G. Marangoni
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Powering the Dutch Greenhouse Energy Transition
Design of a Spectral-filtering agriphotovoltaic (APV) based sustainable multi-commodity energy hub and assessment of grid support, self-sufficiency and economic performance using Python for Power System Analysis (PyPSA)
Dutch greenhouses rely on natural gas-fired combined heat and power (CHP) systems that supply their electricity, heat, and CO\textsubscript{2} while providing flexibility to the grid. As the sector moves away from natural gas, this research investigates how an energy system integrating spectral-filtering agriphotovoltaic (APV) modules can support this transition, and what its impact is on grid support, self-sufficiency, and economic performance.
A sustainable multi-commodity energy hub combining spectral-filtering APV, a battery, geothermal heat, thermal storage, a heat pump, and direct air capture was modelled in Python for Power System Analysis (PyPSA) for a 5.2 ha greenhouse at Koppert Cress in Monster, the Netherlands. The system was built up in three steps: an electrical system with APV and a battery energy storage system (BESS), a heating system adding geothermal heat, thermal storage, and a heat pump, and the full energy hub adding direct air capture with liquid CO\textsubscript{2} storage. Each configuration was evaluated on three key performance indicators mirroring the CHP's functions: Economic Performance, Self-Sufficiency, and Grid Support, the latter based on the imbalance settlement data of the Dutch grid operator. Evaluation was done using Modelling to Generate Alternatives with 5\% and 10\% cost slack, a rolling-horizon optimisation, and a sensitivity analysis on the geothermal capacity and the grid import limit.
The hub can supply the greenhouse's electricity, heat, and CO\textsubscript{2} demand without natural gas. The optimised battery raises the simple self-consumption of the APV electricity from 20.8\% to 90.2\%. Self-sufficiency reaches 16.4\% at the cost-optimal dispatch and 27.0\% within a 5\% cost increase, while the weighted grid alignment can be improved by 34.7\% within the same budget. Economic performance is the main barrier: the APV alone does not pay back within its lifetime, although the combination with the battery pays back in 19.2 years. The hub can therefore take over the CHP's core functions, but mainly when its grid support is actively valued. While these results reflect a single greenhouse, crop, and modelled year, they provide a quantified indication of how sustainable multi-commodity hubs can replace the CHP. ...
A sustainable multi-commodity energy hub combining spectral-filtering APV, a battery, geothermal heat, thermal storage, a heat pump, and direct air capture was modelled in Python for Power System Analysis (PyPSA) for a 5.2 ha greenhouse at Koppert Cress in Monster, the Netherlands. The system was built up in three steps: an electrical system with APV and a battery energy storage system (BESS), a heating system adding geothermal heat, thermal storage, and a heat pump, and the full energy hub adding direct air capture with liquid CO\textsubscript{2} storage. Each configuration was evaluated on three key performance indicators mirroring the CHP's functions: Economic Performance, Self-Sufficiency, and Grid Support, the latter based on the imbalance settlement data of the Dutch grid operator. Evaluation was done using Modelling to Generate Alternatives with 5\% and 10\% cost slack, a rolling-horizon optimisation, and a sensitivity analysis on the geothermal capacity and the grid import limit.
The hub can supply the greenhouse's electricity, heat, and CO\textsubscript{2} demand without natural gas. The optimised battery raises the simple self-consumption of the APV electricity from 20.8\% to 90.2\%. Self-sufficiency reaches 16.4\% at the cost-optimal dispatch and 27.0\% within a 5\% cost increase, while the weighted grid alignment can be improved by 34.7\% within the same budget. Economic performance is the main barrier: the APV alone does not pay back within its lifetime, although the combination with the battery pays back in 19.2 years. The hub can therefore take over the CHP's core functions, but mainly when its grid support is actively valued. While these results reflect a single greenhouse, crop, and modelled year, they provide a quantified indication of how sustainable multi-commodity hubs can replace the CHP. ...
Dutch greenhouses rely on natural gas-fired combined heat and power (CHP) systems that supply their electricity, heat, and CO\textsubscript{2} while providing flexibility to the grid. As the sector moves away from natural gas, this research investigates how an energy system integrating spectral-filtering agriphotovoltaic (APV) modules can support this transition, and what its impact is on grid support, self-sufficiency, and economic performance.
A sustainable multi-commodity energy hub combining spectral-filtering APV, a battery, geothermal heat, thermal storage, a heat pump, and direct air capture was modelled in Python for Power System Analysis (PyPSA) for a 5.2 ha greenhouse at Koppert Cress in Monster, the Netherlands. The system was built up in three steps: an electrical system with APV and a battery energy storage system (BESS), a heating system adding geothermal heat, thermal storage, and a heat pump, and the full energy hub adding direct air capture with liquid CO\textsubscript{2} storage. Each configuration was evaluated on three key performance indicators mirroring the CHP's functions: Economic Performance, Self-Sufficiency, and Grid Support, the latter based on the imbalance settlement data of the Dutch grid operator. Evaluation was done using Modelling to Generate Alternatives with 5\% and 10\% cost slack, a rolling-horizon optimisation, and a sensitivity analysis on the geothermal capacity and the grid import limit.
