Direct wind-to-hydrogen systems, in which a wind farm supplies power to an electrolyser without a firm grid connection, must accommodate the full variability of the wind resource, yet many existing techno-economic and design studies rely on input data with temporal resolutions of several minutes to one hour. This coarse averaging can mask short-term power fluctuations that drive electrolyser performance, cycling and degradation. This paper presents a novel wind-to-electrolyser model that synthesises effective wind speed suitable for a power curve wind model at 1 Hz, combining low-frequency trend variation, mesoscale wind speed changes and Kaimal turbulence, validated against aeroelastic simulations. The resulting power output is supplied to a PEM electrolyser model incorporating bank-level thermal dynamics and steady-state, cyclic and on/off degradation, enabling the effect of wind-power variability on long-term electrolyser performance to be quantified. Using 10-year simulations of a wind farm at a site in Orkney, Scotland, two scenarios are investigated: the effect of wind farm size (1–10 turbines) and input temporal resolution (1 s, 1 min, 10 min) on power variability, degradation and hydrogen production; and the effect of turbine configuration for a fixed installed capacity (three 5 MW turbines versus one 15 MW turbine). Results show that coarser temporal resolution substantially overestimates hydrogen production, particularly for small wind farms, where 10-minute-averaged data increase predicted output by up to 428% relative to 1 s data, primarily due to underestimated on/off and cyclic degradation. Aggregating turbines and, to a lesser extent, using larger rotors both smooth the power input and reduce this sensitivity, though the effect remains significant even for 10-turbine farms. These findings demonstrate that high-frequency wind power variability must be explicitly considered in the design and performance assessment of wind-to-hydrogen systems.
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Stock et al. (2026) studied this question.
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