Distributed ML for Smart Grid Management: Real-Time Demand Prediction and Renewable Integration (Published)
The electric grid infrastructure is transitioning from traditional centralized management to dynamic, bidirectional energy flows, introducing unprecedented complexity due to increased renewable integration. This comprehensive article explores how distributed machine learning systems are revolutionizing smart grid management through real-time demand prediction and renewable integration. The transformation necessitates specialized multi-tier ML infrastructure spanning from edge computing at substations to enterprise-level systems, with each tier addressing unique computational, communication, and security challenges. Architectural patterns like hierarchical forecasting systems, ensemble models, and distributed optimization algorithms enable effective operation across temporal and spatial scales while maintaining physical constraints of power systems. Regional implementations in California, Denmark, India, and urban microgrids demonstrate adaptability to diverse challenges including the “duck curve” phenomenon, high wind penetration, and infrastructure limitations in developing regions. Emerging applications such as predictive maintenance, dynamic pricing optimization, virtual power plant orchestration, and cross-domain integration promise to further enhance grid efficiency, reliability, and resilience. The integration of these distributed ML systems represents a critical enabler for modern electricity systems facing increasing variability and complexity as renewable energy sources continue to proliferate.
Keywords: Predictive Maintenance, Renewable energy integration, distributed machine learning, dynamic pricing optimization, grid resilience, hierarchical forecasting, smart grid management, virtual power plants