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Article

BESS-Enabled Smart Grid Environments: A Comprehensive Framework for Cyber Threat Classification, Cybersecurity, and Operational Resilience

by
Prajwal Priyadarshan Gopinath
1,
Kishore Balasubramanian
1,
Rayappa David Amar Raj
1,
Archana Pallakonda
2,
Rama Muni Reddy Yanamala
3,
Christian Napoli
4,5 and
Cristian Randieri
4,6,*
1
Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore 641112, Tamil Nadu, India
2
Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal 506004, Telangana, India
3
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Design and Manufacturing (IIITD&M) Kancheepuram, Chennai 600127, Tamil Nadu, India
4
Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, Italy
5
Department of Computational Intelligence, Czestochowa University of Technology, ul. Dąbrowskiego 69, 42-201 Czestochowa, Poland
6
Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, Italy
*
Author to whom correspondence should be addressed.
Technologies 2025, 13(9), 423; https://doi.org/10.3390/technologies13090423
Submission received: 21 July 2025 / Revised: 13 September 2025 / Accepted: 17 September 2025 / Published: 20 September 2025
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Abstract

Battery Energy Storage Systems (BESSs) are critical to smart grid functioning but are exposed to mounting cybersecurity threats with their integration into IoT and cloud-based control systems. Current solutions tend to be deficient in proper multi-class attack classification, secure encryption, and full integrity and power quality features. This paper proposes a comprehensive framework that integrates machine learning for attack detection, cryptographic security, data validation, and power quality control. With the BESS-Set dataset for binary classification, Random Forest achieves more than 98.50% accuracy, while LightGBM attains more than 97.60% accuracy for multi-class classification on the resampled data. Principal Component Analysis and feature importance show vital indicators such as State of Charge and battery power. Secure communication is implemented using Elliptic Curve Cryptography and a hybrid Blowfish–RSA encryption method. Data integrity is ensured through applying anomaly detection using Z-scores and redundancy testing, and IEEE 519-2022 power quality compliance is ensured by adaptive filtering and harmonic analysis. Real-time feasibility is demonstrated through hardware implementation on a PYNQ board, thus making this framework a stable and feasible option for BESS security in smart grids.
Keywords: smart grid; distributed energy resources; anomaly detection; total harmonic distortion; artificial neural network; RSA encryption smart grid; distributed energy resources; anomaly detection; total harmonic distortion; artificial neural network; RSA encryption

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MDPI and ACS Style

Gopinath, P.P.; Balasubramanian, K.; Raj, R.D.A.; Pallakonda, A.; Yanamala, R.M.R.; Napoli, C.; Randieri, C. BESS-Enabled Smart Grid Environments: A Comprehensive Framework for Cyber Threat Classification, Cybersecurity, and Operational Resilience. Technologies 2025, 13, 423. https://doi.org/10.3390/technologies13090423

AMA Style

Gopinath PP, Balasubramanian K, Raj RDA, Pallakonda A, Yanamala RMR, Napoli C, Randieri C. BESS-Enabled Smart Grid Environments: A Comprehensive Framework for Cyber Threat Classification, Cybersecurity, and Operational Resilience. Technologies. 2025; 13(9):423. https://doi.org/10.3390/technologies13090423

Chicago/Turabian Style

Gopinath, Prajwal Priyadarshan, Kishore Balasubramanian, Rayappa David Amar Raj, Archana Pallakonda, Rama Muni Reddy Yanamala, Christian Napoli, and Cristian Randieri. 2025. "BESS-Enabled Smart Grid Environments: A Comprehensive Framework for Cyber Threat Classification, Cybersecurity, and Operational Resilience" Technologies 13, no. 9: 423. https://doi.org/10.3390/technologies13090423

APA Style

Gopinath, P. P., Balasubramanian, K., Raj, R. D. A., Pallakonda, A., Yanamala, R. M. R., Napoli, C., & Randieri, C. (2025). BESS-Enabled Smart Grid Environments: A Comprehensive Framework for Cyber Threat Classification, Cybersecurity, and Operational Resilience. Technologies, 13(9), 423. https://doi.org/10.3390/technologies13090423

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