P.W.G. Bots
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1
Bridging the Capacity Gap
A Mixed-Methods Assessment of Project-Level Flexibility and Station-Level Grid Congestion in Laakhavens Hollands Spoor
Corrected abstract
Laakhavens Hollands Spoor is planned as a major high-density redevelopment in The Hague, but the electricity system serving it has almost no room to absorb that growth. Approximately 9,000–11,000 new dwellings are planned alongside employment, commercial and public functions, while station Jan Wapstraat has 38 MW of available transport capacity and reinforcement is not planned until the end of 2036. Development decisions are made largely project by project, yet their electricity demand accumulates at the same station. This thesis examines whether flexibility can bridge part of that capacity gap before reinforcement and how its organisation must evolve as grid pressure increases.
An exploratory sequential mixed-methods case study combines documentary analysis, four stakeholder interviews and quantitative energy-system modelling. The qualitative analysis identifies technically relevant flexibility options and institutional conditions affecting their implementation. These findings inform a Linny-R optimisation model representing eight cumulative development phases and testing flexible WKO/ATES operation, battery storage, smart EV charging and controllable HyER generation at project and area-wide scales.
The results show that the available station margin disappears early. Without congestion-oriented flexibility, corrected functional loading rises from 37.1 MW in Phase 1 to 45.2 MW in Phase 8. The 38 MW limit is already marginally exceeded in Phase 2, while Phase 3 reaches 39.1 MW and confirms the emerging constraint. By Phase 8, the station limit is exceeded during 438 annual hours, with a maximum exceedance of 7.15 MW and approximately 614 MWh above the threshold. The central concern is therefore not only the final deficit: the remaining station margin is consumed while much of the redevelopment is still to follow.
Flexibility can substantially extend the amount of development that can be accommodated, but its effectiveness changes as demand accumulates. Flexible WKO/ATES operation accommodates the modelled programme through Phase 4, while favourable flexibility inherited from earlier projects can extend this through Phase 5. From Phase 6 onward, however, none of the evaluated configurations in which additional flexibility is concentrated in the newly added project fully eliminates congestion. Earlier projects therefore influence not only demand but also the flexibility and headroom available to later developments. Phase 6 marks an important shift: the technical problem has become too cumulative to be addressed solely within the boundary of the next individual project.
The strongest technical result emerges when flexibility is coordinated across the full development portfolio. Under the evaluated assumptions, the integrated Phase 8 scenario eliminates residual congestion during the 336-hour critical period by coordinating WKO/ATES, batteries, smart charging and HyER across all eight projects. However, the station remains exactly at its 38 MW limit for 90 hours. This should therefore be interpreted as an upper technical potential with little robustness margin, not as a guaranteed solution or evidence that reinforcement is unnecessary. Reduced modelled loading also does not automatically create formal transport rights for individual projects.
The institutional analysis shows why this technical potential cannot simply be assumed to materialise. Investment, ownership and operational control remain predominantly project-specific. Area-wide congestion management would require shared information, dispatch authority, performance verification, compensation arrangements and responsibilities that remain workable after projects are completed or transferred. The technically strongest configuration is therefore not currently the most institutionally feasible one.
The strategic implication is to prepare coordination before it becomes technically unavoidable. Phase 2 is an early signpost that unused station headroom can no longer be relied upon; Phase 6 is a stronger trigger showing that additional flexibility concentrated in the newest project is no longer sufficient. The recommended pathway is therefore adaptive: preserve future flexibility options in early projects, develop coordinated project-level arrangements as interdependence increases, and retain deeper area-wide integration as a conditional option rather than an immediate end-state.
The broader lesson extends beyond LHHS. Grid congestion in phased urban redevelopment is not only a shortage of network capacity or flexible technology; it is also a mismatch between the scale and timing of decisions. Projects can be optimised individually while their combined consequences emerge later at a shared network constraint. Flexibility can help bridge the period before reinforcement, but only if early technical and institutional choices preserve the ability to coordinate when that interdependence becomes material. ...
Laakhavens Hollands Spoor is planned as a major high-density redevelopment in The Hague, but the electricity system serving it has almost no room to absorb that growth. Approximately 9,000–11,000 new dwellings are planned alongside employment, commercial and public functions, while station Jan Wapstraat has 38 MW of available transport capacity and reinforcement is not planned until the end of 2036. Development decisions are made largely project by project, yet their electricity demand accumulates at the same station. This thesis examines whether flexibility can bridge part of that capacity gap before reinforcement and how its organisation must evolve as grid pressure increases.
An exploratory sequential mixed-methods case study combines documentary analysis, four stakeholder interviews and quantitative energy-system modelling. The qualitative analysis identifies technically relevant flexibility options and institutional conditions affecting their implementation. These findings inform a Linny-R optimisation model representing eight cumulative development phases and testing flexible WKO/ATES operation, battery storage, smart EV charging and controllable HyER generation at project and area-wide scales.
The results show that the available station margin disappears early. Without congestion-oriented flexibility, corrected functional loading rises from 37.1 MW in Phase 1 to 45.2 MW in Phase 8. The 38 MW limit is already marginally exceeded in Phase 2, while Phase 3 reaches 39.1 MW and confirms the emerging constraint. By Phase 8, the station limit is exceeded during 438 annual hours, with a maximum exceedance of 7.15 MW and approximately 614 MWh above the threshold. The central concern is therefore not only the final deficit: the remaining station margin is consumed while much of the redevelopment is still to follow.
Flexibility can substantially extend the amount of development that can be accommodated, but its effectiveness changes as demand accumulates. Flexible WKO/ATES operation accommodates the modelled programme through Phase 4, while favourable flexibility inherited from earlier projects can extend this through Phase 5. From Phase 6 onward, however, none of the evaluated configurations in which additional flexibility is concentrated in the newly added project fully eliminates congestion. Earlier projects therefore influence not only demand but also the flexibility and headroom available to later developments. Phase 6 marks an important shift: the technical problem has become too cumulative to be addressed solely within the boundary of the next individual project.
The strongest technical result emerges when flexibility is coordinated across the full development portfolio. Under the evaluated assumptions, the integrated Phase 8 scenario eliminates residual congestion during the 336-hour critical period by coordinating WKO/ATES, batteries, smart charging and HyER across all eight projects. However, the station remains exactly at its 38 MW limit for 90 hours. This should therefore be interpreted as an upper technical potential with little robustness margin, not as a guaranteed solution or evidence that reinforcement is unnecessary. Reduced modelled loading also does not automatically create formal transport rights for individual projects.
The institutional analysis shows why this technical potential cannot simply be assumed to materialise. Investment, ownership and operational control remain predominantly project-specific. Area-wide congestion management would require shared information, dispatch authority, performance verification, compensation arrangements and responsibilities that remain workable after projects are completed or transferred. The technically strongest configuration is therefore not currently the most institutionally feasible one.
The strategic implication is to prepare coordination before it becomes technically unavoidable. Phase 2 is an early signpost that unused station headroom can no longer be relied upon; Phase 6 is a stronger trigger showing that additional flexibility concentrated in the newest project is no longer sufficient. The recommended pathway is therefore adaptive: preserve future flexibility options in early projects, develop coordinated project-level arrangements as interdependence increases, and retain deeper area-wide integration as a conditional option rather than an immediate end-state.
The broader lesson extends beyond LHHS. Grid congestion in phased urban redevelopment is not only a shortage of network capacity or flexible technology; it is also a mismatch between the scale and timing of decisions. Projects can be optimised individually while their combined consequences emerge later at a shared network constraint. Flexibility can help bridge the period before reinforcement, but only if early technical and institutional choices preserve the ability to coordinate when that interdependence becomes material. ...
Corrected abstract
Laakhavens Hollands Spoor is planned as a major high-density redevelopment in The Hague, but the electricity system serving it has almost no room to absorb that growth. Approximately 9,000–11,000 new dwellings are planned alongside employment, commercial and public functions, while station Jan Wapstraat has 38 MW of available transport capacity and reinforcement is not planned until the end of 2036. Development decisions are made largely project by project, yet their electricity demand accumulates at the same station. This thesis examines whether flexibility can bridge part of that capacity gap before reinforcement and how its organisation must evolve as grid pressure increases.
An exploratory sequential mixed-methods case study combines documentary analysis, four stakeholder interviews and quantitative energy-system modelling. The qualitative analysis identifies technically relevant flexibility options and institutional conditions affecting their implementation. These findings inform a Linny-R optimisation model representing eight cumulative development phases and testing flexible WKO/ATES operation, battery storage, smart EV charging and controllable HyER generation at project and area-wide scales.
The results show that the available station margin disappears early. Without congestion-oriented flexibility, corrected functional loading rises from 37.1 MW in Phase 1 to 45.2 MW in Phase 8. The 38 MW limit is already marginally exceeded in Phase 2, while Phase 3 reaches 39.1 MW and confirms the emerging constraint. By Phase 8, the station limit is exceeded during 438 annual hours, with a maximum exceedance of 7.15 MW and approximately 614 MWh above the threshold. The central concern is therefore not only the final deficit: the remaining station margin is consumed while much of the redevelopment is still to follow.
Flexibility can substantially extend the amount of development that can be accommodated, but its effectiveness changes as demand accumulates. Flexible WKO/ATES operation accommodates the modelled programme through Phase 4, while favourable flexibility inherited from earlier projects can extend this through Phase 5. From Phase 6 onward, however, none of the evaluated configurations in which additional flexibility is concentrated in the newly added project fully eliminates congestion. Earlier projects therefore influence not only demand but also the flexibility and headroom available to later developments. Phase 6 marks an important shift: the technical problem has become too cumulative to be addressed solely within the boundary of the next individual project.
The strongest technical result emerges when flexibility is coordinated across the full development portfolio. Under the evaluated assumptions, the integrated Phase 8 scenario eliminates residual congestion during the 336-hour critical period by coordinating WKO/ATES, batteries, smart charging and HyER across all eight projects. However, the station remains exactly at its 38 MW limit for 90 hours. This should therefore be interpreted as an upper technical potential with little robustness margin, not as a guaranteed solution or evidence that reinforcement is unnecessary. Reduced modelled loading also does not automatically create formal transport rights for individual projects.
The institutional analysis shows why this technical potential cannot simply be assumed to materialise. Investment, ownership and operational control remain predominantly project-specific. Area-wide congestion management would require shared information, dispatch authority, performance verification, compensation arrangements and responsibilities that remain workable after projects are completed or transferred. The technically strongest configuration is therefore not currently the most institutionally feasible one.
The strategic implication is to prepare coordination before it becomes technically unavoidable. Phase 2 is an early signpost that unused station headroom can no longer be relied upon; Phase 6 is a stronger trigger showing that additional flexibility concentrated in the newest project is no longer sufficient. The recommended pathway is therefore adaptive: preserve future flexibility options in early projects, develop coordinated project-level arrangements as interdependence increases, and retain deeper area-wide integration as a conditional option rather than an immediate end-state.
The broader lesson extends beyond LHHS. Grid congestion in phased urban redevelopment is not only a shortage of network capacity or flexible technology; it is also a mismatch between the scale and timing of decisions. Projects can be optimised individually while their combined consequences emerge later at a shared network constraint. Flexibility can help bridge the period before reinforcement, but only if early technical and institutional choices preserve the ability to coordinate when that interdependence becomes material.
Laakhavens Hollands Spoor is planned as a major high-density redevelopment in The Hague, but the electricity system serving it has almost no room to absorb that growth. Approximately 9,000–11,000 new dwellings are planned alongside employment, commercial and public functions, while station Jan Wapstraat has 38 MW of available transport capacity and reinforcement is not planned until the end of 2036. Development decisions are made largely project by project, yet their electricity demand accumulates at the same station. This thesis examines whether flexibility can bridge part of that capacity gap before reinforcement and how its organisation must evolve as grid pressure increases.
An exploratory sequential mixed-methods case study combines documentary analysis, four stakeholder interviews and quantitative energy-system modelling. The qualitative analysis identifies technically relevant flexibility options and institutional conditions affecting their implementation. These findings inform a Linny-R optimisation model representing eight cumulative development phases and testing flexible WKO/ATES operation, battery storage, smart EV charging and controllable HyER generation at project and area-wide scales.
The results show that the available station margin disappears early. Without congestion-oriented flexibility, corrected functional loading rises from 37.1 MW in Phase 1 to 45.2 MW in Phase 8. The 38 MW limit is already marginally exceeded in Phase 2, while Phase 3 reaches 39.1 MW and confirms the emerging constraint. By Phase 8, the station limit is exceeded during 438 annual hours, with a maximum exceedance of 7.15 MW and approximately 614 MWh above the threshold. The central concern is therefore not only the final deficit: the remaining station margin is consumed while much of the redevelopment is still to follow.
Flexibility can substantially extend the amount of development that can be accommodated, but its effectiveness changes as demand accumulates. Flexible WKO/ATES operation accommodates the modelled programme through Phase 4, while favourable flexibility inherited from earlier projects can extend this through Phase 5. From Phase 6 onward, however, none of the evaluated configurations in which additional flexibility is concentrated in the newly added project fully eliminates congestion. Earlier projects therefore influence not only demand but also the flexibility and headroom available to later developments. Phase 6 marks an important shift: the technical problem has become too cumulative to be addressed solely within the boundary of the next individual project.
The strongest technical result emerges when flexibility is coordinated across the full development portfolio. Under the evaluated assumptions, the integrated Phase 8 scenario eliminates residual congestion during the 336-hour critical period by coordinating WKO/ATES, batteries, smart charging and HyER across all eight projects. However, the station remains exactly at its 38 MW limit for 90 hours. This should therefore be interpreted as an upper technical potential with little robustness margin, not as a guaranteed solution or evidence that reinforcement is unnecessary. Reduced modelled loading also does not automatically create formal transport rights for individual projects.
The institutional analysis shows why this technical potential cannot simply be assumed to materialise. Investment, ownership and operational control remain predominantly project-specific. Area-wide congestion management would require shared information, dispatch authority, performance verification, compensation arrangements and responsibilities that remain workable after projects are completed or transferred. The technically strongest configuration is therefore not currently the most institutionally feasible one.
The strategic implication is to prepare coordination before it becomes technically unavoidable. Phase 2 is an early signpost that unused station headroom can no longer be relied upon; Phase 6 is a stronger trigger showing that additional flexibility concentrated in the newest project is no longer sufficient. The recommended pathway is therefore adaptive: preserve future flexibility options in early projects, develop coordinated project-level arrangements as interdependence increases, and retain deeper area-wide integration as a conditional option rather than an immediate end-state.
The broader lesson extends beyond LHHS. Grid congestion in phased urban redevelopment is not only a shortage of network capacity or flexible technology; it is also a mismatch between the scale and timing of decisions. Projects can be optimised individually while their combined consequences emerge later at a shared network constraint. Flexibility can help bridge the period before reinforcement, but only if early technical and institutional choices preserve the ability to coordinate when that interdependence becomes material.
Early warning model for anticipating extreme electricity prices
A comparison of early warning models for extreme prices in the Dutch day-ahead electricity market, using open data, cross-border information and weather conditions
The Dutch electricity system is becoming increasingly renewable, weather-dependent, and interconnected. This development is essential for the energy transition, but it also changes the behaviour of the day-ahead electricity market. High wind and solar generation can contribute to low or negative prices when supply exceeds demand, while scarcity and limited flexibility can contribute to price spikes. At the same time, the Netherlands is strongly connected to neighbouring bidding zones, which means that Dutch day-ahead prices are influenced not only by domestic conditions, but also by cross-border exchange, neighbouring residual load, renewable generation, and market coupling. Extreme prices should therefore be understood as events in an interconnected regional electricity system.
This creates a challenge for market participants. Traders, retailers, producers, flexibility providers, and large consumers make decisions before delivery, while the actual system conditions are not yet fully known. Anticipating hours with increased downward or upward price risk is therefore valuable. However, negative prices and price spikes are rare, irregular, and difficult to predict with standard price-forecasting approaches. This thesis therefore focuses on probabilistic early warning rather than exact price forecasting. The main research question is:
“How can ex-ante probabilistic machine-learning models be developed and evaluated to estimate hourly probabilities of negative prices, normal prices, and price spikes in the Dutch day-ahead electricity market, using domestic, weather, and cross-border indicators available before market closure, and how can these probabilities support market decision-making under asymmetric error costs?”
The research addresses this question by developing a reproducible ex-ante early-warning framework. Dutch day-ahead prices are translated into a three-class target variable consisting of negative prices, normal prices, and price spikes. Negative prices are defined as prices below 0 EUR/MWh, while price spikes are defined as prices above the 95th percentile of the Dutch day-ahead price distribution within the corresponding month and year. The feature set combines Dutch price-history variables, domestic system forecasts, weather variables, cross-border indicators, neighbouring-system variables, and calendar controls. Only variables that can be retrieved before the day-ahead market closes are included. Multiple probabilistic classification models are compared using a chronological train-validation-test split, in which earlier years are used for model development and 2025 is kept as the final unseen test year. The candidate models are logistic regression, a PNN, an MLP, GBDT, and XGBoost. Because negative prices and price spikes occur much less often than normal prices, model performance is not evaluated through overall accuracy alone. Instead, the evaluation combines class-specific recall and precision, the macro F2 score, balanced accuracy, warning behaviour, probability-quality metrics, calibration diagnostics, target-definition sensitivity, and feature-removal analysis.
The final model comparison shows that tree-based models provide the strongest early-warning performance. XGBoost is selected as the preferred final model because it gives the best overall balance between detecting negative-price and price-spike events, limiting unnecessary warnings, and producing useful probability estimates. On the final 2025 test set, XGBoost achieves a macro F2 score of 0.812 and a balanced accuracy of 0.895, with high recall for both negative prices (0.969) and price spikes (0.845). It also shows the strongest probability-quality results among the compared models. GBDT achieves comparable results and therefore serves as an important benchmark. In line with the reviewed literature, logistic regression and PNN show that models can detect many rare event hours, while still producing lower precision, more false warnings, and weaker probability quality. This confirms that high recall alone is not sufficient for an early-warning model. The predicted probabilities must also translate into useful warning behaviour for decision support.
The results also show that cross-border information improves the prediction of Dutch extreme prices. For XGBoost, the macro F2 score increases from 0.702 with the domestic baseline to 0.813 with the core cross-border feature set. This indicates that Dutch extreme-price prediction should not be treated as a purely domestic problem. The strongest contribution comes from neighbouring-system features, such as load, generation, renewable generation, and residual-load forecasts. Germany/Luxembourg contributes most consistently to the predictive value of the cross-border feature set. The explainability results support this interpretation. Negative-price risk is mainly associated with residual-load conditions, renewable and neighbouring-system indicators, and scheduled exchange, whereas price-spike risk is more strongly linked to residual load, lagged Dutch prices, solar forecasts, and calendar timing. This suggests that Dutch extreme prices are shaped by a combination of domestic market stress, weather-dependent system conditions, and cross-border market coupling.
The estimated probabilities should be interpreted as decision-support signals rather than automatic trading, hedging, or dispatch rules. The model can identify hours with increased negative-price or price-spike risk before day-ahead market outcomes are known, but the appropriate actions depend on the position, contracts, flexibility options, and risk preferences of the user. A lower warning threshold may be suitable when missed events are more costly than false warnings, while a higher threshold may be more appropriate when false warnings trigger costly or unnecessary actions. The validation-selected thresholds are therefore useful for model comparison and warning-behaviour analysis, but actor-specific operational thresholds require additional information about costs and decision context.
The main contribution of this thesis is a Dutch ex-ante probabilistic early-warning framework that combines extreme-price classification, open data, cross-border information, model comparison, explainability, and cost-sensitive interpretation. The practical deliverable is a reproducible modelling pipeline that forms the basis for an early-warning tool. Such a tool could provide hourly probabilities of negative prices and price spikes, together with interpretable drivers that help market participants identify hours requiring closer attention or preparation. At the same time, operational use should also account for possible feedback effects. If many market participants respond to similar warning signals, their bidding, hedging, scheduling, or flexibility decisions may influence the price outcome that the model tries to predict. Several limitations should be taken into account. The final evaluation is based on one out-of-sample test year, and the analysis is limited to hourly day-ahead prices. Intraday and balancing-market outcomes, as well as actor-specific trading and flexibility strategies, are therefore outside the scope of the model. These limitations do not undermine the value of the framework, but they show that operational use requires further development and alignment with actor-specific decision-making processes. ...
This creates a challenge for market participants. Traders, retailers, producers, flexibility providers, and large consumers make decisions before delivery, while the actual system conditions are not yet fully known. Anticipating hours with increased downward or upward price risk is therefore valuable. However, negative prices and price spikes are rare, irregular, and difficult to predict with standard price-forecasting approaches. This thesis therefore focuses on probabilistic early warning rather than exact price forecasting. The main research question is:
“How can ex-ante probabilistic machine-learning models be developed and evaluated to estimate hourly probabilities of negative prices, normal prices, and price spikes in the Dutch day-ahead electricity market, using domestic, weather, and cross-border indicators available before market closure, and how can these probabilities support market decision-making under asymmetric error costs?”
The research addresses this question by developing a reproducible ex-ante early-warning framework. Dutch day-ahead prices are translated into a three-class target variable consisting of negative prices, normal prices, and price spikes. Negative prices are defined as prices below 0 EUR/MWh, while price spikes are defined as prices above the 95th percentile of the Dutch day-ahead price distribution within the corresponding month and year. The feature set combines Dutch price-history variables, domestic system forecasts, weather variables, cross-border indicators, neighbouring-system variables, and calendar controls. Only variables that can be retrieved before the day-ahead market closes are included. Multiple probabilistic classification models are compared using a chronological train-validation-test split, in which earlier years are used for model development and 2025 is kept as the final unseen test year. The candidate models are logistic regression, a PNN, an MLP, GBDT, and XGBoost. Because negative prices and price spikes occur much less often than normal prices, model performance is not evaluated through overall accuracy alone. Instead, the evaluation combines class-specific recall and precision, the macro F2 score, balanced accuracy, warning behaviour, probability-quality metrics, calibration diagnostics, target-definition sensitivity, and feature-removal analysis.
