Robust and Scalable Game-theoretic Security Investment Methods for Voltage Stability of Power Systems

TL;DR

This paper introduces scalable game-theoretic methods to optimize reactive power investments for enhancing voltage stability in power systems under load attack threats, validated on IEEE models.

Contribution

It develops a robust-defense sequential algorithm and a genetic algorithm to improve scalability of security investment strategies in power systems.

Findings

Robust defense is effective unless RPC resources are severely limited.

Methods validated on IEEE power system models with load uncertainties.

Scalable algorithms enable practical security investment planning.

Abstract

We develop investment approaches to secure electric power systems against load attacks where a malicious intruder (the attacker) covertly changes reactive power setpoints of loads to push the grid towards voltage instability while the system operator (the defender) employs reactive power compensation (RPC) to prevent instability. Extending our previously reported Stackelberg game formulation for this problem, we develop a robust-defense sequential algorithm and a novel genetic algorithm that provides scalability to large-scale power system models. The proposed methods are validated using IEEE prototype power system models with time-varying load uncertainties, demonstrating that reliable and robust defense is feasible unless the operator's RPC investment resources are severely limited relative to the attacker's resources.