Resilient Grid Hardening against Multiple Hazards: An Adaptive Two-Stage Stochastic Optimization Approach

TL;DR

This paper presents an adaptive two-stage stochastic optimization framework for resilient power grid hardening against multiple hazards, integrating long-term and short-term measures to improve robustness and cost-effectiveness.

Contribution

It introduces a novel adaptive model that accounts for evolving climate conditions and combines different hardening strategies for enhanced grid resilience.

Findings

Reduces outage and repair costs significantly.

Enhances grid resilience under multiple hazard scenarios.

Demonstrates effectiveness through extensive simulations.

Abstract

The growing prevalence of extreme weather events driven by climate change poses significant challenges to power system resilience. Infrastructure damage and prolonged power outages highlight the urgent need for effective grid-hardening strategies. While some measures provide long-term protection against specific hazards, they can become counterproductive under conflicting threats. In this work, we develop an adaptive two-stage stochastic optimization framework to support dynamic decision-making for hardening critical grid components under multiple hazard exposures. Unlike traditional approaches, our model adapts to evolving climate conditions, enabling more resilient investment strategies. Furthermore, we integrate long-term (undergrounding) and short-term (vegetation management) hardening actions to jointly minimize total system costs. Extensive simulation results validate the effectiveness of the proposed framework in reducing outage and repair costs while enhancing the adaptability and robustness of grid infrastructure planning.