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T. Faghihisenejani
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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 paper proposes a hybrid model predictive control (HMPC) strategy for a three-level neutral-point-clamped (3L NPC) rectifier to optimize the operation of electrolyzers for green hydrogen production. Unlike conventional approaches that focus primarily on inverter applications, the proposed method directly addresses the critical issue of neutral-point voltage imbalance in NPC rectifiers. The control framework combines discrete switching-state selection with adaptive objective prioritization, utilizing real-time capacitor voltage measurements to exploit switching-state redundancy. A multi-objective cost function balances current tracking, DC-link voltage regulation, neutral-point voltage balancing, and switching-loss minimization. An adaptive weighting mechanism dynamically prioritizes control objectives in response to system conditions such as renewable intermittency and grid disturbances. To improve long-term reliability, the strategy introduces state diversity enforcement and inactivity penalties, reducing capacitor stress without compromising harmonic performance. The effectiveness of the proposed method is evaluated using a dynamic equivalent model of a proton exchange membrane (PEM) electrolyzer, enabling a realistic assessment of green hydrogen production. Validation through hardware in the loop (HIL) test demonstrates robust current control and stable DC-link voltages in varying operating scenarios.
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This paper proposes a hybrid model predictive control (HMPC) strategy for a three-level neutral-point-clamped (3L NPC) rectifier to optimize the operation of electrolyzers for green hydrogen production. Unlike conventional approaches that focus primarily on inverter applications, the proposed method directly addresses the critical issue of neutral-point voltage imbalance in NPC rectifiers. The control framework combines discrete switching-state selection with adaptive objective prioritization, utilizing real-time capacitor voltage measurements to exploit switching-state redundancy. A multi-objective cost function balances current tracking, DC-link voltage regulation, neutral-point voltage balancing, and switching-loss minimization. An adaptive weighting mechanism dynamically prioritizes control objectives in response to system conditions such as renewable intermittency and grid disturbances. To improve long-term reliability, the strategy introduces state diversity enforcement and inactivity penalties, reducing capacitor stress without compromising harmonic performance. The effectiveness of the proposed method is evaluated using a dynamic equivalent model of a proton exchange membrane (PEM) electrolyzer, enabling a realistic assessment of green hydrogen production. Validation through hardware in the loop (HIL) test demonstrates robust current control and stable DC-link voltages in varying operating scenarios.
This study proposes a model predictive control (MPC) strategy integrated with closed-loop space vector modulation (CL-SVM) for a three-phase, three-level neutral point clamped (3L-NPC) rectifier supplying two alkaline electrolyzers connected in series. Electrolyzers present a nonlinear and dynamically varying load due to their dependence on temperature, pressure, and electrochemical reaction rates, imposing strict requirements on the stability and responsiveness of the power supply. Among, multi-level converter topologies, the 3L-NPC rectifier is a promising candidate for low to medium-voltage, high power applications due to its reduced harmonic distortion, improved high power handling, and balanced trade-off between complexity and performance. However, maintaining DC-link capacitor voltage balance under dynamic loads remains challenging, risking power quality and system reliability. The proposed approach optimizes voltage vector selection to regulate DC output and minimize neutral-point voltage deviation. Simulation results in MATLAB/Simulink confirm the effectiveness of the designed controller in achieving stable DC voltage and a balanced neutral-point voltage, thereby enhancing the overall performance of the power-electronics interface in electrolyzer applications.
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This study proposes a model predictive control (MPC) strategy integrated with closed-loop space vector modulation (CL-SVM) for a three-phase, three-level neutral point clamped (3L-NPC) rectifier supplying two alkaline electrolyzers connected in series. Electrolyzers present a nonlinear and dynamically varying load due to their dependence on temperature, pressure, and electrochemical reaction rates, imposing strict requirements on the stability and responsiveness of the power supply. Among, multi-level converter topologies, the 3L-NPC rectifier is a promising candidate for low to medium-voltage, high power applications due to its reduced harmonic distortion, improved high power handling, and balanced trade-off between complexity and performance. However, maintaining DC-link capacitor voltage balance under dynamic loads remains challenging, risking power quality and system reliability. The proposed approach optimizes voltage vector selection to regulate DC output and minimize neutral-point voltage deviation. Simulation results in MATLAB/Simulink confirm the effectiveness of the designed controller in achieving stable DC voltage and a balanced neutral-point voltage, thereby enhancing the overall performance of the power-electronics interface in electrolyzer applications.