High-Performance Renewable Mini-Grids Through Adaptive Control Innovation

Abstract

Rural electrification through renewable-powered mini-grids is expanding rapidly, yet maintaining reliable performance under variable generation and demand remains a critical challenge. This study presents an adaptive control framework designed to enhance the efficiency, stability, and resilience of renewable mini-grids. The proposed method integrates a Model Predictive Control (MPC) scheme with a real-time load forecasting module driven by machine learning algorithms. By continuously adjusting inverter setpoints, battery dispatch, and demand-side management strategies, the system responds dynamically to fluctuations in solar and wind inputs as well as load variations. The approach was validated using a hybrid simulation–hardware-in-the-loop platform replicating the conditions of rural micro-communities. Results indicate up to 18% improvement in overall energy utilization efficiency, a 25% reduction in battery cycling stress, and a significant reduction in frequency and voltage deviations compared to conventional rule-based controllers. The findings demonstrate that adaptive control innovations can substantially improve the reliability and cost-effectiveness of rural renewable mini-grids, supporting sustainable electrification in underserved regions.