A Multi-Objective Genetic Algorithm methodology for sizing and power
electronics selection of standalone renewable energy systems
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.