High-performance computing enabled contingency analysis for modern power networks

2025-12-09

AI Summary

Modern power systems face escalating cascading failure risks due to increasing complexity and higher penetration of intermittent generation, rendering the conventional deterministic N-1 security criterion insufficient for comprehensive safety assessment. Method: This paper proposes a scalable security assessment framework that—uniquely—integrates probabilistic N-2 contingency analysis with small-signal stability evaluation. It introduces a component-level risk metric ( R_i ), derived from failure frequency and impact severity, thereby overcoming the limitations of deterministic criteria. The framework leverages PyCOMPSs to enable coordinated parallel execution of VeraGrid (for optimal power flow) and STAMP (for eigenvalue-based small-signal stability analysis). Results: Validated on the IEEE 118-bus system, the framework efficiently evaluates over 57,000 N-2 contingencies, accurately identifies critical components prone to triggering cascading failures, and supports near-real-time, large-scale grid security decision-making.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Computational social science

Abstract

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 extbf{Risk Index ($R_i$)} that weights the deterministic extit{severity} of a contingency (including optimal power flow divergence, islanding, and oscillatory instability) by the extit{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 um{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.

Problem

• Develop a scalable method for vulnerability assessment in modern power networks
• Integrate N-2 contingency analysis with small-signal stability evaluation
• Propose a probabilistic risk index to prioritize critical components

Innovation

• Probabilistic risk index weighting severity by failure frequency
• High-performance computing enabling exhaustive contingency analysis
• Parallel workflow integrating optimal power flow and stability simulations