A.F. Correlje
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1
Towards New Grid Tariffs: Recommendations for Restructuring Grid Tariffs
A Master's Thesis on the Evaluation of Time-of-Use contracted power tariff for large-scale electricity consumers in the Dutch distribution system
Addressing grid congestion in the Dutch distribution grid requires price-based solutions, such as scarcity reflective grid tariffs. One proposed grid tariff structure is the Time-of-Use (ToU) contracted power tariff for large-scale electricity consumers, where power can be contracted hourly at varying prices to incentivize lower electricity usage during peak periods. However, it is unclear whether this tariff meaningfully reduces congestion. This study investigates: “How can Time-of-Use contracted power tariffs contribute to reducing grid congestion and improving efficient grid usage in the Dutch distribution grid, given modeled large-scale electricity user behavior?”.
To answer this question a demand response estimation model was created. Literature was first reviewed to understand consumer behavior in response to price signals, revealing existing models are insufficiently scalable or detailed to analyze grid tariffs on a physical network. A new model was thus created, combining elements from prior literature and an interview with a large-scale electricity consumer. The model assumes cost-minimizing behavior, constrained by consumer-specific historical consumption patterns. The redistribution of a daily amount of energy is optimized considering commodity costs, grid tariffs and penalties for deviations from the reference profile. Penalties are based on price elasticities of electricity demand. Network capacity, electricity prices and total demand were treated as exogenous variables.
The ToU contracted power tariff was evaluated using system efficiency as a guiding principle, operationalized through peak reduction and an adjusted load factor. The adjusted load factor measures the ratio of average load to system peaks. A medium-voltage network segment in the Maasvlakte was modeled to assess the effects of the ToU contracted power tariff on these performance indicators.
The results have shown that the ToU contracted power tariff, as a complement to ToU volumetric and peak tariffs, leads to only a marginal amount of additional peak reduction. The maximum measured reduction is 0,008 MW, and occurs under moderate assumed consumer flexibility. This reduces the system peak from the modeled network from 12.660 MW to 12.652 MW. Also, the ToU contracted power tariff does not consistently improve the adjusted load factor, thus indicating limited incentive for more efficient grid usage.
The structure of the tariff explains its limited effectiveness. The tariff primarily incentivizes load shifting rather than peak reduction. If consumption remains below contracted capacity, additional load does not increase costs. Only the timing of contracted capacity affects pricing. Thus, the load tends to shift across hours rather than reducing peaks, which can lead to lower adjusted load factors.
In conclusion, the ToU contracted power tariff is not a reliable solution for reducing congestion or improving system efficiency. The complementary value to the proposed ToU volumetric and peak tariff is limited, and its complexity may hinder implementation. Also, given that peaks occur infrequently, applying ToU tariffs to an entire year offers limited benefits. Future congestion management should focus on tariffs that directly influence locational, temporal and peak-driven aspects of congestion. Future research into consumer-specific price elasticities could improve on the realism of the created model, and improve future analyses. ...
To answer this question a demand response estimation model was created. Literature was first reviewed to understand consumer behavior in response to price signals, revealing existing models are insufficiently scalable or detailed to analyze grid tariffs on a physical network. A new model was thus created, combining elements from prior literature and an interview with a large-scale electricity consumer. The model assumes cost-minimizing behavior, constrained by consumer-specific historical consumption patterns. The redistribution of a daily amount of energy is optimized considering commodity costs, grid tariffs and penalties for deviations from the reference profile. Penalties are based on price elasticities of electricity demand. Network capacity, electricity prices and total demand were treated as exogenous variables.
The ToU contracted power tariff was evaluated using system efficiency as a guiding principle, operationalized through peak reduction and an adjusted load factor. The adjusted load factor measures the ratio of average load to system peaks. A medium-voltage network segment in the Maasvlakte was modeled to assess the effects of the ToU contracted power tariff on these performance indicators.
The results have shown that the ToU contracted power tariff, as a complement to ToU volumetric and peak tariffs, leads to only a marginal amount of additional peak reduction. The maximum measured reduction is 0,008 MW, and occurs under moderate assumed consumer flexibility. This reduces the system peak from the modeled network from 12.660 MW to 12.652 MW. Also, the ToU contracted power tariff does not consistently improve the adjusted load factor, thus indicating limited incentive for more efficient grid usage.
The structure of the tariff explains its limited effectiveness. The tariff primarily incentivizes load shifting rather than peak reduction. If consumption remains below contracted capacity, additional load does not increase costs. Only the timing of contracted capacity affects pricing. Thus, the load tends to shift across hours rather than reducing peaks, which can lead to lower adjusted load factors.
In conclusion, the ToU contracted power tariff is not a reliable solution for reducing congestion or improving system efficiency. The complementary value to the proposed ToU volumetric and peak tariff is limited, and its complexity may hinder implementation. Also, given that peaks occur infrequently, applying ToU tariffs to an entire year offers limited benefits. Future congestion management should focus on tariffs that directly influence locational, temporal and peak-driven aspects of congestion. Future research into consumer-specific price elasticities could improve on the realism of the created model, and improve future analyses. ...
Addressing grid congestion in the Dutch distribution grid requires price-based solutions, such as scarcity reflective grid tariffs. One proposed grid tariff structure is the Time-of-Use (ToU) contracted power tariff for large-scale electricity consumers, where power can be contracted hourly at varying prices to incentivize lower electricity usage during peak periods. However, it is unclear whether this tariff meaningfully reduces congestion. This study investigates: “How can Time-of-Use contracted power tariffs contribute to reducing grid congestion and improving efficient grid usage in the Dutch distribution grid, given modeled large-scale electricity user behavior?”.
To answer this question a demand response estimation model was created. Literature was first reviewed to understand consumer behavior in response to price signals, revealing existing models are insufficiently scalable or detailed to analyze grid tariffs on a physical network. A new model was thus created, combining elements from prior literature and an interview with a large-scale electricity consumer. The model assumes cost-minimizing behavior, constrained by consumer-specific historical consumption patterns. The redistribution of a daily amount of energy is optimized considering commodity costs, grid tariffs and penalties for deviations from the reference profile. Penalties are based on price elasticities of electricity demand. Network capacity, electricity prices and total demand were treated as exogenous variables.
The ToU contracted power tariff was evaluated using system efficiency as a guiding principle, operationalized through peak reduction and an adjusted load factor. The adjusted load factor measures the ratio of average load to system peaks. A medium-voltage network segment in the Maasvlakte was modeled to assess the effects of the ToU contracted power tariff on these performance indicators.
