Particle swarm optimization of electric power generation via green hydrogen considering the performance of the electrolyzer cell

Abstract

The replacement of energy generation models, from fossil fuels to carbon neutral ones, requires a significant transformation in the generation and consumption of electricity. This work highlights Green Hydrogen as an essential potential to intensify the energy generation process based on renewable sources. The article presents two studies on energy generation models, via Green Hydrogen: Case (i), study of the behavior of the Electric Activation Voltage with ohmic loss influenced by the control variables Electric Current and Operating Temperature. Case (ii), study of the behavior of the Electric Activation Voltage with ohmic loss influenced by the control variables Electric Current and Operating Pressure. For both cases, a computational model stands out, the Proton Exchange Membrane electrolyzer cells, indicated for the generation of renewable energy, from Green Hydrogen. For both cases, a Bioinspired Computing methodology was applied in order to optimize the process. The Optimization technique means, among the various definitions, determining the best set of results, considering the restrictions of the studied phenomenon. Studies indicate that this strategy is increasingly intrinsic to our reality. This paper advocates Particle Swarm Optimization as a mechanism capable of finding the best set of solutions for mathematical models related to Energy Systems. The Swarm Algorithm can be represented as a set of particles that navigate through a space of solutions, where the main element of this method is the exchange of information between the particles of the population, which allows collective mobilization towards the optimal solution. In this case specifically, the values of Electrical Voltage, Electrical Current, Operating Temperature and Pressure form the Particle Examination. The results were exceptional for the Green Hydrogen energy generation system studied. The Particle Swarm Optimization Algorithm was applied to the objective function of multiple variables and satisfactorily incorporated the restrictions and conditions of the electrolysis system. The measurements obtained highlight that the Optimal Values are lower than the Average Values, for each variable analyzed in energy generation, in an electrolyzer. The solution achieved will be of great applicability in the study of energy efficiency in structures linked to this relevant category of renewable energy generation.