Gestión estratégica de costos en microrredes inteligentes: un enfoque de sostenibilidad basado en algoritmos genéticos

Joseph Sosapanta Salas; Ángela Melo Hidalgo

doi:10.22490/25392786.10737

Vol. 14 No. 2 (2025)

Published 2025-08-04

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Artículo de Investigación

Strategic Cost Management in Smart Microgrids: A Sustainability-Driven Approach Using Genetic Algorithms

DOI: https://doi.org/10.22490/25392786.10737

Joseph Sosapanta Salas Institución Universitaria Pascual Bravo, Colombia

Ángela Melo Hidalgo Universidad Nacional Abierta y a Distancia (UNAD), Colombia

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Abstract
References

Introduction: Microgrids, conceived as autonomous power distribution systems, require an advanced controller capable of autonomously determining optimal generation sources to achieve energy self-sufficiency. Methodology: This study delves into the comprehensive optimization of a microgrid through the application of genetic algorithms, simultaneously considering economic and environmental variables within a framework of sustainable management. Two reproduction methods—mutation and crossover—were analyzed for their impact on system performance. Mutation introduces random variations that emulate the introduction of innovations, while crossover combines genetic material from successful configurations to generate potentially superior solutions. Results: Two survival methods—random selection and elitism—were evaluated, revealing that random selection enhances diversity and unpredictability, whereas elitism preserves the most efficient solutions. The results show that random mutations increase the amount of excess energy produced, while combining mutation with elitism improves system efficiency. Likewise, crossover combined with elitism yields the best performance by reproducing only with the most elite chromosome of each generation. These findings provide evidence of the applicability of genetic algorithms in strategic cost management and sustainable optimization of smart microgrids.

keywords: sustainable management, microgrids, cost optimization, genetic algorithms, management strategies, energy transition

Andrade, J., & Olaya, A., “Impactos ambientales asociados a las hidroeléctricas en Colombia,” Revista de Investigación Agraria y Ambiental, vol. 14, no. 2, pp. 217–250, 2023. [Online]. Available: https://doi.org/10.22490/21456453.6074

Arabali, A., Ghofrani, M., Etezadi-Amoli, M., Fadali, M. S., & Baghzouz, Y., “Genetic-algorithm-based optimization approach for energy management,” IEEE Transactions on Power Delivery, vol. 28, no. 1, pp. 162–170, 2013. https://doi.org/10.1109/tpwrd.2012.2219598

Chen, J., “Optimal sizing for grid-tied microgrids with consideration of joint optimization of planning and operation,” IEEE Transactions on Sustainable Energy, vol. 9, no. 1, pp. 237–248, 2018. https://doi.org/10.1109/tste.2017.2724583

Donado, K., Navarro, L., Quintero, M., & Pardo, M., “HYRES: A multi-objective optimization tool for proper configuration of renewable hybrid energy systems,” Energies, vol. 13, no. 1, 2020. https://doi.org/10.3390/en13010026

Ferreira, A. C., Silva, A., Teixeira, J. C., & Teixeira, S., “Multi-objective optimization of solar thermal systems applied to Portuguese dwellings,” Energies, vol. 13, no. 24, 2020. https://doi.org/10.3390/en13246739

Hernández-Sampieri, R., & Mendoza, C., Metodología de la investigación: Las rutas cuantitativa, cualitativa y mixta, Ciudad de México, México: McGraw-Hill Education, 2018. ISBN: 978-1-4562-6096-5.

Janos, M., Szilagyi, A., & Grof, G., “Environmental and economic multi-objective optimization of a household-level hybrid renewable energy system by genetic algorithm,” Applied Energy, vol. 269, 115058, 2020. https://doi.org/10.1016/j.apenergy.2020.115058

KvT, “Microgrid power solutions, the problem: Grid congestion,” 2024. [Online]. Available: https://www.kvt.nl/

Leonori, S., Paschero, M., Frattale, F., & Rizzi, A., “Optimization strategies for microgrid energy management systems by genetic algorithms,” Applied Soft Computing Journal, vol. 86, 105903, 2020. https://doi.org/10.1016/j.asoc.2019.105903

Majeed, M. A., Phichaisawat, S., Asghar, F., & Hussan, U., “Optimal energy management system for grid-tied microgrid: An improved adaptive genetic algorithm,” IEEE Access, vol. 11, pp. 117351–117361, 2023. https://doi.org/10.1109/access.2023.3326505

Ostos Rojas, I. M., Collazos Morales, C. A., Castellanos Acuña, H. E., & Fernández Arévalo, C. P., “Sistema híbrido fotovoltaico (FV) con interacción a la red para zonas rurales de Colombia,” Revista de Investigación Agraria y Ambiental, vol. 8, no. 1, pp. 169–182, 2017. https://doi.org/10.22490/21456453.1847

Raghavan, A., Maan, P., & Shenoy, A. K. B., “Optimization of day-ahead energy storage system scheduling in microgrid using genetic algorithm and particle swarm optimization,” IEEE Access, vol. 8, pp. 173068–173078, 2020. https://doi.org/10.1109/access.2020.3025673

Rúa, E., Barrera, A., & Gómez, M., “Análisis técnico, socioeconómico y ambiental de la electrificación con energía solar fotovoltaica aislada para vivienda rural en Hato Corozal, Casanare, Colombia,” Revista de Investigación Agraria y Ambiental, vol. 8, no. 1, pp. 239–248, 2017. https://doi.org/10.22490/21456453.1853

Saiprasad, N., Kalam, A., & Zayegh, A., “Triple bottom line analysis and optimum sizing of renewable energy using improved hybrid optimization employing the genetic algorithm: A case study from India,” Energies, vol. 12, no. 3, 2019. https://doi.org/10.3390/en12030349

Torkan, R., Ilinca, A., & Ghorbanzadeh, M., “A genetic algorithm optimization approach for smart energy management of microgrids,” Renewable Energy, vol. 197, pp. 852–863, 2022. https://doi.org/10.1016/j.renene.2022.07.055

Umbarila, L., Alfonso, F., & Rivera, J., “Importancia de las energías renovables en la seguridad energética y su relación con el crecimiento económico,” Revista de Investigación Agraria y Ambiental, vol. 6, no. 2, pp. 231–242, 2015. https://doi.org/10.22490/21456453.1419

Vargas Pineda, O. I., Trujillo González, J. M., & Torres Mora, M. A., “La economía verde: un cambio ambiental y social necesario en el mundo actual,” Revista de Investigación Agraria y Ambiental, vol. 8, no. 2, pp. 175–186, 2017. https://doi.org/10.22490/21456453.2044

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How to Cite

Sosapanta Salas, J., & Melo Hidalgo, Ángela. (2025). Strategic Cost Management in Smart Microgrids: A Sustainability-Driven Approach Using Genetic Algorithms. Revista Estrategia Organizacional, 14(2), 63-83. https://doi.org/10.22490/25392786.10737

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