D. Georgiadi
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4 records found
1
Public communication regarding power grid policy is difficult due to the unintuitive behavior of
electricity. This thesis presents the development of an easily understandable yet accurate model of the Dutch electricity network. The topology, production and load of the contemporary grid is studied using publicly available data. An accurate model of the grid is designed using stochastic modeling and data analysis techniques. An attempt is made to simulate the model using DC power flow techniques. The software is developed to be compatible with the Illuminator system, which can visualize the simulation in a physical model. The ultimate implementation of the model was unsuccessful. The design process and attempted simulation of the model is documented in this thesis. ...
electricity. This thesis presents the development of an easily understandable yet accurate model of the Dutch electricity network. The topology, production and load of the contemporary grid is studied using publicly available data. An accurate model of the grid is designed using stochastic modeling and data analysis techniques. An attempt is made to simulate the model using DC power flow techniques. The software is developed to be compatible with the Illuminator system, which can visualize the simulation in a physical model. The ultimate implementation of the model was unsuccessful. The design process and attempted simulation of the model is documented in this thesis. ...
Public communication regarding power grid policy is difficult due to the unintuitive behavior of
electricity. This thesis presents the development of an easily understandable yet accurate model of the Dutch electricity network. The topology, production and load of the contemporary grid is studied using publicly available data. An accurate model of the grid is designed using stochastic modeling and data analysis techniques. An attempt is made to simulate the model using DC power flow techniques. The software is developed to be compatible with the Illuminator system, which can visualize the simulation in a physical model. The ultimate implementation of the model was unsuccessful. The design process and attempted simulation of the model is documented in this thesis.
electricity. This thesis presents the development of an easily understandable yet accurate model of the Dutch electricity network. The topology, production and load of the contemporary grid is studied using publicly available data. An accurate model of the grid is designed using stochastic modeling and data analysis techniques. An attempt is made to simulate the model using DC power flow techniques. The software is developed to be compatible with the Illuminator system, which can visualize the simulation in a physical model. The ultimate implementation of the model was unsuccessful. The design process and attempted simulation of the model is documented in this thesis.
Bachelor thesis
(2025)
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B.L. Dorland, A.M. Vermeer, M. Cvetkovic, J.A. Groen, I.E. Lager, D. Georgiadi
This thesis discusses the integration of the Illuminator software with hardware components, for the purpose of creating a table-top, Plug-and-Play energy system demonstrator. The thesis starts by introducing the motivation for an energy system demonstrator in Chapter 1. An interactive, visual demonstrator can help educate people on energy systems in a way that
is more intuitive. The main product should show the power flow on the table, implement Plug-and-Play dynamics, and be scalable. LED strips are used to visualize power flows in the table-top network, in combination with the Digispark ATtiny85. For determining the topology, static ID pairs were used. A double simulation is used to implement Plug-and-Play dynamics.
The first simulation configures the topology used by the second simulation, based on the hardware connections. The second simulation runs the Illuminator simulation. During this simulation, checks are run to see whether a physical connection has changed, such as a cable being unplugged. The simulation and then starts the reconfiguration process again.
Testing the reliability and run-time of the implementation is documented in Chapter 6, with a focus on how well the implementation scales with the size of the simulation. It was concluded that the Digispark’s communication with the Raspberry Pi would often stall, requiring error correction to be implemented. Even then, the data transfer to the Digispark from the Raspberry Pi fails on the first try an average of 48% of the time. Determining how long a setup takes to reconfigure was estimated using a computer, since the Raspberry Pi’s aren’t powerful enough to simulate dozens of models. It was determined that a reconfiguration of 20 models takes about 100 seconds. ...
is more intuitive. The main product should show the power flow on the table, implement Plug-and-Play dynamics, and be scalable. LED strips are used to visualize power flows in the table-top network, in combination with the Digispark ATtiny85. For determining the topology, static ID pairs were used. A double simulation is used to implement Plug-and-Play dynamics.
