Abstract：Wildfires triggered by extreme drought have become increasingly frequent, posing severe threats to the secure and stable operation of power systems. Such events are characterized by large-scale equipment outages and complex temporal evolution of failures. Existing studies on wildfire-related power system dispatch mainly focus on the independent scheduling of grid control resources or emergency resources, while a systematic modeling framework that captures their coordinated interactions across temporal and spatial dimensions remains lacking. As a result, it is difficult to achieve a unified optimization of system security and economic efficiency during the grid restoration process. Moreover, the coordination of control and emergency resources must account for their inherent discrete characteristics; their large scale and strong coupling lead to complex structural features that distinguish wildfire emergency dispatch from conventional power system scheduling problems, thereby posing significant challenges to computational efficiency and timeliness. However, the variation in computational difficulty of dispatch models under different failure scenarios is still not well understood. Applying identical solution strategies to scenarios with varying levels of difficulty often results in the inability to obtain high-quality restoration dispatch decisions in a timely manner for challenging cases, failing to meet the requirements for rapid restoration under diverse failure scenarios. To address the above challenges, this paper conducts research from two perspectives. First, the coupling mechanisms among repair routing, fault repair duration, and unit scheduling behaviors are explicitly characterized. A mixed-integer linear programming model is developed that jointly incorporates repair crews (emergency resources) and unit commitment and dispatch decisions (control resources) within a unified optimization framework, systematically capturing the coordinated impacts of the two types of resources on power system security and economic efficiency. In addition, focusing on multiple categories of power system failure scenarios induced by wildfires, this study provides an in-depth analysis of how the introduction of emergency resources affects the computational difficulty of wildfire emergency dispatch models. Large-scale numerical experiments are designed and conducted by considering key factors such as the types and numbers of failed components, the temporal evolution of failures, renewable energy penetration levels, and differing emphases on security and economic objectives during dispatch. These experiments identify the critical factors that lead to significant increases in computational complexity. Second, guided by the identified factors influencing computational difficulty, data-driven methods are employed to enable accurate prediction of hard failure scenarios. After wildfire-induced outages occur, the required solution time for obtaining restoration dispatch decisions is estimated in advance, providing decision support for the rational selection of solution strategies within limited dispatch time windows. Using public benchmark test cases, extensive multi-dimensional simulation experiments are conducted to systematically analyze the evolution patterns of computational difficulty and their key influencing factors from both qualitative and quantitative perspectives. The results demonstrate that the number, types, combinations, and temporal evolution of failed components have a significant impact on solution time. In addition, different levels of renewable energy penetration exhibit pronounced nonlinear effects on computational performance. The relative emphasis placed on system security (load shedding) and economic efficiency (start-up and generation costs) during wildfire emergency dispatch is also shown to significantly affect solution efficiency. Furthermore, challenging-case identification experiments achieve an accuracy of 0.95, validating the effectiveness of the proposed analysis. The findings reveal the key factors driving variations in the computational difficulty of wildfire emergency dispatch problems and provide a basis for selecting acceleration strategies under different failure scenarios.
曹一生, 高倩, 余娟, 杨知方. 电网山火灾中调度问题求解难度分析及困难案例预测[J]. 电工技术学报, 2026, 41(19): 6614-6629.
Cao Yisheng, Gao Qian, Yu Juan, Yang Zhifang. Solution Difficulty Analysis and Challenging Case Prediction for Power Grid Wildfire Dispatch Problems. Transactions of China Electrotechnical Society, 2026, 41(19): 6614-6629.
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2026, Vol. 41 