The final model comparison shows that tree-based models provide the strongest early-warning performance. XGBoost is selected as the preferred final model because it gives the best overall balance between detecting negative-price and price-spike events, limiting unnecessary warnings, and producing useful probability estimates. On the final 2025 test set, XGBoost achieves a macro F2 score of 0.812 and a balanced accuracy of 0.895, with high recall for both negative prices (0.969) and price spikes (0.845). It also shows the strongest probability-quality results among the compared models. GBDT achieves comparable results and therefore serves as an important benchmark. In line with the reviewed literature, logistic regression and PNN show that models can detect many rare event hours, while still producing lower precision, more false warnings, and weaker probability quality. This confirms that high recall alone is not sufficient for an early-warning model. The predicted probabilities must also translate into useful warning behaviour for decision support.
The results also show that cross-border information improves the prediction of Dutch extreme prices. For XGBoost, the macro F2 score increases from 0.702 with the domestic baseline to 0.813 with the core cross-border feature set. This indicates that Dutch extreme-price prediction should not be treated as a purely domestic problem. The strongest contribution comes from neighbouring-system features, such as load, generation, renewable generation, and residual-load forecasts. Germany/Luxembourg contributes most consistently to the predictive value of the cross-border feature set. The explainability results support this interpretation. Negative-price risk is mainly associated with residual-load conditions, renewable and neighbouring-system indicators, and scheduled exchange, whereas price-spike risk is more strongly linked to residual load, lagged Dutch prices, solar forecasts, and calendar timing. This suggests that Dutch extreme prices are shaped by a combination of domestic market stress, weather-dependent system conditions, and cross-border market coupling.
The estimated probabilities should be interpreted as decision-support signals rather than automatic trading, hedging, or dispatch rules. The model can identify hours with increased negative-price or price-spike risk before day-ahead market outcomes are known, but the appropriate actions depend on the position, contracts, flexibility options, and risk preferences of the user. A lower warning threshold may be suitable when missed events are more costly than false warnings, while a higher threshold may be more appropriate when false warnings trigger costly or unnecessary actions. The validation-selected thresholds are therefore useful for model comparison and warning-behaviour analysis, but actor-specific operational thresholds require additional information about costs and decision context.
The main contribution of this thesis is a Dutch ex-ante probabilistic early-warning framework that combines extreme-price classification, open data, cross-border information, model comparison, explainability, and cost-sensitive interpretation. The practical deliverable is a reproducible modelling pipeline that forms the basis for an early-warning tool. Such a tool could provide hourly probabilities of negative prices and price spikes, together with interpretable drivers that help market participants identify hours requiring closer attention or preparation. At the same time, operational use should also account for possible feedback effects. If many market participants respond to similar warning signals, their bidding, hedging, scheduling, or flexibility decisions may influence the price outcome that the model tries to predict. Several limitations should be taken into account. The final evaluation is based on one out-of-sample test year, and the analysis is limited to hourly day-ahead prices. Intraday and balancing-market outcomes, as well as actor-specific trading and flexibility strategies, are therefore outside the scope of the model. These limitations do not undermine the value of the framework, but they show that operational use requires further development and alignment with actor-specific decision-making processes. ...
The Dutch electricity system is becoming increasingly renewable, weather-dependent, and interconnected. This development is essential for the energy transition, but it also changes the behaviour of the day-ahead electricity market. High wind and solar generation can contribute to low or negative prices when supply exceeds demand, while scarcity and limited flexibility can contribute to price spikes. At the same time, the Netherlands is strongly connected to neighbouring bidding zones, which means that Dutch day-ahead prices are influenced not only by domestic conditions, but also by cross-border exchange, neighbouring residual load, renewable generation, and market coupling. Extreme prices should therefore be understood as events in an interconnected regional electricity system.
This creates a challenge for market participants. Traders, retailers, producers, flexibility providers, and large consumers make decisions before delivery, while the actual system conditions are not yet fully known. Anticipating hours with increased downward or upward price risk is therefore valuable. However, negative prices and price spikes are rare, irregular, and difficult to predict with standard price-forecasting approaches. This thesis therefore focuses on probabilistic early warning rather than exact price forecasting. The main research question is:
“How can ex-ante probabilistic machine-learning models be developed and evaluated to estimate hourly probabilities of negative prices, normal prices, and price spikes in the Dutch day-ahead electricity market, using domestic, weather, and cross-border indicators available before market closure, and how can these probabilities support market decision-making under asymmetric error costs?”
The research addresses this question by developing a reproducible ex-ante early-warning framework. Dutch day-ahead prices are translated into a three-class target variable consisting of negative prices, normal prices, and price spikes. Negative prices are defined as prices below 0 EUR/MWh, while price spikes are defined as prices above the 95th percentile of the Dutch day-ahead price distribution within the corresponding month and year. The feature set combines Dutch price-history variables, domestic system forecasts, weather variables, cross-border indicators, neighbouring-system variables, and calendar controls. Only variables that can be retrieved before the day-ahead market closes are included. Multiple probabilistic classification models are compared using a chronological train-validation-test split, in which earlier years are used for model development and 2025 is kept as the final unseen test year. The candidate models are logistic regression, a PNN, an MLP, GBDT, and XGBoost. Because negative prices and price spikes occur much less often than normal prices, model performance is not evaluated through overall accuracy alone. Instead, the evaluation combines class-specific recall and precision, the macro F2 score, balanced accuracy, warning behaviour, probability-quality metrics, calibration diagnostics, target-definition sensitivity, and feature-removal analysis.
The final model comparison shows that tree-based models provide the strongest early-warning performance. XGBoost is selected as the preferred final model because it gives the best overall balance between detecting negative-price and price-spike events, limiting unnecessary warnings, and producing useful probability estimates. On the final 2025 test set, XGBoost achieves a macro F2 score of 0.812 and a balanced accuracy of 0.895, with high recall for both negative prices (0.969) and price spikes (0.845). It also shows the strongest probability-quality results among the compared models. GBDT achieves comparable results and therefore serves as an important benchmark. In line with the reviewed literature, logistic regression and PNN show that models can detect many rare event hours, while still producing lower precision, more false warnings, and weaker probability quality. This confirms that high recall alone is not sufficient for an early-warning model. The predicted probabilities must also translate into useful warning behaviour for decision support.
The results also show that cross-border information improves the prediction of Dutch extreme prices. For XGBoost, the macro F2 score increases from 0.702 with the domestic baseline to 0.813 with the core cross-border feature set. This indicates that Dutch extreme-price prediction should not be treated as a purely domestic problem. The strongest contribution comes from neighbouring-system features, such as load, generation, renewable generation, and residual-load forecasts. Germany/Luxembourg contributes most consistently to the predictive value of the cross-border feature set. The explainability results support this interpretation. Negative-price risk is mainly associated with residual-load conditions, renewable and neighbouring-system indicators, and scheduled exchange, whereas price-spike risk is more strongly linked to residual load, lagged Dutch prices, solar forecasts, and calendar timing. This suggests that Dutch extreme prices are shaped by a combination of domestic market stress, weather-dependent system conditions, and cross-border market coupling.
The estimated probabilities should be interpreted as decision-support signals rather than automatic trading, hedging, or dispatch rules. The model can identify hours with increased negative-price or price-spike risk before day-ahead market outcomes are known, but the appropriate actions depend on the position, contracts, flexibility options, and risk preferences of the user. A lower warning threshold may be suitable when missed events are more costly than false warnings, while a higher threshold may be more appropriate when false warnings trigger costly or unnecessary actions. The validation-selected thresholds are therefore useful for model comparison and warning-behaviour analysis, but actor-specific operational thresholds require additional information about costs and decision context.
The main contribution of this thesis is a Dutch ex-ante probabilistic early-warning framework that combines extreme-price classification, open data, cross-border information, model comparison, explainability, and cost-sensitive interpretation. The practical deliverable is a reproducible modelling pipeline that forms the basis for an early-warning tool. Such a tool could provide hourly probabilities of negative prices and price spikes, together with interpretable drivers that help market participants identify hours requiring closer attention or preparation. At the same time, operational use should also account for possible feedback effects. If many market participants respond to similar warning signals, their bidding, hedging, scheduling, or flexibility decisions may influence the price outcome that the model tries to predict. Several limitations should be taken into account. The final evaluation is based on one out-of-sample test year, and the analysis is limited to hourly day-ahead prices. Intraday and balancing-market outcomes, as well as actor-specific trading and flexibility strategies, are therefore outside the scope of the model. These limitations do not undermine the value of the framework, but they show that operational use requires further development and alignment with actor-specific decision-making processes.
This creates a challenge for market participants. Traders, retailers, producers, flexibility providers, and large consumers make decisions before delivery, while the actual system conditions are not yet fully known. Anticipating hours with increased downward or upward price risk is therefore valuable. However, negative prices and price spikes are rare, irregular, and difficult to predict with standard price-forecasting approaches. This thesis therefore focuses on probabilistic early warning rather than exact price forecasting. The main research question is:
“How can ex-ante probabilistic machine-learning models be developed and evaluated to estimate hourly probabilities of negative prices, normal prices, and price spikes in the Dutch day-ahead electricity market, using domestic, weather, and cross-border indicators available before market closure, and how can these probabilities support market decision-making under asymmetric error costs?”
The research addresses this question by developing a reproducible ex-ante early-warning framework. Dutch day-ahead prices are translated into a three-class target variable consisting of negative prices, normal prices, and price spikes. Negative prices are defined as prices below 0 EUR/MWh, while price spikes are defined as prices above the 95th percentile of the Dutch day-ahead price distribution within the corresponding month and year. The feature set combines Dutch price-history variables, domestic system forecasts, weather variables, cross-border indicators, neighbouring-system variables, and calendar controls. Only variables that can be retrieved before the day-ahead market closes are included. Multiple probabilistic classification models are compared using a chronological train-validation-test split, in which earlier years are used for model development and 2025 is kept as the final unseen test year. The candidate models are logistic regression, a PNN, an MLP, GBDT, and XGBoost. Because negative prices and price spikes occur much less often than normal prices, model performance is not evaluated through overall accuracy alone. Instead, the evaluation combines class-specific recall and precision, the macro F2 score, balanced accuracy, warning behaviour, probability-quality metrics, calibration diagnostics, target-definition sensitivity, and feature-removal analysis.
The final model comparison shows that tree-based models provide the strongest early-warning performance. XGBoost is selected as the preferred final model because it gives the best overall balance between detecting negative-price and price-spike events, limiting unnecessary warnings, and producing useful probability estimates. On the final 2025 test set, XGBoost achieves a macro F2 score of 0.812 and a balanced accuracy of 0.895, with high recall for both negative prices (0.969) and price spikes (0.845). It also shows the strongest probability-quality results among the compared models. GBDT achieves comparable results and therefore serves as an important benchmark. In line with the reviewed literature, logistic regression and PNN show that models can detect many rare event hours, while still producing lower precision, more false warnings, and weaker probability quality. This confirms that high recall alone is not sufficient for an early-warning model. The predicted probabilities must also translate into useful warning behaviour for decision support.
The results also show that cross-border information improves the prediction of Dutch extreme prices. For XGBoost, the macro F2 score increases from 0.702 with the domestic baseline to 0.813 with the core cross-border feature set. This indicates that Dutch extreme-price prediction should not be treated as a purely domestic problem. The strongest contribution comes from neighbouring-system features, such as load, generation, renewable generation, and residual-load forecasts. Germany/Luxembourg contributes most consistently to the predictive value of the cross-border feature set. The explainability results support this interpretation. Negative-price risk is mainly associated with residual-load conditions, renewable and neighbouring-system indicators, and scheduled exchange, whereas price-spike risk is more strongly linked to residual load, lagged Dutch prices, solar forecasts, and calendar timing. This suggests that Dutch extreme prices are shaped by a combination of domestic market stress, weather-dependent system conditions, and cross-border market coupling.
The estimated probabilities should be interpreted as decision-support signals rather than automatic trading, hedging, or dispatch rules. The model can identify hours with increased negative-price or price-spike risk before day-ahead market outcomes are known, but the appropriate actions depend on the position, contracts, flexibility options, and risk preferences of the user. A lower warning threshold may be suitable when missed events are more costly than false warnings, while a higher threshold may be more appropriate when false warnings trigger costly or unnecessary actions. The validation-selected thresholds are therefore useful for model comparison and warning-behaviour analysis, but actor-specific operational thresholds require additional information about costs and decision context.
The main contribution of this thesis is a Dutch ex-ante probabilistic early-warning framework that combines extreme-price classification, open data, cross-border information, model comparison, explainability, and cost-sensitive interpretation. The practical deliverable is a reproducible modelling pipeline that forms the basis for an early-warning tool. Such a tool could provide hourly probabilities of negative prices and price spikes, together with interpretable drivers that help market participants identify hours requiring closer attention or preparation. At the same time, operational use should also account for possible feedback effects. If many market participants respond to similar warning signals, their bidding, hedging, scheduling, or flexibility decisions may influence the price outcome that the model tries to predict. Several limitations should be taken into account. The final evaluation is based on one out-of-sample test year, and the analysis is limited to hourly day-ahead prices. Intraday and balancing-market outcomes, as well as actor-specific trading and flexibility strategies, are therefore outside the scope of the model. These limitations do not undermine the value of the framework, but they show that operational use requires further development and alignment with actor-specific decision-making processes.
Green Hydrogen in HIC Rotterdam: Enabling Competitive Domestic Production vs. Imports
A Techno-Economic Modelling (Linny-R) Approach
The Netherlands targets a climate-neutral energy system by 2050, with offshore wind electricity and green hydrogen as key pillars. As an emerging “hydrogen hub”, the Port of Rotterdam is central to this transition. However, domestic hydrogen production in this area is currently economically challenging, while importing hydrogen—mainly via ammonia—is often cheaper but increases dependence on foreign supply.
This thesis examines the techno-economic and strategic conditions under which domestic hydrogen production can compete with imports while strengthening the interconnected hydrogen–electricity system. A location-specific model for the Harbour Industrial Cluster Rotterdam was developed in Linny-R and applied using an exploratory modelling and analysis (EMA) approach. Over 400 scenarios were run to test the system under a range of techno-economic conditions.
The results show three main findings:
1. Domestic production is cost-competitive in only 18% of scenarios, requiring favourable PPA prices, reduced capital costs, and moderately high import prices.
2. System resilience is mainly limited by electricity availability, not hydrogen availability. Robust performance requires grid supply capacity of at least 5.5 GWe, electricity storage of at least 35 GWhe, and stable prices.
3. Domestic production provides strategic value as a buffer against import disruptions and price shocks, even when it is not the cheapest option.
The study concludes that a hybrid system combining domestic production and imports is the most resilient option under uncertainty. It specifies the conditions for cost-competitiveness, highlights risks in PPA design under volatile prices, and offers a policy framework for ports aiming to combine economic efficiency with strategic security. ...
This thesis examines the techno-economic and strategic conditions under which domestic hydrogen production can compete with imports while strengthening the interconnected hydrogen–electricity system. A location-specific model for the Harbour Industrial Cluster Rotterdam was developed in Linny-R and applied using an exploratory modelling and analysis (EMA) approach. Over 400 scenarios were run to test the system under a range of techno-economic conditions.
The results show three main findings:
1. Domestic production is cost-competitive in only 18% of scenarios, requiring favourable PPA prices, reduced capital costs, and moderately high import prices.
2. System resilience is mainly limited by electricity availability, not hydrogen availability. Robust performance requires grid supply capacity of at least 5.5 GWe, electricity storage of at least 35 GWhe, and stable prices.
3. Domestic production provides strategic value as a buffer against import disruptions and price shocks, even when it is not the cheapest option.
The study concludes that a hybrid system combining domestic production and imports is the most resilient option under uncertainty. It specifies the conditions for cost-competitiveness, highlights risks in PPA design under volatile prices, and offers a policy framework for ports aiming to combine economic efficiency with strategic security. ...
The Netherlands targets a climate-neutral energy system by 2050, with offshore wind electricity and green hydrogen as key pillars. As an emerging “hydrogen hub”, the Port of Rotterdam is central to this transition. However, domestic hydrogen production in this area is currently economically challenging, while importing hydrogen—mainly via ammonia—is often cheaper but increases dependence on foreign supply.
This thesis examines the techno-economic and strategic conditions under which domestic hydrogen production can compete with imports while strengthening the interconnected hydrogen–electricity system. A location-specific model for the Harbour Industrial Cluster Rotterdam was developed in Linny-R and applied using an exploratory modelling and analysis (EMA) approach. Over 400 scenarios were run to test the system under a range of techno-economic conditions.
The results show three main findings:
1. Domestic production is cost-competitive in only 18% of scenarios, requiring favourable PPA prices, reduced capital costs, and moderately high import prices.
2. System resilience is mainly limited by electricity availability, not hydrogen availability. Robust performance requires grid supply capacity of at least 5.5 GWe, electricity storage of at least 35 GWhe, and stable prices.
3. Domestic production provides strategic value as a buffer against import disruptions and price shocks, even when it is not the cheapest option.
The study concludes that a hybrid system combining domestic production and imports is the most resilient option under uncertainty. It specifies the conditions for cost-competitiveness, highlights risks in PPA design under volatile prices, and offers a policy framework for ports aiming to combine economic efficiency with strategic security.
This thesis examines the techno-economic and strategic conditions under which domestic hydrogen production can compete with imports while strengthening the interconnected hydrogen–electricity system. A location-specific model for the Harbour Industrial Cluster Rotterdam was developed in Linny-R and applied using an exploratory modelling and analysis (EMA) approach. Over 400 scenarios were run to test the system under a range of techno-economic conditions.
The results show three main findings:
1. Domestic production is cost-competitive in only 18% of scenarios, requiring favourable PPA prices, reduced capital costs, and moderately high import prices.
2. System resilience is mainly limited by electricity availability, not hydrogen availability. Robust performance requires grid supply capacity of at least 5.5 GWe, electricity storage of at least 35 GWhe, and stable prices.
3. Domestic production provides strategic value as a buffer against import disruptions and price shocks, even when it is not the cheapest option.
The study concludes that a hybrid system combining domestic production and imports is the most resilient option under uncertainty. It specifies the conditions for cost-competitiveness, highlights risks in PPA design under volatile prices, and offers a policy framework for ports aiming to combine economic efficiency with strategic security.
Stacking Up
Assessing and improving the digital strategic autonomy of Dutch municipalities
Dutch municipalities are highly digitized and experience an increased digital dependency. Between 2017 and 2025, municipal reliance on Software as a Service (SaaS) for their main tasks has increased from 16% to 70%. Municipalities are highly dependent on a small number of US cloud service providers, which together have a 75-90% EU cloud services market share. This dependency raises democratic and geopolitical concerns. US legislation, including the Foreign Intelligence Surveillance Act (FISA) Section 702, grants the US government access to data stored by US cloud service providers, regardless of where it is stored. Furthermore, US sanctions can terminate access to cloud services. Simultaneously, private cloud service providers can implement unilateral changes to services used by the municipalities, without democratic oversight or explicit municipal consent. Current dependencies are large and run deep. Therefore, any approach should be strategic: full autonomy is unfeasible and undesirable.
This thesis addresses these concerns through the lens of digital strategic autonomy, defined as "the capabilities, capacities, and control to decide and act autonomously on essential digital aspects of our economy, society and democracy", following Timmers. Unlike the binary concept of digital sovereignty, digital strategic autonomy acknowledges degrees of control and better captures the nuanced reality of contemporary digital dependencies.
The research employs a Design Science Research Approach to develop both an analytical theory of digital strategic autonomy and an artefact for municipal application. The methodology combines literature review, semi-structured interviews, actor analysis and legal doctrinal research. The study conceptualizes digital infrastructure through a layered ‘stack’ framework, focusing specifically on the cloud, data & AI, and application layers where municipal vulnerabilities are most pronounced. The research identifies two critical dimensions of digital strategic autonomy: the geopolitical dimension, where foreign jurisdiction poses risks to data confidentiality (the panopticon effect) and service availability (the chokepoint effect), and the public/private dimension, highlighting private actors imposing private values on public service delivery.
Based on this conceptualization, the study develops a self-assessment tool that enables municipalities to evaluate their digital strategic autonomy for specific processes supported by SaaS applications. This tool operationalizes the analytical theory by providing concrete indicators across multiple dimensions. Applying self-assessment tool and an institutional analysis results in technical, institutional and governance measures.
...
This thesis addresses these concerns through the lens of digital strategic autonomy, defined as "the capabilities, capacities, and control to decide and act autonomously on essential digital aspects of our economy, society and democracy", following Timmers. Unlike the binary concept of digital sovereignty, digital strategic autonomy acknowledges degrees of control and better captures the nuanced reality of contemporary digital dependencies.
The research employs a Design Science Research Approach to develop both an analytical theory of digital strategic autonomy and an artefact for municipal application. The methodology combines literature review, semi-structured interviews, actor analysis and legal doctrinal research. The study conceptualizes digital infrastructure through a layered ‘stack’ framework, focusing specifically on the cloud, data & AI, and application layers where municipal vulnerabilities are most pronounced. The research identifies two critical dimensions of digital strategic autonomy: the geopolitical dimension, where foreign jurisdiction poses risks to data confidentiality (the panopticon effect) and service availability (the chokepoint effect), and the public/private dimension, highlighting private actors imposing private values on public service delivery.
Based on this conceptualization, the study develops a self-assessment tool that enables municipalities to evaluate their digital strategic autonomy for specific processes supported by SaaS applications. This tool operationalizes the analytical theory by providing concrete indicators across multiple dimensions. Applying self-assessment tool and an institutional analysis results in technical, institutional and governance measures.
...
Dutch municipalities are highly digitized and experience an increased digital dependency. Between 2017 and 2025, municipal reliance on Software as a Service (SaaS) for their main tasks has increased from 16% to 70%. Municipalities are highly dependent on a small number of US cloud service providers, which together have a 75-90% EU cloud services market share. This dependency raises democratic and geopolitical concerns. US legislation, including the Foreign Intelligence Surveillance Act (FISA) Section 702, grants the US government access to data stored by US cloud service providers, regardless of where it is stored. Furthermore, US sanctions can terminate access to cloud services. Simultaneously, private cloud service providers can implement unilateral changes to services used by the municipalities, without democratic oversight or explicit municipal consent. Current dependencies are large and run deep. Therefore, any approach should be strategic: full autonomy is unfeasible and undesirable.
This thesis addresses these concerns through the lens of digital strategic autonomy, defined as "the capabilities, capacities, and control to decide and act autonomously on essential digital aspects of our economy, society and democracy", following Timmers. Unlike the binary concept of digital sovereignty, digital strategic autonomy acknowledges degrees of control and better captures the nuanced reality of contemporary digital dependencies.