The results have shown that the ToU contracted power tariff, as a complement to ToU volumetric and peak tariffs, leads to only a marginal amount of additional peak reduction. The maximum measured reduction is 0,008 MW, and occurs under moderate assumed consumer flexibility. This reduces the system peak from the modeled network from 12.660 MW to 12.652 MW. Also, the ToU contracted power tariff does not consistently improve the adjusted load factor, thus indicating limited incentive for more efficient grid usage.
The structure of the tariff explains its limited effectiveness. The tariff primarily incentivizes load shifting rather than peak reduction. If consumption remains below contracted capacity, additional load does not increase costs. Only the timing of contracted capacity affects pricing. Thus, the load tends to shift across hours rather than reducing peaks, which can lead to lower adjusted load factors.
In conclusion, the ToU contracted power tariff is not a reliable solution for reducing congestion or improving system efficiency. The complementary value to the proposed ToU volumetric and peak tariff is limited, and its complexity may hinder implementation. Also, given that peaks occur infrequently, applying ToU tariffs to an entire year offers limited benefits. Future congestion management should focus on tariffs that directly influence locational, temporal and peak-driven aspects of congestion. Future research into consumer-specific price elasticities could improve on the realism of the created model, and improve future analyses.
To answer this question a demand response estimation model was created. Literature was first reviewed to understand consumer behavior in response to price signals, revealing existing models are insufficiently scalable or detailed to analyze grid tariffs on a physical network. A new model was thus created, combining elements from prior literature and an interview with a large-scale electricity consumer. The model assumes cost-minimizing behavior, constrained by consumer-specific historical consumption patterns. The redistribution of a daily amount of energy is optimized considering commodity costs, grid tariffs and penalties for deviations from the reference profile. Penalties are based on price elasticities of electricity demand. Network capacity, electricity prices and total demand were treated as exogenous variables.
The ToU contracted power tariff was evaluated using system efficiency as a guiding principle, operationalized through peak reduction and an adjusted load factor. The adjusted load factor measures the ratio of average load to system peaks. A medium-voltage network segment in the Maasvlakte was modeled to assess the effects of the ToU contracted power tariff on these performance indicators.
The results have shown that the ToU contracted power tariff, as a complement to ToU volumetric and peak tariffs, leads to only a marginal amount of additional peak reduction. The maximum measured reduction is 0,008 MW, and occurs under moderate assumed consumer flexibility. This reduces the system peak from the modeled network from 12.660 MW to 12.652 MW. Also, the ToU contracted power tariff does not consistently improve the adjusted load factor, thus indicating limited incentive for more efficient grid usage.
The structure of the tariff explains its limited effectiveness. The tariff primarily incentivizes load shifting rather than peak reduction. If consumption remains below contracted capacity, additional load does not increase costs. Only the timing of contracted capacity affects pricing. Thus, the load tends to shift across hours rather than reducing peaks, which can lead to lower adjusted load factors.
In conclusion, the ToU contracted power tariff is not a reliable solution for reducing congestion or improving system efficiency. The complementary value to the proposed ToU volumetric and peak tariff is limited, and its complexity may hinder implementation. Also, given that peaks occur infrequently, applying ToU tariffs to an entire year offers limited benefits. Future congestion management should focus on tariffs that directly influence locational, temporal and peak-driven aspects of congestion. Future research into consumer-specific price elasticities could improve on the realism of the created model, and improve future analyses.
Governing maritime safety in a crowded sea
A multi-layer framework for integrating shipping safety into offshore wind planning in the Netherlands and the United Kingdom
Offshore wind expansion in the North Sea is putting pressure on maritime safety, as increasing fixed infrastructure reduces manoeuvring space and increases collision and disruption risk. This thesis examines how shipping safety is integrated into offshore wind planning as the sector scales up. It compares governance arrangements in the Netherlands and the United Kingdom, two mature offshore wind contexts with similar geography but different institutional designs.
The central research question asks how vertical coordination, horizontal coordination, Safety-I/Safety-II approaches, and compliance design shape the integration of shipping safety into offshore wind planning in both countries as offshore wind scales up. The study applies a comparative case design combining document analysis and expert interviews, structured around four analytical dimensions: vertical allocation of responsibilities, horizontal cross-sector coordination, safety mode (Safety-I versus Safety-II), and dual-layer compliance architecture.
Both countries channel navigational safety through a multi-stage governance funnel, but differ in where safety alignment is stabilised. The Netherlands employs upstream stabilisation through plan-led steering: safety principles and spatial assumptions are embedded early via marine spatial planning and state-led site preparation, pushing consequential trade-offs to pre-tendering stages. The United Kingdom relies on downstream stabilisation through developer-led proof in consenting: early screening processes exist, but most safety issues are resolved through Navigational Risk Assessments and evidence during the consenting phase, preserving flexibility while increasing vulnerability to late lock-in and costly redesign.
Several findings emerge. Decision power does not eliminate the need for evidence, but it changes how evidence must be produced and justified. Safety-I continues to provide the non-negotiable prescriptive baseline, while Safety-II is growing in importance as sea space becomes denser and cumulative risks harder to anticipate through incident-based learning alone. Scenario modelling, near-miss learning, and structured learning loops are becoming essential evidence-production tools. Both systems depend on a dual-layer compliance structure combining prescriptive baselines with performance-based proof, but this functions robustly only when review capacity, consistent methods, and shared data baselines are in place.
Based on these findings, the thesis identifies four practical priorities for strengthening navigational safety governance as offshore wind scales. Institutionalising anticipatory evidence production through routine scenario-based reviews and near-miss learning. Strengthening upstream screening before commercial and procedural lock-in occurs. Investing in review capacity and consistency so regulators can evaluate deviations from prescriptive baselines transparently. Building a shared evidence base of traffic patterns, manoeuvring needs, and port practice projections to enable cumulative impact assessment across projects and institutions. ...
The central research question asks how vertical coordination, horizontal coordination, Safety-I/Safety-II approaches, and compliance design shape the integration of shipping safety into offshore wind planning in both countries as offshore wind scales up. The study applies a comparative case design combining document analysis and expert interviews, structured around four analytical dimensions: vertical allocation of responsibilities, horizontal cross-sector coordination, safety mode (Safety-I versus Safety-II), and dual-layer compliance architecture.
Both countries channel navigational safety through a multi-stage governance funnel, but differ in where safety alignment is stabilised. The Netherlands employs upstream stabilisation through plan-led steering: safety principles and spatial assumptions are embedded early via marine spatial planning and state-led site preparation, pushing consequential trade-offs to pre-tendering stages. The United Kingdom relies on downstream stabilisation through developer-led proof in consenting: early screening processes exist, but most safety issues are resolved through Navigational Risk Assessments and evidence during the consenting phase, preserving flexibility while increasing vulnerability to late lock-in and costly redesign.
