ArticleScientific reports2026
Modelling and metrics for optimal sizing of renewable power plants supplying green hydrogen generation systems.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This paper presents a modular modelling approach for long-term analysis and design of renewable-powered hydrogen generation and storage facilities, encompassing both power generation and hydrogen system components. The proposed model can be used to integrate different sizes of solar and wind energy resources, different battery energy storage systems, a backup power source (if required), and main hydrogen system modules in power demand calculations. As a part of the paper's novelty, the proposed modelling approach is modular and case study-free, which allows for generalisation to a variety of case studies. The expandability of the modelling method is strengthened by presenting a unified modelling framework for all modules required in modelling the system. As the second main paper's contribution, a comprehensive set of performance metrics is proposed to support a multi-objective optimisation framework for optimal sizing of system components. Although the metrics focus on different technical and economic aspects, environmental issues can be covered using some metrics, like the grid share of total energy requirements for the hydrogen system. Both proposed modelling and sizing methods enable renewable power plant designers to evaluate different configurations and make informed decisions based on weighted performance criteria. The proposed model and sizing problem are implemented in a combined Editor and Simulink environment in MATLAB for a case study as a real feasibility study in the UK to operate a renewable-supplied hydrogen system, including a 1 MW electrolyser. Simulation results for the representative case study validate the model's behaviour and its reliability through various primary output profiles, e.g., power profiles, and secondary outputs, e.g., met hydrogen demand and levelised cost of hydrogen. The proposed modelling and optimisation methods can easily be expanded for case studies with more technical data or different load demands, e.g., combined hydrogen, heat, and power.
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