Hybrid energy systems sizing for the colombian context: A genetic algorithm and particle swarm optimization approach

José Luis Torres-Madroñero, César Nieto-Londoño, Julián Sierra-Pérez

    Research output: Contribution to journalArticle in an indexed scientific journalpeer-review

    24 Scopus citations


    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.

    Original languageEnglish
    Article number5648
    Issue number21
    StatePublished - 1 Nov 2020

    Bibliographical note

    Publisher Copyright:
    © 2020 by the authors. Licensee MDPI, Basel, Switzerland.


    • Genetic algorithm
    • Hybrid systems
    • Particle swarm optimization
    • Renewable energies
    • Solar energy
    • Wind energy

    Types Minciencias

    • Artículos de investigación con calidad A2 / Q2


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