Several findings emerge. Decision power does not eliminate the need for evidence, but it changes how evidence must be produced and justified. Safety-I continues to provide the non-negotiable prescriptive baseline, while Safety-II is growing in importance as sea space becomes denser and cumulative risks harder to anticipate through incident-based learning alone. Scenario modelling, near-miss learning, and structured learning loops are becoming essential evidence-production tools. Both systems depend on a dual-layer compliance structure combining prescriptive baselines with performance-based proof, but this functions robustly only when review capacity, consistent methods, and shared data baselines are in place.
Based on these findings, the thesis identifies four practical priorities for strengthening navigational safety governance as offshore wind scales. Institutionalising anticipatory evidence production through routine scenario-based reviews and near-miss learning. Strengthening upstream screening before commercial and procedural lock-in occurs. Investing in review capacity and consistency so regulators can evaluate deviations from prescriptive baselines transparently. Building a shared evidence base of traffic patterns, manoeuvring needs, and port practice projections to enable cumulative impact assessment across projects and institutions. ...
Offshore wind expansion in the North Sea is putting pressure on maritime safety, as increasing fixed infrastructure reduces manoeuvring space and increases collision and disruption risk. This thesis examines how shipping safety is integrated into offshore wind planning as the sector scales up. It compares governance arrangements in the Netherlands and the United Kingdom, two mature offshore wind contexts with similar geography but different institutional designs.
The central research question asks how vertical coordination, horizontal coordination, Safety-I/Safety-II approaches, and compliance design shape the integration of shipping safety into offshore wind planning in both countries as offshore wind scales up. The study applies a comparative case design combining document analysis and expert interviews, structured around four analytical dimensions: vertical allocation of responsibilities, horizontal cross-sector coordination, safety mode (Safety-I versus Safety-II), and dual-layer compliance architecture.
Both countries channel navigational safety through a multi-stage governance funnel, but differ in where safety alignment is stabilised. The Netherlands employs upstream stabilisation through plan-led steering: safety principles and spatial assumptions are embedded early via marine spatial planning and state-led site preparation, pushing consequential trade-offs to pre-tendering stages. The United Kingdom relies on downstream stabilisation through developer-led proof in consenting: early screening processes exist, but most safety issues are resolved through Navigational Risk Assessments and evidence during the consenting phase, preserving flexibility while increasing vulnerability to late lock-in and costly redesign.
Several findings emerge. Decision power does not eliminate the need for evidence, but it changes how evidence must be produced and justified. Safety-I continues to provide the non-negotiable prescriptive baseline, while Safety-II is growing in importance as sea space becomes denser and cumulative risks harder to anticipate through incident-based learning alone. Scenario modelling, near-miss learning, and structured learning loops are becoming essential evidence-production tools. Both systems depend on a dual-layer compliance structure combining prescriptive baselines with performance-based proof, but this functions robustly only when review capacity, consistent methods, and shared data baselines are in place.
Based on these findings, the thesis identifies four practical priorities for strengthening navigational safety governance as offshore wind scales. Institutionalising anticipatory evidence production through routine scenario-based reviews and near-miss learning. Strengthening upstream screening before commercial and procedural lock-in occurs. Investing in review capacity and consistency so regulators can evaluate deviations from prescriptive baselines transparently. Building a shared evidence base of traffic patterns, manoeuvring needs, and port practice projections to enable cumulative impact assessment across projects and institutions.
The central research question asks how vertical coordination, horizontal coordination, Safety-I/Safety-II approaches, and compliance design shape the integration of shipping safety into offshore wind planning in both countries as offshore wind scales up. The study applies a comparative case design combining document analysis and expert interviews, structured around four analytical dimensions: vertical allocation of responsibilities, horizontal cross-sector coordination, safety mode (Safety-I versus Safety-II), and dual-layer compliance architecture.
Both countries channel navigational safety through a multi-stage governance funnel, but differ in where safety alignment is stabilised. The Netherlands employs upstream stabilisation through plan-led steering: safety principles and spatial assumptions are embedded early via marine spatial planning and state-led site preparation, pushing consequential trade-offs to pre-tendering stages. The United Kingdom relies on downstream stabilisation through developer-led proof in consenting: early screening processes exist, but most safety issues are resolved through Navigational Risk Assessments and evidence during the consenting phase, preserving flexibility while increasing vulnerability to late lock-in and costly redesign.
Several findings emerge. Decision power does not eliminate the need for evidence, but it changes how evidence must be produced and justified. Safety-I continues to provide the non-negotiable prescriptive baseline, while Safety-II is growing in importance as sea space becomes denser and cumulative risks harder to anticipate through incident-based learning alone. Scenario modelling, near-miss learning, and structured learning loops are becoming essential evidence-production tools. Both systems depend on a dual-layer compliance structure combining prescriptive baselines with performance-based proof, but this functions robustly only when review capacity, consistent methods, and shared data baselines are in place.
Based on these findings, the thesis identifies four practical priorities for strengthening navigational safety governance as offshore wind scales. Institutionalising anticipatory evidence production through routine scenario-based reviews and near-miss learning. Strengthening upstream screening before commercial and procedural lock-in occurs. Investing in review capacity and consistency so regulators can evaluate deviations from prescriptive baselines transparently. Building a shared evidence base of traffic patterns, manoeuvring needs, and port practice projections to enable cumulative impact assessment across projects and institutions.
Study into the role that measurement uncertainty of solar irradiance data plays in industrial applications in the utility-scale solar PV industry. Study was performed in collaboration with Hukx Sensor Technology.
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Study into the role that measurement uncertainty of solar irradiance data plays in industrial applications in the utility-scale solar PV industry. Study was performed in collaboration with Hukx Sensor Technology.
The goal of this thesis is to simulate and benchmark centralised and decentralised capacity mechanisms against an Energy-Only Market (EOM). The study uses a stylised model of the Greek energy system to investigate whether a decentralised market design can offer a more efficient alternative to the prevailing centralised capacity market approach in addressing the 'missing money' problem....
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The goal of this thesis is to simulate and benchmark centralised and decentralised capacity mechanisms against an Energy-Only Market (EOM). The study uses a stylised model of the Greek energy system to investigate whether a decentralised market design can offer a more efficient alternative to the prevailing centralised capacity market approach in addressing the 'missing money' problem....
Resilience Beyond Technical Systems
Designing a framework for assessing resilience in the energy sector
Organisations in the energy sector increasingly face complex disruptions and long-term stresses that threaten their ability to deliver essential services. While resilience has emerged as a key concept for addressing such challenges, it remains conceptually fragmented and methodologically inconsistent, particularly within the context of critical entities more commonly known by their previous terminology, critical infrastructures. This study addresses this gap by developing a framework for assessing resilience in the critical entity energy.
