Mitigating Renewable Power Fluctuations through Adaptive Predictive Control for Grid Stability Enhancement
Document Type : Original Research Article
Received: 18 June 2026
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Revised: 08 July 2026
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Accepted: 20 July 2026
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Published: 28 July 2026
Authors
JSEEGT Author 3
1,*
1
Independent Researcher
*
Author to whom correspondence should be addressed.
Abstract
The fast adoption of renewable sources such as solar and wind energy increases sustainability in electricity generation in modern smart grids but leads to problems related to intermittency, power fluctuation, voltage instabilities, frequency changes and power imbalance that may impact the reliability and resilience of the grid. In order to solve these problems, the research proposed an Adaptive Predictive Renewable Stability Network (APRSN), which is an intelligent system for renewable energy management and increased grid stability. The APRSN integrates renewable power prediction, Adaptive Predictive Control (APC), Renewable Stability Index (RSI) based grid stability evaluation, Battery Energy Storage System (BESS) coordination and adaptive optimization into one system. APRSN constantly analyzes the information on renewable generation, demand, voltage, frequency, weather and battery states to predict the future state of the grid, evaluate its stability and take necessary control actions. The BESS coordination takes place depending on power imbalances in order to store extra renewable power and used the grid with generation deficit. The experimental evaluation of APRSN showed 99.1% accuracy of forecasting along with the improved voltage and frequency stability, reduction of renewable power fluctuations and higher renewable energy utilization and strengthening grid resilience. The results demonstrate the potential of APRSN as an integrated approach for reliable renewable-energy management in smart-grid environments.
Graphical Abstract
Keywords:
Adaptive Predictive Control, Battery Energy Storage System, Grid Stability, Renewable Energy Forecasting, Renewable Stability Index, Smart Grid Resilience.
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