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D.O. Schat
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1
H2 Integration Into a Residential Energy Hub
A Case Study, Data-Driven Model-Based Approach
Master thesis
(2025)
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D.O. Schat, J. Dong, Hani Vahedi, M. Khosravi, M. Cvetkovic, Azadeh Kermansaravi
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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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.
...
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.
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.
UV-C LED Seed Disinfection
Mechanics Group
Bachelor thesis
(2023)
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M. Mazurovs, D.O. Schat, H.W. van Zeijl, J. van Turnhout, L.F.A. Wymenga, I.E. Lager, S. Izadkhast
This thesis presents the design and development of a UV-C LED-based seed treatment machine aimed at enhancing seed quality by the extermination of pathogens. The research covers design choices, including a round irradiation pattern, consisting of two rings with three and nine LEDs for the inner and outer ring respectively, the use of a quartz plate as a holding plate for seeds for its high UV-C light permeability capabilities, the use of a vibration motor underneath the main operational stack for seed movement, and the use of Ethernet ports for power distribution and communication. The thesis discusses a comparative study between square and circular plate configurations, evaluating their performance using simulation results. Safety considerations were prioritized in the design, and appropriate precautions were implemented throughout the design process. The thesis also highlights the iterative design process for the mechanical system, discussing challenges encountered and improvements made to achieve a functional and robust prototype. Results demonstrate successful integration of components and achievement of objectives. The thesis concludes with discussions on the strengths, limitations, and future enhancements of the UV-C LED-based seed treatment machine. The research presented in this thesis provides valuable insights for further advancements in seed treatment technology, contributing to sustainable agricultural practices. Due to time constraints, conclusive results of testing on seed with this machine could not be obtained yet.
...
This thesis presents the design and development of a UV-C LED-based seed treatment machine aimed at enhancing seed quality by the extermination of pathogens. The research covers design choices, including a round irradiation pattern, consisting of two rings with three and nine LEDs for the inner and outer ring respectively, the use of a quartz plate as a holding plate for seeds for its high UV-C light permeability capabilities, the use of a vibration motor underneath the main operational stack for seed movement, and the use of Ethernet ports for power distribution and communication. The thesis discusses a comparative study between square and circular plate configurations, evaluating their performance using simulation results. Safety considerations were prioritized in the design, and appropriate precautions were implemented throughout the design process. The thesis also highlights the iterative design process for the mechanical system, discussing challenges encountered and improvements made to achieve a functional and robust prototype. Results demonstrate successful integration of components and achievement of objectives. The thesis concludes with discussions on the strengths, limitations, and future enhancements of the UV-C LED-based seed treatment machine. The research presented in this thesis provides valuable insights for further advancements in seed treatment technology, contributing to sustainable agricultural practices. Due to time constraints, conclusive results of testing on seed with this machine could not be obtained yet.