High-Performance Renewable Mini-Grids through Adaptive Control Innovation
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| Natura: | Recurso digital |
| Lingua: | inglese |
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2025
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| _version_ | 1866901997514391552 |
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| author | Ibanibo Tamunotonye Sotonye Ekeng Effiong Lawrence Madu Matthew Chuks |
| author_facet | Ibanibo Tamunotonye Sotonye Ekeng Effiong Lawrence Madu Matthew Chuks |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17519112 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | High-Performance Renewable Mini-Grids through Adaptive Control Innovation Ibanibo Tamunotonye Sotonye Ekeng Effiong Lawrence Madu Matthew Chuks Renewable, Mini-grids, Rural electrification, MPC, Adaptive control, EMS <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> |
| title | High-Performance Renewable Mini-Grids through Adaptive Control Innovation |
| topic | Renewable, Mini-grids, Rural electrification, MPC, Adaptive control, EMS |
| url | https://doi.org/10.5281/zenodo.17519112 |