增强决策依赖不确定性下电网应对野火的运行韧性
Enhancing Operational Grid Resilience Against Wildfires Under Decision-Dependent Uncertainties
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中文总结 AI 辅助
本文提出含决策依赖不确定性（DDU）的自动化决策框架，结合多阶段优化模型与数学分解算法，在IEEE 30节点系统验证其可增强电网应对野火的运行韧性。
中文摘要 AI 辅助
本文提出一种新的自动化决策框架，通过考虑决策依赖不确定性（DDU）的运行策略，增强电力系统应对野火的韧性。该框架整合预防与校正措施，可在野火场景演变过程中实现自适应自动化决策。首先，提出基线多阶段优化模型作为野火驱动运行决策的基础；随后，将DDU纳入模型，即前期做出的公共安全断电（PSPS）决策会影响未来野火场景的概率与参数。为高效求解该复杂优化问题，采用数学分解算法。通过IEEE 30节点系统的案例研究验证了所提方法的有效性，仿真结果证实，在优化过程中纳入DDU的影响，能在不断演变的野火威胁下提供更贴合实际、具备运行韧性的解决方案。
英文摘要
This paper proposes a new automated decision-making framework to enhance the resilience of electrical systems against wildfires by applying operational strategies that account for decision-dependent uncertainty (DDU). The proposed framework incorporates both preventive and corrective measures, enabling adaptive and automated decision-making throughout the course of evolving wildfire scenarios. First, a baseline multistage optimization model is presented as a foundation to support wildfire-driven operational decision-making. The model then incorporates DDU, wherein Public Safety Power Shutoff (PSPS) decisions made in earlier stages influence the probabilities and parameters of future wildfire scenarios. To efficiently solve the resulting complex optimization problem, a mathematical decomposition algorithm is employed. The effectiveness of the proposed approach is demonstrated through case studies on the IEEE 30-bus system. Simulation results confirm that incorporating the impact of DDU into the optimization process provides more realistic, and operationally resilient solutions in the face of evolving wildfire threats.