The research employs a Design Science Research Approach to develop both an analytical theory of digital strategic autonomy and an artefact for municipal application. The methodology combines literature review, semi-structured interviews, actor analysis and legal doctrinal research. The study conceptualizes digital infrastructure through a layered ‘stack’ framework, focusing specifically on the cloud, data & AI, and application layers where municipal vulnerabilities are most pronounced. The research identifies two critical dimensions of digital strategic autonomy: the geopolitical dimension, where foreign jurisdiction poses risks to data confidentiality (the panopticon effect) and service availability (the chokepoint effect), and the public/private dimension, highlighting private actors imposing private values on public service delivery.
Based on this conceptualization, the study develops a self-assessment tool that enables municipalities to evaluate their digital strategic autonomy for specific processes supported by SaaS applications. This tool operationalizes the analytical theory by providing concrete indicators across multiple dimensions. Applying self-assessment tool and an institutional analysis results in technical, institutional and governance measures.
This thesis addresses these concerns through the lens of digital strategic autonomy, defined as "the capabilities, capacities, and control to decide and act autonomously on essential digital aspects of our economy, society and democracy", following Timmers. Unlike the binary concept of digital sovereignty, digital strategic autonomy acknowledges degrees of control and better captures the nuanced reality of contemporary digital dependencies.
The research employs a Design Science Research Approach to develop both an analytical theory of digital strategic autonomy and an artefact for municipal application. The methodology combines literature review, semi-structured interviews, actor analysis and legal doctrinal research. The study conceptualizes digital infrastructure through a layered ‘stack’ framework, focusing specifically on the cloud, data & AI, and application layers where municipal vulnerabilities are most pronounced. The research identifies two critical dimensions of digital strategic autonomy: the geopolitical dimension, where foreign jurisdiction poses risks to data confidentiality (the panopticon effect) and service availability (the chokepoint effect), and the public/private dimension, highlighting private actors imposing private values on public service delivery.
Based on this conceptualization, the study develops a self-assessment tool that enables municipalities to evaluate their digital strategic autonomy for specific processes supported by SaaS applications. This tool operationalizes the analytical theory by providing concrete indicators across multiple dimensions. Applying self-assessment tool and an institutional analysis results in technical, institutional and governance measures.
Analysing the economic viability of hydrogen-fueled gas turbines in a sustainable Dutch electricity system
An exploratory modelling approach
Master thesis
(2025)
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M.N. van Kaauwen, L.J. de Vries, P.W.G. Bots, W. Nijmeijer, Wouter van den Berg
The Dutch government aims to establish a CO₂-neutral electricity system by 2035, phasing out natural gas and coal while increasing the share of variable renewable energy sources. However, the intermittency of these sources presents challenges for maintaining system reliability, particularly during prolonged periods of low renewable generation, known as Dunkelflautes. Hydrogen-fueled gas turbines have been identified as a potential solution, offering CO₂-free electricity generation and system flexibility.
This study assesses the potential role of hydrogen-fueled gas turbines in ensuring system adequacy and evaluates whether, and to what extent, government intervention is necessary to stimulate investments. A quantitative exploratory modelling approach is employed to analyse the economic viability of hydrogen turbines and system reliability under various uncertainties and market conditions. A simulation model of the Dutch electricity system is developed in Linny-R, a graphical modelling tool specifically designed for the formulation of MILP problems. Through various scenarios for the period from 2030 to 2050, the influence of deep uncertainties is analysed. Additionally, qualitative insights from expert interviews complement the analysis.
The findings indicate that, under anticipated government scenarios, significant electricity shortages are expected, particularly in later years as vRES capacity increases. While demand flexibility and battery storage alleviate shortages to some extent, they are insufficient to fully replace dispatchable generation. Hydrogen turbines can mitigate these shortages, provided sufficient hydrogen is available. However, their financial viability remains highly uncertain, as profits are highly dependent on scarcity rents rather than consistent operational hours.
Due to uncertainties, market failures and policy expectation, the study concludes that market forces alone are unlikely to drive sufficient investment in hydrogen turbines. Without intervention, investment risks remain high, leading to underinvestment and reliability issues. Among the policy measures evaluated, capacity remuneration mechanisms (CRMs) emerge as the most robust solution, as they reduce revenue uncertainty and improve financial feasibility. Reliability options, a specific form of CRM, prevent excessive generator profits through a clawback mechanism while ensuring electricity affordability.
For interventions to be effective and facilitate large-scale hydrogen turbine deployment, a coordinated approach is necessary to support the maturation of the entire hydrogen value chain. Parallel development of hydrogen production, storage, and infrastructure must be actively monitored, with targeted support where needed. A proactive approach from the government is important to prevent supply shortages and avoid the higher costs associated with delayed intervention. Without decisive action, the transition to a sustainable, reliable, and affordable Dutch electricity system faces significant risks and delays. ...
This study assesses the potential role of hydrogen-fueled gas turbines in ensuring system adequacy and evaluates whether, and to what extent, government intervention is necessary to stimulate investments. A quantitative exploratory modelling approach is employed to analyse the economic viability of hydrogen turbines and system reliability under various uncertainties and market conditions. A simulation model of the Dutch electricity system is developed in Linny-R, a graphical modelling tool specifically designed for the formulation of MILP problems. Through various scenarios for the period from 2030 to 2050, the influence of deep uncertainties is analysed. Additionally, qualitative insights from expert interviews complement the analysis.
The findings indicate that, under anticipated government scenarios, significant electricity shortages are expected, particularly in later years as vRES capacity increases. While demand flexibility and battery storage alleviate shortages to some extent, they are insufficient to fully replace dispatchable generation. Hydrogen turbines can mitigate these shortages, provided sufficient hydrogen is available. However, their financial viability remains highly uncertain, as profits are highly dependent on scarcity rents rather than consistent operational hours.
Due to uncertainties, market failures and policy expectation, the study concludes that market forces alone are unlikely to drive sufficient investment in hydrogen turbines. Without intervention, investment risks remain high, leading to underinvestment and reliability issues. Among the policy measures evaluated, capacity remuneration mechanisms (CRMs) emerge as the most robust solution, as they reduce revenue uncertainty and improve financial feasibility. Reliability options, a specific form of CRM, prevent excessive generator profits through a clawback mechanism while ensuring electricity affordability.
For interventions to be effective and facilitate large-scale hydrogen turbine deployment, a coordinated approach is necessary to support the maturation of the entire hydrogen value chain. Parallel development of hydrogen production, storage, and infrastructure must be actively monitored, with targeted support where needed. A proactive approach from the government is important to prevent supply shortages and avoid the higher costs associated with delayed intervention. Without decisive action, the transition to a sustainable, reliable, and affordable Dutch electricity system faces significant risks and delays. ...
The Dutch government aims to establish a CO₂-neutral electricity system by 2035, phasing out natural gas and coal while increasing the share of variable renewable energy sources. However, the intermittency of these sources presents challenges for maintaining system reliability, particularly during prolonged periods of low renewable generation, known as Dunkelflautes. Hydrogen-fueled gas turbines have been identified as a potential solution, offering CO₂-free electricity generation and system flexibility.
This study assesses the potential role of hydrogen-fueled gas turbines in ensuring system adequacy and evaluates whether, and to what extent, government intervention is necessary to stimulate investments. A quantitative exploratory modelling approach is employed to analyse the economic viability of hydrogen turbines and system reliability under various uncertainties and market conditions. A simulation model of the Dutch electricity system is developed in Linny-R, a graphical modelling tool specifically designed for the formulation of MILP problems. Through various scenarios for the period from 2030 to 2050, the influence of deep uncertainties is analysed. Additionally, qualitative insights from expert interviews complement the analysis.
The findings indicate that, under anticipated government scenarios, significant electricity shortages are expected, particularly in later years as vRES capacity increases. While demand flexibility and battery storage alleviate shortages to some extent, they are insufficient to fully replace dispatchable generation. Hydrogen turbines can mitigate these shortages, provided sufficient hydrogen is available. However, their financial viability remains highly uncertain, as profits are highly dependent on scarcity rents rather than consistent operational hours.
Due to uncertainties, market failures and policy expectation, the study concludes that market forces alone are unlikely to drive sufficient investment in hydrogen turbines. Without intervention, investment risks remain high, leading to underinvestment and reliability issues. Among the policy measures evaluated, capacity remuneration mechanisms (CRMs) emerge as the most robust solution, as they reduce revenue uncertainty and improve financial feasibility. Reliability options, a specific form of CRM, prevent excessive generator profits through a clawback mechanism while ensuring electricity affordability.
For interventions to be effective and facilitate large-scale hydrogen turbine deployment, a coordinated approach is necessary to support the maturation of the entire hydrogen value chain. Parallel development of hydrogen production, storage, and infrastructure must be actively monitored, with targeted support where needed. A proactive approach from the government is important to prevent supply shortages and avoid the higher costs associated with delayed intervention. Without decisive action, the transition to a sustainable, reliable, and affordable Dutch electricity system faces significant risks and delays.
This study assesses the potential role of hydrogen-fueled gas turbines in ensuring system adequacy and evaluates whether, and to what extent, government intervention is necessary to stimulate investments. A quantitative exploratory modelling approach is employed to analyse the economic viability of hydrogen turbines and system reliability under various uncertainties and market conditions. A simulation model of the Dutch electricity system is developed in Linny-R, a graphical modelling tool specifically designed for the formulation of MILP problems. Through various scenarios for the period from 2030 to 2050, the influence of deep uncertainties is analysed. Additionally, qualitative insights from expert interviews complement the analysis.
The findings indicate that, under anticipated government scenarios, significant electricity shortages are expected, particularly in later years as vRES capacity increases. While demand flexibility and battery storage alleviate shortages to some extent, they are insufficient to fully replace dispatchable generation. Hydrogen turbines can mitigate these shortages, provided sufficient hydrogen is available. However, their financial viability remains highly uncertain, as profits are highly dependent on scarcity rents rather than consistent operational hours.
Due to uncertainties, market failures and policy expectation, the study concludes that market forces alone are unlikely to drive sufficient investment in hydrogen turbines. Without intervention, investment risks remain high, leading to underinvestment and reliability issues. Among the policy measures evaluated, capacity remuneration mechanisms (CRMs) emerge as the most robust solution, as they reduce revenue uncertainty and improve financial feasibility. Reliability options, a specific form of CRM, prevent excessive generator profits through a clawback mechanism while ensuring electricity affordability.
For interventions to be effective and facilitate large-scale hydrogen turbine deployment, a coordinated approach is necessary to support the maturation of the entire hydrogen value chain. Parallel development of hydrogen production, storage, and infrastructure must be actively monitored, with targeted support where needed. A proactive approach from the government is important to prevent supply shortages and avoid the higher costs associated with delayed intervention. Without decisive action, the transition to a sustainable, reliable, and affordable Dutch electricity system faces significant risks and delays.
Modeling the Energy Transition of an Integrated Steel Site
The Case of Tata Steel’s IJmuiden Site
The steel industry is one of the most energy-intensive and carbon-emitting sectors, accounting for nearly 10% of global greenhouse gas (GHG) emissions. Tata Steel IJmuiden faces growing pressure to decarbonize due to rising CO₂ costs and increasing demand for green steel. This study develops an optimization model to evaluate Tata Steel’s current operations and identify feasible transition pathways towards carbon-neutral steel production. The model simulates energy flows, material usage, and economic impacts under different decarbonization scenarios, using real operational data and future energy price projections.
Three progressive transition phases from 2030 to 2050 are proposed: (1) a shift from the traditional blast furnace–basic oxygen furnace (BF-BOF) route to a hybrid model incorporating direct reduced iron (DRI) and electric arc furnace (EAF) technologies, (2) increased reliance on hydrogen-based steelmaking, and (3) full adoption of hydrogen-powered DRI plants. Results indicate that while green steel production is achievable, it will lead to higher costs due to electricity price volatility, hydrogen dependency, and network constraints. Key recommendations include optimizing capital investments, enhancing energy flexibility through EAFs, accelerating hydrogen infrastructure development, and advocating for policy reforms.
This study provides critical insights for Tata Steel and the broader industrial sector, demonstrating that strategic planning and investment in energy-efficient technologies are essential for a sustainable and competitive steel industry. ...
Three progressive transition phases from 2030 to 2050 are proposed: (1) a shift from the traditional blast furnace–basic oxygen furnace (BF-BOF) route to a hybrid model incorporating direct reduced iron (DRI) and electric arc furnace (EAF) technologies, (2) increased reliance on hydrogen-based steelmaking, and (3) full adoption of hydrogen-powered DRI plants. Results indicate that while green steel production is achievable, it will lead to higher costs due to electricity price volatility, hydrogen dependency, and network constraints. Key recommendations include optimizing capital investments, enhancing energy flexibility through EAFs, accelerating hydrogen infrastructure development, and advocating for policy reforms.
This study provides critical insights for Tata Steel and the broader industrial sector, demonstrating that strategic planning and investment in energy-efficient technologies are essential for a sustainable and competitive steel industry. ...
The steel industry is one of the most energy-intensive and carbon-emitting sectors, accounting for nearly 10% of global greenhouse gas (GHG) emissions. Tata Steel IJmuiden faces growing pressure to decarbonize due to rising CO₂ costs and increasing demand for green steel. This study develops an optimization model to evaluate Tata Steel’s current operations and identify feasible transition pathways towards carbon-neutral steel production. The model simulates energy flows, material usage, and economic impacts under different decarbonization scenarios, using real operational data and future energy price projections.
Three progressive transition phases from 2030 to 2050 are proposed: (1) a shift from the traditional blast furnace–basic oxygen furnace (BF-BOF) route to a hybrid model incorporating direct reduced iron (DRI) and electric arc furnace (EAF) technologies, (2) increased reliance on hydrogen-based steelmaking, and (3) full adoption of hydrogen-powered DRI plants. Results indicate that while green steel production is achievable, it will lead to higher costs due to electricity price volatility, hydrogen dependency, and network constraints. Key recommendations include optimizing capital investments, enhancing energy flexibility through EAFs, accelerating hydrogen infrastructure development, and advocating for policy reforms.
This study provides critical insights for Tata Steel and the broader industrial sector, demonstrating that strategic planning and investment in energy-efficient technologies are essential for a sustainable and competitive steel industry.
Three progressive transition phases from 2030 to 2050 are proposed: (1) a shift from the traditional blast furnace–basic oxygen furnace (BF-BOF) route to a hybrid model incorporating direct reduced iron (DRI) and electric arc furnace (EAF) technologies, (2) increased reliance on hydrogen-based steelmaking, and (3) full adoption of hydrogen-powered DRI plants. Results indicate that while green steel production is achievable, it will lead to higher costs due to electricity price volatility, hydrogen dependency, and network constraints. Key recommendations include optimizing capital investments, enhancing energy flexibility through EAFs, accelerating hydrogen infrastructure development, and advocating for policy reforms.
This study provides critical insights for Tata Steel and the broader industrial sector, demonstrating that strategic planning and investment in energy-efficient technologies are essential for a sustainable and competitive steel industry.
Flexmeister
Assessing system performance of an integrated balancing and redispatch market design on the Dutch electricity grid
The increasing demand for electricity and fluctuating renewable generation intensify congestion risks on the Dutch transmission grid, challenging the reliability ensured by TenneT, the national Transmission System Operator (TSO). Currently, redispatch and balancing are operated in separate markets, limiting efficient use of flexibility and increasing system costs. This study develops and evaluates Flexmeister, an integrated balancing and redispatch market design, where suppliers provide a uniform flexibility product that TenneT can allocate in real time. The simulations in this study were based on the actual Dutch high-voltage grid topology, physical power flow constraints, realistic generator locations, parametrised bidding behaviour and representative time-series data. To evaluate the performance of Flexmeister, four market designs were simulated: the Current (separated) design, Gross, Net, and All-in-one (one-step optimisation). Three market designs were structured around three sequential market rounds: DA, redispatch and imbalance, whereas All-in-one executed redispatch and imbalance at the same time-step. The benchmark results demonstrated that integrated designs consistently outperformed the current design across all key performance indicators: extra capacity, total costs, activated volumes and clearing prices up and down. Simulations consistently reported the performance in the following order: All-in-one > Net > Gross > Current. Unlike the current market, the integrated designs required no additional out-of-market capacity, thereby improving reliability without costly infrastructure investments. The findings provide the first quantitative evidence that market integration can strengthen grid reliability and lower societal costs. Moreover, the developed simulation model bridges economic market design and technical system modelling, offering policymakers and grid operators a practical tool to support the energy transition.
...
The increasing demand for electricity and fluctuating renewable generation intensify congestion risks on the Dutch transmission grid, challenging the reliability ensured by TenneT, the national Transmission System Operator (TSO). Currently, redispatch and balancing are operated in separate markets, limiting efficient use of flexibility and increasing system costs. This study develops and evaluates Flexmeister, an integrated balancing and redispatch market design, where suppliers provide a uniform flexibility product that TenneT can allocate in real time. The simulations in this study were based on the actual Dutch high-voltage grid topology, physical power flow constraints, realistic generator locations, parametrised bidding behaviour and representative time-series data. To evaluate the performance of Flexmeister, four market designs were simulated: the Current (separated) design, Gross, Net, and All-in-one (one-step optimisation). Three market designs were structured around three sequential market rounds: DA, redispatch and imbalance, whereas All-in-one executed redispatch and imbalance at the same time-step. The benchmark results demonstrated that integrated designs consistently outperformed the current design across all key performance indicators: extra capacity, total costs, activated volumes and clearing prices up and down. Simulations consistently reported the performance in the following order: All-in-one > Net > Gross > Current. Unlike the current market, the integrated designs required no additional out-of-market capacity, thereby improving reliability without costly infrastructure investments. The findings provide the first quantitative evidence that market integration can strengthen grid reliability and lower societal costs. Moreover, the developed simulation model bridges economic market design and technical system modelling, offering policymakers and grid operators a practical tool to support the energy transition.
The production of carbon dioxide (CO2) is the largest contributor to global warming. The primary source of these emissions is petroleum-derived fuel used in transportation, specifically in internal combustion engine vehicles. In response, the European Union (EU) has implemented a low-emission mobility strategy, aiming to shift towards low-carbon transportation. This strategy includes ambitious targets for electric vehicle (EV) adoption with at least 30 million zero-emission vehicles expected to be on European roads by 2030. This push towards EVs is driving up demand for lithium-ion batteries (LIBs).
While efforts to improve efficiency in EV battery production and use are intended to reduce environmental impacts, they might potentially lead to unintended consequences known as rebound effects. Rebound effects occur when efficiency improvements result in increased consumption or production elsewhere in the system, negating the expected benefits. These effects can arise from both behavioural and systemic responses to efficiency gains. Current discussions and policy frameworks often overlook the potential rebound effects in CE activities, particularly concerning EV batteries. This oversight poses a significant risk to the effectiveness of initiatives aimed at reducing CO2 emissions and advancing CE practices. Therefore, it is crucial to investigate these rebound effects within the CE context for EV batteries to develop effective mitigation strategies.
There is a noticeable gap in the existing literature regarding the interconnected dynamics of CE practices and rebound effects in the context of EV batteries. While some studies have explored rebound effects in other sectors, limited research examines how CE practices for EV batteries contribute to rebound effects across different lifecycle stages. To ensure the sustainability of CE initiatives, it is essential to adopt a systemic view that considers these effects. By doing so, businesses can develop strategies that not only focus on recycling and efficiency but also address broader systemic impacts, ensuring that increased demand does not negate the benefits of circular practices.
The research employs qualitative SD to examine potential rebound effects in the CE system for EV batteries. This approach allows for a comprehensive understanding of the system's dynamics by identifying causal relationships between physical and behavioural components. The study uses CLDs to represent the interconnections and feedback processes within the circular economy of EV batteries, helping to identify reinforcing and balancing feedback loops that drive system behaviour. The initial phase involves identifying key variables that influence the system's behaviour. These variables include economic incentives, technological advancements, regulatory frameworks, consumer behaviour, and environmental impacts. The relationships between these variables are mapped to create CLDs, which are then iteratively refined based on expert feedback to ensure accuracy and relevance.
Expert interviews are conducted to validate and refine the constructed CLDs. Professionals with in-depth knowledge of the circular economy and EV batteries provide insights that help verify the model's assumptions, structure, and behaviour. This validation process includes discussions on potential oversights or nuances that the initial model may not fully capture. The methodology also involves developing Circular Business Models (CBMs) as strategies to mitigate rebound effects. A review of existing business model innovations in the CE context is conducted to identify patterns relevant to EV batteries. These business model patterns are categorized to align with the rebound mechanism categories, facilitating a cohesive analysis.
The study identifies key mechanisms within the EV battery lifecycle that can lead to rebound effects, particularly during the usage phase. Three significant mechanisms—Income, Substitution, and Demand Adjustment by Efficiency—were found to be most prominent in this phase. To mitigate these effects, the research proposes various strategies. Dynamic Pricing emerges as the most effective strategy, capable of addressing all three mechanisms by adjusting prices in response to real-time market conditions, thereby preventing excessive consumption and production. Alternative strategies such as Pay per Use and Subscription models also show promise in mitigating rebound effects by promoting efficient use and reducing the need for outright ownership.
The research highlights the importance of understanding these mechanisms and selecting suitable strategies to mitigate rebound effects. Firms must focus on the identified mechanisms and integrate appropriate strategies into their business models to ensure sustainable practices. Furthermore, the study underscores the necessity of engaging a diverse range of stakeholders, including consumers, policymakers, and industry practitioners, to develop comprehensive and inclusive CE strategies. Future research should continue to refine these strategies and explore the dynamic interactions within the EV battery lifecycle to enhance the effectiveness of CE initiatives. ...
While efforts to improve efficiency in EV battery production and use are intended to reduce environmental impacts, they might potentially lead to unintended consequences known as rebound effects. Rebound effects occur when efficiency improvements result in increased consumption or production elsewhere in the system, negating the expected benefits. These effects can arise from both behavioural and systemic responses to efficiency gains. Current discussions and policy frameworks often overlook the potential rebound effects in CE activities, particularly concerning EV batteries. This oversight poses a significant risk to the effectiveness of initiatives aimed at reducing CO2 emissions and advancing CE practices. Therefore, it is crucial to investigate these rebound effects within the CE context for EV batteries to develop effective mitigation strategies.
