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Open AccessArticle

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

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Grupo de Investigación en Ingeniería Aeroespacial, Universidad Pontificia Bolivariana, Medellín 050031, Colombia
2
Grupo de Energía y Termodinámica, Universidad Pontificia Bolivariana, Medellín 050031, Colombia
*
Author to whom correspondence should be addressed.
Energies 2020, 13(21), 5648; https://doi.org/10.3390/en13215648
Received: 16 September 2020 / Revised: 20 October 2020 / Accepted: 22 October 2020 / Published: 28 October 2020
(This article belongs to the Special Issue Computational Intelligence Applications in Smart Grid Optimization)
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. View Full-Text
Keywords: hybrid systems; renewable energies; wind energy; solar energy; genetic algorithm; particle swarm optimization hybrid systems; renewable energies; wind energy; solar energy; genetic algorithm; particle swarm optimization
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MDPI and ACS Style

Torres-Madroñero, J.L.; Nieto-Londoño, C.; Sierra-Pérez, J. Hybrid Energy Systems Sizing for the Colombian Context: A Genetic Algorithm and Particle Swarm Optimization Approach. Energies 2020, 13, 5648.

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