Using a Design Science Research (DSR) approach, the research integrates theoretical insights from resilience literature with empirical input from industry observations and semi-structured interviews. First, a systems-oriented definition of resilience is established. Subsequently, relevant resilience criteria and indicators are identified and structured into a multi-criteria assessment framework, grounded in the Technical, Organisational, Social, and Economic (TOSE) dimensions, which have been identified as the aspects of resilience.
The resulting framework, with its four aspects, comprises 19 criteria and 89 indicators. Enabling organisations to systematically evaluate their resilience across interconnected aspects, thereby supporting strategic and operational decision-making in asset-intensive energy systems. Evaluation through expert interviews confirms the framework’s relevance, novelty, and usability, highlighting its potential to translate the abstract concept of resilience into a comprehensive practical approach.
The resulting framework aims to provide a structured, transferable, and practice-oriented approach to resilience assessment for critical energy entities. A mechanism for continuously reviewing, expanding and refining that list, as the risks and vulnerabilities evolve. ...
Using a Design Science Research (DSR) approach, the research integrates theoretical insights from resilience literature with empirical input from industry observations and semi-structured interviews. First, a systems-oriented definition of resilience is established. Subsequently, relevant resilience criteria and indicators are identified and structured into a multi-criteria assessment framework, grounded in the Technical, Organisational, Social, and Economic (TOSE) dimensions, which have been identified as the aspects of resilience.
The resulting framework, with its four aspects, comprises 19 criteria and 89 indicators. Enabling organisations to systematically evaluate their resilience across interconnected aspects, thereby supporting strategic and operational decision-making in asset-intensive energy systems. Evaluation through expert interviews confirms the framework’s relevance, novelty, and usability, highlighting its potential to translate the abstract concept of resilience into a comprehensive practical approach.
The resulting framework aims to provide a structured, transferable, and practice-oriented approach to resilience assessment for critical energy entities. A mechanism for continuously reviewing, expanding and refining that list, as the risks and vulnerabilities evolve. ...
Organisations in the energy sector increasingly face complex disruptions and long-term stresses that threaten their ability to deliver essential services. While resilience has emerged as a key concept for addressing such challenges, it remains conceptually fragmented and methodologically inconsistent, particularly within the context of critical entities more commonly known by their previous terminology, critical infrastructures. This study addresses this gap by developing a framework for assessing resilience in the critical entity energy.
Using a Design Science Research (DSR) approach, the research integrates theoretical insights from resilience literature with empirical input from industry observations and semi-structured interviews. First, a systems-oriented definition of resilience is established. Subsequently, relevant resilience criteria and indicators are identified and structured into a multi-criteria assessment framework, grounded in the Technical, Organisational, Social, and Economic (TOSE) dimensions, which have been identified as the aspects of resilience.
The resulting framework, with its four aspects, comprises 19 criteria and 89 indicators. Enabling organisations to systematically evaluate their resilience across interconnected aspects, thereby supporting strategic and operational decision-making in asset-intensive energy systems. Evaluation through expert interviews confirms the framework’s relevance, novelty, and usability, highlighting its potential to translate the abstract concept of resilience into a comprehensive practical approach.
The resulting framework aims to provide a structured, transferable, and practice-oriented approach to resilience assessment for critical energy entities. A mechanism for continuously reviewing, expanding and refining that list, as the risks and vulnerabilities evolve.
Using a Design Science Research (DSR) approach, the research integrates theoretical insights from resilience literature with empirical input from industry observations and semi-structured interviews. First, a systems-oriented definition of resilience is established. Subsequently, relevant resilience criteria and indicators are identified and structured into a multi-criteria assessment framework, grounded in the Technical, Organisational, Social, and Economic (TOSE) dimensions, which have been identified as the aspects of resilience.
The resulting framework, with its four aspects, comprises 19 criteria and 89 indicators. Enabling organisations to systematically evaluate their resilience across interconnected aspects, thereby supporting strategic and operational decision-making in asset-intensive energy systems. Evaluation through expert interviews confirms the framework’s relevance, novelty, and usability, highlighting its potential to translate the abstract concept of resilience into a comprehensive practical approach.
The resulting framework aims to provide a structured, transferable, and practice-oriented approach to resilience assessment for critical energy entities. A mechanism for continuously reviewing, expanding and refining that list, as the risks and vulnerabilities evolve.
Looking Beyond Reliability
Safety Governance for Evolving Gas Distribution Systems
This dissertation is about safety in gas distribution systems that transport renewable gases. The dangers of gases that we use to heat homes, power industries, and cook food are well-known. Gasses are often flammable, explosive and toxic. Even so, the abundance of gas and its clear advantages over coal and oil have made it a popular source of energy in many countries (Arapostathis et al., 2019; Correljé et al., 2003). Today, gas infrastructures primarily transport natural gas. Over time, gas infrastructures have evolved so that they are omnipresent in daily life. They connect underground gas deposits with households and industries. This infrastructure comprises gas pipelines that run through not only sparsely populated areas, but also under city sidewalks and into our houses and kitchens. The safety of gas production, transport, and usage is not questioned very often. More than two hundred years of using gas to heat homes and power industries has resulted in all kinds of rules and technologies that render these systems now generally safe.
Yet, the combustion of natural gas emits greenhouse gases, giving rise to global warming. To curb global warming, natural gas is to be substituted for other types of gases that emit no or less greenhouse gases. Two important examples of such renewable gases are biogas and hydrogen. Hence, gas systems are undergoing major changes as they are expected to transport increasing volumes of biogas and hydrogen. In this dissertation, I take the Netherlands gas system as an example to investigate how safety can be maintained in evolving gas systems. I focus on biogas, because it is the only renewable gas that is currently available in the Netherlands gas system in significant amounts. Biogas, once modified, can be transported through the existing gas pipelines. Yet the gas system will require changes to safely transport it. These changes concern both the technology as well as the way in which various users of the gas system are organized. These changes influence safety in different ways and inform the main research question of this dissertation… ...
Yet, the combustion of natural gas emits greenhouse gases, giving rise to global warming. To curb global warming, natural gas is to be substituted for other types of gases that emit no or less greenhouse gases. Two important examples of such renewable gases are biogas and hydrogen. Hence, gas systems are undergoing major changes as they are expected to transport increasing volumes of biogas and hydrogen. In this dissertation, I take the Netherlands gas system as an example to investigate how safety can be maintained in evolving gas systems. I focus on biogas, because it is the only renewable gas that is currently available in the Netherlands gas system in significant amounts. Biogas, once modified, can be transported through the existing gas pipelines. Yet the gas system will require changes to safely transport it. These changes concern both the technology as well as the way in which various users of the gas system are organized. These changes influence safety in different ways and inform the main research question of this dissertation… ...