There is a noticeable gap in the existing literature regarding the interconnected dynamics of CE practices and rebound effects in the context of EV batteries. While some studies have explored rebound effects in other sectors, limited research examines how CE practices for EV batteries contribute to rebound effects across different lifecycle stages. To ensure the sustainability of CE initiatives, it is essential to adopt a systemic view that considers these effects. By doing so, businesses can develop strategies that not only focus on recycling and efficiency but also address broader systemic impacts, ensuring that increased demand does not negate the benefits of circular practices.
The research employs qualitative SD to examine potential rebound effects in the CE system for EV batteries. This approach allows for a comprehensive understanding of the system's dynamics by identifying causal relationships between physical and behavioural components. The study uses CLDs to represent the interconnections and feedback processes within the circular economy of EV batteries, helping to identify reinforcing and balancing feedback loops that drive system behaviour. The initial phase involves identifying key variables that influence the system's behaviour. These variables include economic incentives, technological advancements, regulatory frameworks, consumer behaviour, and environmental impacts. The relationships between these variables are mapped to create CLDs, which are then iteratively refined based on expert feedback to ensure accuracy and relevance.
Expert interviews are conducted to validate and refine the constructed CLDs. Professionals with in-depth knowledge of the circular economy and EV batteries provide insights that help verify the model's assumptions, structure, and behaviour. This validation process includes discussions on potential oversights or nuances that the initial model may not fully capture. The methodology also involves developing Circular Business Models (CBMs) as strategies to mitigate rebound effects. A review of existing business model innovations in the CE context is conducted to identify patterns relevant to EV batteries. These business model patterns are categorized to align with the rebound mechanism categories, facilitating a cohesive analysis.
The study identifies key mechanisms within the EV battery lifecycle that can lead to rebound effects, particularly during the usage phase. Three significant mechanisms—Income, Substitution, and Demand Adjustment by Efficiency—were found to be most prominent in this phase. To mitigate these effects, the research proposes various strategies. Dynamic Pricing emerges as the most effective strategy, capable of addressing all three mechanisms by adjusting prices in response to real-time market conditions, thereby preventing excessive consumption and production. Alternative strategies such as Pay per Use and Subscription models also show promise in mitigating rebound effects by promoting efficient use and reducing the need for outright ownership.
The research highlights the importance of understanding these mechanisms and selecting suitable strategies to mitigate rebound effects. Firms must focus on the identified mechanisms and integrate appropriate strategies into their business models to ensure sustainable practices. Furthermore, the study underscores the necessity of engaging a diverse range of stakeholders, including consumers, policymakers, and industry practitioners, to develop comprehensive and inclusive CE strategies. Future research should continue to refine these strategies and explore the dynamic interactions within the EV battery lifecycle to enhance the effectiveness of CE initiatives. ...
The production of carbon dioxide (CO2) is the largest contributor to global warming. The primary source of these emissions is petroleum-derived fuel used in transportation, specifically in internal combustion engine vehicles. In response, the European Union (EU) has implemented a low-emission mobility strategy, aiming to shift towards low-carbon transportation. This strategy includes ambitious targets for electric vehicle (EV) adoption with at least 30 million zero-emission vehicles expected to be on European roads by 2030. This push towards EVs is driving up demand for lithium-ion batteries (LIBs).
While efforts to improve efficiency in EV battery production and use are intended to reduce environmental impacts, they might potentially lead to unintended consequences known as rebound effects. Rebound effects occur when efficiency improvements result in increased consumption or production elsewhere in the system, negating the expected benefits. These effects can arise from both behavioural and systemic responses to efficiency gains. Current discussions and policy frameworks often overlook the potential rebound effects in CE activities, particularly concerning EV batteries. This oversight poses a significant risk to the effectiveness of initiatives aimed at reducing CO2 emissions and advancing CE practices. Therefore, it is crucial to investigate these rebound effects within the CE context for EV batteries to develop effective mitigation strategies.
There is a noticeable gap in the existing literature regarding the interconnected dynamics of CE practices and rebound effects in the context of EV batteries. While some studies have explored rebound effects in other sectors, limited research examines how CE practices for EV batteries contribute to rebound effects across different lifecycle stages. To ensure the sustainability of CE initiatives, it is essential to adopt a systemic view that considers these effects. By doing so, businesses can develop strategies that not only focus on recycling and efficiency but also address broader systemic impacts, ensuring that increased demand does not negate the benefits of circular practices.
The research employs qualitative SD to examine potential rebound effects in the CE system for EV batteries. This approach allows for a comprehensive understanding of the system's dynamics by identifying causal relationships between physical and behavioural components. The study uses CLDs to represent the interconnections and feedback processes within the circular economy of EV batteries, helping to identify reinforcing and balancing feedback loops that drive system behaviour. The initial phase involves identifying key variables that influence the system's behaviour. These variables include economic incentives, technological advancements, regulatory frameworks, consumer behaviour, and environmental impacts. The relationships between these variables are mapped to create CLDs, which are then iteratively refined based on expert feedback to ensure accuracy and relevance.
Expert interviews are conducted to validate and refine the constructed CLDs. Professionals with in-depth knowledge of the circular economy and EV batteries provide insights that help verify the model's assumptions, structure, and behaviour. This validation process includes discussions on potential oversights or nuances that the initial model may not fully capture. The methodology also involves developing Circular Business Models (CBMs) as strategies to mitigate rebound effects. A review of existing business model innovations in the CE context is conducted to identify patterns relevant to EV batteries. These business model patterns are categorized to align with the rebound mechanism categories, facilitating a cohesive analysis.
The study identifies key mechanisms within the EV battery lifecycle that can lead to rebound effects, particularly during the usage phase. Three significant mechanisms—Income, Substitution, and Demand Adjustment by Efficiency—were found to be most prominent in this phase. To mitigate these effects, the research proposes various strategies. Dynamic Pricing emerges as the most effective strategy, capable of addressing all three mechanisms by adjusting prices in response to real-time market conditions, thereby preventing excessive consumption and production. Alternative strategies such as Pay per Use and Subscription models also show promise in mitigating rebound effects by promoting efficient use and reducing the need for outright ownership.
The research highlights the importance of understanding these mechanisms and selecting suitable strategies to mitigate rebound effects. Firms must focus on the identified mechanisms and integrate appropriate strategies into their business models to ensure sustainable practices. Furthermore, the study underscores the necessity of engaging a diverse range of stakeholders, including consumers, policymakers, and industry practitioners, to develop comprehensive and inclusive CE strategies. Future research should continue to refine these strategies and explore the dynamic interactions within the EV battery lifecycle to enhance the effectiveness of CE initiatives.
While efforts to improve efficiency in EV battery production and use are intended to reduce environmental impacts, they might potentially lead to unintended consequences known as rebound effects. Rebound effects occur when efficiency improvements result in increased consumption or production elsewhere in the system, negating the expected benefits. These effects can arise from both behavioural and systemic responses to efficiency gains. Current discussions and policy frameworks often overlook the potential rebound effects in CE activities, particularly concerning EV batteries. This oversight poses a significant risk to the effectiveness of initiatives aimed at reducing CO2 emissions and advancing CE practices. Therefore, it is crucial to investigate these rebound effects within the CE context for EV batteries to develop effective mitigation strategies.
There is a noticeable gap in the existing literature regarding the interconnected dynamics of CE practices and rebound effects in the context of EV batteries. While some studies have explored rebound effects in other sectors, limited research examines how CE practices for EV batteries contribute to rebound effects across different lifecycle stages. To ensure the sustainability of CE initiatives, it is essential to adopt a systemic view that considers these effects. By doing so, businesses can develop strategies that not only focus on recycling and efficiency but also address broader systemic impacts, ensuring that increased demand does not negate the benefits of circular practices.
The research employs qualitative SD to examine potential rebound effects in the CE system for EV batteries. This approach allows for a comprehensive understanding of the system's dynamics by identifying causal relationships between physical and behavioural components. The study uses CLDs to represent the interconnections and feedback processes within the circular economy of EV batteries, helping to identify reinforcing and balancing feedback loops that drive system behaviour. The initial phase involves identifying key variables that influence the system's behaviour. These variables include economic incentives, technological advancements, regulatory frameworks, consumer behaviour, and environmental impacts. The relationships between these variables are mapped to create CLDs, which are then iteratively refined based on expert feedback to ensure accuracy and relevance.
Expert interviews are conducted to validate and refine the constructed CLDs. Professionals with in-depth knowledge of the circular economy and EV batteries provide insights that help verify the model's assumptions, structure, and behaviour. This validation process includes discussions on potential oversights or nuances that the initial model may not fully capture. The methodology also involves developing Circular Business Models (CBMs) as strategies to mitigate rebound effects. A review of existing business model innovations in the CE context is conducted to identify patterns relevant to EV batteries. These business model patterns are categorized to align with the rebound mechanism categories, facilitating a cohesive analysis.
The study identifies key mechanisms within the EV battery lifecycle that can lead to rebound effects, particularly during the usage phase. Three significant mechanisms—Income, Substitution, and Demand Adjustment by Efficiency—were found to be most prominent in this phase. To mitigate these effects, the research proposes various strategies. Dynamic Pricing emerges as the most effective strategy, capable of addressing all three mechanisms by adjusting prices in response to real-time market conditions, thereby preventing excessive consumption and production. Alternative strategies such as Pay per Use and Subscription models also show promise in mitigating rebound effects by promoting efficient use and reducing the need for outright ownership.
The research highlights the importance of understanding these mechanisms and selecting suitable strategies to mitigate rebound effects. Firms must focus on the identified mechanisms and integrate appropriate strategies into their business models to ensure sustainable practices. Furthermore, the study underscores the necessity of engaging a diverse range of stakeholders, including consumers, policymakers, and industry practitioners, to develop comprehensive and inclusive CE strategies. Future research should continue to refine these strategies and explore the dynamic interactions within the EV battery lifecycle to enhance the effectiveness of CE initiatives.
Modeling Water Resources for Everyone
Transparent and Effective Approaches for Complex Systems: Case Study of the Lower Omo Basin
Master thesis
(2024)
-
Y. Bangar, J. Zatarain Salazar, P.W.G. Bots, P.H.A.J.M. van Gelder, Seleshi Yalew
This study introduces HydroWizard, an innovative framework addressing critical challenges in water resource modeling through enhanced transparency, efficiency, reproducibility, and extensibility. Integrating a YAML-based Model Specification Language with a sophisticated Execution Engine, HydroWizard enables accessible modeling of complex water systems.
Applied to the Lower Omo-Gibe River Basin in Ethiopia, the study employs Evolutionary Multi-Objective Direct Policy Search to identify 283 Pareto-optimal policies. Findings reveal nuanced trade-offs: irrigation-optimized policies eliminate demand deficits but reduce environmental flows by up to 48%, while environmentally-focused policies show opposite effects. Notably, mean power generation remains relatively consistent across policies, challenging assumptions about water resource allocation trade-offs.
HydroWizard introduces novel visualization techniques, including animated rule curves and system state graphs, enhancing strategy interpretability. Its versatility is demonstrated through application to diverse water systems, including the Zambezi River Basin.
This research marks a significant advancement in water resource modeling, offering an open-source, accessible tool for complex water system analysis. It contributes valuable insights for sustainable water management and sets a new standard for global water resource management studies, emerging as an innovative solution to intensifying water management challenges worldwide. ...
Applied to the Lower Omo-Gibe River Basin in Ethiopia, the study employs Evolutionary Multi-Objective Direct Policy Search to identify 283 Pareto-optimal policies. Findings reveal nuanced trade-offs: irrigation-optimized policies eliminate demand deficits but reduce environmental flows by up to 48%, while environmentally-focused policies show opposite effects. Notably, mean power generation remains relatively consistent across policies, challenging assumptions about water resource allocation trade-offs.
HydroWizard introduces novel visualization techniques, including animated rule curves and system state graphs, enhancing strategy interpretability. Its versatility is demonstrated through application to diverse water systems, including the Zambezi River Basin.
This research marks a significant advancement in water resource modeling, offering an open-source, accessible tool for complex water system analysis. It contributes valuable insights for sustainable water management and sets a new standard for global water resource management studies, emerging as an innovative solution to intensifying water management challenges worldwide. ...
This study introduces HydroWizard, an innovative framework addressing critical challenges in water resource modeling through enhanced transparency, efficiency, reproducibility, and extensibility. Integrating a YAML-based Model Specification Language with a sophisticated Execution Engine, HydroWizard enables accessible modeling of complex water systems.
Applied to the Lower Omo-Gibe River Basin in Ethiopia, the study employs Evolutionary Multi-Objective Direct Policy Search to identify 283 Pareto-optimal policies. Findings reveal nuanced trade-offs: irrigation-optimized policies eliminate demand deficits but reduce environmental flows by up to 48%, while environmentally-focused policies show opposite effects. Notably, mean power generation remains relatively consistent across policies, challenging assumptions about water resource allocation trade-offs.
HydroWizard introduces novel visualization techniques, including animated rule curves and system state graphs, enhancing strategy interpretability. Its versatility is demonstrated through application to diverse water systems, including the Zambezi River Basin.
This research marks a significant advancement in water resource modeling, offering an open-source, accessible tool for complex water system analysis. It contributes valuable insights for sustainable water management and sets a new standard for global water resource management studies, emerging as an innovative solution to intensifying water management challenges worldwide.
Applied to the Lower Omo-Gibe River Basin in Ethiopia, the study employs Evolutionary Multi-Objective Direct Policy Search to identify 283 Pareto-optimal policies. Findings reveal nuanced trade-offs: irrigation-optimized policies eliminate demand deficits but reduce environmental flows by up to 48%, while environmentally-focused policies show opposite effects. Notably, mean power generation remains relatively consistent across policies, challenging assumptions about water resource allocation trade-offs.
HydroWizard introduces novel visualization techniques, including animated rule curves and system state graphs, enhancing strategy interpretability. Its versatility is demonstrated through application to diverse water systems, including the Zambezi River Basin.
This research marks a significant advancement in water resource modeling, offering an open-source, accessible tool for complex water system analysis. It contributes valuable insights for sustainable water management and sets a new standard for global water resource management studies, emerging as an innovative solution to intensifying water management challenges worldwide.
Underground hydrogen storage
Researching security of supply in a net-zero future
Master thesis
(2024)
-
E.C. Reddingius, L.J. de Vries, P.W.G. Bots, Barthold Schroot, Timme van Melle
Climate change demands a transition to a carbon-neutral energy system. In the Netherlands, this shift involves electrifying sectors, predominantly by increasing energy sources like solar PV and wind power. This comes with flexibility challenges during times with insufficient variable Renewable Energy Sources and extended periods with wind droughts, or a dunkleflaute which is a period of more or less two weeks with insufficient wind and a cold front. Electricity generation from molecule based fuels can play this flexible role to balance the system in the future. Hydrogen is a versatile energy carrier that can play this role in a low carbon manner. Hydrogen can be stored in large volumes underground in salt caverns and in depleted gas fields. To ensure security of supply in different scenarios it is necessary to understand what volume of hydrogen should be stored in storage facilities to withstand a dunkleflaute. What is also necessary is to understand with what speed this hydrogen should be injected and produced from storage facilities, in other words: in how many days the storage can empty and fill. The production of green hydrogen with which the storage is filled is produced through the process of electrolysis. There are different types of technologies to produce green hydrogen. This research also focuses on the trends in developments regarding electrolysers and their impact on the system. As well as on the policy goals set by European Union and the Dutch government. The method in which the research was set up is by determining the system characteristics and uncertainties and going through the motion of the modelling cycle multiple times. This research is conducted by simulating the Dutch energy system in Linny-R, a mixed integer linear programming software tool. The model is made for the years 2035 and 2050. A sensitivity analysis is done to determine the most influential keys in the system. Based on literature and this sensitivity analysis, a scenario analysis was determined. Many scenario’s were then tested in the model to determine their impact. The system is measured with the following KPI’s: Carbon emissions, Loss of Load Expectation and Expected Energy Not Supplied, System costs and Production cycles. The conceptualisation in this thesis details the intricacies of the Dutch energy system and its simulation within the Linny-R model, offering a foundational understanding essential for later analyses. This chapter dissects the components and operational settings of the model, emphasizing their significance in shaping outcomes. The Dutch energy system’s transition from fossil fuels to renewable energy sources is mapped within Linny-R, highlighting the interplay of various energy forms and the constraints inherent to the system. The setup includes explanations of Linny-R’s key entities and discusses the model’s execution over a yearly timeline with hourly steps to capture dynamic system behaviours. Special attention is given to the settings impacting computational efficiency and the incorporation of historical weather data to account for variability in renewable energy production. After discussing the system a framework is presented, detailing the external factors, policy levers, system relationships, and performance metrics that guide the analysis. The analysis focuses on verifying the model, conducting a sensitivity analysis, and beginning scenario discovery to test the model’s performance. Verification ensures the model accurately represents real-world systems by examining storage facilities’ behaviors and hydrogen-to-power conversion during low variable renewable energy supply (vRES) periods. Sensitivity analysis tests how changes in input parameters impact outputs, identifying critical inputs that influence the model’s performance and ensuring robustness. Key findings show that hydrogen and electricity demand parameters significantly affect system costs and efficiency. Scenario discovery assesses the model’s resilience under various conditions, including extreme weather years, to identify potential bottlenecks. This chapter establishes the model’s reliability and guides improvements in its predictive capabilities. The results show the findings from the scenario analysis aimed at evaluating the potential of underground hydrogen storage in the future energy system. These experiments investigate various performance parameters to inform decision-making for effective integration of underground hydrogen storage. Key findings include the impact of electrolyser capacity on hydrogen production and system efficiency, ii iii revealing that increased electrolyser capacity significantly reduces carbon emissions, though it must scale parallel to storage capacity to be effective. The analysis of different storage types highlights the importance of sufficient electrolysis capacity to fully utilize storage facilities. Concluding their dependence on one another. Additionally, scenarios exploring battery capacity, gas power plant use, and demand variations provide insights into optimizing the energy system while achieving policy goals. The results underscore the complex interplay between various components of the hydrogen storage system, emphasizing the need for balanced and integrated approaches to meet future energy demands and sustainability targets. The discussion interprets the research findings, emphasizing the anticipated demand for underground hydrogen storage and the required production capacity for 2035 and 2050. The study reveals that while there will be significant storage needs in 2035 due to hydrogen shortage, the demand will decrease by 2050 as renewable energy sources and electrolysis capacity increases. Economically and functionally, a combination of salt caverns and gas fields is recommended, with optimal configurations identified for injection and production capacities. The research also highlights the critical role of electrolysers and their efficiency. Limitations of the study are discussed and recommendations for future research directions suggest exploring blue hydrogen, integrating hydrogen flow dynamics, and considering regional energy needs. The study’s findings have practical implications for policy decisions for Dutch hydrogen storage
...
Climate change demands a transition to a carbon-neutral energy system. In the Netherlands, this shift involves electrifying sectors, predominantly by increasing energy sources like solar PV and wind power. This comes with flexibility challenges during times with insufficient variable Renewable Energy Sources and extended periods with wind droughts, or a dunkleflaute which is a period of more or less two weeks with insufficient wind and a cold front. Electricity generation from molecule based fuels can play this flexible role to balance the system in the future. Hydrogen is a versatile energy carrier that can play this role in a low carbon manner. Hydrogen can be stored in large volumes underground in salt caverns and in depleted gas fields. To ensure security of supply in different scenarios it is necessary to understand what volume of hydrogen should be stored in storage facilities to withstand a dunkleflaute. What is also necessary is to understand with what speed this hydrogen should be injected and produced from storage facilities, in other words: in how many days the storage can empty and fill. The production of green hydrogen with which the storage is filled is produced through the process of electrolysis. There are different types of technologies to produce green hydrogen. This research also focuses on the trends in developments regarding electrolysers and their impact on the system. As well as on the policy goals set by European Union and the Dutch government. The method in which the research was set up is by determining the system characteristics and uncertainties and going through the motion of the modelling cycle multiple times. This research is conducted by simulating the Dutch energy system in Linny-R, a mixed integer linear programming software tool. The model is made for the years 2035 and 2050. A sensitivity analysis is done to determine the most influential keys in the system. Based on literature and this sensitivity analysis, a scenario analysis was determined. Many scenario’s were then tested in the model to determine their impact. The system is measured with the following KPI’s: Carbon emissions, Loss of Load Expectation and Expected Energy Not Supplied, System costs and Production cycles. The conceptualisation in this thesis details the intricacies of the Dutch energy system and its simulation within the Linny-R model, offering a foundational understanding essential for later analyses. This chapter dissects the components and operational settings of the model, emphasizing their significance in shaping outcomes. The Dutch energy system’s transition from fossil fuels to renewable energy sources is mapped within Linny-R, highlighting the interplay of various energy forms and the constraints inherent to the system. The setup includes explanations of Linny-R’s key entities and discusses the model’s execution over a yearly timeline with hourly steps to capture dynamic system behaviours. Special attention is given to the settings impacting computational efficiency and the incorporation of historical weather data to account for variability in renewable energy production. After discussing the system a framework is presented, detailing the external factors, policy levers, system relationships, and performance metrics that guide the analysis. The analysis focuses on verifying the model, conducting a sensitivity analysis, and beginning scenario discovery to test the model’s performance. Verification ensures the model accurately represents real-world systems by examining storage facilities’ behaviors and hydrogen-to-power conversion during low variable renewable energy supply (vRES) periods. Sensitivity analysis tests how changes in input parameters impact outputs, identifying critical inputs that influence the model’s performance and ensuring robustness. Key findings show that hydrogen and electricity demand parameters significantly affect system costs and efficiency. Scenario discovery assesses the model’s resilience under various conditions, including extreme weather years, to identify potential bottlenecks. This chapter establishes the model’s reliability and guides improvements in its predictive capabilities. The results show the findings from the scenario analysis aimed at evaluating the potential of underground hydrogen storage in the future energy system. These experiments investigate various performance parameters to inform decision-making for effective integration of underground hydrogen storage. Key findings include the impact of electrolyser capacity on hydrogen production and system efficiency, ii iii revealing that increased electrolyser capacity significantly reduces carbon emissions, though it must scale parallel to storage capacity to be effective. The analysis of different storage types highlights the importance of sufficient electrolysis capacity to fully utilize storage facilities. Concluding their dependence on one another. Additionally, scenarios exploring battery capacity, gas power plant use, and demand variations provide insights into optimizing the energy system while achieving policy goals. The results underscore the complex interplay between various components of the hydrogen storage system, emphasizing the need for balanced and integrated approaches to meet future energy demands and sustainability targets. The discussion interprets the research findings, emphasizing the anticipated demand for underground hydrogen storage and the required production capacity for 2035 and 2050. The study reveals that while there will be significant storage needs in 2035 due to hydrogen shortage, the demand will decrease by 2050 as renewable energy sources and electrolysis capacity increases. Economically and functionally, a combination of salt caverns and gas fields is recommended, with optimal configurations identified for injection and production capacities. The research also highlights the critical role of electrolysers and their efficiency. Limitations of the study are discussed and recommendations for future research directions suggest exploring blue hydrogen, integrating hydrogen flow dynamics, and considering regional energy needs. The study’s findings have practical implications for policy decisions for Dutch hydrogen storage
The impact of UPHS on the high-voltage grid
A case study in Zeeland, the Netherlands
Underground Pumped Hydro Storage is considered as power storage asset in Zeeland. Using Linny-R, a cost-driven model is made representing the power flows in the high-voltage grid of Zeeland. This model includes power production by nuclear power, fossil power generators, solar PV and windturbines, power demand from industrial companies, agriculture, mobility and dwellings, and the current market functioning using the day-ahead price and marginal costs. Modelling various scenarios, the impact of UPHS on the high-voltage grid is analysed, and discussed in a broader perspective of societal added value.
