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.