Research Article

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

Authors

  • Vivekananda Reddy Chittireddy Independent Researcher, USA

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.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

7 (8)

Pages

588-595

Published

2025-08-04

How to Cite

Vivekananda Reddy Chittireddy. (2025). Streaming Analytics for Sustainable Energy Grid Management: Balancing Renewable Integration at Scale. Journal of Computer Science and Technology Studies, 7(8), 588-595. https://doi.org/10.32996/jcsts.2025.7.8.68

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Keywords:

Streaming analytics, Renewable energy integration, Grid stability, Virtual power plants, Predictive intelligence