High-performance computing enabled contingency analysis for modern power networks

TL;DR

This paper tackles the computational challenge of exhaustively evaluating $N-2$ contingencies in modern power networks by integrating AC-OPF feasibility, small-signal stability, and islanding into a single probabilistic risk metric. Using HPC with PyCOMPSs, VeraGrid, and STAMP, it analyzes over 57,000 contingencies on the IEEE 118-bus system to rank component criticality via the $R_i$ index, weighted by reliability data. The results reveal that risk is highly concentrated in a small subset of transmission lines, with $N-2$ interactions playing a dominant role, offering a scalable framework for near-real-time operator support and proactive grid security. The work provides a replicable workflow for probabilistic security assessment that can guide maintenance, protection, and contingency planning in large-scale networks.

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 \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.

Figures (5)

• Figure 1: Topological representation of the IEEE 118-bus test system.
• Figure 2: Risk Index ($R_i$) for transmission lines.
• Figure 3: Risk Index ($R_i$) for generators.
• Figure 4: Risk Index ($R_i$) for transformers.
• Figure 5: Combined Risk Index ($R_i$) for all elements.