Adaptive nesting of evolutionary algorithms for the optimization of Microgrid’s sizing and operation scheduling
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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.
KeywordsMicrogrids Microgrid design Microgrid operation Energy storage system scheduling Evolutionary algorithms Nesting algorithms
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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Conflicts of interest
All authors declare that they have no conflict of interest.
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