...
Underground Pumped Hydro Storage is considered as power storage asset in Zeeland. Using Linny-R, a cost-driven model is made representing the power flows in the high-voltage grid of Zeeland. This model includes power production by nuclear power, fossil power generators, solar PV and windturbines, power demand from industrial companies, agriculture, mobility and dwellings, and the current market functioning using the day-ahead price and marginal costs. Modelling various scenarios, the impact of UPHS on the high-voltage grid is analysed, and discussed in a broader perspective of societal added value.
Operationally robust offshore bidding zones
Market design for efficient balancing
The large-scale deployment of offshore wind farms (OWFs) in the North Sea, targeting 300 GW by 2050, presents opportunities for carbon-neutral energy but challenges conventional grid connection methods. Offshore bidding zones (OBZs), characterised by variable generation and limited dispatchable demand, require innovative imbalance management to maintain energy balance. This thesis models the bidding behaviour of balancing responsible parties (BRPs) in sequential electricity markets using Linny-R, examining the impact of production uncertainty, price risks, and gate closure times on imbalance volumes. Results highlight trade-offs between shortages and curtailment under varying imbalance penalties and gate closure times, with reduced gate closure times significantly mitigating imbalances. Policy recommendations emphasise the need for flexible demand, risk mitigation mechanisms, and coupled imbalance pricing to enhance market efficiency and energy balancing incentivise in OBZs.
...
The large-scale deployment of offshore wind farms (OWFs) in the North Sea, targeting 300 GW by 2050, presents opportunities for carbon-neutral energy but challenges conventional grid connection methods. Offshore bidding zones (OBZs), characterised by variable generation and limited dispatchable demand, require innovative imbalance management to maintain energy balance. This thesis models the bidding behaviour of balancing responsible parties (BRPs) in sequential electricity markets using Linny-R, examining the impact of production uncertainty, price risks, and gate closure times on imbalance volumes. Results highlight trade-offs between shortages and curtailment under varying imbalance penalties and gate closure times, with reduced gate closure times significantly mitigating imbalances. Policy recommendations emphasise the need for flexible demand, risk mitigation mechanisms, and coupled imbalance pricing to enhance market efficiency and energy balancing incentivise in OBZs.
The future of green electrolytic hydrogen production in the Netherlands
Assessing uncertainty for determining a prudent production capacity
The transition to a carbon-neutral power system has become a global imperative, ever more driven due to recent events such as the energy crisis resulting from the Ukraine invasion. The Netherlands, devoted to the large-scale development of offshore wind and solar PV, see great potential in the use of hydrogen as a gas replacing energy carrier. A knowledge gap has been identified on what this role of electrolysis will be in the future energy system and more specifically, in what quantities electricity can and should be converted to hydrogen. This research aims to address these uncertainties and provide insights into the prudent capacity for green electrolytic hydrogen production in the Netherlands by 2030 and 2040.
To this end, a model of the combined electricity and hydrogen system of the Netherlands is constructed in Linny-R, an executable graphical representation language for Mixed Integer Linear Programming (MILP). A vast array of output data from forecasting scenarios is gathered and consolidated to establish the spectrum of input variables. By systematically assessing the uncertainty, based on these ranges of input variables, and the impact, based on a sensitivity analysis, an experiment design is composed to encompass the most complete reflection of all uncertainties within computation limits. Numerous experiments are then conducted to generate outcomes, determine prudent capacity, identify influential uncertainties, and evaluate the impact of large-scale hydrogen storage on electrolysis potential.
The research results demonstrate that the potential for electrolysis increases from 8-12 GW in 2030 to 8-44 GW in 2040, indicating the cruciality of flexibility for the Dutch energy system in the coming years. The most prudent capacities for 2030 and 2040 are identified as 12 GW and 38 GW, respectively, with 9 GW being identified as the no-regret capacity to be installed by 2030. Therefore, the research recommends stimulating additional investments beyond the initial targets of 3-4 GW for 2030 to foster the Dutch hydrogen economy. Moreover, there is substantial risk of overinvestment associated with the prudent capacities, especially as a result of the growing uncertainty in 2040. Deploying additional flexible system capacity to prevent electrolysis from acting as a loss of load mitigation measure can lower the prudent capacity and reduce the risk of overinvestment. Furthermore, policy measures should focus on stimulating influential uncertainties such as hydrogen demand, peak residual load and hydrogen storage. Every additional 100GWh of underground hydrogen storage capacity enables 500MW of electrolysis potential. Moreover, the last few gigawatts of the prudent capacities will primarily serve as peak production plants with limited operational hours and margins starting at 11.00 EURO/kg required to recoup the investments.
Further research opportunities include incorporating geographical and grid aspects into the model, exploring profitable conditions for investments in the last few GW of electrolysis capacity, investigating the role of large-scale hydrogen storage, and studying the potential of electrolysis beyond national borders in the North Sea interconnected electricity generation hub. ...
To this end, a model of the combined electricity and hydrogen system of the Netherlands is constructed in Linny-R, an executable graphical representation language for Mixed Integer Linear Programming (MILP). A vast array of output data from forecasting scenarios is gathered and consolidated to establish the spectrum of input variables. By systematically assessing the uncertainty, based on these ranges of input variables, and the impact, based on a sensitivity analysis, an experiment design is composed to encompass the most complete reflection of all uncertainties within computation limits. Numerous experiments are then conducted to generate outcomes, determine prudent capacity, identify influential uncertainties, and evaluate the impact of large-scale hydrogen storage on electrolysis potential.
The research results demonstrate that the potential for electrolysis increases from 8-12 GW in 2030 to 8-44 GW in 2040, indicating the cruciality of flexibility for the Dutch energy system in the coming years. The most prudent capacities for 2030 and 2040 are identified as 12 GW and 38 GW, respectively, with 9 GW being identified as the no-regret capacity to be installed by 2030. Therefore, the research recommends stimulating additional investments beyond the initial targets of 3-4 GW for 2030 to foster the Dutch hydrogen economy. Moreover, there is substantial risk of overinvestment associated with the prudent capacities, especially as a result of the growing uncertainty in 2040. Deploying additional flexible system capacity to prevent electrolysis from acting as a loss of load mitigation measure can lower the prudent capacity and reduce the risk of overinvestment. Furthermore, policy measures should focus on stimulating influential uncertainties such as hydrogen demand, peak residual load and hydrogen storage. Every additional 100GWh of underground hydrogen storage capacity enables 500MW of electrolysis potential. Moreover, the last few gigawatts of the prudent capacities will primarily serve as peak production plants with limited operational hours and margins starting at 11.00 EURO/kg required to recoup the investments.
Further research opportunities include incorporating geographical and grid aspects into the model, exploring profitable conditions for investments in the last few GW of electrolysis capacity, investigating the role of large-scale hydrogen storage, and studying the potential of electrolysis beyond national borders in the North Sea interconnected electricity generation hub. ...
The transition to a carbon-neutral power system has become a global imperative, ever more driven due to recent events such as the energy crisis resulting from the Ukraine invasion. The Netherlands, devoted to the large-scale development of offshore wind and solar PV, see great potential in the use of hydrogen as a gas replacing energy carrier. A knowledge gap has been identified on what this role of electrolysis will be in the future energy system and more specifically, in what quantities electricity can and should be converted to hydrogen. This research aims to address these uncertainties and provide insights into the prudent capacity for green electrolytic hydrogen production in the Netherlands by 2030 and 2040.
To this end, a model of the combined electricity and hydrogen system of the Netherlands is constructed in Linny-R, an executable graphical representation language for Mixed Integer Linear Programming (MILP). A vast array of output data from forecasting scenarios is gathered and consolidated to establish the spectrum of input variables. By systematically assessing the uncertainty, based on these ranges of input variables, and the impact, based on a sensitivity analysis, an experiment design is composed to encompass the most complete reflection of all uncertainties within computation limits. Numerous experiments are then conducted to generate outcomes, determine prudent capacity, identify influential uncertainties, and evaluate the impact of large-scale hydrogen storage on electrolysis potential.
The research results demonstrate that the potential for electrolysis increases from 8-12 GW in 2030 to 8-44 GW in 2040, indicating the cruciality of flexibility for the Dutch energy system in the coming years. The most prudent capacities for 2030 and 2040 are identified as 12 GW and 38 GW, respectively, with 9 GW being identified as the no-regret capacity to be installed by 2030. Therefore, the research recommends stimulating additional investments beyond the initial targets of 3-4 GW for 2030 to foster the Dutch hydrogen economy. Moreover, there is substantial risk of overinvestment associated with the prudent capacities, especially as a result of the growing uncertainty in 2040. Deploying additional flexible system capacity to prevent electrolysis from acting as a loss of load mitigation measure can lower the prudent capacity and reduce the risk of overinvestment. Furthermore, policy measures should focus on stimulating influential uncertainties such as hydrogen demand, peak residual load and hydrogen storage. Every additional 100GWh of underground hydrogen storage capacity enables 500MW of electrolysis potential. Moreover, the last few gigawatts of the prudent capacities will primarily serve as peak production plants with limited operational hours and margins starting at 11.00 EURO/kg required to recoup the investments.
Further research opportunities include incorporating geographical and grid aspects into the model, exploring profitable conditions for investments in the last few GW of electrolysis capacity, investigating the role of large-scale hydrogen storage, and studying the potential of electrolysis beyond national borders in the North Sea interconnected electricity generation hub.
To this end, a model of the combined electricity and hydrogen system of the Netherlands is constructed in Linny-R, an executable graphical representation language for Mixed Integer Linear Programming (MILP). A vast array of output data from forecasting scenarios is gathered and consolidated to establish the spectrum of input variables. By systematically assessing the uncertainty, based on these ranges of input variables, and the impact, based on a sensitivity analysis, an experiment design is composed to encompass the most complete reflection of all uncertainties within computation limits. Numerous experiments are then conducted to generate outcomes, determine prudent capacity, identify influential uncertainties, and evaluate the impact of large-scale hydrogen storage on electrolysis potential.
The research results demonstrate that the potential for electrolysis increases from 8-12 GW in 2030 to 8-44 GW in 2040, indicating the cruciality of flexibility for the Dutch energy system in the coming years. The most prudent capacities for 2030 and 2040 are identified as 12 GW and 38 GW, respectively, with 9 GW being identified as the no-regret capacity to be installed by 2030. Therefore, the research recommends stimulating additional investments beyond the initial targets of 3-4 GW for 2030 to foster the Dutch hydrogen economy. Moreover, there is substantial risk of overinvestment associated with the prudent capacities, especially as a result of the growing uncertainty in 2040. Deploying additional flexible system capacity to prevent electrolysis from acting as a loss of load mitigation measure can lower the prudent capacity and reduce the risk of overinvestment. Furthermore, policy measures should focus on stimulating influential uncertainties such as hydrogen demand, peak residual load and hydrogen storage. Every additional 100GWh of underground hydrogen storage capacity enables 500MW of electrolysis potential. Moreover, the last few gigawatts of the prudent capacities will primarily serve as peak production plants with limited operational hours and margins starting at 11.00 EURO/kg required to recoup the investments.
Further research opportunities include incorporating geographical and grid aspects into the model, exploring profitable conditions for investments in the last few GW of electrolysis capacity, investigating the role of large-scale hydrogen storage, and studying the potential of electrolysis beyond national borders in the North Sea interconnected electricity generation hub.
Identifying weather robust high-performing energy system configurations to aid decision-making
A case of the North Sea energy system
The ever-rising greenhouse gas emissions move the European Union to transition towards a future decarbonized energy system. To achieve this, the future energy system will mainly comprise variable renewable energy generation sources. Energy production from these sources is susceptible to weather fluctuations. Uncertainty of future weather scenarios translates into energy system models that help decision-makers to design the future renewable energy system. Energy system models often use a single weather year to simulate weather behaviour. Thus, many energy system models fail to take weather fluctuations between various years into account. Identifying energy system designs that are robust to weather fluctuations, i.e. systems that are able to suffice demand regardless of the weather circumstances, is therefore key.
The research described in this thesis has aimed to develop and test a method to give insight to decision-makers into the composition of robust and efficient energy systems, given weather uncertainty. To achieve this, the SPORES methodology has been used and extended to identify energy system configurations that are both robust and efficient. For this, a decision option space for decision-makers has been created that has been diversified based on renewable energy generation and storage technologies. To test the developed method, the North Sea region has been used as a case, as this is the region thought by policy to have great potential to house renewable generation sources in Europe.
The method developed in this research systematically covers the decision option space over three weather scenarios (worst, typical and best). From these decision spaces, configurations that meet demand with installed capacities that exist across the whole weather options space have been selected as robust. Clustering was used to identify types of energy system configurations, having commonalities in the installed generation capacities. Energy efficiency has been identified as key for measuring energy system performance. This research, therefore, takes curtailment and energy system yield into account to quantify efficiency. Using a Pareto analysis, both robust and efficiency-wise high-performing energy system configurations were identified as most promising for decision-makers.
The no-regret decisions, visualized by the SPORE-core, are minimum capacities required across the whole decision space. Results showed that robust energy systems are typically comprised of balanced configurations, meaning that solar PV and wind power both have the largest capacity of energy generation sources. The balanced configurations also contain high transmission capacities and typically no storage capacities indicating energy is distributed rather than stored. The robust and efficient configurations need additional capacity investments on top of the no-regret decisions. Especially solar PV needs a large increase in capacity when robustness and efficiency are required. Combined heat and power from biofuels and electrolysis capacity are also key to robust and efficient configurations. Additional results showed that the majority of robust and efficient configurations utilised more offshore than onshore wind capacity.
The findings of this research are based on a case of the North Sea energy system with a high level of aggregation and are thus of limited use for precise designs of the North Sea energy system. The method created in this study can be adapted to contain more detail and offers space for researchers to include their own performance indicators. However, this research already used significant computational efforts, so adding more resolution and detail will mean the computational process can be restricting. Future research should focus on using the developed method to select promising and robust energy system configurations with higher levels of detail and conduct further weather scenario analyses on the selected configuration. ...
The research described in this thesis has aimed to develop and test a method to give insight to decision-makers into the composition of robust and efficient energy systems, given weather uncertainty. To achieve this, the SPORES methodology has been used and extended to identify energy system configurations that are both robust and efficient. For this, a decision option space for decision-makers has been created that has been diversified based on renewable energy generation and storage technologies. To test the developed method, the North Sea region has been used as a case, as this is the region thought by policy to have great potential to house renewable generation sources in Europe.
The method developed in this research systematically covers the decision option space over three weather scenarios (worst, typical and best). From these decision spaces, configurations that meet demand with installed capacities that exist across the whole weather options space have been selected as robust. Clustering was used to identify types of energy system configurations, having commonalities in the installed generation capacities. Energy efficiency has been identified as key for measuring energy system performance. This research, therefore, takes curtailment and energy system yield into account to quantify efficiency. Using a Pareto analysis, both robust and efficiency-wise high-performing energy system configurations were identified as most promising for decision-makers.
The no-regret decisions, visualized by the SPORE-core, are minimum capacities required across the whole decision space. Results showed that robust energy systems are typically comprised of balanced configurations, meaning that solar PV and wind power both have the largest capacity of energy generation sources. The balanced configurations also contain high transmission capacities and typically no storage capacities indicating energy is distributed rather than stored. The robust and efficient configurations need additional capacity investments on top of the no-regret decisions. Especially solar PV needs a large increase in capacity when robustness and efficiency are required. Combined heat and power from biofuels and electrolysis capacity are also key to robust and efficient configurations. Additional results showed that the majority of robust and efficient configurations utilised more offshore than onshore wind capacity.
The findings of this research are based on a case of the North Sea energy system with a high level of aggregation and are thus of limited use for precise designs of the North Sea energy system. The method created in this study can be adapted to contain more detail and offers space for researchers to include their own performance indicators. However, this research already used significant computational efforts, so adding more resolution and detail will mean the computational process can be restricting. Future research should focus on using the developed method to select promising and robust energy system configurations with higher levels of detail and conduct further weather scenario analyses on the selected configuration. ...
The ever-rising greenhouse gas emissions move the European Union to transition towards a future decarbonized energy system. To achieve this, the future energy system will mainly comprise variable renewable energy generation sources. Energy production from these sources is susceptible to weather fluctuations. Uncertainty of future weather scenarios translates into energy system models that help decision-makers to design the future renewable energy system. Energy system models often use a single weather year to simulate weather behaviour. Thus, many energy system models fail to take weather fluctuations between various years into account. Identifying energy system designs that are robust to weather fluctuations, i.e. systems that are able to suffice demand regardless of the weather circumstances, is therefore key.
The research described in this thesis has aimed to develop and test a method to give insight to decision-makers into the composition of robust and efficient energy systems, given weather uncertainty. To achieve this, the SPORES methodology has been used and extended to identify energy system configurations that are both robust and efficient. For this, a decision option space for decision-makers has been created that has been diversified based on renewable energy generation and storage technologies. To test the developed method, the North Sea region has been used as a case, as this is the region thought by policy to have great potential to house renewable generation sources in Europe.
The method developed in this research systematically covers the decision option space over three weather scenarios (worst, typical and best). From these decision spaces, configurations that meet demand with installed capacities that exist across the whole weather options space have been selected as robust. Clustering was used to identify types of energy system configurations, having commonalities in the installed generation capacities. Energy efficiency has been identified as key for measuring energy system performance. This research, therefore, takes curtailment and energy system yield into account to quantify efficiency. Using a Pareto analysis, both robust and efficiency-wise high-performing energy system configurations were identified as most promising for decision-makers.
The no-regret decisions, visualized by the SPORE-core, are minimum capacities required across the whole decision space. Results showed that robust energy systems are typically comprised of balanced configurations, meaning that solar PV and wind power both have the largest capacity of energy generation sources. The balanced configurations also contain high transmission capacities and typically no storage capacities indicating energy is distributed rather than stored. The robust and efficient configurations need additional capacity investments on top of the no-regret decisions. Especially solar PV needs a large increase in capacity when robustness and efficiency are required. Combined heat and power from biofuels and electrolysis capacity are also key to robust and efficient configurations. Additional results showed that the majority of robust and efficient configurations utilised more offshore than onshore wind capacity.
The findings of this research are based on a case of the North Sea energy system with a high level of aggregation and are thus of limited use for precise designs of the North Sea energy system. The method created in this study can be adapted to contain more detail and offers space for researchers to include their own performance indicators. However, this research already used significant computational efforts, so adding more resolution and detail will mean the computational process can be restricting. Future research should focus on using the developed method to select promising and robust energy system configurations with higher levels of detail and conduct further weather scenario analyses on the selected configuration.
The research described in this thesis has aimed to develop and test a method to give insight to decision-makers into the composition of robust and efficient energy systems, given weather uncertainty. To achieve this, the SPORES methodology has been used and extended to identify energy system configurations that are both robust and efficient. For this, a decision option space for decision-makers has been created that has been diversified based on renewable energy generation and storage technologies. To test the developed method, the North Sea region has been used as a case, as this is the region thought by policy to have great potential to house renewable generation sources in Europe.
The method developed in this research systematically covers the decision option space over three weather scenarios (worst, typical and best). From these decision spaces, configurations that meet demand with installed capacities that exist across the whole weather options space have been selected as robust. Clustering was used to identify types of energy system configurations, having commonalities in the installed generation capacities. Energy efficiency has been identified as key for measuring energy system performance. This research, therefore, takes curtailment and energy system yield into account to quantify efficiency. Using a Pareto analysis, both robust and efficiency-wise high-performing energy system configurations were identified as most promising for decision-makers.
The no-regret decisions, visualized by the SPORE-core, are minimum capacities required across the whole decision space. Results showed that robust energy systems are typically comprised of balanced configurations, meaning that solar PV and wind power both have the largest capacity of energy generation sources. The balanced configurations also contain high transmission capacities and typically no storage capacities indicating energy is distributed rather than stored. The robust and efficient configurations need additional capacity investments on top of the no-regret decisions. Especially solar PV needs a large increase in capacity when robustness and efficiency are required. Combined heat and power from biofuels and electrolysis capacity are also key to robust and efficient configurations. Additional results showed that the majority of robust and efficient configurations utilised more offshore than onshore wind capacity.
The findings of this research are based on a case of the North Sea energy system with a high level of aggregation and are thus of limited use for precise designs of the North Sea energy system. The method created in this study can be adapted to contain more detail and offers space for researchers to include their own performance indicators. However, this research already used significant computational efforts, so adding more resolution and detail will mean the computational process can be restricting. Future research should focus on using the developed method to select promising and robust energy system configurations with higher levels of detail and conduct further weather scenario analyses on the selected configuration.
Uncertainty on transmission grids
An exploratory modeling study on transmission grids
Following the climate goals of the Dutch government, a tremendous amount of vRES (variable Renewable Energy Sources) will be built. These energy sources will produce large amounts of power, which must travel great distances on the transmission grid. The Dutch TSO, TenneT, is responsible for the transmission grid's design, construction, and maintenance by European and Dutch law. The planning for transmission grids has become more complex since the liberalization of power markets. Because of this complexity, the planned expansions are published every two years. In energy systems, the ever-changing context of the system makes the possible future states of the system uncertain. In this research, we 1. identify the relevant uncertainties for the transmission system, 2. Identify the most vulnerable lines under these uncertainties, and 3. Look at the effect of the delays on the performance of the overall power system. This gives insights into how the transmission system must evolve to maintain the grid's performance.