This dissertation is about safety in gas distribution systems that transport renewable gases. The dangers of gases that we use to heat homes, power industries, and cook food are well-known. Gasses are often flammable, explosive and toxic. Even so, the abundance of gas and its clear advantages over coal and oil have made it a popular source of energy in many countries (Arapostathis et al., 2019; Correljé et al., 2003). Today, gas infrastructures primarily transport natural gas. Over time, gas infrastructures have evolved so that they are omnipresent in daily life. They connect underground gas deposits with households and industries. This infrastructure comprises gas pipelines that run through not only sparsely populated areas, but also under city sidewalks and into our houses and kitchens. The safety of gas production, transport, and usage is not questioned very often. More than two hundred years of using gas to heat homes and power industries has resulted in all kinds of rules and technologies that render these systems now generally safe.
Yet, the combustion of natural gas emits greenhouse gases, giving rise to global warming. To curb global warming, natural gas is to be substituted for other types of gases that emit no or less greenhouse gases. Two important examples of such renewable gases are biogas and hydrogen. Hence, gas systems are undergoing major changes as they are expected to transport increasing volumes of biogas and hydrogen. In this dissertation, I take the Netherlands gas system as an example to investigate how safety can be maintained in evolving gas systems. I focus on biogas, because it is the only renewable gas that is currently available in the Netherlands gas system in significant amounts. Biogas, once modified, can be transported through the existing gas pipelines. Yet the gas system will require changes to safely transport it. These changes concern both the technology as well as the way in which various users of the gas system are organized. These changes influence safety in different ways and inform the main research question of this dissertation…
Yet, the combustion of natural gas emits greenhouse gases, giving rise to global warming. To curb global warming, natural gas is to be substituted for other types of gases that emit no or less greenhouse gases. Two important examples of such renewable gases are biogas and hydrogen. Hence, gas systems are undergoing major changes as they are expected to transport increasing volumes of biogas and hydrogen. In this dissertation, I take the Netherlands gas system as an example to investigate how safety can be maintained in evolving gas systems. I focus on biogas, because it is the only renewable gas that is currently available in the Netherlands gas system in significant amounts. Biogas, once modified, can be transported through the existing gas pipelines. Yet the gas system will require changes to safely transport it. These changes concern both the technology as well as the way in which various users of the gas system are organized. These changes influence safety in different ways and inform the main research question of this dissertation…
Optimizing Grid Flexibility
An Agent-Based Analysis of Alternative Transport Rights for Large Energy Consumers in the Dutch Electricity Grid
The accelerating integration of renewable energy sources in the Netherlands has led to increasing congestion in the electricity grid, posing a significant challenge to system reliability and the energy transition. In response, the Dutch Authority for Consumers and Markets (ACM) introduced Alternative Transport Rights (ATR), including Time-Duration-Based (TDTR) and Time-Block-Based Transport Rights (TBTR), which offer conditional grid access and financial incentives for large energy consumers (LECs) to shift demand to off-peak periods. Despite their potential, limited practical guidance exists for LECs on how to operationalize flexibility under these new regulatory instruments.
This thesis explores how LECs can leverage data and technology to comply with ATR and evaluates the system-level impacts of ATR adoption using a mixed-methods approach. Qualitative insights from stakeholder interviews and literature review informed the development of sector-specific scenarios, which were tested in an agent-based model built on the ASSUME framework. The simulation results show that TDTR significantly reduces peak loads at the national level, improving grid stability but leading to moderate price increases due to reliance on fossil generation in off-peak periods. TBTR effectively redistributes demand at the regional level but may create secondary peaks under full adoption due to rigid scheduling.
Findings emphasize the critical role of enterprise data management, automation, and organizational adaptation in enabling ATR compliance. The study concludes with actionable recommendations for LECs, grid operators, and policymakers to enhance implementation, align tariff structures with flexibility goals, and support a broader transition to a more resilient and dynamic electricity system. ...
This thesis explores how LECs can leverage data and technology to comply with ATR and evaluates the system-level impacts of ATR adoption using a mixed-methods approach. Qualitative insights from stakeholder interviews and literature review informed the development of sector-specific scenarios, which were tested in an agent-based model built on the ASSUME framework. The simulation results show that TDTR significantly reduces peak loads at the national level, improving grid stability but leading to moderate price increases due to reliance on fossil generation in off-peak periods. TBTR effectively redistributes demand at the regional level but may create secondary peaks under full adoption due to rigid scheduling.
Findings emphasize the critical role of enterprise data management, automation, and organizational adaptation in enabling ATR compliance. The study concludes with actionable recommendations for LECs, grid operators, and policymakers to enhance implementation, align tariff structures with flexibility goals, and support a broader transition to a more resilient and dynamic electricity system. ...
The accelerating integration of renewable energy sources in the Netherlands has led to increasing congestion in the electricity grid, posing a significant challenge to system reliability and the energy transition. In response, the Dutch Authority for Consumers and Markets (ACM) introduced Alternative Transport Rights (ATR), including Time-Duration-Based (TDTR) and Time-Block-Based Transport Rights (TBTR), which offer conditional grid access and financial incentives for large energy consumers (LECs) to shift demand to off-peak periods. Despite their potential, limited practical guidance exists for LECs on how to operationalize flexibility under these new regulatory instruments.
This thesis explores how LECs can leverage data and technology to comply with ATR and evaluates the system-level impacts of ATR adoption using a mixed-methods approach. Qualitative insights from stakeholder interviews and literature review informed the development of sector-specific scenarios, which were tested in an agent-based model built on the ASSUME framework. The simulation results show that TDTR significantly reduces peak loads at the national level, improving grid stability but leading to moderate price increases due to reliance on fossil generation in off-peak periods. TBTR effectively redistributes demand at the regional level but may create secondary peaks under full adoption due to rigid scheduling.
Findings emphasize the critical role of enterprise data management, automation, and organizational adaptation in enabling ATR compliance. The study concludes with actionable recommendations for LECs, grid operators, and policymakers to enhance implementation, align tariff structures with flexibility goals, and support a broader transition to a more resilient and dynamic electricity system.
This thesis explores how LECs can leverage data and technology to comply with ATR and evaluates the system-level impacts of ATR adoption using a mixed-methods approach. Qualitative insights from stakeholder interviews and literature review informed the development of sector-specific scenarios, which were tested in an agent-based model built on the ASSUME framework. The simulation results show that TDTR significantly reduces peak loads at the national level, improving grid stability but leading to moderate price increases due to reliance on fossil generation in off-peak periods. TBTR effectively redistributes demand at the regional level but may create secondary peaks under full adoption due to rigid scheduling.
Findings emphasize the critical role of enterprise data management, automation, and organizational adaptation in enabling ATR compliance. The study concludes with actionable recommendations for LECs, grid operators, and policymakers to enhance implementation, align tariff structures with flexibility goals, and support a broader transition to a more resilient and dynamic electricity system.
