Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach

TL;DR

This paper introduces a tri-level optimization framework for power system operations that enhances resilience against cyberattacks and extreme events by integrating economic dispatch, vulnerability assessment, and mitigation strategies.

Contribution

It presents a novel tri-level approach combining economic dispatch, adversarial attack modeling, and mitigation with energy storage, improving power system robustness against worst-case threats.

Findings

Effective in identifying worst-case attack scenarios

Reduces system vulnerability using fast-response energy storage

Validated on IEEE-33 node system showing improved resilience

Abstract

In power systems, unpredictable events like extreme weather, equipment failures, and cyberattacks present significant challenges to ensuring safety and reliability. Ensuring resilience in the face of these uncertainties is crucial for reliable and efficient operations. This paper presents a tri-level optimization approach for robust power system operations that effectively address worst-case attacks. The first stage focuses on optimizing economic dispatch under normal operating conditions, aiming to minimize generation costs while maintaining the supply-demand balance. The second stage introduces an adversarial attack model, identifying worst-case scenarios that maximize the system's vulnerability by targeting distributed generation (DG). In the third stage, mitigation strategies are developed using fast-response energy storage systems (ESS) to minimize disruptions caused by these attacks. By integrating economic dispatch, vulnerability assessment, and mitigation into a unified framework, this approach provides a robust solution for enhancing power system resilience and safety against evolving adversarial threats. The approach is validated using the IEEE-33 node distribution system to demonstrate its effectiveness in achieving both cost efficiency and system resilience.