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Adaptive nesting of evolutionary algorithms for the optimization of Microgrid’s sizing and operation scheduling

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Abstract

This paper proposes a novel adaptive nesting Evolutionary Algorithm to jointly optimize two important aspects of the configuration and planning of a Microgrid (MG): the structure’s design and the way it is operated in time (specifically, the charging and discharging scheduling of the Energy Storage System, ESS, elements). For this purpose, a real MG scenario consisting of a wind and a photovoltaic generator, an ESS made up of one electrochemical battery, and residential and industrial loads is considered. Optimization is addressed by nesting a two-steps procedure [the first step optimizes the structure using an Evolutionary Algorithm (EA), and the second step optimizes the scheduling using another EA] following different adaptive approaches that determine the number of fitness function evaluations to perform in each EA. Finally, results obtained are compared to non-nesting 2-steps algorithm evolving following a classical scheme. Results obtained show a 3.5 % improvement with respect to the baseline scenario (the non-nesting 2-steps algorithm), or a 21 % improvement when the initial solution obtained with the Baseline Charge and Discharge Procedure is used as reference.

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Acknowledgments

This study was partially funded by the Spanish Ministerial Commission of Science and Technology (MICYT, Project Number TIN2014-54583-C2-2-R) and by Comunidad Autónoma de Madrid (Project Number S2013ICE-2933_02).

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Authors and Affiliations

  1. Department of Signal Processing and Communications, Universidad de Alcalá, Madrid, Spain

    R. Mallol-Poyato, S. Jiménez-Fernández, P. Díaz-Villar & S. Salcedo-Sanz

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  1. R. Mallol-Poyato
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  2. S. Jiménez-Fernández
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  3. P. Díaz-Villar
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  4. S. Salcedo-Sanz
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Corresponding author

Correspondence to S. Jiménez-Fernández.

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All authors declare that they have no conflict of interest.

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Communicated by C. Analide.

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Mallol-Poyato, R., Jiménez-Fernández, S., Díaz-Villar, P. et al. Adaptive nesting of evolutionary algorithms for the optimization of Microgrid’s sizing and operation scheduling. Soft Comput 21, 4845–4857 (2017). https://doi.org/10.1007/s00500-016-2373-x

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  • DOI: https://doi.org/10.1007/s00500-016-2373-x

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