Enabling aggregators to deploy battery capacity for congestion management
Designing a dynamic congestion management framework at Frank Energie
The rapid electrification of the Dutch energy system, coupled with the increasing penetration of intermittent renewable generation, is intensifying congestion in local electricity distribution grids. While residential battery storage offers potential for alleviating such congestion, current operational practices by aggregators can inadvertently exacerbate local overloads. In response, Distribution System Operators (DSOs) are considering static restrictions on battery dispatch during peak hours. Although effective in preventing transformer congestion, such measures significantly erode the economic viability of residential batteries and block their deployment even when local capacity is available.
This thesis investigates how aggregators, exemplified by Frank Energie, can deploy residential battery capacity in a manner that mitigates local congestion while minimising revenue loss. Using a design science and systems engineering approach, the research proceeds through five phases: (1) mapping interactions between the day-ahead, imbalance, and congestion markets; (2) analysing institutional and regulatory frameworks; (3) identifying technical constraints and stakeholder objectives; (4) developing feasible dynamic congestion management concepts; and (5) evaluating these options against criteria including effectiveness, fairness, regulatory compatibility, and economic efficiency.
Four solution archetypes were identified: distribution-level locational marginal pricing (DLMP), local flexibility markets, dynamic network tariffs, and dynamic capacity tariffs. Comparative analysis finds that local flexibility markets, where DSOs procure targeted flexibility services from aggregators at specific times and locations, offer the most balanced approach. This mechanism directly addresses congestion events without unnecessary curtailment, provides fair compensation to flexibility providers, and aligns with EU and Dutch policy trends toward market-based congestion management, as exemplified by the GOPACS platform.
The study recommends establishing local flexibility markets for the low-voltage grid, expanding existing platforms to include smaller assets, lowering minimum bid thresholds, and improving near-real-time data sharing between DSOs and aggregators. Such reforms could enable aggregators to integrate local congestion signals into dispatch algorithms, aligning commercial incentives with grid stability needs.
By bridging technical, regulatory, and market design perspectives, this research demonstrates that well-structured local flexibility markets can transform residential batteries from a perceived threat into an essential tool for congestion management supporting both the profitability of aggregators and the resilience of the electricity grid during the ongoing energy transition.
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This thesis investigates how aggregators, exemplified by Frank Energie, can deploy residential battery capacity in a manner that mitigates local congestion while minimising revenue loss. Using a design science and systems engineering approach, the research proceeds through five phases: (1) mapping interactions between the day-ahead, imbalance, and congestion markets; (2) analysing institutional and regulatory frameworks; (3) identifying technical constraints and stakeholder objectives; (4) developing feasible dynamic congestion management concepts; and (5) evaluating these options against criteria including effectiveness, fairness, regulatory compatibility, and economic efficiency.
Four solution archetypes were identified: distribution-level locational marginal pricing (DLMP), local flexibility markets, dynamic network tariffs, and dynamic capacity tariffs. Comparative analysis finds that local flexibility markets, where DSOs procure targeted flexibility services from aggregators at specific times and locations, offer the most balanced approach. This mechanism directly addresses congestion events without unnecessary curtailment, provides fair compensation to flexibility providers, and aligns with EU and Dutch policy trends toward market-based congestion management, as exemplified by the GOPACS platform.
The study recommends establishing local flexibility markets for the low-voltage grid, expanding existing platforms to include smaller assets, lowering minimum bid thresholds, and improving near-real-time data sharing between DSOs and aggregators. Such reforms could enable aggregators to integrate local congestion signals into dispatch algorithms, aligning commercial incentives with grid stability needs.
By bridging technical, regulatory, and market design perspectives, this research demonstrates that well-structured local flexibility markets can transform residential batteries from a perceived threat into an essential tool for congestion management supporting both the profitability of aggregators and the resilience of the electricity grid during the ongoing energy transition.
...
The rapid electrification of the Dutch energy system, coupled with the increasing penetration of intermittent renewable generation, is intensifying congestion in local electricity distribution grids. While residential battery storage offers potential for alleviating such congestion, current operational practices by aggregators can inadvertently exacerbate local overloads. In response, Distribution System Operators (DSOs) are considering static restrictions on battery dispatch during peak hours. Although effective in preventing transformer congestion, such measures significantly erode the economic viability of residential batteries and block their deployment even when local capacity is available.
This thesis investigates how aggregators, exemplified by Frank Energie, can deploy residential battery capacity in a manner that mitigates local congestion while minimising revenue loss. Using a design science and systems engineering approach, the research proceeds through five phases: (1) mapping interactions between the day-ahead, imbalance, and congestion markets; (2) analysing institutional and regulatory frameworks; (3) identifying technical constraints and stakeholder objectives; (4) developing feasible dynamic congestion management concepts; and (5) evaluating these options against criteria including effectiveness, fairness, regulatory compatibility, and economic efficiency.
Four solution archetypes were identified: distribution-level locational marginal pricing (DLMP), local flexibility markets, dynamic network tariffs, and dynamic capacity tariffs. Comparative analysis finds that local flexibility markets, where DSOs procure targeted flexibility services from aggregators at specific times and locations, offer the most balanced approach. This mechanism directly addresses congestion events without unnecessary curtailment, provides fair compensation to flexibility providers, and aligns with EU and Dutch policy trends toward market-based congestion management, as exemplified by the GOPACS platform.
The study recommends establishing local flexibility markets for the low-voltage grid, expanding existing platforms to include smaller assets, lowering minimum bid thresholds, and improving near-real-time data sharing between DSOs and aggregators. Such reforms could enable aggregators to integrate local congestion signals into dispatch algorithms, aligning commercial incentives with grid stability needs.
By bridging technical, regulatory, and market design perspectives, this research demonstrates that well-structured local flexibility markets can transform residential batteries from a perceived threat into an essential tool for congestion management supporting both the profitability of aggregators and the resilience of the electricity grid during the ongoing energy transition.
This thesis investigates how aggregators, exemplified by Frank Energie, can deploy residential battery capacity in a manner that mitigates local congestion while minimising revenue loss. Using a design science and systems engineering approach, the research proceeds through five phases: (1) mapping interactions between the day-ahead, imbalance, and congestion markets; (2) analysing institutional and regulatory frameworks; (3) identifying technical constraints and stakeholder objectives; (4) developing feasible dynamic congestion management concepts; and (5) evaluating these options against criteria including effectiveness, fairness, regulatory compatibility, and economic efficiency.
