Genetic algorithm-based methodology for hydrogen network planning and optimization: application in a Portuguese national project

Abstract

Green hydrogen is a priority on the agendas of nearly all developed nations seeking to decarbonize the increasingly complex energy system, providing a sustainable solution for hard-to-abate industries. This study intends to provide a useful and generalizable methodology for planning hydrogen infrastructures, considering specific drivers, including geographic characteristics, production and consumption uncertainty, and financial viability. This novelty approach comprises three primary processes, each supported by a dedicated Python algorithm. The first phase involves mapping consumption that could be replaced by green hydrogen. The second phase features a Decision Support System to define consumption scenarios. The final phase employs a broadly applicable Genetic Algorithm (GA), enabling project developers to determine optimal network parameters. The methodology was applied to the CelZa project, demonstrating its practical application, which can be adapted to other projects. The results reveal a potential national hydrogen consumption around the network ranging from 3.1 to 7.7 TWh by 2030. The average annual carbon dioxide emissions reduction was approximately one megaton, representing 12.5% of the National Strategy for Hydrogen target for 2030. The GA results demonstrate a decreasing transportation cost as consumption increases, indicating that the project's economic viability could be achieved with one-third of the predicted electrolysis capacity for Portugal in 2030 (5.5 GW).