This thesis explores the integration of hydrogen ( H2) into a residential hybrid energy hub and presents results based on a case study of such an energy hub located at The Green Village, an open-field lab environment at Delft university of Technology. The study focusses on analysing real operational data from the energy hub to assess the performance, behaviour, and integration of the system. The energy hub combines photovoltaic (PV) power generation, battery storage, and hydrogen-based components including an AEM electrolyser, hydrogen storage, and a PEM fuel cell.
First, a detailed data analysis of the energy hub components is performed which lays a basis for the model of the energy hub. Particular attention was paid to the ramp-up and ramp-down dynamics, the power consumption and generation capabilities, and hydrogen consumption and generation of the electrolyser and fuel cell, as these affect the overall efficiency and responsiveness of the system. In addition, insights are given into the real capacity of the energy hub, and a comparison between the intended operation of the energy hub and the real operation is given. Following insights gained from the data analysis, a first step toward integrating machine learning into the Energy Management System (EMS) was taken. One potential improvement identified was the use of a machine learning algorithm that uses weather forecasts into EMS decision-making for the electrolyser. This study explores both binary classification and regression models using outside temperature and PV inverter power as inputs. Since inverter power correlates strongly with solar irradiance, these inputs were considered sufficient for developing a preliminary machine learning model aimed at enabling smarter control of the electrolyser.
A data-driven Simulink model of the energy hub was developed to simulate various operational scenarios, sizes, and edge cases. These simulations revealed how the behaviour of the system changes under different profiles of PV generation and residential demand, even when total energy consumption remains constant.
The data results create a solid foundation for the development of the Simulink model to run experiments with. The model has successfully demonstrated that the integration of hydrogen into the energy hub facilitates the coverage of seasonal energy demands, adding flexibility, and flattening the energy demands of the grid. However, it also showed that the system performance is highly sensitive to operating conditions and system sizing. Recommendations for future energy hub upgrades are provided through control optimisation and system sizing.
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