计算复杂性，我们设计了一种问题特定的外近似算法，嵌入在列生成和约束生成框架中。计算结果表明，所提出的模型和求解方法可以在合理时间内为中等规模实例达到标准最优性间隙。
英文摘要：
Extreme weather events in electric power systems can cause line trips or physical damage to transmission infrastructure, potentially leading to large-scale load shedding. To mitigate this risk, we propose a framework that strategically pre-positions the commitment of generation units--particularly slow-start units--to adapt to transmission topologies that may arise following such events. The objective is to minimize load shedding under worst-case conditions. This paper makes two main contributions. First, we provide a more accurate representation of the underlying physical laws than those used in prior studies. Second, we develop a highly efficient solution algorithm that outperforms state-of-the-art, off-the-shelf solvers. The proposed framework is formulated as a two-stage robust optimization model. In the first stage, generation units are scheduled in anticipation of disruptions. In the second stage, power dispatch decisions are optimized to minimize load shedding under the worst-case transmission topology. To ensure system reliability and security, we incorporate convexified AC power flow constraints. The resulting model is a tri-level mixed-integer nonlinear optimization problem. To address its computational complexity, we design a problem-specific outer approximation algorithm embedded within a column-and-constraint generation framework. Computational results show that the proposed model and solution approach can achieve solutions within a standard optimality gap in a reasonable time for moderately large instances.
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