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| Autores principales: | , , |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2025
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| Materias: | |
| Acceso en línea: | https://doi.org/10.5281/zenodo.17519112 |
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- <p><em><span lang="EN-GB">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.</span></em></p>