Federated Learning-based Intelligent Energy Management for Distributed Power Electronic Networks in Smart Grids
DOI:
https://doi.org/10.71426/jmt.v3.i1.pp446-456Keywords:
Smart Grid, Federated learning, Intelligent energy management, Distributed power electronic networks, Distributed energy resources, Deep reinforcement learning, Converter-based energy systems.Abstract
The increasing integration of Distributed Energy Resources (DERs), renewable generation systems, converter-interfaced loads, battery energy storage systems, and intelligent power electronic controllers has transformed conventional power grids into highly decentralized and data-intensive smart energy networks. However, traditional centralized energy management frameworks face significant challenges including excessive communication overhead, limited scalability, latency constraints, cybersecurity risks, and concerns related to operational data privacy. To address these limitations, this paper proposes a Federated Learning-Based Intelligent Energy Management (FL-IEM) framework for distributed power electronic networks in smart grids. The proposed architecture enables multiple geographically distributed converter controllers to collaboratively train a shared intelligence model without exchanging raw local data, thereby preserving privacy while maintaining coordinated decision-making capabilities. A federated optimization layer based on Federated Averaging (FedAvg) is integrated with a Deep Reinforcement Learning (DRL)-assisted energy dispatch mechanism to enable adaptive control, dynamic power sharing, and real-time energy scheduling under varying renewable generation and load conditions. The proposed framework is validated using an IEEE 33-bus smart distribution system integrated with photovoltaic units, battery energy storage systems, and converter-interfaced loads. Simulation results demonstrate superior performance over conventional centralized learning approaches, achieving reduced operational energy cost, lower communication burden, enhanced voltage regulation, improved renewable utilization, and accelerated model convergence. The proposed FL-IEM framework provides a scalable, privacy-preserving, and intelligent solution for next-generation distributed smart grid energy management.
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