High-performance computing enabled contingency analysis for modern power networks

📄 arXiv: 2512.08465v1 📥 PDF

作者: Alexandre Gracia-Calvo, Francesca Rossi, Eduardo Iraola, Juan Carlos Olives-Camps, Eduardo Prieto-Araujo

分类: eess.SY, cs.PF

发布日期: 2025-12-09

备注: 10 apges, 5 figures, pending to be submitted on IJEPES

💡 一句话要点

提出高性能计算方法以解决现代电力网络的脆弱性评估问题

🎯 匹配领域: 支柱四：生成式动作 (Generative Motion)

关键词: 电力网络 级联故障 高性能计算 风险评估 小信号稳定性 概率风险指数 安全评估

📋 核心要点

  1. 现有方法在进行N-2故障分析时面临计算可行性不足的问题，难以满足现代电力网络的复杂性需求。
  2. 本文提出了一种结合N-2故障分析与小信号稳定性评估的可扩展方法，利用概率风险指数对关键组件进行优先排序。
  3. 在IEEE 118-bus测试系统上验证了该方法，处理超过57000种场景，成功识别出传统N-1标准常常忽视的关键资产。

📝 摘要（中文）

现代电力网络因复杂性增加和间歇性资源渗透而面临更高的级联故障脆弱性，迫切需要超越传统的N-1标准进行严格的安全评估。现有方法在进行N-2故障分析时常常难以实现所需的计算可行性。本文提出了一种可扩展的方法，结合N-2故障分析与小信号稳定性评估，利用概率风险指数（Ri）对关键组件进行优先排序。该框架通过高性能计算技术实现，能够处理超过57000种场景，识别出易触发级联故障的组件，显著提高了安全评估的效率和准确性。

🔬 方法详解

问题定义：本文旨在解决现代电力网络在面对级联故障时的脆弱性评估问题。现有方法在进行N-2故障分析时，计算可行性不足，无法有效评估复杂网络的安全性。

核心思路：论文提出了一种新的方法，将N-2故障分析与小信号稳定性评估相结合，利用概率风险指数（Ri）来加权确定性严重性与故障频率，从而优先识别关键组件。

技术框架：整体架构包括三个主要模块：首先，通过PyCOMPSs并行编程库实现高性能计算；其次，进行最优潮流仿真（VeraGrid）与小信号分析（STAMP）；最后，进行大规模故障集的全面探索与评估。

关键创新：最重要的创新在于引入了概率风险指数（Ri），这一指标能够有效整合故障的严重性与频率，优先识别出在传统N-1标准下被忽视的关键资产。

关键设计：在设计中，采用了高性能计算技术，确保能够处理大规模的故障场景，并通过可靠性数据来计算故障频率，优化了评估流程。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，所提出的风险基础方法能够有效识别出传统N-1标准下常被忽视的关键资产。在IEEE 118-bus测试系统中，处理超过57000种场景，显著提高了对潜在级联故障的识别能力。

🎯 应用场景

该研究的潜在应用领域包括电力系统的安全评估与管理，尤其适用于大型电力网络的实时监控与决策支持。通过提供更为精准的脆弱性评估，能够帮助电力运营商在复杂环境中做出更为有效的决策，降低级联故障的风险。

📄 摘要（原文）

Modern power networks face increasing vulnerability to cascading failures due to high complexity and the growing penetration of intermittent resources, necessitating rigorous security assessment beyond the conventional $N-1$ criterion. Current approaches often struggle to achieve the computational tractability required for exhaustive $N-2$ contingency analysis integrated with complex stability evaluations like small-signal stability. Addressing this computational bottleneck and the limitations of deterministic screening, this paper presents a scalable methodology for the vulnerability assessment of modern power networks, integrating $N-2$ contingency analysis with small-signal stability evaluation. To prioritize critical components, we propose a probabilistic \textbf{Risk Index ($R_i$)} that weights the deterministic \textit{severity} of a contingency (including optimal power flow divergence, islanding, and oscillatory instability) by the \textit{failure frequency} of the involved elements based on reliability data. The proposed framework is implemented using High-Performance Computing (HPC) techniques through the PyCOMPSs parallel programming library, orchestrating optimal power flow simulations (VeraGrid) and small-signal analysis (STAMP) to enable the exhaustive exploration of massive contingency sets. The methodology is validated on the IEEE 118-bus test system, processing more than \num{57000} scenarios to identify components prone to triggering cascading failures. Results demonstrate that the risk-based approach effectively isolates critical assets that deterministic $N-1$ criteria often overlook. This work establishes a replicable and efficient workflow for probabilistic security assessment, suitable for large-scale networks and capable of supporting operator decision-making in near real-time environments.