The hub can supply the greenhouse's electricity, heat, and CO\textsubscript{2} demand without natural gas. The optimised battery raises the simple self-consumption of the APV electricity from 20.8\% to 90.2\%. Self-sufficiency reaches 16.4\% at the cost-optimal dispatch and 27.0\% within a 5\% cost increase, while the weighted grid alignment can be improved by 34.7\% within the same budget. Economic performance is the main barrier: the APV alone does not pay back within its lifetime, although the combination with the battery pays back in 19.2 years. The hub can therefore take over the CHP's core functions, but mainly when its grid support is actively valued. While these results reflect a single greenhouse, crop, and modelled year, they provide a quantified indication of how sustainable multi-commodity hubs can replace the CHP.
A sustainable multi-commodity energy hub combining spectral-filtering APV, a battery, geothermal heat, thermal storage, a heat pump, and direct air capture was modelled in Python for Power System Analysis (PyPSA) for a 5.2 ha greenhouse at Koppert Cress in Monster, the Netherlands. The system was built up in three steps: an electrical system with APV and a battery energy storage system (BESS), a heating system adding geothermal heat, thermal storage, and a heat pump, and the full energy hub adding direct air capture with liquid CO\textsubscript{2} storage. Each configuration was evaluated on three key performance indicators mirroring the CHP's functions: Economic Performance, Self-Sufficiency, and Grid Support, the latter based on the imbalance settlement data of the Dutch grid operator. Evaluation was done using Modelling to Generate Alternatives with 5\% and 10\% cost slack, a rolling-horizon optimisation, and a sensitivity analysis on the geothermal capacity and the grid import limit.
The hub can supply the greenhouse's electricity, heat, and CO\textsubscript{2} demand without natural gas. The optimised battery raises the simple self-consumption of the APV electricity from 20.8\% to 90.2\%. Self-sufficiency reaches 16.4\% at the cost-optimal dispatch and 27.0\% within a 5\% cost increase, while the weighted grid alignment can be improved by 34.7\% within the same budget. Economic performance is the main barrier: the APV alone does not pay back within its lifetime, although the combination with the battery pays back in 19.2 years. The hub can therefore take over the CHP's core functions, but mainly when its grid support is actively valued. While these results reflect a single greenhouse, crop, and modelled year, they provide a quantified indication of how sustainable multi-commodity hubs can replace the CHP.
Analyzing Storage Needs in Energy Systems with Variable Renewable Energy Integration
Lessons from Calliope for WITCH
This thesis explores the integration of flexibility measures in the European energy system. The thesis uses an integrated assessment model to simulate the integration of flexibility based on parameters from an energy system model with a high spatial and temporal scope.
The key objectives include evaluating different flexibility technologies' roles in enhancing future energy systems' reliability and resilience.
The foundation of the thesis is identifying and evaluating the most promising storage technologies. Thereafter, the storage technologies are placed into the context of energy modeling, highlighting their strengths, weaknesses, and ability to be modeled.
The research uses the WITCH (World Induced Technical Change Hybrid) model. However, this research is based only on the region of Europe. The WITCH model can run simulations under different climate policy scenarios, including the business-as-usual (BAU) and carbon tax (ctax) pathways. Variables and parameters such as flexibility measures and associated costs are modeled to reflect future energy system configurations based on pre-run cost-optimal configurations from the Calliope framework.
The thesis results show that the main flexibility measures from the literature are storage, grid expansion, demand response, and sector coupling. These measures can enhance the energy system's integration of variable renewable energy sources. Climate policies, i.e., carbon taxes, enable higher levels of VRE and, therefore, flexibility measures, resulting in lower emissions and more efficient energy systems. Fundamentally, the results show a different approach to flexibility than that utilized in long-term models. Using aggregated parameters from energy systems models' pre-run configuration is a novel method of informing other models. This coupling method is effective when the variables of the two models can be harmonized.
The thesis discussion raises areas for future research. The main discussion point is the effectiveness of using pre-run optimization results. The 2030 and 2050-based data provide the energy system's transitional nature. However, extracting insight for a purely transitional model like WITCH proved challenging. Furthermore, the impact of scaling the data from Calliope to match the WITCH data ranges needs further investigation. Lastly, the implications of the elasticity of substitution between the individual flexibility measures, e.g., between storage capacity expansion and transmission grid expansion. ...
The key objectives include evaluating different flexibility technologies' roles in enhancing future energy systems' reliability and resilience.
The foundation of the thesis is identifying and evaluating the most promising storage technologies. Thereafter, the storage technologies are placed into the context of energy modeling, highlighting their strengths, weaknesses, and ability to be modeled.
