Modelling a small-scale hydrogen valley: Optimisation under techno-economic and environmental perspectives

Renewable hydrogen is a promising pathway to decarbonise hard-to-electrify sectors, though its widespread deployment remains hindered by economic challenges. Hydrogen valleys, integrated regional systems, have emerged as a strategic solution to scale up hydrogen infrastructure and demand. This study assesses the techno-economic feasibility of a hydrogen valley in southeastern Crete, based on the CRAVE-H 2 project, using a Mixed-Integer Linear Programming (MILP) optimisation model. The system serves multiple end-uses: touristic fuel cell buses and a vessel, as well as cold ironing for ships at berth. In addition to renewable generators, electricity can be supplied via a hybrid storage system or purchased from the grid, with dispatch optimised according to hourly market prices. A customised modelling framework is developed within PyPSA using the Linopy extension, enabling the inclusion of piecewise affine approximations of non-linear performance curves for electrolysers and fuel cells, alongside operating range constraints. Hydrogen leakage is also explicitly modelled to assess its environmental and economic implications. The model delivers optimal component sizing, energy dispatch strategies, and key performance metrics, including Levelised Cost Of Hydrogen (LCOH), aggregated Levelised Cost Of Energy (LCOE) and carbon intensity. Most scenarios yield competitive LCOH values between 5.36 and 8.21 €/kgH 2 , increasing to 15 €/kgH 2 under full decarbonisation due to extensive storage investments. Hydrogen emissions, that may exceed 10 % of total production in worst-case scenarios, become more pronounced in fully decarbonised scenarios. These findings underline the importance of emissions tracking and provide practical insights to inform the design of cost-effective, low-emission hydrogen valleys. • MILP model of CRAVE-H 2 valley for touristic mobility and port cold ironing. • PWA curves for electrolyser and fuel cell using PyPSA with Linopy extension. • Non-interconnected island; excess RES sold via dedicated Feed-in Tariff. • H 2 leakages assessed; up to 12 % of production in worst-case scenario. • Cost-optimal configuration with a balanced mix of RES and grid electricity.