Not only more power from vRES will be generated, but coal plants will also be phased out, and the demand for other energy sources (fuel, gas, etc.) will be electrified. More power flow will lead to congestion on the current grid. The Dutch Transmission System Operator (TSO) uses congestion management to alleviate acute congestion and expansion planning to prevent future congestion. To decide whether grid expansion should be used, Production Cost Models (PCMs) are used. To account for uncertainties in a system, the concept of deep uncertainty can be employed to map the uncertainty of a system. Exploratory modeling uses this concept to understand the consequences of deep uncertainty. Experimental modeling and analysis could thus be used to understand the effects of vRES on the power grid.
Direct Current Optimal Power Flow (DCOPF) is used for the formulation of the Production Cost Model (PCM) for the power network modeling. The chosen exploratory modeling framework is a simplified form of the XLRM framework, in which the policies in the model (L) is not altered. The data used as input for the model is selected from a wide range of sources, following the format of the DCOPF formulation. The tool used to build and simulate the DCOPF model is Scenario Analysis Interface for Energy Systems (SAInt) software. The EMA workbench is used to perform exploratory modeling and analysis. Two times 1,000 experiments for a week period have been run with these tools, one set in January and one set in June. To assess which lines in the transmission network might be susceptible to congestion, we analyzed the experiments' outcomes with a congestion-seeking algorithm. The adopted methodology has been verified using a 5-node model.
The results of these experiments are divided into two types of outcomes: the general system outcomes and the line outcomes; the latter has been split up based on the urgency of capacity expansion. The analysis tools by the EMA workbench were used to analyze the results of the experiments, especially the Patient Rule Induction Method (PRIM) algorithm. The experiments showed significant differences in congestion between June and January...
...
Not only more power from vRES will be generated, but coal plants will also be phased out, and the demand for other energy sources (fuel, gas, etc.) will be electrified. More power flow will lead to congestion on the current grid. The Dutch Transmission System Operator (TSO) uses congestion management to alleviate acute congestion and expansion planning to prevent future congestion. To decide whether grid expansion should be used, Production Cost Models (PCMs) are used. To account for uncertainties in a system, the concept of deep uncertainty can be employed to map the uncertainty of a system. Exploratory modeling uses this concept to understand the consequences of deep uncertainty. Experimental modeling and analysis could thus be used to understand the effects of vRES on the power grid.
Direct Current Optimal Power Flow (DCOPF) is used for the formulation of the Production Cost Model (PCM) for the power network modeling. The chosen exploratory modeling framework is a simplified form of the XLRM framework, in which the policies in the model (L) is not altered. The data used as input for the model is selected from a wide range of sources, following the format of the DCOPF formulation. The tool used to build and simulate the DCOPF model is Scenario Analysis Interface for Energy Systems (SAInt) software. The EMA workbench is used to perform exploratory modeling and analysis. Two times 1,000 experiments for a week period have been run with these tools, one set in January and one set in June. To assess which lines in the transmission network might be susceptible to congestion, we analyzed the experiments' outcomes with a congestion-seeking algorithm. The adopted methodology has been verified using a 5-node model.
The results of these experiments are divided into two types of outcomes: the general system outcomes and the line outcomes; the latter has been split up based on the urgency of capacity expansion. The analysis tools by the EMA workbench were used to analyze the results of the experiments, especially the Patient Rule Induction Method (PRIM) algorithm. The experiments showed significant differences in congestion between June and January...
...
Following the climate goals of the Dutch government, a tremendous amount of vRES (variable Renewable Energy Sources) will be built. These energy sources will produce large amounts of power, which must travel great distances on the transmission grid. The Dutch TSO, TenneT, is responsible for the transmission grid's design, construction, and maintenance by European and Dutch law. The planning for transmission grids has become more complex since the liberalization of power markets. Because of this complexity, the planned expansions are published every two years. In energy systems, the ever-changing context of the system makes the possible future states of the system uncertain. In this research, we 1. identify the relevant uncertainties for the transmission system, 2. Identify the most vulnerable lines under these uncertainties, and 3. Look at the effect of the delays on the performance of the overall power system. This gives insights into how the transmission system must evolve to maintain the grid's performance.
Not only more power from vRES will be generated, but coal plants will also be phased out, and the demand for other energy sources (fuel, gas, etc.) will be electrified. More power flow will lead to congestion on the current grid. The Dutch Transmission System Operator (TSO) uses congestion management to alleviate acute congestion and expansion planning to prevent future congestion. To decide whether grid expansion should be used, Production Cost Models (PCMs) are used. To account for uncertainties in a system, the concept of deep uncertainty can be employed to map the uncertainty of a system. Exploratory modeling uses this concept to understand the consequences of deep uncertainty. Experimental modeling and analysis could thus be used to understand the effects of vRES on the power grid.
Direct Current Optimal Power Flow (DCOPF) is used for the formulation of the Production Cost Model (PCM) for the power network modeling. The chosen exploratory modeling framework is a simplified form of the XLRM framework, in which the policies in the model (L) is not altered. The data used as input for the model is selected from a wide range of sources, following the format of the DCOPF formulation. The tool used to build and simulate the DCOPF model is Scenario Analysis Interface for Energy Systems (SAInt) software. The EMA workbench is used to perform exploratory modeling and analysis. Two times 1,000 experiments for a week period have been run with these tools, one set in January and one set in June. To assess which lines in the transmission network might be susceptible to congestion, we analyzed the experiments' outcomes with a congestion-seeking algorithm. The adopted methodology has been verified using a 5-node model.
The results of these experiments are divided into two types of outcomes: the general system outcomes and the line outcomes; the latter has been split up based on the urgency of capacity expansion. The analysis tools by the EMA workbench were used to analyze the results of the experiments, especially the Patient Rule Induction Method (PRIM) algorithm. The experiments showed significant differences in congestion between June and January...
Not only more power from vRES will be generated, but coal plants will also be phased out, and the demand for other energy sources (fuel, gas, etc.) will be electrified. More power flow will lead to congestion on the current grid. The Dutch Transmission System Operator (TSO) uses congestion management to alleviate acute congestion and expansion planning to prevent future congestion. To decide whether grid expansion should be used, Production Cost Models (PCMs) are used. To account for uncertainties in a system, the concept of deep uncertainty can be employed to map the uncertainty of a system. Exploratory modeling uses this concept to understand the consequences of deep uncertainty. Experimental modeling and analysis could thus be used to understand the effects of vRES on the power grid.
Direct Current Optimal Power Flow (DCOPF) is used for the formulation of the Production Cost Model (PCM) for the power network modeling. The chosen exploratory modeling framework is a simplified form of the XLRM framework, in which the policies in the model (L) is not altered. The data used as input for the model is selected from a wide range of sources, following the format of the DCOPF formulation. The tool used to build and simulate the DCOPF model is Scenario Analysis Interface for Energy Systems (SAInt) software. The EMA workbench is used to perform exploratory modeling and analysis. Two times 1,000 experiments for a week period have been run with these tools, one set in January and one set in June. To assess which lines in the transmission network might be susceptible to congestion, we analyzed the experiments' outcomes with a congestion-seeking algorithm. The adopted methodology has been verified using a 5-node model.
The results of these experiments are divided into two types of outcomes: the general system outcomes and the line outcomes; the latter has been split up based on the urgency of capacity expansion. The analysis tools by the EMA workbench were used to analyze the results of the experiments, especially the Patient Rule Induction Method (PRIM) algorithm. The experiments showed significant differences in congestion between June and January...
Electrification by means of renewable energy sources in electricity production (RES-E) is a key strategy to meet global climate goals established in the Paris Agreement. The increasing share of RES-E as a result of this has significant implications on the functioning of the electricity system and the adequacy of the electricity market design, as these technologies exhibit different characteristics compared to conventional generators. Their intermittent output combined with near negligible marginal costs has an effect on electricity price formation and will lead to an increased demand for flexibility services to level out these fluctuations. These trends will induce an increased investment risk for flexible generation capacity. This increased investment risk feeds into the risk-averse behaviour of investors that is already present due to market failures that exist in electricity markets. It remains unclear whether the liberalised neoclassical electricity market design provides sufficient incentives to invest in generation capacity in order to maintain system adequacy during the energy transition.
One possible market design option that is proposed to address this increased investment risk are Capacity Mechanisms. Capacity Mechanisms allow for an adequate remuneration of (flexible) generation capacity, by providing a steady income stream to firm capacity on top of revenues obtained from selling electricity. The purpose of this thesis is to evaluate the effectiveness of a capacity market - a specification of a capacity mechanism - in maintaining system adequacy in a system with an increasing share of RES-E. Additionally, it aims to fulfil the need for electricity system models that allow for performing robustness analysis as this it is seen as increasingly important to adequately account for uncertainty that is inherently related to the unfolding of the energy transition. This research was carried out by extending an existing quantitative model of the Dutch electricity system, referred to as Myopic Optimisation Detailed Operational (or MODO), with a capacity market. The methodology applied relies on myopic optimisation.
Conceptualisation of the capacity market model is based largely on the design of the NYISO Installed Capacity Market (NYISO-ICAP), as this design is relatively simple whilst being considered a successful capacity market. A thorough literature review on the NYISO-ICAP was performed to establish a profound understanding of real-world capacity market dynamics and how these can be translated to the model. The conceptualisation of the capacity market forms a basis for formalisation and is used to establish the formal rules of the capacity market model complying with the basic structure of linear programming problems. This serves as input for the implementation of the extension of the capacity market in MODO, and thus in Linny-R.
With the resulting model, the effectiveness of a capacity market in maintaining system adequacy was assessed on the performance of several Key Performance Indicators. These encompass the Supply Ratio, the average annual volume of Energy-not-Served [MWh], the average Electricity Price [€/MWh] and the total Consumer Spending [€]. A comparative analysis was performed between the performance of the energy-only market and the capacity market on the identified Key Performance Indicators in four pre-specified scenarios. The four scenarios are varied on key uncertainties underlying the unfolding of the energy transition to explore the robustness of results, such as weather conditions and the level of risk aversion of investors.
From this thesis, it can be concluded that a capacity market can be an effective and robust policy instrument to maintain system adequacy during the energy transition at a lower cost to consumers. This can specifically be seen in the increased number of investments in peaking generators compared to the energy-only market when forecasted revenues of the capacity market are sufficient, which has a positive effect on the electricity system's robustness to different weather conditions. Furthermore, when the level of a risk aversion of investors is high, a capacity market is more robust in ensuring system adequacy compared to an energy-only market.
Nonetheless, a capacity market can be prone to investment cycles which can have a negative influence on the effectiveness of a capacity market to maintain system adequacy at all times. These investment cycles are a result of the bounded rationality and myopia experienced by investors, ultimately leading to imperfect forecasting of the revenues of the capacity market. Results of this thesis have shown that in busts of investment cycles, investments in generation capacity can be insufficient to maintain system adequacy.
To conclude, a capacity market can be a viable option in order to maintain system adequacy in the energy transition. However, whether it is the most suitable option to maintain system adequacy is not clear. Novel flexibility options such as storage could potentially serve a significant role in maintaining system adequacy. If additional flexibility options have a lower perceived investment risk compared to flexible generation units, the electricity market can potentially continue to rely on its initial liberalised neoclassical market design whilst maintaining system adequacy. This requires further exploration in future research.
Furthermore, MODO and its capacity market extension fill the need for electricity system models that allow for performing robustness analysis, as it has a relatively low computation burden. Hence, an interesting avenue for future research is to evaluate the effectiveness of a capacity market in many possible futures. ...
One possible market design option that is proposed to address this increased investment risk are Capacity Mechanisms. Capacity Mechanisms allow for an adequate remuneration of (flexible) generation capacity, by providing a steady income stream to firm capacity on top of revenues obtained from selling electricity. The purpose of this thesis is to evaluate the effectiveness of a capacity market - a specification of a capacity mechanism - in maintaining system adequacy in a system with an increasing share of RES-E. Additionally, it aims to fulfil the need for electricity system models that allow for performing robustness analysis as this it is seen as increasingly important to adequately account for uncertainty that is inherently related to the unfolding of the energy transition. This research was carried out by extending an existing quantitative model of the Dutch electricity system, referred to as Myopic Optimisation Detailed Operational (or MODO), with a capacity market. The methodology applied relies on myopic optimisation.
Conceptualisation of the capacity market model is based largely on the design of the NYISO Installed Capacity Market (NYISO-ICAP), as this design is relatively simple whilst being considered a successful capacity market. A thorough literature review on the NYISO-ICAP was performed to establish a profound understanding of real-world capacity market dynamics and how these can be translated to the model. The conceptualisation of the capacity market forms a basis for formalisation and is used to establish the formal rules of the capacity market model complying with the basic structure of linear programming problems. This serves as input for the implementation of the extension of the capacity market in MODO, and thus in Linny-R.
With the resulting model, the effectiveness of a capacity market in maintaining system adequacy was assessed on the performance of several Key Performance Indicators. These encompass the Supply Ratio, the average annual volume of Energy-not-Served [MWh], the average Electricity Price [€/MWh] and the total Consumer Spending [€]. A comparative analysis was performed between the performance of the energy-only market and the capacity market on the identified Key Performance Indicators in four pre-specified scenarios. The four scenarios are varied on key uncertainties underlying the unfolding of the energy transition to explore the robustness of results, such as weather conditions and the level of risk aversion of investors.
From this thesis, it can be concluded that a capacity market can be an effective and robust policy instrument to maintain system adequacy during the energy transition at a lower cost to consumers. This can specifically be seen in the increased number of investments in peaking generators compared to the energy-only market when forecasted revenues of the capacity market are sufficient, which has a positive effect on the electricity system's robustness to different weather conditions. Furthermore, when the level of a risk aversion of investors is high, a capacity market is more robust in ensuring system adequacy compared to an energy-only market.
Nonetheless, a capacity market can be prone to investment cycles which can have a negative influence on the effectiveness of a capacity market to maintain system adequacy at all times. These investment cycles are a result of the bounded rationality and myopia experienced by investors, ultimately leading to imperfect forecasting of the revenues of the capacity market. Results of this thesis have shown that in busts of investment cycles, investments in generation capacity can be insufficient to maintain system adequacy.
To conclude, a capacity market can be a viable option in order to maintain system adequacy in the energy transition. However, whether it is the most suitable option to maintain system adequacy is not clear. Novel flexibility options such as storage could potentially serve a significant role in maintaining system adequacy. If additional flexibility options have a lower perceived investment risk compared to flexible generation units, the electricity market can potentially continue to rely on its initial liberalised neoclassical market design whilst maintaining system adequacy. This requires further exploration in future research.
Furthermore, MODO and its capacity market extension fill the need for electricity system models that allow for performing robustness analysis, as it has a relatively low computation burden. Hence, an interesting avenue for future research is to evaluate the effectiveness of a capacity market in many possible futures. ...
Electrification by means of renewable energy sources in electricity production (RES-E) is a key strategy to meet global climate goals established in the Paris Agreement. The increasing share of RES-E as a result of this has significant implications on the functioning of the electricity system and the adequacy of the electricity market design, as these technologies exhibit different characteristics compared to conventional generators. Their intermittent output combined with near negligible marginal costs has an effect on electricity price formation and will lead to an increased demand for flexibility services to level out these fluctuations. These trends will induce an increased investment risk for flexible generation capacity. This increased investment risk feeds into the risk-averse behaviour of investors that is already present due to market failures that exist in electricity markets. It remains unclear whether the liberalised neoclassical electricity market design provides sufficient incentives to invest in generation capacity in order to maintain system adequacy during the energy transition.
One possible market design option that is proposed to address this increased investment risk are Capacity Mechanisms. Capacity Mechanisms allow for an adequate remuneration of (flexible) generation capacity, by providing a steady income stream to firm capacity on top of revenues obtained from selling electricity. The purpose of this thesis is to evaluate the effectiveness of a capacity market - a specification of a capacity mechanism - in maintaining system adequacy in a system with an increasing share of RES-E. Additionally, it aims to fulfil the need for electricity system models that allow for performing robustness analysis as this it is seen as increasingly important to adequately account for uncertainty that is inherently related to the unfolding of the energy transition. This research was carried out by extending an existing quantitative model of the Dutch electricity system, referred to as Myopic Optimisation Detailed Operational (or MODO), with a capacity market. The methodology applied relies on myopic optimisation.
Conceptualisation of the capacity market model is based largely on the design of the NYISO Installed Capacity Market (NYISO-ICAP), as this design is relatively simple whilst being considered a successful capacity market. A thorough literature review on the NYISO-ICAP was performed to establish a profound understanding of real-world capacity market dynamics and how these can be translated to the model. The conceptualisation of the capacity market forms a basis for formalisation and is used to establish the formal rules of the capacity market model complying with the basic structure of linear programming problems. This serves as input for the implementation of the extension of the capacity market in MODO, and thus in Linny-R.
With the resulting model, the effectiveness of a capacity market in maintaining system adequacy was assessed on the performance of several Key Performance Indicators. These encompass the Supply Ratio, the average annual volume of Energy-not-Served [MWh], the average Electricity Price [€/MWh] and the total Consumer Spending [€]. A comparative analysis was performed between the performance of the energy-only market and the capacity market on the identified Key Performance Indicators in four pre-specified scenarios. The four scenarios are varied on key uncertainties underlying the unfolding of the energy transition to explore the robustness of results, such as weather conditions and the level of risk aversion of investors.
From this thesis, it can be concluded that a capacity market can be an effective and robust policy instrument to maintain system adequacy during the energy transition at a lower cost to consumers. This can specifically be seen in the increased number of investments in peaking generators compared to the energy-only market when forecasted revenues of the capacity market are sufficient, which has a positive effect on the electricity system's robustness to different weather conditions. Furthermore, when the level of a risk aversion of investors is high, a capacity market is more robust in ensuring system adequacy compared to an energy-only market.
Nonetheless, a capacity market can be prone to investment cycles which can have a negative influence on the effectiveness of a capacity market to maintain system adequacy at all times. These investment cycles are a result of the bounded rationality and myopia experienced by investors, ultimately leading to imperfect forecasting of the revenues of the capacity market. Results of this thesis have shown that in busts of investment cycles, investments in generation capacity can be insufficient to maintain system adequacy.
To conclude, a capacity market can be a viable option in order to maintain system adequacy in the energy transition. However, whether it is the most suitable option to maintain system adequacy is not clear. Novel flexibility options such as storage could potentially serve a significant role in maintaining system adequacy. If additional flexibility options have a lower perceived investment risk compared to flexible generation units, the electricity market can potentially continue to rely on its initial liberalised neoclassical market design whilst maintaining system adequacy. This requires further exploration in future research.
Furthermore, MODO and its capacity market extension fill the need for electricity system models that allow for performing robustness analysis, as it has a relatively low computation burden. Hence, an interesting avenue for future research is to evaluate the effectiveness of a capacity market in many possible futures.
One possible market design option that is proposed to address this increased investment risk are Capacity Mechanisms. Capacity Mechanisms allow for an adequate remuneration of (flexible) generation capacity, by providing a steady income stream to firm capacity on top of revenues obtained from selling electricity. The purpose of this thesis is to evaluate the effectiveness of a capacity market - a specification of a capacity mechanism - in maintaining system adequacy in a system with an increasing share of RES-E. Additionally, it aims to fulfil the need for electricity system models that allow for performing robustness analysis as this it is seen as increasingly important to adequately account for uncertainty that is inherently related to the unfolding of the energy transition. This research was carried out by extending an existing quantitative model of the Dutch electricity system, referred to as Myopic Optimisation Detailed Operational (or MODO), with a capacity market. The methodology applied relies on myopic optimisation.
Conceptualisation of the capacity market model is based largely on the design of the NYISO Installed Capacity Market (NYISO-ICAP), as this design is relatively simple whilst being considered a successful capacity market. A thorough literature review on the NYISO-ICAP was performed to establish a profound understanding of real-world capacity market dynamics and how these can be translated to the model. The conceptualisation of the capacity market forms a basis for formalisation and is used to establish the formal rules of the capacity market model complying with the basic structure of linear programming problems. This serves as input for the implementation of the extension of the capacity market in MODO, and thus in Linny-R.
With the resulting model, the effectiveness of a capacity market in maintaining system adequacy was assessed on the performance of several Key Performance Indicators. These encompass the Supply Ratio, the average annual volume of Energy-not-Served [MWh], the average Electricity Price [€/MWh] and the total Consumer Spending [€]. A comparative analysis was performed between the performance of the energy-only market and the capacity market on the identified Key Performance Indicators in four pre-specified scenarios. The four scenarios are varied on key uncertainties underlying the unfolding of the energy transition to explore the robustness of results, such as weather conditions and the level of risk aversion of investors.
From this thesis, it can be concluded that a capacity market can be an effective and robust policy instrument to maintain system adequacy during the energy transition at a lower cost to consumers. This can specifically be seen in the increased number of investments in peaking generators compared to the energy-only market when forecasted revenues of the capacity market are sufficient, which has a positive effect on the electricity system's robustness to different weather conditions. Furthermore, when the level of a risk aversion of investors is high, a capacity market is more robust in ensuring system adequacy compared to an energy-only market.
Nonetheless, a capacity market can be prone to investment cycles which can have a negative influence on the effectiveness of a capacity market to maintain system adequacy at all times. These investment cycles are a result of the bounded rationality and myopia experienced by investors, ultimately leading to imperfect forecasting of the revenues of the capacity market. Results of this thesis have shown that in busts of investment cycles, investments in generation capacity can be insufficient to maintain system adequacy.
To conclude, a capacity market can be a viable option in order to maintain system adequacy in the energy transition. However, whether it is the most suitable option to maintain system adequacy is not clear. Novel flexibility options such as storage could potentially serve a significant role in maintaining system adequacy. If additional flexibility options have a lower perceived investment risk compared to flexible generation units, the electricity market can potentially continue to rely on its initial liberalised neoclassical market design whilst maintaining system adequacy. This requires further exploration in future research.
Furthermore, MODO and its capacity market extension fill the need for electricity system models that allow for performing robustness analysis, as it has a relatively low computation burden. Hence, an interesting avenue for future research is to evaluate the effectiveness of a capacity market in many possible futures.
