This study aims to design an efficient and sustainable hydrogen logistical network (HLN) that utilizes hybrid energy sources, including local power grids and wind turbines, for water electrolysis to produce hydrogen, thereby meeting the demand for clean energy carriers.
We develop a new mixed-integer nonlinear programming (MINLP) model and propose an innovative opposition-based learning enhanced DJAYA optimization algorithm to optimize the HLN design. This includes determining the number of wind turbines, locations of hydrogen production plants and storage facilities, transportation modes and satisfying hydrogen demand.
Case study analysis under various scenarios validates the efficiency of our proposed model and algorithm in minimizing total supply chain costs, underscoring the advantages of wind-powered hydrogen production.
This research addresses a novel problem in HLN design by focusing on the integration of wind energy for hydrogen production, offering an innovative approach to cost-efficient, sustainable energy supply chain frameworks for related companies.
