SK
S. Khodakaramzadeh
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This article provides a comprehensive review of power electronics converter control and energy management for hydrogen production systems through water electrolysis. Hydrogen production from renewable energy sources is a key pathway toward decarbonizing energy systems and enabling large-scale energy storage. Efficient and dependable operation necessitates addressing the dynamic properties of electrolyzers, the intermittent nature of renewable sources, and the coordination among numerous power electronic interfaces. Unlike earlier studies that addressed these aspects separately, this review systematically connects electrolyzer modeling, converter design, control architectures, and energy management to reveal their critical interdependence. By examining these connections, the analysis reveals critical research gaps in real-time coordination, parameter adaptation, and scalable architectures, outlining pathways toward intelligent and grid-independent hydrogen production systems. This review integrates electrolyzer modeling, power converter control algorithms, AC and DC energy hub architectures, hierarchical control schemes, and energy management systems from classical to advanced methods.
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This article provides a comprehensive review of power electronics converter control and energy management for hydrogen production systems through water electrolysis. Hydrogen production from renewable energy sources is a key pathway toward decarbonizing energy systems and enabling large-scale energy storage. Efficient and dependable operation necessitates addressing the dynamic properties of electrolyzers, the intermittent nature of renewable sources, and the coordination among numerous power electronic interfaces. Unlike earlier studies that addressed these aspects separately, this review systematically connects electrolyzer modeling, converter design, control architectures, and energy management to reveal their critical interdependence. By examining these connections, the analysis reveals critical research gaps in real-time coordination, parameter adaptation, and scalable architectures, outlining pathways toward intelligent and grid-independent hydrogen production systems. This review integrates electrolyzer modeling, power converter control algorithms, AC and DC energy hub architectures, hierarchical control schemes, and energy management systems from classical to advanced methods.
This research presents a neural-adaptive control technique for DC microgrids in renewable hydrogen production systems. The proposed approach tackles voltage stability issues arising from variable solar production and fluctuating electrolyzer loads with an adaptive neural network-based proportional-integral (ANN-PI) controller with online system identification. The control architecture utilizes two concurrent multilayer perceptron (MLP) networks: one for real-time system identification to estimate the Jacobian matrix, and another for adaptive proportional-integral (PI) parameter adjustment. The decentralized architecture removes communication dependencies among converters, hence improving reliability and scalability. Simulation results indicate a better dynamic response with a 50% decrease in settling time, increased voltage stability retaining the DC bus voltage within ±2% of the nominal 400 V, and resilient performance under diverse situations, including load transitions and changes in solar irradiation. The neural-adaptive method effectively facilitates intelligent, model-free regulation for electric-hydrogen DC microgrids.
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This research presents a neural-adaptive control technique for DC microgrids in renewable hydrogen production systems. The proposed approach tackles voltage stability issues arising from variable solar production and fluctuating electrolyzer loads with an adaptive neural network-based proportional-integral (ANN-PI) controller with online system identification. The control architecture utilizes two concurrent multilayer perceptron (MLP) networks: one for real-time system identification to estimate the Jacobian matrix, and another for adaptive proportional-integral (PI) parameter adjustment. The decentralized architecture removes communication dependencies among converters, hence improving reliability and scalability. Simulation results indicate a better dynamic response with a 50% decrease in settling time, increased voltage stability retaining the DC bus voltage within ±2% of the nominal 400 V, and resilient performance under diverse situations, including load transitions and changes in solar irradiation. The neural-adaptive method effectively facilitates intelligent, model-free regulation for electric-hydrogen DC microgrids.