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

This research aims to model and compare two strategies for locating new hydrogen refueling stations (HRS) in a context of high uncertainty on H<sub>2</sub> demand the spatial distribution points. The first strategy S1 represented by an agent-based integrating particle swarm optimization metaheuristic consists finding best HRS locations adapting real evolution demand. A second S2 solving classical capacitated <em>p</em>-median problem based consumption forecasts over given deterministic horizon order define advance <em>p</em> optimal future locations. Assuming that same distributor gradually implements HRSs area between 2023 2030, both models minimize sum travel distances each point its assigned SRH. results show during growth phase fuel cell electric vehicle (FCEV) market, with different compound annual rates (medium strong), conservative performs better than as these increase. However, while remains suboptimal throughout sales period, it becomes more effective once stabilizes. Another is uniform distributions points space have only small long-term influence performance models. advises investors study location final network region. Models can be easily configured adapted particular specific environment, flexible production capabilities, or behaviors FCEV drivers could geo-located.