The first simulation configures the topology used by the second simulation, based on the hardware connections. The second simulation runs the Illuminator simulation. During this simulation, checks are run to see whether a physical connection has changed, such as a cable being unplugged. The simulation and then starts the reconfiguration process again.
Testing the reliability and run-time of the implementation is documented in Chapter 6, with a focus on how well the implementation scales with the size of the simulation. It was concluded that the Digispark’s communication with the Raspberry Pi would often stall, requiring error correction to be implemented. Even then, the data transfer to the Digispark from the Raspberry Pi fails on the first try an average of 48% of the time. Determining how long a setup takes to reconfigure was estimated using a computer, since the Raspberry Pi’s aren’t powerful enough to simulate dozens of models. It was determined that a reconfiguration of 20 models takes about 100 seconds. ...
This thesis discusses the integration of the Illuminator software with hardware components, for the purpose of creating a table-top, Plug-and-Play energy system demonstrator. The thesis starts by introducing the motivation for an energy system demonstrator in Chapter 1. An interactive, visual demonstrator can help educate people on energy systems in a way that
is more intuitive. The main product should show the power flow on the table, implement Plug-and-Play dynamics, and be scalable. LED strips are used to visualize power flows in the table-top network, in combination with the Digispark ATtiny85. For determining the topology, static ID pairs were used. A double simulation is used to implement Plug-and-Play dynamics.
The first simulation configures the topology used by the second simulation, based on the hardware connections. The second simulation runs the Illuminator simulation. During this simulation, checks are run to see whether a physical connection has changed, such as a cable being unplugged. The simulation and then starts the reconfiguration process again.
Testing the reliability and run-time of the implementation is documented in Chapter 6, with a focus on how well the implementation scales with the size of the simulation. It was concluded that the Digispark’s communication with the Raspberry Pi would often stall, requiring error correction to be implemented. Even then, the data transfer to the Digispark from the Raspberry Pi fails on the first try an average of 48% of the time. Determining how long a setup takes to reconfigure was estimated using a computer, since the Raspberry Pi’s aren’t powerful enough to simulate dozens of models. It was determined that a reconfiguration of 20 models takes about 100 seconds.
is more intuitive. The main product should show the power flow on the table, implement Plug-and-Play dynamics, and be scalable. LED strips are used to visualize power flows in the table-top network, in combination with the Digispark ATtiny85. For determining the topology, static ID pairs were used. A double simulation is used to implement Plug-and-Play dynamics.
The first simulation configures the topology used by the second simulation, based on the hardware connections. The second simulation runs the Illuminator simulation. During this simulation, checks are run to see whether a physical connection has changed, such as a cable being unplugged. The simulation and then starts the reconfiguration process again.
Testing the reliability and run-time of the implementation is documented in Chapter 6, with a focus on how well the implementation scales with the size of the simulation. It was concluded that the Digispark’s communication with the Raspberry Pi would often stall, requiring error correction to be implemented. Even then, the data transfer to the Digispark from the Raspberry Pi fails on the first try an average of 48% of the time. Determining how long a setup takes to reconfigure was estimated using a computer, since the Raspberry Pi’s aren’t powerful enough to simulate dozens of models. It was determined that a reconfiguration of 20 models takes about 100 seconds.
Bachelor thesis
(2025)
-
R. Bonouvrie, J.I. Libier, M. Cvetkovic, J.A. Groen, I.E. Lager, D. Georgiadi
The Energy System Integration Demonstrator, developed by the Illuminator team at TU Delft, is a modular, tabletop tool designed to educate and engage citizens in the benefits and challenges of the energy transition. It simulates important aspects of national electricity grids through a combination of Raspberry Pi control systems, 3D models, and LED visualizations. The B.Sc. graduation project focused on enhancing the demonstrator’s visualization and interaction capabilities. The project subgroup developed dynamic 3D models such as houses, windmills, cars, solar panels, carbon emissions, and batteries. On-screen elements are also created to represent key simulation agents like the battery state-of-charge and green energy percentage. Multiple dashboard versions were designed to tailor the experience to different stakeholder audiences. The report details the design, implementation, and validation of the visualized components.
...
