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Hybrid Energy Systems Sizing for the Colombian Context: A Genetic Algorithm and Particle Swarm Optimization Approach

Author

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  • José Luis Torres-Madroñero

    (Grupo de Investigación en Ingeniería Aeroespacial, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • César Nieto-Londoño

    (Grupo de Energía y Termodinámica, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Julián Sierra-Pérez

    (Grupo de Investigación en Ingeniería Aeroespacial, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

Abstract

The use of fossil resources for electricity production is one of the primary reasons for increasing greenhouse emissions and is a non-renewable resource. Therefore, the electricity generation by wind and solar resources have had greater applicability in recent years. Hybrid Renewable Energy Systems (HRES) integrates renewable sources and storage systems, increasing the reliability of generators. For the sizing of HRES, Artificial Intelligence (AI) methods such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) stand out. This article presents the sizing of an HRES for the Colombian context, taking into account the energy consumption by three typical demands, four types of wind turbines, three types of solar panels, and a storage system for the system configuration. Two optimization approaches were set-up with both optimization strategies (i.e., GA and PSO). The first one implies the minimization of the Loss Power Supply Probability (LPSP). In contrast, the second one concerns adding the Total Annual Cost (TAC) or the Levelized Cost of Energy (LCOE) to the objective function. Results obtained show that HRES can supply the energy demand, where the PSO method gives configurations that are more adjusted to the considered electricity demands.

Suggested Citation

  • José Luis Torres-Madroñero & César Nieto-Londoño & Julián Sierra-Pérez, 2020. "Hybrid Energy Systems Sizing for the Colombian Context: A Genetic Algorithm and Particle Swarm Optimization Approach," Energies, MDPI, vol. 13(21), pages 1-30, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5648-:d:436414
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    References listed on IDEAS

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    Cited by:

    1. Jing Wu & Zhongfu Tan & Keke Wang & Yi Liang & Jinghan Zhou, 2021. "Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    2. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.
    3. José Luis Torres-Madroñero & Joham Alvarez-Montoya & Daniel Restrepo-Montoya & Jorge Mario Tamayo-Avendaño & César Nieto-Londoño & Julián Sierra-Pérez, 2020. "Technological and Operational Aspects That Limit Small Wind Turbines Performance," Energies, MDPI, vol. 13(22), pages 1-39, November.
    4. Ludmil Stoyanov & Ivan Bachev & Zahari Zarkov & Vladimir Lazarov & Gilles Notton, 2021. "Multivariate Analysis of a Wind–PV-Based Water Pumping Hybrid System for Irrigation Purposes," Energies, MDPI, vol. 14(11), pages 1-28, May.
    5. Paweł Ocłoń & Maciej Ławryńczuk & Marek Czamara, 2021. "A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation," Energies, MDPI, vol. 14(16), pages 1-15, August.
    6. Rovick Tarife & Yosuke Nakanishi & Yining Chen & Yicheng Zhou & Noel Estoperez & Anacita Tahud, 2022. "Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines," Energies, MDPI, vol. 15(6), pages 1-29, March.

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