A Multi-Model Probabilistic Framework for Seismic Risk Assessment and Retrofit Planning of Electric Power Networks

TL;DR

This paper introduces a comprehensive probabilistic framework that assesses seismic risk and guides retrofit planning for electric power networks by modeling systemic behavior, damage, cascading failures, and operational constraints.

Contribution

It integrates seismic hazard, component damage, system cascading effects, and optimization into a unified, probabilistic approach for more accurate risk assessment and retrofit decision-making.

Findings

Captures cascading failures and network interdependencies.

Identifies critical components for retrofit prioritization.

Demonstrates effectiveness on IEEE 24-bus system.

Abstract

Electric power networks are critical lifelines, and their disruption during earthquakes can lead to severe cascading failures and significantly hinder post-disaster recovery. Enhancing their seismic resilience requires identifying and strengthening vulnerable components in a cost-effective and system-aware manner. However, existing studies often overlook the systemic behavior of power networks under seismic loading. Common limitations include isolated component analyses that neglect network-wide interdependencies, oversimplified damage models assuming binary states or damage independence, and the exclusion of electrical operational constraints. These simplifications can result in inaccurate risk estimates and inefficient retrofit decisions. This study proposes a multi-model probabilistic framework for seismic risk assessment and retrofit planning of electric power systems. The approach integrates: (1) regional seismic hazard characterization with ground motion prediction and spatial correlation models; (2) component-level damage analysis using fragility functions and multi-state damage-functionality mappings; (3) system-level cascading impact evaluation through graph-based island detection and constrained optimal power flow analysis; and (4) retrofit planning via heuristic optimization to minimize expected annual functionality loss (EAFL) under budget constraints. Uncertainty is propagated throughout the framework using Monte Carlo simulation. The methodology is demonstrated on the IEEE 24-bus Reliability Test System, showcasing its ability to capture cascading failures, identify critical components, and generate effective retrofit strategies. Results underscore the potential of the framework as a scalable, data-informed decision-support tool for enhancing the seismic resilience of power infrastructure.