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Open AccessFeature PaperArticle

Optimization of Isolated Hybrid Microgrids with Renewable Energy Based on Different Battery Models and Technologies

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Electronic Engineering Department, San Buenaventura University, Carrera 8H # 172-20 Bogota, Colombia
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Electrical Engineering Department, University of Zaragoza, Calle María de Luna 3, 50018 Zaragoza, Spain
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Author to whom correspondence should be addressed.
Energies 2020, 13(3), 581; https://doi.org/10.3390/en13030581
Received: 27 December 2019 / Revised: 20 January 2020 / Accepted: 22 January 2020 / Published: 26 January 2020
Energy supply in remote areas (mainly in developing countries such as Colombia) has become a challenge. Hybrid microgrids are local and reliable sources of energy for these areas where access to the power grid is generally limited or unavailable. These systems generally include a diesel generator, solar modules, wind turbines, and storage devices such as batteries. Battery life estimation is an essential factor in the optimization of a hybrid microgrid since it determines the system’s final costs, including future battery replacements. This article presents a comparison of different technologies and battery models in a hybrid microgrid. The optimization is achieved using the iHOGA software, based on data from a real microgrid in Colombia. The simulation results allowed the comparison of prediction models for lifespan calculation for both lead–acid and lithium batteries in a hybrid microgrid, showing that the most accurate models are more realistic in predicting battery life by closely estimating real lifespans that are shorter, unlike other simplified methods that obtain much longer and unrealistic lifetimes. View Full-Text
Keywords: lead–acid batteries; lithium batteries; aging models; optimization; hybrid microgrids lead–acid batteries; lithium batteries; aging models; optimization; hybrid microgrids
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García-Vera, Y.E.; Dufo-López, R.; Bernal-Agustín, J.L. Optimization of Isolated Hybrid Microgrids with Renewable Energy Based on Different Battery Models and Technologies. Energies 2020, 13, 581.

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