Vulnerability-Based Optimal Grid Defense Strategies for Enhancing Cyber-Physical Energy System Resilience

TL;DR

This work targets resilience of cyber-physical energy systems against high-impact-low-probability events by replacing integrated trilevel defender-attacker-defender optimization with a modular vulnerability-based defense framework. It first identifies multiple critical attack scenarios (CASs) via bilevel network interdiction models, producing a list of CASs and associated lost load, and then solves an integer program to select protected components subject to a defense budget $X^{\text{max}}$, aiming to maximize the number of consecutively excluded CASs $y_w$ while respecting feasibility constraints. The methodology is agnostic to the exact vulnerability assessment method, enabling integration with heuristics, learning-based approaches, or AC OPF representations, and is demonstrated on IEEE 9- and 30-bus, CIGRE MV, and SimBench grids with runtimes around one second. Results show meaningful reductions in the remaining worst-case lost load with modest protection budgets, and identify components (e.g., transformers, certain branches) that are frequently critical across configurations, supporting scalable, transparent resilience planning for CPES.

Abstract

An approach is proposed to identify optimal asset protection strategies based on vulnerability assessment outcomes. Traditional bilevel attacker-defender models emphasize worst-case scenarios but offer limited defensive guidance. In contrast, trilevel models introduce high computational complexity and rely on fixed network configurations. The proposed critical-components method leverages vulnerability assessment results to determine protection strategies, effectively outsourcing the upper-level defense decision. This enables adaptability to diverse network topologies, assessment techniques, and cyber-physical energy systems without the overhead of multi-level optimization. Case studies demonstrate the potential for improved system resilience across varying operational conditions.

Figures (4)

• Figure 1: Schematic comparison of integrated trilevel defender-attacker-defender models and the proposed vulnerability-based approach to identify optimal asset protection strategies.
• Figure 2: Simulation procedure applied in the case studies.
• Figure 3: Optimal protected sets of components with varying $X^\text{max}$ in the IEEE 30-bus grid.
• Figure 4: Effect of varying $X^\text{max}$ on the reduction of remaining worst-case lost load and the total number of excluded CASs.