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L. Trinh
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Modelling Future Power Systems
Integrating offshore wind energy and electrolysers in a PyPSA-based model and discovering policy trade-offs
Master thesis
(2026)
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L. Trinh, Stefan Pfenninger, F. Lombardi, Ö. Okur, Michel Dubbelboer, Viktor Beelen
The Dutch electricity grid is increasingly congested due to rapid electrification and the growing share of variable renewable energy. This congestion results from temporal mismatches between generation and demand, causing overloaded transmission lines and curtailment of renewable production. Policymakers and grid operators therefore require fast, regional insights into where and in which technologies to invest, accounting for transmission constraints and cost uncertainties. While TenneT currently relies on detailed planning tools such as PLEXOS and PowerFactory/PSSE, their long computation times limit rapid scenario exploration. This thesis investigates whether a simplified model built in the open-source software PyPSA can provide reliable quantitative insights for early-stage planning, particularly for offshore wind integration and electrolysers in the 2040 Koersvaste Middenweg scenario.
A PyPSA model of the Dutch electricity system was developed iteratively and validated against TenneT’s reference models. First, the Netherlands was represented as a single node to reproduce dispatch results comparable to PLEXOS. Second, the transmission network was added to incorporate power flows and grid constraints, with results compared to PowerFactory/PSSE load-flow outcomes. Finally, offshore wind farms and electrolysers were included to assess their system impacts. After validation, the EMA Workbench was used to explore investment cost uncertainty by varying key technology costs between −20% and +40%, identifying when cost-driven trade-offs between technologies occur.
Results show that large solar capacity is consistently cost-effective but requires flexibility options due to variability. In the single-node model, PyPSA favors batteries and gas plants for flexibility, whereas PLEXOS indicates a stronger role for offshore wind, likely due to more detailed cost representations. When transmission constraints are included, offshore wind becomes more attractive because its coastal location reduces congestion and long-distance transport to major industrial demand centers. Load-flow comparisons indicate that PyPSA captures overall flow patterns well: approximately 70–75% of flow directions match TenneT models, and congestion hotspots align with planned reinforcements.
Introducing electrolysers increases annual electricity consumption but does not raise peak demand, as they primarily operate during surplus, low-price periods. Consequently, total optimal generation capacity remains largely unchanged. Sensitivity analyses confirm that PyPSA responds to cost variations similarly to PLEXOS, increasing confidence in its use for scenario analysis. The EMA Workbench results reveal that the future system does not converge to a single optimal configuration; instead, multiple viable configurations balance costs, emissions, and flexibility differently. Gas plants reduce investment costs and peak prices but increase emissions, offshore wind lowers emissions but raises upfront costs, solar-battery systems require large capacities, and nuclear becomes competitive only under substantially lower costs.
Computation time is significantly reduced compared to detailed models. The most detailed PyPSA setup simulates a full year in about 1.5 hours, down from more than a day, and time-aggregation techniques can reduce runtime to minutes. This enables rapid exploratory analysis.
Overall, the study concludes that PyPSA is suitable for supporting early-stage planning of the Dutch transmission system, especially under uncertainty. The future electricity system appears robust but highly sensitive to technology costs, implying that policymakers should prepare flexible strategies that remain effective across multiple possible development pathways rather than relying on a single optimal scenario. ...
A PyPSA model of the Dutch electricity system was developed iteratively and validated against TenneT’s reference models. First, the Netherlands was represented as a single node to reproduce dispatch results comparable to PLEXOS. Second, the transmission network was added to incorporate power flows and grid constraints, with results compared to PowerFactory/PSSE load-flow outcomes. Finally, offshore wind farms and electrolysers were included to assess their system impacts. After validation, the EMA Workbench was used to explore investment cost uncertainty by varying key technology costs between −20% and +40%, identifying when cost-driven trade-offs between technologies occur.
Results show that large solar capacity is consistently cost-effective but requires flexibility options due to variability. In the single-node model, PyPSA favors batteries and gas plants for flexibility, whereas PLEXOS indicates a stronger role for offshore wind, likely due to more detailed cost representations. When transmission constraints are included, offshore wind becomes more attractive because its coastal location reduces congestion and long-distance transport to major industrial demand centers. Load-flow comparisons indicate that PyPSA captures overall flow patterns well: approximately 70–75% of flow directions match TenneT models, and congestion hotspots align with planned reinforcements.
Introducing electrolysers increases annual electricity consumption but does not raise peak demand, as they primarily operate during surplus, low-price periods. Consequently, total optimal generation capacity remains largely unchanged. Sensitivity analyses confirm that PyPSA responds to cost variations similarly to PLEXOS, increasing confidence in its use for scenario analysis. The EMA Workbench results reveal that the future system does not converge to a single optimal configuration; instead, multiple viable configurations balance costs, emissions, and flexibility differently. Gas plants reduce investment costs and peak prices but increase emissions, offshore wind lowers emissions but raises upfront costs, solar-battery systems require large capacities, and nuclear becomes competitive only under substantially lower costs.
Computation time is significantly reduced compared to detailed models. The most detailed PyPSA setup simulates a full year in about 1.5 hours, down from more than a day, and time-aggregation techniques can reduce runtime to minutes. This enables rapid exploratory analysis.
