电网山火灾中调度问题求解难度分析及困难案例预测

doi:10.19595/j.cnki.1000-6753.tces.251541

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摘要

极端干旱引发的山火灾害频发,严重威胁电网安全稳定运行,呈现出大规模设备故障及故障时序演变复杂等特征。现有电网山火灾中调度研究多聚焦于电网调控资源或应急资源的单独调度,尚缺乏对两类资源在时空维度上协同作用机制的系统建模,难以实现电网恢复过程中的安全性与经济性统一优化。同时,调控资源与应急资源的协同需要兼顾各自的离散特性,其规模庞大、耦合性强,呈现出区别于传统电网调度问题的复杂结构特征,对电网山火灾中调度计算的时效性提出挑战。然而,当前对于不同故障场景下求解难度的变化仍不清晰,难以实现针对不同故障场景的定制化快速恢复的需求。针对上述挑战,本文从两个方面展开研究：首先,构建融合应急资源与电网调控资源的电网山火灾中调度模型,围绕山火引发的典型电力系统故障场景开展求解难度定性与定量评估,识别导致模型求解难度显著增加的关键因素。其次,以分析得到的模型求解难度影响因素作为引导,结合数据驱动方法实现对困难场景的精准预测,为在有限调度时间窗口内合理选择求解策略提供支撑。研究结果揭示了电网山火灾中调度问题求解难度变化的影响因素,并为不同故障场景下加速策略的选择提供依据。

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关键词 ：山火, 应急与调控资源, 电网调度, 求解难度分析, 困难案例预测

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 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.

Key words： Wildfire emergency and dispatching resources power grid scheduling solution difficulty analysis challenging case prediction

收稿日期: 2025-09-04

PACS:

TM73

基金资助:

国家重点研发计划项目（2024YFB2409100）和重庆市研究生科研创新项目（CYB25032）资助

通讯作者: 杨知方男,1992年生,博士,教授,博士生导师,研究方向为电力系统优化、电力市场。E-mail：zfyang@cqu.edu.cn

作者简介: 曹一生男,1996年生,博士研究生,研究方向为电网山火灾害韧性。E-mail：20241101062@stu.cqu.edu.cn

引用本文:

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, 0, (): 8-. 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, 0, (): 8-.

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