Because of the climate crisis the world is facing, all sectors must move towards a more sustainable future. In the Netherlands, the industry sector emits large amounts of CO2 because of the heavy reliance on fossil-fuels and large electricity demand. The Paris agreement and the Green Deal forces the industry sector to rapidly transition towards more sustainable practices, which can be achieved if the correct synergies are established. The multi-actor nature and the technical and operational dependencies that industrial clusters have, makes optimal decision-making extremely important in working towards that net zero future. Innovations that re- duce the emissions are limited, but they exist and provide (intermediary) solutions to adhere with the imposed regulations. Furthermore, the industrial clusters are subject to numerous different factors that influences the behaviour of actors within the cluster. The issue faced is to understand how the transition of an industrial cluster is influenced by exogenous factors and how actors in such an integrated environment should invest, while keeping in mind that both the industrial clusters and the individual actors have to remain profitable and have differing investment behaviour. Current studies fall short in identifying, analysing and understanding how multi-actors in an institutional en- vironment relate to the technical options and the exogenous factors engaging with that system in an industrial setting. Identifying an optimal investment trajectory that such an industrial cluster should follow to adhere with the regulations whilst staying profitable with a multi-actor configuration requires different integrated methods and other tools. This thesis will address that problem through the following main research question: What is the effect of multiple exogenous factors on the optimal investment trajectories of indus- trial clusters in the Netherlands with multiple investment options? This question has been answered through exploratory research combined with a modelling approach. The first step in the exploratory research required attaining insights in what the current state of is multi-actor invest- ments. This in combination with looking at how they are structured contractually has been the main foundation for the literature review. A case study analysis has been conducted of an industrial cluster located in Geleen, called Chemelot. This allowed for analysing decarbonizing investment options and looking at what exogenous factors influence the industrial cluster. The investment options attempt to move Chemelot away from fossil-fuels as energy source and move towards lower emitting sources such as electricity. The information gathered served as an input to put together a methodology which captured the exogenous factors, interactions in the industrial cluster and the sustainable investment options in an optimization model. Using this, the optimization model was constructed using Linny-R, a Mixed Integer Linear Programming op- timization software developed by Dr. P.W.G. Bots. The model produced quantifiable results as well as serve as a proof of concept for the methodology that is presented to incorporate exogenous factors and sustainable investment options in the energy transition. The model also gives insights in what effect the exogenous factors have on the investment behaviour of actors within industrial clusters by showing cash flows, both individually and collectively and through an investment curve for the cluster. The economic performance is considered to be the leading metric in this research. Finally, using the results, the implications of implementing exogenous factors and sustainable investment options in an optimization model are discussed. The research outcomes show that CCS and electrification are favourable investments for Chemelot to remain profitable and cope with increasing prices of commodities and CO2. The model results has provided insights in the effect that exogenous factors have on the energy transition of an industrial cluster. More specifically, it showed that a cap on CO2 and increasing the price of CO2 emitted really accelerates the rate at which actors make investments in sustainable options. Added to that, increasing the price of other commodities such as natural gas and naphtha also forces actors to move away from those commodities and look for alternatives that provide the same product or energy without having to compensate the CO2 emissions related to them. Other factors such as limited infrastructure restrict industrial clusters in the amount of products they can produce and restrict them from innovating towards less emitting processes. Electrification of certain processes is possible, but increases the electricity demand with enormous amounts. With limited electricity infrastructure to provide those amounts, the innovation cannot be realized and therefore, halting the transition for an industrial cluster. The findings suggest that because of these exogenous factors, the actors will move towards electricity demanding innovations that do not make use of fossil-fuels and invest in CCS options. However, the methodology presented should be used as directing future research in looking at how industrial clusters should engage in the energy transition. The dependencies existing between actors in an industrial clusters and how different investments II may adjust these dependencies are identifiable using the methodology presented in this research. Ending with a general conclusion, the investment behaviour of industrial cluster changes because of multiple exogenous factors that influence the system. The increase in commodity prices combined with CO2 capping reg- ulations cause actors to invest more quickly into sustainable investment options to adhere with the regulations, but it also causes them to compromise their production levels in some scenarios to remain profitable. Actors downstream are dependent on the investments made by actors upstream since they rely on the output of those upstream actors to develop the final products that they sale. This combination generates valuable insights from a systems perspective but also from a multi-actor perspective.
...
Because of the climate crisis the world is facing, all sectors must move towards a more sustainable future. In the Netherlands, the industry sector emits large amounts of CO2 because of the heavy reliance on fossil-fuels and large electricity demand. The Paris agreement and the Green Deal forces the industry sector to rapidly transition towards more sustainable practices, which can be achieved if the correct synergies are established. The multi-actor nature and the technical and operational dependencies that industrial clusters have, makes optimal decision-making extremely important in working towards that net zero future. Innovations that re- duce the emissions are limited, but they exist and provide (intermediary) solutions to adhere with the imposed regulations. Furthermore, the industrial clusters are subject to numerous different factors that influences the behaviour of actors within the cluster. The issue faced is to understand how the transition of an industrial cluster is influenced by exogenous factors and how actors in such an integrated environment should invest, while keeping in mind that both the industrial clusters and the individual actors have to remain profitable and have differing investment behaviour. Current studies fall short in identifying, analysing and understanding how multi-actors in an institutional en- vironment relate to the technical options and the exogenous factors engaging with that system in an industrial setting. Identifying an optimal investment trajectory that such an industrial cluster should follow to adhere with the regulations whilst staying profitable with a multi-actor configuration requires different integrated methods and other tools. This thesis will address that problem through the following main research question: What is the effect of multiple exogenous factors on the optimal investment trajectories of indus- trial clusters in the Netherlands with multiple investment options? This question has been answered through exploratory research combined with a modelling approach. The first step in the exploratory research required attaining insights in what the current state of is multi-actor invest- ments. This in combination with looking at how they are structured contractually has been the main foundation for the literature review. A case study analysis has been conducted of an industrial cluster located in Geleen, called Chemelot. This allowed for analysing decarbonizing investment options and looking at what exogenous factors influence the industrial cluster. The investment options attempt to move Chemelot away from fossil-fuels as energy source and move towards lower emitting sources such as electricity. The information gathered served as an input to put together a methodology which captured the exogenous factors, interactions in the industrial cluster and the sustainable investment options in an optimization model. Using this, the optimization model was constructed using Linny-R, a Mixed Integer Linear Programming op- timization software developed by Dr. P.W.G. Bots. The model produced quantifiable results as well as serve as a proof of concept for the methodology that is presented to incorporate exogenous factors and sustainable investment options in the energy transition. The model also gives insights in what effect the exogenous factors have on the investment behaviour of actors within industrial clusters by showing cash flows, both individually and collectively and through an investment curve for the cluster. The economic performance is considered to be the leading metric in this research. Finally, using the results, the implications of implementing exogenous factors and sustainable investment options in an optimization model are discussed. The research outcomes show that CCS and electrification are favourable investments for Chemelot to remain profitable and cope with increasing prices of commodities and CO2. The model results has provided insights in the effect that exogenous factors have on the energy transition of an industrial cluster. More specifically, it showed that a cap on CO2 and increasing the price of CO2 emitted really accelerates the rate at which actors make investments in sustainable options. Added to that, increasing the price of other commodities such as natural gas and naphtha also forces actors to move away from those commodities and look for alternatives that provide the same product or energy without having to compensate the CO2 emissions related to them. Other factors such as limited infrastructure restrict industrial clusters in the amount of products they can produce and restrict them from innovating towards less emitting processes. Electrification of certain processes is possible, but increases the electricity demand with enormous amounts. With limited electricity infrastructure to provide those amounts, the innovation cannot be realized and therefore, halting the transition for an industrial cluster. The findings suggest that because of these exogenous factors, the actors will move towards electricity demanding innovations that do not make use of fossil-fuels and invest in CCS options. However, the methodology presented should be used as directing future research in looking at how industrial clusters should engage in the energy transition. The dependencies existing between actors in an industrial clusters and how different investments II may adjust these dependencies are identifiable using the methodology presented in this research. Ending with a general conclusion, the investment behaviour of industrial cluster changes because of multiple exogenous factors that influence the system. The increase in commodity prices combined with CO2 capping reg- ulations cause actors to invest more quickly into sustainable investment options to adhere with the regulations, but it also causes them to compromise their production levels in some scenarios to remain profitable. Actors downstream are dependent on the investments made by actors upstream since they rely on the output of those upstream actors to develop the final products that they sale. This combination generates valuable insights from a systems perspective but also from a multi-actor perspective.
Technology adoption at the PoR chlorine cluster
A study on the effect of market & behavioural barriers on technology adoption at the Port of Rotterdam chlorine cluster
In order for the Netherlands
to reach CO2 neutrality by 2050, large investments in zero emission
technologies are needed. These investments would comprise out of renewable
energy generation, higher energy efficiency alternatives and electrification of
end uses. Although climate mitigation has become an ever growing societal
concern since the ratification of "het klimaatakkoord", progress in
the industrial sector has been seriously lagging in the Netherlands. In 2021
the reduction of CO2 emissions in the Netherlands stagnated and the emissions
of the industrial sector actually slightly rose. So too in the Port of
Rotterdam, where the chlorine cluster is not showing any significant CO2
reductions. There are several potential explanations why there is a gap between
what should be invested and what actually is invested. They define the gap as,
’the apparent reality that some technologies that would pay off for adopters
are, nevertheless, not adopted’. So, why do decision makers under invest in
zero emission technologies? The explanations of fall into two broad categories:
•Market barriers
•Behavioural barriers
Following the problem situation, this thesis
focused on researching to what extent market & behavioural barriers
contribute to the investment gap in the Port of Rotterdam Chlorine cluster, by
incorporating both market and behavioural barriers in a quantitative investment
model. The incorporation of these barriers in the model result in a range of
investment types, some of them non-optimal and varying in perspectives on
valuing the future. By simulating technology adoption under the assumption of
this range of varying configurations of market and behavioural barriers, it is
possible to determine the effect of those two categories of barriers on
technology adoption at the PoR chlorine cluster. The obtained insights of this
research feed into the larger study of quantitative decision models for the
industrial sector. Where the following main research question is answered:
What is the effect of market and behavioural
barriers on zero emission technology adoption at the PoR chlorine cluster?
To answer the main research question, the model
represents the PoR chlorine cluster on a highly detailed level and bases it's
technical system's configuration on a thorough plant-process-product &
zero-emission technology inventarisation. These two inventarisations give the
current and possible future configuration of the PoR chlorine cluster's
technical system. Consequently, this makes it possible to explicitly model the
technology stock at the chlorine cluster and determine technology adoption on
an asset level. The market & behavioural barriers are represented by 8
evaluation types. These evaluation types represent 8 varying configurations of
the market & behavioural barriers. Consequently, a scenario analysis was
conducted with the model.
The scenario analysis resulted in transition
pathways, total CO2 emissions and total cash flows between 2022-2050. The model
results show that the incorporation of market and behavioural barriers lead to
postponed adoption of zero emission technology adoption. This is reflected by
the lower number of years that these alternatives are installed between 2022
and 2050. The lower adoption lead to an increase of 288% total CO2 emissions
between 2022-2050, compared to the optimal solution. Underneath, the key
findings from the model results are presented…
...
In order for the Netherlands
to reach CO2 neutrality by 2050, large investments in zero emission
technologies are needed. These investments would comprise out of renewable
energy generation, higher energy efficiency alternatives and electrification of
end uses. Although climate mitigation has become an ever growing societal
concern since the ratification of "het klimaatakkoord", progress in
the industrial sector has been seriously lagging in the Netherlands. In 2021
the reduction of CO2 emissions in the Netherlands stagnated and the emissions
of the industrial sector actually slightly rose. So too in the Port of
Rotterdam, where the chlorine cluster is not showing any significant CO2
reductions. There are several potential explanations why there is a gap between
what should be invested and what actually is invested. They define the gap as,
’the apparent reality that some technologies that would pay off for adopters
are, nevertheless, not adopted’. So, why do decision makers under invest in
zero emission technologies? The explanations of fall into two broad categories:
•Market barriers
•Behavioural barriers
Following the problem situation, this thesis
focused on researching to what extent market & behavioural barriers
contribute to the investment gap in the Port of Rotterdam Chlorine cluster, by
incorporating both market and behavioural barriers in a quantitative investment
model. The incorporation of these barriers in the model result in a range of
investment types, some of them non-optimal and varying in perspectives on
valuing the future. By simulating technology adoption under the assumption of
this range of varying configurations of market and behavioural barriers, it is
possible to determine the effect of those two categories of barriers on
technology adoption at the PoR chlorine cluster. The obtained insights of this
research feed into the larger study of quantitative decision models for the
industrial sector. Where the following main research question is answered:
What is the effect of market and behavioural
barriers on zero emission technology adoption at the PoR chlorine cluster?
To answer the main research question, the model
represents the PoR chlorine cluster on a highly detailed level and bases it's
technical system's configuration on a thorough plant-process-product &
zero-emission technology inventarisation. These two inventarisations give the
current and possible future configuration of the PoR chlorine cluster's
technical system. Consequently, this makes it possible to explicitly model the
technology stock at the chlorine cluster and determine technology adoption on
an asset level. The market & behavioural barriers are represented by 8
evaluation types. These evaluation types represent 8 varying configurations of
the market & behavioural barriers. Consequently, a scenario analysis was
conducted with the model.
The scenario analysis resulted in transition
pathways, total CO2 emissions and total cash flows between 2022-2050. The model
results show that the incorporation of market and behavioural barriers lead to
postponed adoption of zero emission technology adoption. This is reflected by
the lower number of years that these alternatives are installed between 2022
and 2050. The lower adoption lead to an increase of 288% total CO2 emissions
between 2022-2050, compared to the optimal solution. Underneath, the key
findings from the model results are presented…
The urgency for decarbonization of the energy grid is ever increasing and fossil energy sources must be replaced by sustainable sources of energy to prevent detrimental levels of global warming. Renewable energy sources wind and solar show potential and contribute to decarbonization but are intermitted and require storage solutions that are exposed to further constrictions and costs. Nuclear fusion is regarded as the most promising new energy source since scientists first learned about it and in theory poses the ideal properties to meet the needs of the world's future energy system. Despite fusion’s potential as the ideal energy source for innovating and decarbonizing the energy system, the reality is that 70 years of research and development have not yet resulted in fusion energy on the grid. Overpromises by fusion scientists since the seventies have resulted in scepticism and cynicism towards fusion development with the longstanding quip that “Fusion energy is thirty years away…and always will be”. Nevertheless, 2021 saw many important breakthroughs and achievements in fusion research which resulted in it being titled “The best year in the history of fusion development” and led to revived optimism and claims that fusion development is moving from the lab to the grid and commercialization is closer than ever before.
In order to truly understand the process of commercializing fusion energy, the barriers need to be known and understood so that these can be addressed specifically, and the process can be accelerated. Adding to that is, that there are also numerous different technical approaches to fusion, each with their own characteristics. However, currently comprehensive knowledge of these barriers is missing. Information is highly scattered as it is focusses on specific topics of fusion development, mostly the scientific or technical barriers. Adding to that is that most information is on separate technologies or specific experiments. As a result, there is a severe lack of knowledge: it is unknown what all the barriers towards commercialization are, how these barriers differ in severity and how they differ amongst the numerous fusion technologies. In an attempt to tackle this knowledge gap and enable a better understanding of the fusion development, the objective of this research was to develop a comprehensive list of barriers and subsequently study and assess this list for the different approaches to fusion with the intention of increasing the understanding of the pathway for commercialization of fusion energy.
Before starting this endeavour, a conceptual analysis was performed to define the concepts “barrier” and “commercialization”. Using these definitions an extensive literature study was performed, alongside 23 semi-structured interviews with almost all the leading fusion institutes and companies. Using predefined selection criteria to deal with the vast amounts of information, a list of fifteen relevant barriers was identified. The barriers described in literature were complemented and extended by empirical experiences and practical examples obtained in the interviews, resulting in a manageable but comprehensive list of fusion barriers towards commercialization, including several barriers that have received very little attention to date.
In an attempt to gain further insight into these barriers and research how these are different for the various approaches to fusion, a methodology was developed to assess the barriers in a standardized way. Based on the principles of the Y-factor method developed by (Chappin et al., 2020), a customized framework was developed for the commercialization of fusion energy technologies. Each barrier was concisely described and subsequently the identified barriers were organized into five categories: Technology, Operation, Cost & Financing, Governance and Engineering. The framework assesses the barriers on a tripartite scale, scoring a value of 0 indicating no barrier, 1; indicating a potential barrier and 2; indicating a significant barrier. For every barrier the scoring criteria were detailed to allow for accurate scoring.
The five most developed and pursued technical approaches to fusion (Tokamak, Spherical Tokamak, Stellarator, Field Reversed Configuration and Inertial Confinement Fusion) were assessed using the designed framework. This was done by three sperate expert interviews. These respondents were selected because they all had high expertise of both fusion energy and experience within the fusion industry and hence contribute to the validity of the research. Analysis of the results lead to numerous interesting findings
• Barriers generally apply to all technologies: Although the difference between the fusion technologies were identified and acknowledged by the various respondents, this did not result in notable differences in the scoring of these technologies. Instead, most barriers apply in a similar severity for all technologies.
• Experts disagree on fundamental barriers: Two of the respondents disagreed strongly on the scoring of several barriers, such as “Plasma physics”, “Radiation shielding” and “Energy production”. The fact that these respondents both have a PHD in plasma physics, demonstrates the uncertainty of fusion development and underlines the complexity and difficulty of predicting the pathway of fusion technologies. In this particular case the differences mostly originated from the reasoning of the respondents; one argued more from a theoretical point of view while the other purely looked at results to date, exposing that the framework can be interpreted differently by different respondents.
• Barriers have a strong time element: The abovementioned disagreements can be partially explained by time. The application of the framework exposed that nearly all barriers are characterized by a strong time dependency and that the barrier value is heavily dependent on the timeframe it is evaluated in. Fusion technology is still under development and while an active effort was made to describe the scoring criteria as closely as possible during the synthesis of the framework, the time dependency and the interpretability that comes with it could not be eliminated
• Hierarchy within barriers: The application of the framework also exposed a certain degree of hierarchy within the barriers and found that there was an order of urgency within the barrier categories. A clear and logical pathway could be observed; firstly the “Technology” barriers must be resolved, afterwards the category of “Operation” barriers become most urgent and finally the “Engineering” category. This was substantiated by the scores as these categories received the highest scores. The remaining categories “Governance” and “Cost & Financing” are present throughout the entire innovation pathway.
All in all, the research has three main contributions. The first contribution is the identification of a comprehensive list of fifteen barriers that is validated by experts, can be used to assess all fusion technologies and captures the complete commercialization pathway. Secondly, the developed framework is the first tool that can be used to uniformly assess these barriers and compare them amongst different technologies. Finally, application of the framework increased understanding of the time-dependency and hierarchy of the barriers. Despite the limited value of the quantitative output, the qualitative findings have certainly increased understanding of the barriers and complexity of fusion energy development and showed that the use of the method can enable insightful discussions.
It should be noted that in spite of continuous attempts at safeguarding the validity of the research, there are a number of limitations that should be taken into account when interpreting the research and its results. The development of the scoring criteria is subjective and can be interpreted differently by different respondents, despite the effort to formulate these with a high accuracy and clarity. Simultaneously, because the barrier definitions and scoring criteria are newly designed, these are also constrained by the perception and interpretation of the researcher. Lastly it is important to note that the application of the designed framework was limited to only 3 respondents and the outcomes are therefore based on a small sample size. The overall results of applying and scoring the framework is greatly determined by the individual views and can’t be generalized.
...
In order to truly understand the process of commercializing fusion energy, the barriers need to be known and understood so that these can be addressed specifically, and the process can be accelerated. Adding to that is, that there are also numerous different technical approaches to fusion, each with their own characteristics. However, currently comprehensive knowledge of these barriers is missing. Information is highly scattered as it is focusses on specific topics of fusion development, mostly the scientific or technical barriers. Adding to that is that most information is on separate technologies or specific experiments. As a result, there is a severe lack of knowledge: it is unknown what all the barriers towards commercialization are, how these barriers differ in severity and how they differ amongst the numerous fusion technologies. In an attempt to tackle this knowledge gap and enable a better understanding of the fusion development, the objective of this research was to develop a comprehensive list of barriers and subsequently study and assess this list for the different approaches to fusion with the intention of increasing the understanding of the pathway for commercialization of fusion energy.
Before starting this endeavour, a conceptual analysis was performed to define the concepts “barrier” and “commercialization”. Using these definitions an extensive literature study was performed, alongside 23 semi-structured interviews with almost all the leading fusion institutes and companies. Using predefined selection criteria to deal with the vast amounts of information, a list of fifteen relevant barriers was identified. The barriers described in literature were complemented and extended by empirical experiences and practical examples obtained in the interviews, resulting in a manageable but comprehensive list of fusion barriers towards commercialization, including several barriers that have received very little attention to date.
In an attempt to gain further insight into these barriers and research how these are different for the various approaches to fusion, a methodology was developed to assess the barriers in a standardized way. Based on the principles of the Y-factor method developed by (Chappin et al., 2020), a customized framework was developed for the commercialization of fusion energy technologies. Each barrier was concisely described and subsequently the identified barriers were organized into five categories: Technology, Operation, Cost & Financing, Governance and Engineering. The framework assesses the barriers on a tripartite scale, scoring a value of 0 indicating no barrier, 1; indicating a potential barrier and 2; indicating a significant barrier. For every barrier the scoring criteria were detailed to allow for accurate scoring.
The five most developed and pursued technical approaches to fusion (Tokamak, Spherical Tokamak, Stellarator, Field Reversed Configuration and Inertial Confinement Fusion) were assessed using the designed framework. This was done by three sperate expert interviews. These respondents were selected because they all had high expertise of both fusion energy and experience within the fusion industry and hence contribute to the validity of the research. Analysis of the results lead to numerous interesting findings
• Barriers generally apply to all technologies: Although the difference between the fusion technologies were identified and acknowledged by the various respondents, this did not result in notable differences in the scoring of these technologies. Instead, most barriers apply in a similar severity for all technologies.
• Experts disagree on fundamental barriers: Two of the respondents disagreed strongly on the scoring of several barriers, such as “Plasma physics”, “Radiation shielding” and “Energy production”. The fact that these respondents both have a PHD in plasma physics, demonstrates the uncertainty of fusion development and underlines the complexity and difficulty of predicting the pathway of fusion technologies. In this particular case the differences mostly originated from the reasoning of the respondents; one argued more from a theoretical point of view while the other purely looked at results to date, exposing that the framework can be interpreted differently by different respondents.
