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A Multi-Objective Genetic Algorithm methodology for sizing and power electronics selection of standalone renewable energy systems
  • Marco Virgili ,
  • Andrew J. Forsyth ,
  • Pete James
Marco Virgili
University of Manchester

Corresponding Author:[email protected]

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Andrew J. Forsyth
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Pete James
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This work proposes a design methodology to optimize multiple design metrics of a stand-alone PV/battery system at the same time. The relevance of each objective can be adjusted by the designer and this paper explores the correlations among them. An application example is proposed, where the objectives are the minimization of investment and operational cost, with a boundary set on the system reliability. The variables are six and represent the size of the generation, storage, and power conversion elements, as well as the converters selection. The example design is repeated with two battery types, Lead-Acid and Li-Ion. The use of a genetic algorithm reduces the computational power, allowing the quick execution of several optimizations with different settings.