## 3 Results
### Current Status, Challenges and Future Developments
#### 3.2.3 Renewable Hydrogen Transport and Distribution
[120] use a GIS model to integrate physical obstacles and technical restrictions and propose an innovative analysis based on genetic obstacle algorithms, which allows the calculation of optimal routes with variable costs considering hydraulic, geographical, and economic constraints. It is especially useful for scenarios where ground topography influences the feasibility of routes. [PERSON], [PERSON] et al. [121] use GIS to locate the productions and consumptions regions this integration is not detailed, which limits optimization and investigate the effects of hydrogen mixing on existing gas pipelines reducing the need for new transport investments. However, the technical and regulatory limitations associated with different mixing percentages are also analyzed, and [PERSON], [PERSON] et al. [122] the study optimizes the system size of hydrogen production prioritizing the wind energy production system, compress the hydrogen before the storage-GIS is used to pair each wind-hydrogen system with its nearest gas pipeline and identify the shortest road route between them. Other studies on transport optimization focus on evaluating on-site hydrogen production, thereby reducing the need for transport, by using GIS models and satellite imagery to measure solar and wind capacity as a source of hydrogen. [PERSON], [PERSON] et al. [125] use geospatial techniques to locate hydrogen stations and assess the availability of excess renewable energy focus on using this excess renewable of energy for the Hydrogen Refuelling Stations (HRS) through electrolysis. Furthermore, [PERSON], [PERSON] et al. [126] propose to integrate renewable hydrogen into smart grids highlighting its potential to transform energy systems towards more sustainable and decarbonized models. Although the article does not focus specifically on geospatial tools, integration with existing electricity networks involves spatial analysis. and, [PERSON], [PERSON] et al. [127] make a techno-economic study to power Irlanda's buses with hydrogen in order to decarbonize the bus and coach networks. A location-allocation algorithm in a GIS environment optimizes the distributed hydrogen supply chain from each wind farm to a hypothetical HRS in the nearest city, identifying efficient transport routes and optimal locations for HRS. [PERSON], [PERSON] et al. [129] focus their efforts on assessing the feasibility of using hydrogen in the fuel cell trains operating between Berlin and Brandenburg. Geospatial techniques are used to detect optimal locations for hydrogen production and its efficient distribution along regional railway routes, and the integration of wind power production with regional transport by fuel cell trains is evaluated. In Brandenburg, around 10.1 million train-kilometers per year could be converted to fuel cell electric train operations. The global study conducted by [PERSON], [PERSON] et al. [124] models transcontinental supply chains including ship and pipeline transport. Its approach enables integration with the electricity and gas sectors, use GIS to create, transform, analyze and interpret geographic data. The main difficulty found in this study is the lack of standardized data and variability in existing infrastructure. While other studies look at the costs of retrofitting and transporting existing pipelines [PERSON], [PERSON] et al. [128] present a model for the investment evaluation of Power-to-Gas (PtG) plants to generate hydrogen from electricity. GIS is used to evaluate and display data such as renewable resources, existing infrastructure, and energy demand. [PERSON], [PERSON] et al. [130] make a techno-economic study evaluating the economic viability of integrating renewable hydrogen into existing pipe infrastructure in Wyoming, identifying optimal locations to mix hydrogen with gas- Meanwhile, [PERSON], [PERSON] et al. [131] uses geospatial techniques to determine the potential of renewable energy sources in different regions. It faces the added difficulty of using hourly data from different geographical areas for renewable energy production.