Four solution archetypes were identified: distribution-level locational marginal pricing (DLMP), local flexibility markets, dynamic network tariffs, and dynamic capacity tariffs. Comparative analysis finds that local flexibility markets, where DSOs procure targeted flexibility services from aggregators at specific times and locations, offer the most balanced approach. This mechanism directly addresses congestion events without unnecessary curtailment, provides fair compensation to flexibility providers, and aligns with EU and Dutch policy trends toward market-based congestion management, as exemplified by the GOPACS platform.
The study recommends establishing local flexibility markets for the low-voltage grid, expanding existing platforms to include smaller assets, lowering minimum bid thresholds, and improving near-real-time data sharing between DSOs and aggregators. Such reforms could enable aggregators to integrate local congestion signals into dispatch algorithms, aligning commercial incentives with grid stability needs.
By bridging technical, regulatory, and market design perspectives, this research demonstrates that well-structured local flexibility markets can transform residential batteries from a perceived threat into an essential tool for congestion management supporting both the profitability of aggregators and the resilience of the electricity grid during the ongoing energy transition.
Towards a Just Energy Transition in Arnhem
Exploring the Role of the DSO in Achieving an Equitable Energy System
The Netherlands strives to achieve carbon neutrality by 2050, but the transition to renewable energy risks deepening existing social inequalities, particularly energy poverty. In 2021, over 600,000 Dutch households experienced energy poverty, struggling to afford clean energy due to high costs and systemic barriers. Arnhem, a mid-sized city in the eastern Netherlands, exemplifies these challenges. With a high proportion of outdated housing stock and marked socio-economic disparities across its neighborhoods, Arnhem is particularly vulnerable to the uneven impacts of the energy transition. This thesis investigates how a Distribution System Operator (DSO), specifically Alliander, can contribute to a more just and equitable energy transition in Arnhem. As the operator responsible for managing and expanding the regional electricity and gas infrastructure, Alliander plays a central role in enabling access to energy by facilitating the energy infrastructure and shaping the conditions and tariffs under regulatory oversight. This research therefore explores how Alliander, in collaboration with local stakeholders such as the municipality, regulators, and civil society organizations, can help ensure that the energy transition does not leave vulnerable groups behind...
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The Netherlands strives to achieve carbon neutrality by 2050, but the transition to renewable energy risks deepening existing social inequalities, particularly energy poverty. In 2021, over 600,000 Dutch households experienced energy poverty, struggling to afford clean energy due to high costs and systemic barriers. Arnhem, a mid-sized city in the eastern Netherlands, exemplifies these challenges. With a high proportion of outdated housing stock and marked socio-economic disparities across its neighborhoods, Arnhem is particularly vulnerable to the uneven impacts of the energy transition. This thesis investigates how a Distribution System Operator (DSO), specifically Alliander, can contribute to a more just and equitable energy transition in Arnhem. As the operator responsible for managing and expanding the regional electricity and gas infrastructure, Alliander plays a central role in enabling access to energy by facilitating the energy infrastructure and shaping the conditions and tariffs under regulatory oversight. This research therefore explores how Alliander, in collaboration with local stakeholders such as the municipality, regulators, and civil society organizations, can help ensure that the energy transition does not leave vulnerable groups behind...
Assumptions in Action: Impact of Assumptions on the Relation between Electrolysis Integration and Renewable Energy
A Focus on North-Western Europe
The energy transition toward a carbon-neutral energy system presents complex challenges that require reliable system-level insights to guide investment and policy. Energy system models are essential tools in this context. They support planning by simulating interactions between technologies, markets, and policies under various future scenarios. Their strength lies in their ability to highlight structural system relationships and test the feasibility of different energy strategies.
At the foundation of these models lie assumptions and simplifications that define the internal logic of an energy system model. Importantly, a distinction must be made between assumptions (e.g., cost or efficiency parameters) and simplifications (e.g., ignoring demand fluctuations or omitting battery interaction). While simplifications make models tractable and transparent, they also risk overlooking key real-world constraints. This is why testing the impact of these assumptions and simplifications is critical: doing so ensures that model outcomes are robust and that their conclusions remain meaningful in practical applications.
Energy modelling simulates the operation and evolution of energy systems to support decision-making and policy planning. It helps simplify complex systems, forecast scenarios, and evaluate the effects of different strategies. While models are never perfectly accurate, their usefulness depends on data quality, transparent assumptions, and iterative refinement. These assumptions directly shape model credibility and must be rigorously tested to avoid the risk of unvalidated assumptions becoming accepted truths that undermine decision-making.
One such model is the Kramer and Koning Model (KKM), a stylised energy model developed to analyse the relationship between renewable electricity generation and hydrogen capacity. The KKM is appreciated for its simplicity and its capacity to clarify the fundamental relationship between renewable energy generation and electrolyser capacity - the r : e relationship. However, this simplicity raises the question of how sensitive its results are to added real-world complexities and how valid its outcomes remain. This study addresses that knowledge gap by investigating: "How Do Key Model Assumptions in the KKM Influence the Relationship Between Renewable Energy and Electrolysis Deployment?".
To evaluate the validity of KKM outcomes, this study introduces the Electrolyser Battery Balancing Model (EBBM) - a more detailed cost optimisation model operating under the same logic as the KKM, but with extensive additional parameters. The EBBM simulates hourly interactions between renewable supply, demand, electrolysers, and batteries. Developed in collaboration with Gasunie, a key player in the Dutch gas infrastructure and hydrogen transition, the EBBM is specifically designed to test real-world factors and find the cost-optimum interplay between renewable, electrolysis, and battery capacity. It is well-suited to validate the simplified relationships modelled by the KKM.
Firstly, a systematic identification of assumptions in the KKM was made. These were categorised as either explicit or implicit. Implicit assumptions were further divided into (1) real-world system simplifications (e.g., omitting compressors, conversion losses), and (2) wider context simplifications (e.g., sector coupling, market conditions). Based on their role in the model and feasibility for testing in the EBBM, a focused selection of assumptions was made, grouped into four categories: renewable energy, hydrogen, cost, and system simplifications. The eventual selection consisted of:
• Generation Mix;
• Electrolyser Efficiency;
• Electrolyser Limitations;
• Hydrogen Storage Cost;
• Cost Ratio between Renewables and Electrolysers;
• Neglect of Demand Fluctuations;
• Battery Interaction Exclusion;
• Demand Flexibility.
Moving on with the selected set of assumptions and simplifications, a sensitivity analysis was first conducted by incrementally reintroducing high-certainty system simplifications to the KKM base case. This included adding demand fluctuations, battery interaction, electrolyser efficiency curves, hydrogen storage cost and electrolyser limitations to create a new, more realistic base case. This updated case was then used to test the impact of four key parameters: electrolyser efficiency, demand flexibility, solar share, and the cost ratio between renewables an electrolysers. In each case, a high and low value was tested. These variations were used to assess how much each assumption shifts the r : e relationship, battery sizing (r : b), and total system cost (c).
