Title | ||
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Adaptive nesting of evolutionary algorithms for the optimization of Microgrid's sizing and operation scheduling. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2017 | 10.1007/s00500-016-2373-x | Soft Comput. |
Keywords | Field | DocType |
Microgrids, Microgrid design, Microgrid operation, Energy storage system scheduling, Evolutionary algorithms, Nesting algorithms | Energy storage,Mathematical optimization,Evolutionary algorithm,Computer science,Scheduling (computing),Fitness function,Sizing,Battery (electricity),Operation scheduling,Microgrid | Journal |
Volume | Issue | ISSN |
21 | 17 | 1433-7479 |
Citations | PageRank | References |
1 | 0.36 | 6 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
R. Mallol-Poyato | 1 | 10 | 1.90 |
Silvia Jiménez-Fernández | 2 | 29 | 5.46 |
P. Díaz-Villar | 3 | 1 | 1.04 |
Sancho Salcedo-Sanz | 4 | 580 | 71.21 |