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Open Access

ARTICLE

Detecting and Mitigating Cyberattacks on Load Frequency Control with Battery Energy Storage System

Yunhao Yu1, Fuhua Luo1, Zhenyong Zhang2,*
1 Electric Power Dispatching and Control Center, Guizhou Power Grid Co., Ltd., Guiyang, 550002, China
2 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
* Corresponding Author: Zhenyong Zhang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.074277

Received 07 October 2025; Accepted 26 November 2025; Published online 19 December 2025

Abstract

This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control (LFC) systems integrated with Battery Energy Storage Systems (BESS). As renewable energy sources gain greater penetration, power grids are becoming increasingly vulnerable to cyber threats, potentially leading to frequency instability and widespread disruptions. We model two significant attack vectors: load-altering attacks (LAAs) and false data injection attacks (FDIAs) that corrupt frequency measurements. These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models, incorporating generation rate constraints and nonlinear loads. A coordinated attack strategy is presented, combining LAAs and FDIAs to achieve stealthiness by concealing frequency deviations from system operators, thereby maximizing disruption while evading traditional detection. To counteract these threats, we propose an Unknown Input Observer (UIO)-based detection framework for linear and nonlinear LFCs. The UIO is designed using linear matrix inequalities (LMIs) to estimate system states while isolating unknown attack inputs, enabling attack detection through monitoring measurement residuals against a predefined threshold. For mitigation, we leverage BESS capabilities with two adaptive strategies: dynamic mitigation for dynamic LAAs, which tunes BESS parameters to enhance the system’s stability margin and accelerate convergence to equilibrium; and static mitigation for static LAAs and FDIAs. Simulations show that the UIO achieves high detection accuracy, with residuals exceeding thresholds promptly under coordinated attacks, even in nonlinear models. Mitigation strategies reduce frequency deviations by up to 80% compared to unmitigated cases, restoring stability within seconds.

Keywords

Load frequency control; cybersecurity; unknown input observer; battery energy storage system

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