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

Power systems must withstand extreme weather, failures, and cyberattacks, requiring a resilient operational strategy. Therefore, this paper presents a tri-level optimization approach integrating robust optimization for worst-case scenarios. First, it optimizes economic dispatch to minimize costs while maintaining supply-demand balance. Next, it identifies worst-case attacks on distributed generation (DG) to assess system vulnerabilities. Finally, it deploys fast-response energy storage systems (ESS) to mitigate disruptions. By combining these stages, the approach enhances both cost efficiency and resilience. Its effectiveness is demonstrated on the IEEE-33 node distribution system.

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Publication Details

Published
2025-04-24
DOI
https://doi.org/10.1109/kpec65465.2025.11045031
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
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article

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

Tong Wu, Saman Mazaheri Khamaneh
Adversarial Robustness in Machine Learning
article

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

Tong Wu, Saman Mazaheri Khamaneh
article en

Abstract

Power systems must withstand extreme weather, failures, and cyberattacks, requiring a resilient operational strategy. Therefore, this paper presents a tri-level optimization approach integrating robust optimization for worst-case scenarios. First, it optimizes economic dispatch to minimize costs while maintaining supply-demand balance. Next, it identifies worst-case attacks on distributed generation (DG) to assess system vulnerabilities. Finally, it deploys fast-response energy storage systems (ESS) to mitigate disruptions. By combining these stages, the approach enhances both cost efficiency and resilience. Its effectiveness is demonstrated on the IEEE-33 node distribution system.

University of Central Florida (US)
Openalex Percentile: Top 93%
Adversarial Robustness in Machine Learning
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