Federated Learning for Distributed Optimal Power Flow Solutions in Smart Grids

The rapid evolution of smart grids has necessitated the development of advanced computational techniques to ensure efficient and reliable power distribution. This paper introduces a novel approach to solving the Optimal Power Flow (OPF) problem by leveraging Federated Learning (FL) - a decentralized machine learning paradigm. Traditional centralized methods for OPF solutions often face scalability issues and data privacy concerns when applied to large-scale, distributed smart grids. The proposed FL-based method addresses these challenges by allowing individual grid nodes to locally compute model updates without sharing raw data. This not only preserves data privacy but also reduces the communication overhead. The paper presents a comprehensive mathematical formulation of the FL-based OPF problem, followed by a detailed analysis of its convergence properties and computational efficiency. Comparative studies with conventional methods demonstrate the superiority of the proposed approach in terms of accuracy, speed, and scalability. The findings suggest that FL can serve as a robust framework for distributed OPF solutions, paving the way for more resilient and efficient smart grid operations.