Comparing Two Strategies for Locating Hydrogen Refueling Stations under High Demand Uncertainty

Abstract

This research aims to model and compare two strategies for locating new hydrogen refueling stations (HRS) in a context of high uncertainty on H₂ demand and on the spatial distribution of demand points. The first strategy S1 represented by an agent-based model integrating a particle swarm optimization metaheuristic consists of finding the best HRS locations by adapting to the real evolution of the demand. A second strategy S2 consists in solving a classical capacitated <em>p</em>-median problem based on H₂ consumption forecasts over a given deterministic horizon in order to define in advance <em>p</em> optimal future HRS locations. Assuming that the same distributor gradually implements future HRSs in a given area between 2023 and 2030, both models minimize the sum of travel distances between each demand point and its assigned SRH. The results show that during the growth phase of the fuel cell electric vehicle (FCEV) market, with two different compound annual growth rates (medium and strong), the conservative S1 strategy performs better than S2 as these rates increase. However, while S2 remains suboptimal throughout the sales growth period, it becomes more effective once demand stabilizes. Another finding is that different uniform distributions of H₂ demand points in the same space have only a small long-term influence on the performance of these two models. This research advises investors to study the influence of different location strategies and models on the performance of a final HRS network in a given region. Models can be easily configured and adapted to a particular spatial distribution of demand points in a specific environment, more flexible H₂ production capabilities, or different behaviors of FCEV drivers that could be geo-located.