Evolutionary optimization approaches for direct coupling photovoltaic-electrolyzer systems

Hydrogen is an important storage medium and can be produced by the water electrolysis. In this research, energy transfer loss between a photovoltaic (PV) unit and electrolyzer is minimized by optimizing the size and operating condition of an electrolyzer directly connected to a PV module. In directly coupled photovoltaic-electrolyzer (PV/EL) systems, there is a mismatch between output PV´s maximum power point characteristic and input PEM electrolyzer´s characteristic. With proper sizing optimization methods, it is possible to directly couple photovoltaic-electrolyzer systems. The evolutionary optimization algorithms like genetic algorithm (GA), particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) are ideal for handling this kind of problems due to nonlinear behavior of the system during a year. However, each algorithm has its own advantages and disadvantages. In this paper a PV/EL system is simulated and then comparisons among the three evolutionary algorithms are presented for optimization of the system; in terms of processing time, convergence speed, and quality of the results. Based on the comparative analysis, the performance of the algorithms differs in various aspects which make them more or less best suited for such a kind of problem.