Streaming Analytics for Sustainable Energy Grid Management: Balancing Renewable Integration at Scale

Abstract

This article examines a streaming analytics architecture designed specifically for high-renewable penetration scenarios in modern power grids. The framework continuously processes sensor data from distributed resources, enabling sub-second response to generation variability. Central to this approach are specialized machine learning algorithms for ultra-short-term forecasting, edge computing for localized decision-making, and complex event processing for pattern recognition across disparate systems. Implementation challenges addressed include legacy SCADA integration, imperfect data quality management, and cross-jurisdictional coordination mechanisms. Field deployments demonstrate that continuous real-time processing, rather than traditional batch analysis, creates the necessary conditions for reliable grid operation at renewable penetration levels sufficient to meet established climate targets. The architecture represents a critical advancement in reconciling variable generation with stringent grid stability requirements.