A Multi-Objective Genetic Algorithm Methodology for the Design of Standalone 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 energy availability granted by the system. There are six variables, representing the size of the generation, storage, and power conversion elements, as well as the converters. The example design is repeated with two battery types, lead-acid and Li-ion. The use of a genetic algorithm (GA) reduces the computational load, allowing the quick execution of several optimizations with different settings. In the example of application, the proposed methodology showed almost identical results to a brute force search, but carrying out about 85% less iterations. Furthermore, the results highlighted the major role of the operational cost in standalone systems, as well as the correlation between this parameter and energy availability.