Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization

TL;DR

This paper introduces a tri-level optimization model for electricity system resilience, integrating proactive, adversarial, and reactive strategies, enhanced by conformal prediction for uncertainty characterization, outperforming traditional methods.

Contribution

It presents a novel tri-level optimization framework combined with conformal prediction for uncertainty modeling in electricity resilience planning.

Findings

Outperforms conventional robust and two-stage methods in numerical tests.

Effectively models uncertainty with distribution-free guarantees.

Integrates proactive, adversarial, and reactive decision layers.

Abstract

Extreme weather is straining electricity systems, exposing the limitations of reactive responses, and prompting the need for proactive resilience planning. Most existing approaches to enhance electricity system resilience employ simplified uncertainty models and decouple proactive and reactive decisions. This paper proposes a novel tri-level optimization model that integrates proactive actions, adversarial disruptions, and reactive responses. Conformal prediction is used to construct distribution-free system-disruption uncertainty sets with coverage guarantees. The tri-level problem is solved by using duality theory to derive a bi-level reformulation and employing Bender's decomposition. Numerical experiments demonstrate that our approach outperforms conventional robust and two-stage methods.