[EN] Renewable hydrogen is an emerging solution to the need for decarbonization of the current society, with local deployments being at the core of most implementations. It is currently in early stage of implementation, so there are not many previous experiences to standardize decision-making and the most relevant criteria. However, the lack of experts in the field of renewable hydrogen makes it difficult to design an optimal value chain. For this reason, this paper proposes a specific framework based on Geographic Information Systems, Multi-Criteria Decision Analysis and Intelligent Optimization (specifically two Genetic Algorithms denoted as Methods A and B) for the decision-making regarding the selection of optimal sites for the implementation of the renewable energy value chain from a holistic perspective; that is, considering topographic, economic, social, environmental, and demand criteria. The proposed framework is validated through the comparison of its results with those of the most extended methods in the state of the art. The results show that the application of the proposed framework implies an increase in accuracy in the determination of the locations with the highest Land Suitability Index for the renewable hydrogen value chain. Specifically, an increase in accuracy of 1.35 % (Method A) and 3.25 % (Method B) is observed with respect to the most widely used method in the literature: Analytic Hierarchy Process. Spain has been selected as a case study to validate the applicability of the proposed framework, which has facilitated the identification the optimal municipalities for the local implementation of renewable hydrogen in the country. It has been demonstrated that of the 60 projects in advanced levels of development, 87% (50 projects) have a high level of Land Suitability Index placing them in the first quartile of the ranking. In terms of investment, these projects represent around €468 million (87.7 %). It can therefore be concluded that the renewable hydrogen financing strategy of Spain can be slightly improved with the results of the proposed framework.
