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

TL;DR

This work tackles grid resilience under multiple natural hazards by formulating an adaptive two-stage stochastic optimization that jointly optimizes long-term undergrounding and short-term vegetation management. The method uses scenario trees with backward pruning to capture evolving climate risk while allowing a one-time revision of initial decisions, balancing flexibility with practicality. By incorporating both earthquakes and high winds, and integrating UG and VM costs, the approach demonstrates substantial cost savings and improved robustness compared with traditional two-stage models, especially with larger uncertainty horizons. The framework is validated on a modified IEEE 22-bus system, showing scalable performance and clear benefits from multi-hazard, multi-strategy planning for long-term grid reliability.

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.

Figures (7)

• Figure 1: Scenario tree annotated with notation: parent node $P(n)$, the set of children nodes $C(n)$, ancestor nodes $A(n)$, set of nodes belong to time $t$ as $S_{t}$, and the time belongs to node $n$ as $t_{n}$.
• Figure 2: Decision structure of first-stage variable $(\sqsubset \! \sqsupset)$ (a) two-stage; (b) adaptive two-stage; and (c) decision structure of second-stage variable $(\triangle)$ for both two-stage and adaptive two-stage.
• Figure 3: For $T=4$, (a) full scenario tree and (b) pruned scenario tree.
• Figure 4: Risk level for power lines facing (a) high wind (b) earthquake.
• Figure 5: Line undergrounding decision $(\alpha)$ with respect to relative load shedding cost $(\text{L}^\text{CF})$, line outage probability for high wind $(\varrho^{\text{W}})$, earthquake $(\varrho^{\text{EQ}})$ and fallen trees $(\varrho^{\text{VM}})$.