The Energy System Integration Demonstrator, developed by the Illuminator team at TU Delft, is a modular, tabletop tool designed to educate and engage citizens in the benefits and challenges of the energy transition. It simulates important aspects of national electricity grids through a combination of Raspberry Pi control systems, 3D models, and LED visualizations. The B.Sc. graduation project focused on enhancing the demonstrator’s visualization and interaction capabilities. The project subgroup developed dynamic 3D models such as houses, windmills, cars, solar panels, carbon emissions, and batteries. On-screen elements are also created to represent key simulation agents like the battery state-of-charge and green energy percentage. Multiple dashboard versions were designed to tailor the experience to different stakeholder audiences. The report details the design, implementation, and validation of the visualized components.
Energy Hubs within the Built Environment
Exploring opportunities and challenges for the low-voltage grid
A scalable model was developed based on the 24/7 Energy Hub at The Green Village. The system includes short-term balancing through a battery, hydrogen production through an electrolyzer, and winter supply from a fuel cell. The validated component models were applied to a representative Dutch neighborhood consisting of 155 households (archetype 3) with an 250 kVA transformer, under projected 2050 demand and generation conditions. A set of 320 configurations was evaluated across multiple transformer capacities and hydrogen production sources. Feasible configurations were defined as those eliminating transformer violations while maintaining a positive annual hydrogen balance. The 2050 baseline exhibited 67.9 MWh/year of congestion. Of the 320 evaluated configurations, 56 met the feasibility criteria, all requiring a transformer upgrade from 250 kVA to 400 kVA. Two lowest-cost feasible designs were identified: a PV-only hydrogen configuration and a configuration that explicitly used the grid to produce hydrogen in off-peak winter periods. Both eliminated congestion while maintaining hydrogen self-sufficiency. However, total system investment ranged between €0.7–€4.6M, significantly higher than traditional transformer reinforcement (€34k).
The results from the case study show that decentralized hydrogen storage can technically resolve transformer congestion, but only when combined with moderate reinforcement and at substantially higher cost than conventional upgrading. Under current cost and efficiency assumptions, energy hubs have a flexibility purpose rather than being an economic substitute for grid reinforcement. ...
The results from the case study show that decentralized hydrogen storage can technically resolve transformer congestion, but only when combined with moderate reinforcement and at substantially higher cost than conventional upgrading. Under current cost and efficiency assumptions, energy hubs have a flexibility purpose rather than being an economic substitute for grid reinforcement. ...
A scalable model was developed based on the 24/7 Energy Hub at The Green Village. The system includes short-term balancing through a battery, hydrogen production through an electrolyzer, and winter supply from a fuel cell. The validated component models were applied to a representative Dutch neighborhood consisting of 155 households (archetype 3) with an 250 kVA transformer, under projected 2050 demand and generation conditions. A set of 320 configurations was evaluated across multiple transformer capacities and hydrogen production sources. Feasible configurations were defined as those eliminating transformer violations while maintaining a positive annual hydrogen balance. The 2050 baseline exhibited 67.9 MWh/year of congestion. Of the 320 evaluated configurations, 56 met the feasibility criteria, all requiring a transformer upgrade from 250 kVA to 400 kVA. Two lowest-cost feasible designs were identified: a PV-only hydrogen configuration and a configuration that explicitly used the grid to produce hydrogen in off-peak winter periods. Both eliminated congestion while maintaining hydrogen self-sufficiency. However, total system investment ranged between €0.7–€4.6M, significantly higher than traditional transformer reinforcement (€34k).
The results from the case study show that decentralized hydrogen storage can technically resolve transformer congestion, but only when combined with moderate reinforcement and at substantially higher cost than conventional upgrading. Under current cost and efficiency assumptions, energy hubs have a flexibility purpose rather than being an economic substitute for grid reinforcement.
The results from the case study show that decentralized hydrogen storage can technically resolve transformer congestion, but only when combined with moderate reinforcement and at substantially higher cost than conventional upgrading. Under current cost and efficiency assumptions, energy hubs have a flexibility purpose rather than being an economic substitute for grid reinforcement.