A. Amirreza Silani
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Modeling of an AEM Electrolyzer for Distribution Grid Support
A Dual Framework for Hybrid Component Modeling and Data-Driven Grid Reconstruction
The global energy transition towards Renewable Energy Sources (RES) introduces significant challenges to grid stability due to the inherent intermittency of sources like solar and wind. Hybrid Energy Storage (HES) systems, particularly those integrating Power-to-Hydrogen-to- Power (P2H2P) technologies, are critical for mitigating these issues. However, the effective planning and control of these systems depend on accurate models of both the HES components and the Low Voltage (LV) distribution grids they support. This thesis addresses this dual modeling challenge, which is often hindered by complex component dynamics and limited grid data.
This research develops and validates two distinct frameworks. First, a control-oriented, hybrid dynamic model for an Anion Exchange Membrane (AEM) electrolyzer is proposed. This model integrates a physics-based Equivalent Circuit Model (ECM) to capture primary electrochemical dynamics with a shallow neural network trained to correct for residual, unmodeled thermal dynamics. Second, a complete, data-driven pipeline is developed to construct "digital twins" of LV distribution grids from limited static data. This pipeline uses an Integer Linear Programming (ILP) algorithm to reconstruct the most probable network topology, which is then parameterized for power flow analysis.
The hybrid electrolyzer model was validated using operational data from a commercial Enapter EL 2.1 unit, demonstrating high fidelity with a 92.03 % fit, drastically outperforming the 26.00% fit of the standalone ECM. The grid modeling pipeline was validated in a case study of a 160kVA substation in Bonaire. Despite incomplete GIS data, the resulting model accurately simulated aggregate load profiles, achieving a low Root Mean Square Error (RMSE) of approximately 6% for both active and reactive power when compared to real-world measurements.
In synthesis, this thesis delivers a robust and validated toolset for analyzing HES-grid inter- action: a high-fidelity, computationally efficient model of an AEM electrolyzer and a viable method for accurately modeling limited-data LV grids. These components enable advanced co- simulation and control design to study the role of AEM electrolyzers in providing distribution grid support. ...
The global energy transition towards Renewable Energy Sources (RES) introduces significant challenges to grid stability due to the inherent intermittency of sources like solar and wind. Hybrid Energy Storage (HES) systems, particularly those integrating Power-to-Hydrogen-to- Power (P2H2P) technologies, are critical for mitigating these issues. However, the effective planning and control of these systems depend on accurate models of both the HES components and the Low Voltage (LV) distribution grids they support. This thesis addresses this dual modeling challenge, which is often hindered by complex component dynamics and limited grid data.
This research develops and validates two distinct frameworks. First, a control-oriented, hybrid dynamic model for an Anion Exchange Membrane (AEM) electrolyzer is proposed. This model integrates a physics-based Equivalent Circuit Model (ECM) to capture primary electrochemical dynamics with a shallow neural network trained to correct for residual, unmodeled thermal dynamics. Second, a complete, data-driven pipeline is developed to construct "digital twins" of LV distribution grids from limited static data. This pipeline uses an Integer Linear Programming (ILP) algorithm to reconstruct the most probable network topology, which is then parameterized for power flow analysis.
The hybrid electrolyzer model was validated using operational data from a commercial Enapter EL 2.1 unit, demonstrating high fidelity with a 92.03 % fit, drastically outperforming the 26.00% fit of the standalone ECM. The grid modeling pipeline was validated in a case study of a 160kVA substation in Bonaire. Despite incomplete GIS data, the resulting model accurately simulated aggregate load profiles, achieving a low Root Mean Square Error (RMSE) of approximately 6% for both active and reactive power when compared to real-world measurements.
In synthesis, this thesis delivers a robust and validated toolset for analyzing HES-grid inter- action: a high-fidelity, computationally efficient model of an AEM electrolyzer and a viable method for accurately modeling limited-data LV grids. These components enable advanced co- simulation and control design to study the role of AEM electrolyzers in providing distribution grid support.