SDCL: A Framework for Secure, Distributed, and Collaborative Learning in Smart Grids

Abstract

The future of electric grids is undergoing a remarkable transformation driven by the increasing adoption of emerging technologies, notably Artificial Intelligence (AI) and Blockchain. These innovative technologies are revolutionizing smart grid management by introducing novel approaches that enhance efficiency, reliability, and sustainability, all while securing information across distributed grid components. AI empowers predictive analytics and real-time optimization, while Blockchain ensures secure and transparent transactions, laying the foundation for a more resilient and adaptive electrical grid system. This article introduces a novel Secure, Distributed, and Collaborative Learning (SDCL) framework for the smart grid. The SDCL framework leverages advances in distributed learning and blockchain technologies to provide scalability, secure data exchange, and rapid response capabilities. The proposed architecture not only enables secure data and model exchange among different microgrids but also facilitates the integration of multiple microgrids and distributed network operators. This integration enables the correlation of unforeseen events and enhances the management and control of emerging failures. Our resilient, blockchain-based architecture optimizes information sharing and security levels within the blockchain, accommodating diverse requirements for smart grid services. Finally, we highlight the advantages of the proposed SDCL framework and outline future research directions that warrant further investigation.