The research uses the WITCH (World Induced Technical Change Hybrid) model. However, this research is based only on the region of Europe. The WITCH model can run simulations under different climate policy scenarios, including the business-as-usual (BAU) and carbon tax (ctax) pathways. Variables and parameters such as flexibility measures and associated costs are modeled to reflect future energy system configurations based on pre-run cost-optimal configurations from the Calliope framework.
The thesis results show that the main flexibility measures from the literature are storage, grid expansion, demand response, and sector coupling. These measures can enhance the energy system's integration of variable renewable energy sources. Climate policies, i.e., carbon taxes, enable higher levels of VRE and, therefore, flexibility measures, resulting in lower emissions and more efficient energy systems. Fundamentally, the results show a different approach to flexibility than that utilized in long-term models. Using aggregated parameters from energy systems models' pre-run configuration is a novel method of informing other models. This coupling method is effective when the variables of the two models can be harmonized.
The thesis discussion raises areas for future research. The main discussion point is the effectiveness of using pre-run optimization results. The 2030 and 2050-based data provide the energy system's transitional nature. However, extracting insight for a purely transitional model like WITCH proved challenging. Furthermore, the impact of scaling the data from Calliope to match the WITCH data ranges needs further investigation. Lastly, the implications of the elasticity of substitution between the individual flexibility measures, e.g., between storage capacity expansion and transmission grid expansion. ...
This thesis explores the integration of flexibility measures in the European energy system. The thesis uses an integrated assessment model to simulate the integration of flexibility based on parameters from an energy system model with a high spatial and temporal scope.
The key objectives include evaluating different flexibility technologies' roles in enhancing future energy systems' reliability and resilience.
The foundation of the thesis is identifying and evaluating the most promising storage technologies. Thereafter, the storage technologies are placed into the context of energy modeling, highlighting their strengths, weaknesses, and ability to be modeled.
The research uses the WITCH (World Induced Technical Change Hybrid) model. However, this research is based only on the region of Europe. The WITCH model can run simulations under different climate policy scenarios, including the business-as-usual (BAU) and carbon tax (ctax) pathways. Variables and parameters such as flexibility measures and associated costs are modeled to reflect future energy system configurations based on pre-run cost-optimal configurations from the Calliope framework.
The thesis results show that the main flexibility measures from the literature are storage, grid expansion, demand response, and sector coupling. These measures can enhance the energy system's integration of variable renewable energy sources. Climate policies, i.e., carbon taxes, enable higher levels of VRE and, therefore, flexibility measures, resulting in lower emissions and more efficient energy systems. Fundamentally, the results show a different approach to flexibility than that utilized in long-term models. Using aggregated parameters from energy systems models' pre-run configuration is a novel method of informing other models. This coupling method is effective when the variables of the two models can be harmonized.
The thesis discussion raises areas for future research. The main discussion point is the effectiveness of using pre-run optimization results. The 2030 and 2050-based data provide the energy system's transitional nature. However, extracting insight for a purely transitional model like WITCH proved challenging. Furthermore, the impact of scaling the data from Calliope to match the WITCH data ranges needs further investigation. Lastly, the implications of the elasticity of substitution between the individual flexibility measures, e.g., between storage capacity expansion and transmission grid expansion.
The key objectives include evaluating different flexibility technologies' roles in enhancing future energy systems' reliability and resilience.
The foundation of the thesis is identifying and evaluating the most promising storage technologies. Thereafter, the storage technologies are placed into the context of energy modeling, highlighting their strengths, weaknesses, and ability to be modeled.
The research uses the WITCH (World Induced Technical Change Hybrid) model. However, this research is based only on the region of Europe. The WITCH model can run simulations under different climate policy scenarios, including the business-as-usual (BAU) and carbon tax (ctax) pathways. Variables and parameters such as flexibility measures and associated costs are modeled to reflect future energy system configurations based on pre-run cost-optimal configurations from the Calliope framework.
The thesis results show that the main flexibility measures from the literature are storage, grid expansion, demand response, and sector coupling. These measures can enhance the energy system's integration of variable renewable energy sources. Climate policies, i.e., carbon taxes, enable higher levels of VRE and, therefore, flexibility measures, resulting in lower emissions and more efficient energy systems. Fundamentally, the results show a different approach to flexibility than that utilized in long-term models. Using aggregated parameters from energy systems models' pre-run configuration is a novel method of informing other models. This coupling method is effective when the variables of the two models can be harmonized.
The thesis discussion raises areas for future research. The main discussion point is the effectiveness of using pre-run optimization results. The 2030 and 2050-based data provide the energy system's transitional nature. However, extracting insight for a purely transitional model like WITCH proved challenging. Furthermore, the impact of scaling the data from Calliope to match the WITCH data ranges needs further investigation. Lastly, the implications of the elasticity of substitution between the individual flexibility measures, e.g., between storage capacity expansion and transmission grid expansion.