Grid-integrated hydrogen production systems
A holistic analytical modeling framework for stability assessment and dynamic interaction
Chunjun Huang (TU Delft - Electrical Engineering, Mathematics and Computer Science)
José Luis Rueda Torres (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Nakul Narayanan Kuruveettil (Government Engineering College, Thrissur)
Xin Jin (Norwegian University of Science and Technology (NTNU))
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Abstract
Grid-integrated electrolyzer systems are increasingly deployed for green hydrogen production, which is a promising pathway for energy decarbonization. However, their operation is challenged by insufficiently understood dynamic interactions among the grid-side rectifier, the buck converter, their control loops, and the electrolyzer stack. To address this issue, this paper develops a holistic analytical framework for such systems. A unified model is derived by integrating the rectifier, the buck converter, their control loops, and the electrolyzer stack. Based on this model, eigenvalue, participation-factor, and frequency-response analyses are conducted to systematically quantify stability characteristics, internal dynamic couplings, and parameter sensitivities. For a 2 MW electrolyzer case, the results reveal that excessive rectifier or buck-control bandwidths can independently trigger distinct oscillatory instabilities. On this basis, engineering-oriented controller-tuning guidelines are established, recommending about 10–50 Hz for the phase-locked loop, below about 60 Hz for the DC-link voltage controller, and about 20–150 Hz for the buck power controller. The analysis further shows that properly designed buck bandwidth renders the system-level power response weakly sensitive to slow electrolyzer dynamics dominated by double-layer capacitance, thereby mitigating uncertainty in this capacitance and clarifying the applicability of reduced-order electrolyzer models. These findings are corroborated by PSCAD/EMTDC time-domain simulations, verifying the effectiveness of the proposed analytical model. Additional verifications under frequency and voltage disturbances further confirm the model’s predictive capability, with maximum relative errors of 0.264%–1.486% and 0.153%–5.922%, respectively. Overall, this work offers an efficient analytical tool for stability-oriented control design, model-fidelity selection, and dynamic interaction analysis of grid-integrated electrolyzer systems.
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