• Barriers have a strong time element: The abovementioned disagreements can be partially explained by time. The application of the framework exposed that nearly all barriers are characterized by a strong time dependency and that the barrier value is heavily dependent on the timeframe it is evaluated in. Fusion technology is still under development and while an active effort was made to describe the scoring criteria as closely as possible during the synthesis of the framework, the time dependency and the interpretability that comes with it could not be eliminated
• Hierarchy within barriers: The application of the framework also exposed a certain degree of hierarchy within the barriers and found that there was an order of urgency within the barrier categories. A clear and logical pathway could be observed; firstly the “Technology” barriers must be resolved, afterwards the category of “Operation” barriers become most urgent and finally the “Engineering” category. This was substantiated by the scores as these categories received the highest scores. The remaining categories “Governance” and “Cost & Financing” are present throughout the entire innovation pathway.
All in all, the research has three main contributions. The first contribution is the identification of a comprehensive list of fifteen barriers that is validated by experts, can be used to assess all fusion technologies and captures the complete commercialization pathway. Secondly, the developed framework is the first tool that can be used to uniformly assess these barriers and compare them amongst different technologies. Finally, application of the framework increased understanding of the time-dependency and hierarchy of the barriers. Despite the limited value of the quantitative output, the qualitative findings have certainly increased understanding of the barriers and complexity of fusion energy development and showed that the use of the method can enable insightful discussions.
It should be noted that in spite of continuous attempts at safeguarding the validity of the research, there are a number of limitations that should be taken into account when interpreting the research and its results. The development of the scoring criteria is subjective and can be interpreted differently by different respondents, despite the effort to formulate these with a high accuracy and clarity. Simultaneously, because the barrier definitions and scoring criteria are newly designed, these are also constrained by the perception and interpretation of the researcher. Lastly it is important to note that the application of the designed framework was limited to only 3 respondents and the outcomes are therefore based on a small sample size. The overall results of applying and scoring the framework is greatly determined by the individual views and can’t be generalized.
...
The urgency for decarbonization of the energy grid is ever increasing and fossil energy sources must be replaced by sustainable sources of energy to prevent detrimental levels of global warming. Renewable energy sources wind and solar show potential and contribute to decarbonization but are intermitted and require storage solutions that are exposed to further constrictions and costs. Nuclear fusion is regarded as the most promising new energy source since scientists first learned about it and in theory poses the ideal properties to meet the needs of the world's future energy system. Despite fusion’s potential as the ideal energy source for innovating and decarbonizing the energy system, the reality is that 70 years of research and development have not yet resulted in fusion energy on the grid. Overpromises by fusion scientists since the seventies have resulted in scepticism and cynicism towards fusion development with the longstanding quip that “Fusion energy is thirty years away…and always will be”. Nevertheless, 2021 saw many important breakthroughs and achievements in fusion research which resulted in it being titled “The best year in the history of fusion development” and led to revived optimism and claims that fusion development is moving from the lab to the grid and commercialization is closer than ever before.
In order to truly understand the process of commercializing fusion energy, the barriers need to be known and understood so that these can be addressed specifically, and the process can be accelerated. Adding to that is, that there are also numerous different technical approaches to fusion, each with their own characteristics. However, currently comprehensive knowledge of these barriers is missing. Information is highly scattered as it is focusses on specific topics of fusion development, mostly the scientific or technical barriers. Adding to that is that most information is on separate technologies or specific experiments. As a result, there is a severe lack of knowledge: it is unknown what all the barriers towards commercialization are, how these barriers differ in severity and how they differ amongst the numerous fusion technologies. In an attempt to tackle this knowledge gap and enable a better understanding of the fusion development, the objective of this research was to develop a comprehensive list of barriers and subsequently study and assess this list for the different approaches to fusion with the intention of increasing the understanding of the pathway for commercialization of fusion energy.
Before starting this endeavour, a conceptual analysis was performed to define the concepts “barrier” and “commercialization”. Using these definitions an extensive literature study was performed, alongside 23 semi-structured interviews with almost all the leading fusion institutes and companies. Using predefined selection criteria to deal with the vast amounts of information, a list of fifteen relevant barriers was identified. The barriers described in literature were complemented and extended by empirical experiences and practical examples obtained in the interviews, resulting in a manageable but comprehensive list of fusion barriers towards commercialization, including several barriers that have received very little attention to date.
In an attempt to gain further insight into these barriers and research how these are different for the various approaches to fusion, a methodology was developed to assess the barriers in a standardized way. Based on the principles of the Y-factor method developed by (Chappin et al., 2020), a customized framework was developed for the commercialization of fusion energy technologies. Each barrier was concisely described and subsequently the identified barriers were organized into five categories: Technology, Operation, Cost & Financing, Governance and Engineering. The framework assesses the barriers on a tripartite scale, scoring a value of 0 indicating no barrier, 1; indicating a potential barrier and 2; indicating a significant barrier. For every barrier the scoring criteria were detailed to allow for accurate scoring.
The five most developed and pursued technical approaches to fusion (Tokamak, Spherical Tokamak, Stellarator, Field Reversed Configuration and Inertial Confinement Fusion) were assessed using the designed framework. This was done by three sperate expert interviews. These respondents were selected because they all had high expertise of both fusion energy and experience within the fusion industry and hence contribute to the validity of the research. Analysis of the results lead to numerous interesting findings
• Barriers generally apply to all technologies: Although the difference between the fusion technologies were identified and acknowledged by the various respondents, this did not result in notable differences in the scoring of these technologies. Instead, most barriers apply in a similar severity for all technologies.
• Experts disagree on fundamental barriers: Two of the respondents disagreed strongly on the scoring of several barriers, such as “Plasma physics”, “Radiation shielding” and “Energy production”. The fact that these respondents both have a PHD in plasma physics, demonstrates the uncertainty of fusion development and underlines the complexity and difficulty of predicting the pathway of fusion technologies. In this particular case the differences mostly originated from the reasoning of the respondents; one argued more from a theoretical point of view while the other purely looked at results to date, exposing that the framework can be interpreted differently by different respondents.
• Barriers have a strong time element: The abovementioned disagreements can be partially explained by time. The application of the framework exposed that nearly all barriers are characterized by a strong time dependency and that the barrier value is heavily dependent on the timeframe it is evaluated in. Fusion technology is still under development and while an active effort was made to describe the scoring criteria as closely as possible during the synthesis of the framework, the time dependency and the interpretability that comes with it could not be eliminated
• Hierarchy within barriers: The application of the framework also exposed a certain degree of hierarchy within the barriers and found that there was an order of urgency within the barrier categories. A clear and logical pathway could be observed; firstly the “Technology” barriers must be resolved, afterwards the category of “Operation” barriers become most urgent and finally the “Engineering” category. This was substantiated by the scores as these categories received the highest scores. The remaining categories “Governance” and “Cost & Financing” are present throughout the entire innovation pathway.
All in all, the research has three main contributions. The first contribution is the identification of a comprehensive list of fifteen barriers that is validated by experts, can be used to assess all fusion technologies and captures the complete commercialization pathway. Secondly, the developed framework is the first tool that can be used to uniformly assess these barriers and compare them amongst different technologies. Finally, application of the framework increased understanding of the time-dependency and hierarchy of the barriers. Despite the limited value of the quantitative output, the qualitative findings have certainly increased understanding of the barriers and complexity of fusion energy development and showed that the use of the method can enable insightful discussions.
It should be noted that in spite of continuous attempts at safeguarding the validity of the research, there are a number of limitations that should be taken into account when interpreting the research and its results. The development of the scoring criteria is subjective and can be interpreted differently by different respondents, despite the effort to formulate these with a high accuracy and clarity. Simultaneously, because the barrier definitions and scoring criteria are newly designed, these are also constrained by the perception and interpretation of the researcher. Lastly it is important to note that the application of the designed framework was limited to only 3 respondents and the outcomes are therefore based on a small sample size. The overall results of applying and scoring the framework is greatly determined by the individual views and can’t be generalized.
In order to truly understand the process of commercializing fusion energy, the barriers need to be known and understood so that these can be addressed specifically, and the process can be accelerated. Adding to that is, that there are also numerous different technical approaches to fusion, each with their own characteristics. However, currently comprehensive knowledge of these barriers is missing. Information is highly scattered as it is focusses on specific topics of fusion development, mostly the scientific or technical barriers. Adding to that is that most information is on separate technologies or specific experiments. As a result, there is a severe lack of knowledge: it is unknown what all the barriers towards commercialization are, how these barriers differ in severity and how they differ amongst the numerous fusion technologies. In an attempt to tackle this knowledge gap and enable a better understanding of the fusion development, the objective of this research was to develop a comprehensive list of barriers and subsequently study and assess this list for the different approaches to fusion with the intention of increasing the understanding of the pathway for commercialization of fusion energy.
Before starting this endeavour, a conceptual analysis was performed to define the concepts “barrier” and “commercialization”. Using these definitions an extensive literature study was performed, alongside 23 semi-structured interviews with almost all the leading fusion institutes and companies. Using predefined selection criteria to deal with the vast amounts of information, a list of fifteen relevant barriers was identified. The barriers described in literature were complemented and extended by empirical experiences and practical examples obtained in the interviews, resulting in a manageable but comprehensive list of fusion barriers towards commercialization, including several barriers that have received very little attention to date.
In an attempt to gain further insight into these barriers and research how these are different for the various approaches to fusion, a methodology was developed to assess the barriers in a standardized way. Based on the principles of the Y-factor method developed by (Chappin et al., 2020), a customized framework was developed for the commercialization of fusion energy technologies. Each barrier was concisely described and subsequently the identified barriers were organized into five categories: Technology, Operation, Cost & Financing, Governance and Engineering. The framework assesses the barriers on a tripartite scale, scoring a value of 0 indicating no barrier, 1; indicating a potential barrier and 2; indicating a significant barrier. For every barrier the scoring criteria were detailed to allow for accurate scoring.
The five most developed and pursued technical approaches to fusion (Tokamak, Spherical Tokamak, Stellarator, Field Reversed Configuration and Inertial Confinement Fusion) were assessed using the designed framework. This was done by three sperate expert interviews. These respondents were selected because they all had high expertise of both fusion energy and experience within the fusion industry and hence contribute to the validity of the research. Analysis of the results lead to numerous interesting findings
• Barriers generally apply to all technologies: Although the difference between the fusion technologies were identified and acknowledged by the various respondents, this did not result in notable differences in the scoring of these technologies. Instead, most barriers apply in a similar severity for all technologies.
• Experts disagree on fundamental barriers: Two of the respondents disagreed strongly on the scoring of several barriers, such as “Plasma physics”, “Radiation shielding” and “Energy production”. The fact that these respondents both have a PHD in plasma physics, demonstrates the uncertainty of fusion development and underlines the complexity and difficulty of predicting the pathway of fusion technologies. In this particular case the differences mostly originated from the reasoning of the respondents; one argued more from a theoretical point of view while the other purely looked at results to date, exposing that the framework can be interpreted differently by different respondents.
• Barriers have a strong time element: The abovementioned disagreements can be partially explained by time. The application of the framework exposed that nearly all barriers are characterized by a strong time dependency and that the barrier value is heavily dependent on the timeframe it is evaluated in. Fusion technology is still under development and while an active effort was made to describe the scoring criteria as closely as possible during the synthesis of the framework, the time dependency and the interpretability that comes with it could not be eliminated
• Hierarchy within barriers: The application of the framework also exposed a certain degree of hierarchy within the barriers and found that there was an order of urgency within the barrier categories. A clear and logical pathway could be observed; firstly the “Technology” barriers must be resolved, afterwards the category of “Operation” barriers become most urgent and finally the “Engineering” category. This was substantiated by the scores as these categories received the highest scores. The remaining categories “Governance” and “Cost & Financing” are present throughout the entire innovation pathway.
All in all, the research has three main contributions. The first contribution is the identification of a comprehensive list of fifteen barriers that is validated by experts, can be used to assess all fusion technologies and captures the complete commercialization pathway. Secondly, the developed framework is the first tool that can be used to uniformly assess these barriers and compare them amongst different technologies. Finally, application of the framework increased understanding of the time-dependency and hierarchy of the barriers. Despite the limited value of the quantitative output, the qualitative findings have certainly increased understanding of the barriers and complexity of fusion energy development and showed that the use of the method can enable insightful discussions.
It should be noted that in spite of continuous attempts at safeguarding the validity of the research, there are a number of limitations that should be taken into account when interpreting the research and its results. The development of the scoring criteria is subjective and can be interpreted differently by different respondents, despite the effort to formulate these with a high accuracy and clarity. Simultaneously, because the barrier definitions and scoring criteria are newly designed, these are also constrained by the perception and interpretation of the researcher. Lastly it is important to note that the application of the designed framework was limited to only 3 respondents and the outcomes are therefore based on a small sample size. The overall results of applying and scoring the framework is greatly determined by the individual views and can’t be generalized.
Modelling of Global Energy Demand in the Transportation Sector
A Country by Country Approach
This study deals with the modelling of the future global energy demand in
the transportation sector, how and in what extend this could be limited and
which low carbon fuels and technologies could contribute to the mitigation of
the energy demand. In the introduction of the current report, the greenhouse
effect, the global warming and the climate change are defined. Then, the
actions taken and the agreements made to deal with global warming and
climate change are presented and the need for combating them is highlighted.
After the introduction, a literature review is conducted in order to spot
the knowledge gap and formulate the research questions. In the literature
review, the ICCT’s and IEA’s studies are mainly discussed due to their high
quality research and the big amount of published reports. More specifically,
their models, scenarios, policies and results are discussed in detail in
order to accurately define the knowledge gap and formulate the research
questions. Thus, the goal and the main research question of this research
is to answer how could the future global energy demand be mitigated and
which low carbon fuels and technologies could significantly penetrate into
the transportation sector.
In the beginning of this research, the conceptual and the theoretical
frameworks are presented in order to assist with the outline of the thesis
and create the theoretical background for the development of the model.
Then, in order for the research questions to be answered, a forecasting model
calculating the transportation future energy demand by country, transport
mode, technology and energy carrier throughout the period 2015-2050 is developed.
The main features that distinguish this model from models used in
similar studies are the strong focus on the diffusion of low carbon fuels and
technologies, the use of a different diffusion model (the Bass s-curve) and
the country by country with one-year time increments approach.
After the model is verified and validated through comparison with similar
studies and a sensitivity analysis, results are presented for two different
scenarios. The first scenario is called Current Policies scenario and aims to
show a potential pathway of the future global energy demand in the transportation
sector that could happen if no more policies are applied after 2020
and the second scenario is called Accelerated Policies scenario and its target
is to represent a pathway that could happen if new policies are adopted and
stricter implementation is applied.
The results of the two scenarios show that it is possible to achieve a
bending of the energy demand and a diffusion enhancement of low carbon
fuels and technologies if new and stricter policies that motivate technology
improvements and fossil vehicles ban are applied. In particular, the results
indicate that the implementation of new and stricter policies, which could
lead to efficiency improvements, and to more effective diffusion of low carbon
fuels and technologies could achieve reduction of the energy demand after 2029. Moreover, according to the results, electricity is expected to dominate in the transportation sector, while biofuels, hydrogen and ammonia are also
expected to be highly used. However, without further policy action, the
global energy demand is expected to follow a constantly increasing trend
in the future while the penetration of low carbon fuels is expected to be
significantly lower. ...
the transportation sector, how and in what extend this could be limited and
which low carbon fuels and technologies could contribute to the mitigation of
the energy demand. In the introduction of the current report, the greenhouse
effect, the global warming and the climate change are defined. Then, the
actions taken and the agreements made to deal with global warming and
climate change are presented and the need for combating them is highlighted.
After the introduction, a literature review is conducted in order to spot
the knowledge gap and formulate the research questions. In the literature
review, the ICCT’s and IEA’s studies are mainly discussed due to their high
quality research and the big amount of published reports. More specifically,
their models, scenarios, policies and results are discussed in detail in
order to accurately define the knowledge gap and formulate the research
questions. Thus, the goal and the main research question of this research
is to answer how could the future global energy demand be mitigated and
which low carbon fuels and technologies could significantly penetrate into
the transportation sector.
In the beginning of this research, the conceptual and the theoretical
frameworks are presented in order to assist with the outline of the thesis
and create the theoretical background for the development of the model.
Then, in order for the research questions to be answered, a forecasting model
calculating the transportation future energy demand by country, transport
mode, technology and energy carrier throughout the period 2015-2050 is developed.
The main features that distinguish this model from models used in
similar studies are the strong focus on the diffusion of low carbon fuels and
technologies, the use of a different diffusion model (the Bass s-curve) and
the country by country with one-year time increments approach.
After the model is verified and validated through comparison with similar
studies and a sensitivity analysis, results are presented for two different
scenarios. The first scenario is called Current Policies scenario and aims to
show a potential pathway of the future global energy demand in the transportation
sector that could happen if no more policies are applied after 2020
and the second scenario is called Accelerated Policies scenario and its target
is to represent a pathway that could happen if new policies are adopted and
stricter implementation is applied.
The results of the two scenarios show that it is possible to achieve a
bending of the energy demand and a diffusion enhancement of low carbon
fuels and technologies if new and stricter policies that motivate technology
improvements and fossil vehicles ban are applied. In particular, the results
indicate that the implementation of new and stricter policies, which could
lead to efficiency improvements, and to more effective diffusion of low carbon
fuels and technologies could achieve reduction of the energy demand after 2029. Moreover, according to the results, electricity is expected to dominate in the transportation sector, while biofuels, hydrogen and ammonia are also
expected to be highly used. However, without further policy action, the
global energy demand is expected to follow a constantly increasing trend
in the future while the penetration of low carbon fuels is expected to be
significantly lower. ...
This study deals with the modelling of the future global energy demand in
the transportation sector, how and in what extend this could be limited and
which low carbon fuels and technologies could contribute to the mitigation of
the energy demand. In the introduction of the current report, the greenhouse
effect, the global warming and the climate change are defined. Then, the
actions taken and the agreements made to deal with global warming and
climate change are presented and the need for combating them is highlighted.
After the introduction, a literature review is conducted in order to spot
the knowledge gap and formulate the research questions. In the literature
review, the ICCT’s and IEA’s studies are mainly discussed due to their high
quality research and the big amount of published reports. More specifically,
their models, scenarios, policies and results are discussed in detail in
order to accurately define the knowledge gap and formulate the research
questions. Thus, the goal and the main research question of this research
is to answer how could the future global energy demand be mitigated and
which low carbon fuels and technologies could significantly penetrate into
the transportation sector.
In the beginning of this research, the conceptual and the theoretical
frameworks are presented in order to assist with the outline of the thesis
and create the theoretical background for the development of the model.
Then, in order for the research questions to be answered, a forecasting model
calculating the transportation future energy demand by country, transport
mode, technology and energy carrier throughout the period 2015-2050 is developed.
The main features that distinguish this model from models used in
similar studies are the strong focus on the diffusion of low carbon fuels and
technologies, the use of a different diffusion model (the Bass s-curve) and
the country by country with one-year time increments approach.
After the model is verified and validated through comparison with similar
studies and a sensitivity analysis, results are presented for two different
scenarios. The first scenario is called Current Policies scenario and aims to
show a potential pathway of the future global energy demand in the transportation
sector that could happen if no more policies are applied after 2020
and the second scenario is called Accelerated Policies scenario and its target
is to represent a pathway that could happen if new policies are adopted and
stricter implementation is applied.
The results of the two scenarios show that it is possible to achieve a
bending of the energy demand and a diffusion enhancement of low carbon
fuels and technologies if new and stricter policies that motivate technology
improvements and fossil vehicles ban are applied. In particular, the results
indicate that the implementation of new and stricter policies, which could
lead to efficiency improvements, and to more effective diffusion of low carbon
fuels and technologies could achieve reduction of the energy demand after 2029. Moreover, according to the results, electricity is expected to dominate in the transportation sector, while biofuels, hydrogen and ammonia are also
expected to be highly used. However, without further policy action, the
global energy demand is expected to follow a constantly increasing trend
in the future while the penetration of low carbon fuels is expected to be
significantly lower.
the transportation sector, how and in what extend this could be limited and
which low carbon fuels and technologies could contribute to the mitigation of
the energy demand. In the introduction of the current report, the greenhouse
effect, the global warming and the climate change are defined. Then, the
actions taken and the agreements made to deal with global warming and
climate change are presented and the need for combating them is highlighted.
After the introduction, a literature review is conducted in order to spot
the knowledge gap and formulate the research questions. In the literature
review, the ICCT’s and IEA’s studies are mainly discussed due to their high
quality research and the big amount of published reports. More specifically,
their models, scenarios, policies and results are discussed in detail in
order to accurately define the knowledge gap and formulate the research
questions. Thus, the goal and the main research question of this research
is to answer how could the future global energy demand be mitigated and
which low carbon fuels and technologies could significantly penetrate into
the transportation sector.
In the beginning of this research, the conceptual and the theoretical
frameworks are presented in order to assist with the outline of the thesis
and create the theoretical background for the development of the model.
Then, in order for the research questions to be answered, a forecasting model
calculating the transportation future energy demand by country, transport
mode, technology and energy carrier throughout the period 2015-2050 is developed.
The main features that distinguish this model from models used in
similar studies are the strong focus on the diffusion of low carbon fuels and
technologies, the use of a different diffusion model (the Bass s-curve) and
the country by country with one-year time increments approach.
After the model is verified and validated through comparison with similar
studies and a sensitivity analysis, results are presented for two different
scenarios. The first scenario is called Current Policies scenario and aims to
show a potential pathway of the future global energy demand in the transportation
sector that could happen if no more policies are applied after 2020
and the second scenario is called Accelerated Policies scenario and its target
is to represent a pathway that could happen if new policies are adopted and
stricter implementation is applied.
The results of the two scenarios show that it is possible to achieve a
bending of the energy demand and a diffusion enhancement of low carbon
fuels and technologies if new and stricter policies that motivate technology
improvements and fossil vehicles ban are applied. In particular, the results
indicate that the implementation of new and stricter policies, which could
lead to efficiency improvements, and to more effective diffusion of low carbon
fuels and technologies could achieve reduction of the energy demand after 2029. Moreover, according to the results, electricity is expected to dominate in the transportation sector, while biofuels, hydrogen and ammonia are also
expected to be highly used. However, without further policy action, the
global energy demand is expected to follow a constantly increasing trend
in the future while the penetration of low carbon fuels is expected to be
significantly lower.