Impact of Data Poisoning Attacks on Feasibility and Optimality of Neural Power System Optimizers

TL;DR

This paper investigates how data poisoning attacks affect the reliability and optimality of machine learning-based power system optimization methods, highlighting vulnerabilities and comparing different mitigation strategies.

Contribution

It provides a comparative analysis of three ML-based power system optimizers under data poisoning attacks, focusing on their resilience and robustness.

Findings

Penalty-based method shows higher resilience.

Post-repair approach improves feasibility after attacks.

Direct mapping method is most vulnerable.

Abstract

The increased integration of clean yet stochastic energy resources and the growing number of extreme weather events are narrowing the decision-making window of power grid operators. This time constraint is fueling a plethora of research on Machine Learning-, or ML-, based optimization proxies. While finding a fast solution is appealing, the inherent vulnerabilities of the learning-based methods are hindering their adoption. One of these vulnerabilities is data poisoning attacks, which adds perturbations to ML training data, leading to incorrect decisions. The impact of poisoning attacks on learning-based power system optimizers have not been thoroughly studied, which creates a critical vulnerability. In this paper, we examine the impact of data poisoning attacks on ML-based optimization proxies that are used to solve the DC Optimal Power Flow problem. Specifically, we compare the resilience of three different methods-a penalty-based method, a post-repair approach, and a direct mapping approach-against the adverse effects of poisoning attacks. We will use the optimality and feasibility of these proxies as performance metrics. The insights of this work will establish a foundation for enhancing the resilience of neural power system optimizers.