Research on Dynamic Response and Predictive Smoothing Control of AEM Water Electrolyzers Considering Renewable Energy Fluctuations

To address current-density ramping, cell-voltage increase, and elevated operating stress in anion exchange membrane water electrolyzers (AEMWEs) under fluctuating wind and photovoltaic (PV) power inputs, this study develops a dynamic AEMWE model that couples voltage losses, thermal dynamics, and water-management states. A forecast-assisted reference governor based on short-term power prediction and dynamic constraints is further proposed. Using German Open Power System Data (OPSD) wind and PV, the effects of power-command correction on current density, voltage efficiency, specific energy consumption (SEC), voltage-limit exceedance, and a degradation-related stress proxy are analyzed. The steady-state benchmark distinguishes a separate 21-point empirical polarization fit (same-set RMSE 0.0059 V and MAPE 0.292%) from the mechanistic voltage-loss model actually used in the dynamic simulations (same-set RMSE 0.5063 V and MAPE 28.42%); consequently, the dynamic results are treated as model-conditioned rather than experimentally validated. In the 24 h case, explicit state-constrained smoothing reduced the maximum cell voltage from 2.2479 to 2.1501 V and SEC from 59.4755 to 58.1968 kWh kg−1, but hydrogen yield and renewable-energy utilization decreased from 1.51997 to 0.54293 kg d−1 and from 99.52% to 34.78%, respectively. The forecast envelope did not bind on the selected smooth day; across 30 representative days it reduced the stress proxy relative to the no-look-ahead state-constrained baseline on only 3–6 days depending on wind–PV composition, while in a diagnostic ramp-down case it activated 10 times and reduced cumulative ramping by 6.7% at the cost of a 16.9% hydrogen-yield loss. These results provide model-based exploratory evidence and separate the benefit of state projection from the incremental value of prediction.

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Publication Details

Journal
Electronics
Published
2026-09-14
DOI
https://doi.org/10.3390/electronics15184160
Primary Topic
Hybrid Renewable Energy Systems
Type
article
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article

Research on Dynamic Response and Predictive Smoothing Control of AEM Water Electrolyzers Considering Renewable Energy Fluctuations

Gaojun Meng, Hu Hairui, Fengwei Jin, Xinyang Chai et al.
Electronics
Hybrid Renewable Energy Systems
article

Research on Dynamic Response and Predictive Smoothing Control of AEM Water Electrolyzers Considering Renewable Energy Fluctuations

Gaojun Meng, Hu Hairui, Fengwei Jin, Xinyang Chai, Geyang Xu
article en

Abstract

To address current-density ramping, cell-voltage increase, and elevated operating stress in anion exchange membrane water electrolyzers (AEMWEs) under fluctuating wind and photovoltaic (PV) power inputs, this study develops a dynamic AEMWE model that couples voltage losses, thermal dynamics, and water-management states. A forecast-assisted reference governor based on short-term power prediction and dynamic constraints is further proposed. Using German Open Power System Data (OPSD) wind and PV, the effects of power-command correction on current density, voltage efficiency, specific energy consumption (SEC), voltage-limit exceedance, and a degradation-related stress proxy are analyzed. The steady-state benchmark distinguishes a separate 21-point empirical polarization fit (same-set RMSE 0.0059 V and MAPE 0.292%) from the mechanistic voltage-loss model actually used in the dynamic simulations (same-set RMSE 0.5063 V and MAPE 28.42%); consequently, the dynamic results are treated as model-conditioned rather than experimentally validated. In the 24 h case, explicit state-constrained smoothing reduced the maximum cell voltage from 2.2479 to 2.1501 V and SEC from 59.4755 to 58.1968 kWh kg−1, but hydrogen yield and renewable-energy utilization decreased from 1.51997 to 0.54293 kg d−1 and from 99.52% to 34.78%, respectively. The forecast envelope did not bind on the selected smooth day; across 30 representative days it reduced the stress proxy relative to the no-look-ahead state-constrained baseline on only 3–6 days depending on wind–PV composition, while in a diagnostic ramp-down case it activated 10 times and reduced cumulative ramping by 6.7% at the cost of a 16.9% hydrogen-yield loss. These results provide model-based exploratory evidence and separate the benefit of state projection from the incremental value of prediction.

ElectronicsVol. 15(18)
Nanjing Institute of Technology (CN), Inner Mongolia Electric Power Survey & Design Institute (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 23%
Hybrid Renewable Energy Systems
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