A Privacy-Preserving RL-Based Secure Charging Coordinator Using Efficient FL for Smart Grid Home Batteries

Amr A. Elshazly, Islam Elgarhy, Mohamed Mahmoud, Mohamed I. Ibrahem () and Maazen Alsabaan
Additional contact information
Amr A. Elshazly: Department of Computer Science, Tennessee Technological University, Cookeville, TN 38505, USA
Islam Elgarhy: Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
Mohamed Mahmoud: Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
Mohamed I. Ibrahem: School of Computer and Cyber Sciences, Augusta University, Augusta, GA 30912, USA
Maazen Alsabaan: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia

Energies, 2025, vol. 18, issue 4, 1-34

Abstract: Smart power grids (SGs) enhance efficiency, reliability, and sustainability by integrating distributed energy resources (DERs) such as solar panels and wind turbines. A key challenge in SGs is managing home battery charging during periods of insufficient renewable energy generation to ensure fairness, efficiency, and customer satisfaction. This paper introduces a secure reinforcement learning (RL)-based framework for optimizing battery charging coordination while addressing privacy concerns and false data injection (FDI) attacks. Privacy is preserved through Federated Learning (FL), enabling collaborative model training without sharing sensitive State of Charge (SoC) data that could reveal personal routines. To combat FDI attacks, Deep Learning (DL)-based detectors are deployed to identify malicious SoC data manipulation. To improve FL efficiency, the Change and Transmit (CAT) technique reduces communication overhead by transmitting only model parameters that experience enough change comparing to the last round. Extensive experiments validate the framework’s efficacy. The RL-based charging coordinator ensures fairness by maintaining SoC levels within thresholds and reduces overall power utilization through optimal grid power allocation. The CAT-FL approach achieves up to 93.5% communication overhead reduction, while DL-based detectors maintain high accuracy, with supervised models reaching 99.84% and anomaly detection models achieving 92.1%. Moreover, the RL agent trained via FL demonstrates strong generalization, achieving high cumulative rewards and equitable power allocation when applied to new data owners which did not participate in FL training. This framework provides a scalable, privacy-preserving, and efficient solution for energy management in SGs, offering high accuracy against FDI attacks and paving the way for the future of smart grid deployments.

Keywords: privacy-preservation; security; federated learning; charging coordination; false data injection; reinforcement learning; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/4/961/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/4/961/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:4:p:961-:d:1593163

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().