Cadena-Zarate, Cristian, Tucci, Ilaria, Scalla, Dario Della, Garcia, Jersson, Crouzier, Maurine, Cambron, Phillipe, Carreau, Michel, Rousse, Daniel R. et Ilinca, Adrian.
2026.
« Renewable energy optimization in isolated microgrids: A Python-based tool for cost-effective solutions using genetic algorithms ».
Energy Conversion and Management: X, vol. 30.
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Résumé
Isolated areas often rely on diesel generators for electricity production, which is associated with high costs and environmental impacts. Microgrids (MG) that integrate renewable energy and storage offer a more sustainable alternative. To support the techno-economic planning of such systems, this paper presents a modular Python-based tool for evaluating renewable energy penetration in isolated hybrid microgrids through single- or bi-objective optimization using genetic algorithms (GA). The tool combines a rule-based dispatch simulator with a GA optimizer and supports both hourly and minute-resolution data. It enables users to assess and optimize key performance indicators such as diesel consumption and Levelized Cost of Energy (LCOE). Applied to a real case study in Nunavik, Quebec, the tool evaluates five scenarios including wind integration and storage. Results indicate that optimized scenarios can reduce diesel consumption by up to 87% and the LCOE by up to 58% relative to diesel-only configurations. The proposed tool provides a flexible and practical framework for assessing and optimizing renewable integration in isolated MGs.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Chercheur(-euse): | Chercheur(-euse) Rousse, Daniel R. Ilinca, Adrian |
| Affiliation: | Génie mécanique, Génie mécanique |
| Date de dépôt: | 01 avr. 2026 20:19 |
| Dernière modification: | 22 avr. 2026 20:07 |
| URI: | https://espace2.etsmtl.ca/id/eprint/33544 |
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