Overall, the study concludes that PyPSA is suitable for supporting early-stage planning of the Dutch transmission system, especially under uncertainty. The future electricity system appears robust but highly sensitive to technology costs, implying that policymakers should prepare flexible strategies that remain effective across multiple possible development pathways rather than relying on a single optimal scenario. ...
The Dutch electricity grid is increasingly congested due to rapid electrification and the growing share of variable renewable energy. This congestion results from temporal mismatches between generation and demand, causing overloaded transmission lines and curtailment of renewable production. Policymakers and grid operators therefore require fast, regional insights into where and in which technologies to invest, accounting for transmission constraints and cost uncertainties. While TenneT currently relies on detailed planning tools such as PLEXOS and PowerFactory/PSSE, their long computation times limit rapid scenario exploration. This thesis investigates whether a simplified model built in the open-source software PyPSA can provide reliable quantitative insights for early-stage planning, particularly for offshore wind integration and electrolysers in the 2040 Koersvaste Middenweg scenario.
A PyPSA model of the Dutch electricity system was developed iteratively and validated against TenneT’s reference models. First, the Netherlands was represented as a single node to reproduce dispatch results comparable to PLEXOS. Second, the transmission network was added to incorporate power flows and grid constraints, with results compared to PowerFactory/PSSE load-flow outcomes. Finally, offshore wind farms and electrolysers were included to assess their system impacts. After validation, the EMA Workbench was used to explore investment cost uncertainty by varying key technology costs between −20% and +40%, identifying when cost-driven trade-offs between technologies occur.
Results show that large solar capacity is consistently cost-effective but requires flexibility options due to variability. In the single-node model, PyPSA favors batteries and gas plants for flexibility, whereas PLEXOS indicates a stronger role for offshore wind, likely due to more detailed cost representations. When transmission constraints are included, offshore wind becomes more attractive because its coastal location reduces congestion and long-distance transport to major industrial demand centers. Load-flow comparisons indicate that PyPSA captures overall flow patterns well: approximately 70–75% of flow directions match TenneT models, and congestion hotspots align with planned reinforcements.
Introducing electrolysers increases annual electricity consumption but does not raise peak demand, as they primarily operate during surplus, low-price periods. Consequently, total optimal generation capacity remains largely unchanged. Sensitivity analyses confirm that PyPSA responds to cost variations similarly to PLEXOS, increasing confidence in its use for scenario analysis. The EMA Workbench results reveal that the future system does not converge to a single optimal configuration; instead, multiple viable configurations balance costs, emissions, and flexibility differently. Gas plants reduce investment costs and peak prices but increase emissions, offshore wind lowers emissions but raises upfront costs, solar-battery systems require large capacities, and nuclear becomes competitive only under substantially lower costs.
Computation time is significantly reduced compared to detailed models. The most detailed PyPSA setup simulates a full year in about 1.5 hours, down from more than a day, and time-aggregation techniques can reduce runtime to minutes. This enables rapid exploratory analysis.
Overall, the study concludes that PyPSA is suitable for supporting early-stage planning of the Dutch transmission system, especially under uncertainty. The future electricity system appears robust but highly sensitive to technology costs, implying that policymakers should prepare flexible strategies that remain effective across multiple possible development pathways rather than relying on a single optimal scenario.
A PyPSA model of the Dutch electricity system was developed iteratively and validated against TenneT’s reference models. First, the Netherlands was represented as a single node to reproduce dispatch results comparable to PLEXOS. Second, the transmission network was added to incorporate power flows and grid constraints, with results compared to PowerFactory/PSSE load-flow outcomes. Finally, offshore wind farms and electrolysers were included to assess their system impacts. After validation, the EMA Workbench was used to explore investment cost uncertainty by varying key technology costs between −20% and +40%, identifying when cost-driven trade-offs between technologies occur.
Results show that large solar capacity is consistently cost-effective but requires flexibility options due to variability. In the single-node model, PyPSA favors batteries and gas plants for flexibility, whereas PLEXOS indicates a stronger role for offshore wind, likely due to more detailed cost representations. When transmission constraints are included, offshore wind becomes more attractive because its coastal location reduces congestion and long-distance transport to major industrial demand centers. Load-flow comparisons indicate that PyPSA captures overall flow patterns well: approximately 70–75% of flow directions match TenneT models, and congestion hotspots align with planned reinforcements.
Introducing electrolysers increases annual electricity consumption but does not raise peak demand, as they primarily operate during surplus, low-price periods. Consequently, total optimal generation capacity remains largely unchanged. Sensitivity analyses confirm that PyPSA responds to cost variations similarly to PLEXOS, increasing confidence in its use for scenario analysis. The EMA Workbench results reveal that the future system does not converge to a single optimal configuration; instead, multiple viable configurations balance costs, emissions, and flexibility differently. Gas plants reduce investment costs and peak prices but increase emissions, offshore wind lowers emissions but raises upfront costs, solar-battery systems require large capacities, and nuclear becomes competitive only under substantially lower costs.
Computation time is significantly reduced compared to detailed models. The most detailed PyPSA setup simulates a full year in about 1.5 hours, down from more than a day, and time-aggregation techniques can reduce runtime to minutes. This enables rapid exploratory analysis.
Overall, the study concludes that PyPSA is suitable for supporting early-stage planning of the Dutch transmission system, especially under uncertainty. The future electricity system appears robust but highly sensitive to technology costs, implying that policymakers should prepare flexible strategies that remain effective across multiple possible development pathways rather than relying on a single optimal scenario.