Genetic algorithms-based size optimization of directly and indirectly coupled photovoltaic-electrolyzer systems

Since relatively high costs and low efficiencies are usually associated with photovoltaic-electrolyzer (PV-EL) systems, the coupling of a PV system to an EL one is a critical aspect when sizing PV-EL systems. Accordingly, using a genetic algorithms-based optimization approach and considering a hydrogen production target of 100 g per day, the size of different directly and indirectly coupled PV-EL systems is optimized in this work. The referred optimization processes are carried out for five PV-EL system configurations, one related to directly coupled systems and four (one per each DC/DC converter topology accounted for) to indirectly coupled ones. In addition, seeking to maximize hydrogen production, minimize losses, and increase system efficiency, four objective functions are assessed. Some of the results highlight that, when using system cost and overall efficiency as objective functions, properly sized indirectly coupled PV-EL systems feature lower implementation costs than directly coupled ones. In addition, the differences in the overall efficiencies characterizing the optimum directly and indirectly coupled PV-EL systems so determined are relatively small (>1%). One of the original contributions of this work relates to the fact that this is one of the first works dealing with optimization processes of both directly and indirectly coupled PV-EL systems, where optimum system configurations are compared with each other.