Firstly, the incremental addition of complexities resulted in a flatter slope and lower overall system cost compared to the original KKM. Further results showed that parameters like solar share and cost ratio significantly affect infrastructure allocation between batteries and electrolysers, while demand flexibility and efficiency assumptions moderately shift total system cost and capacity sizing. The r : e relationship remained structurally linear in all cases but varied in slope and magnitude. Notably, the combination of battery interaction and electrolyser efficiency assumptions produced the largest cost savings, lowering total decarbonisation cost by several hundred euros per kW relative to the KKM.
A robustness analysis followed, designed to assess whether model outcomes remain valid under extreme input conditions (edge cases). These edge cases were selected for the same assumptions as for the sensitivity analysis. The aim was to evaluate whether the KKM’s simplified relations hold up under stress. The results indicated that while the relationship itself remains observable, its quantitative implications (e.g., cost and deployment levels) vary substantially, suggesting that the relation needs to be interpreted as directional rather than predictive.
To further contextualise the findings, a comparative model analysis was conducted. This compared the r : e relationship in the KKM against other existing energy system models. A longlist was developed and refined to three studies: CE Delft, E-Bridge, and a NSWPH study. Extracted data confirmed that while each model uses different frameworks, a consistent structural trend in the r : e relation is present, supporting the underlying logic of the KKM, albeit under different boundary conditions.
Despite differences in geography, modelling scope, and sectoral integration, all three studies showed a similar acceleration in electrolyser deployment relative to renewable generation, particularly beyond 2040. This convergence across models suggests that the r : e relationship is a robust feature of future energy system dynamics, rather than an artefact of a specific model setup. It reinforces the validity of the KKM’s structural assumptions, even if absolute outcomes vary. As such, the r : e relation emerges as a valuable comparative indicator for system modellers and energy planners aiming to align infrastructure scaling with decarbonisation timelines.
In the discussion, the findings reveal that while the KKM offers a robust conceptual tool, its practical outputs are assumption-sensitive.. Key limitations include the use of a single weather year to simulate renewable variability, a strictly unidimensional approach to parameter varying, and the degree of certainty with which a particular impact can be attributed to an assumption in another model. These issues are particularly important for policymakers or investors relying on model outputs for long-term infrastructure decisions.
The conclusion confirms that the KKM captures a fundamental structural relationship between renewables and hydrogen capacity, which reappears when evaluating other models. However, the outputs of the KKM are highly dependent on assumption quality and scope, especially regarding solar share and the cost ratio between renewables and electrolysis. The research shows that integrating high-certainty simplifications and testing uncertain variables adds valuable depth. Therefore, the KKM proves useful for identifying strategic trends in the r : e relation. Future research should extend this work by incorporating power-to-heat, more detailed battery interaction, and policy scenarios to increase applicability in real-world system design.
...
At the foundation of these models lie assumptions and simplifications that define the internal logic of an energy system model. Importantly, a distinction must be made between assumptions (e.g., cost or efficiency parameters) and simplifications (e.g., ignoring demand fluctuations or omitting battery interaction). While simplifications make models tractable and transparent, they also risk overlooking key real-world constraints. This is why testing the impact of these assumptions and simplifications is critical: doing so ensures that model outcomes are robust and that their conclusions remain meaningful in practical applications.
Energy modelling simulates the operation and evolution of energy systems to support decision-making and policy planning. It helps simplify complex systems, forecast scenarios, and evaluate the effects of different strategies. While models are never perfectly accurate, their usefulness depends on data quality, transparent assumptions, and iterative refinement. These assumptions directly shape model credibility and must be rigorously tested to avoid the risk of unvalidated assumptions becoming accepted truths that undermine decision-making.
One such model is the Kramer and Koning Model (KKM), a stylised energy model developed to analyse the relationship between renewable electricity generation and hydrogen capacity. The KKM is appreciated for its simplicity and its capacity to clarify the fundamental relationship between renewable energy generation and electrolyser capacity - the r : e relationship. However, this simplicity raises the question of how sensitive its results are to added real-world complexities and how valid its outcomes remain. This study addresses that knowledge gap by investigating: "How Do Key Model Assumptions in the KKM Influence the Relationship Between Renewable Energy and Electrolysis Deployment?".
To evaluate the validity of KKM outcomes, this study introduces the Electrolyser Battery Balancing Model (EBBM) - a more detailed cost optimisation model operating under the same logic as the KKM, but with extensive additional parameters. The EBBM simulates hourly interactions between renewable supply, demand, electrolysers, and batteries. Developed in collaboration with Gasunie, a key player in the Dutch gas infrastructure and hydrogen transition, the EBBM is specifically designed to test real-world factors and find the cost-optimum interplay between renewable, electrolysis, and battery capacity. It is well-suited to validate the simplified relationships modelled by the KKM.
Firstly, a systematic identification of assumptions in the KKM was made. These were categorised as either explicit or implicit. Implicit assumptions were further divided into (1) real-world system simplifications (e.g., omitting compressors, conversion losses), and (2) wider context simplifications (e.g., sector coupling, market conditions). Based on their role in the model and feasibility for testing in the EBBM, a focused selection of assumptions was made, grouped into four categories: renewable energy, hydrogen, cost, and system simplifications. The eventual selection consisted of:
• Generation Mix;
• Electrolyser Efficiency;
• Electrolyser Limitations;
• Hydrogen Storage Cost;
• Cost Ratio between Renewables and Electrolysers;
• Neglect of Demand Fluctuations;
• Battery Interaction Exclusion;
• Demand Flexibility.
Moving on with the selected set of assumptions and simplifications, a sensitivity analysis was first conducted by incrementally reintroducing high-certainty system simplifications to the KKM base case. This included adding demand fluctuations, battery interaction, electrolyser efficiency curves, hydrogen storage cost and electrolyser limitations to create a new, more realistic base case. This updated case was then used to test the impact of four key parameters: electrolyser efficiency, demand flexibility, solar share, and the cost ratio between renewables an electrolysers. In each case, a high and low value was tested. These variations were used to assess how much each assumption shifts the r : e relationship, battery sizing (r : b), and total system cost (c).
Firstly, the incremental addition of complexities resulted in a flatter slope and lower overall system cost compared to the original KKM. Further results showed that parameters like solar share and cost ratio significantly affect infrastructure allocation between batteries and electrolysers, while demand flexibility and efficiency assumptions moderately shift total system cost and capacity sizing. The r : e relationship remained structurally linear in all cases but varied in slope and magnitude. Notably, the combination of battery interaction and electrolyser efficiency assumptions produced the largest cost savings, lowering total decarbonisation cost by several hundred euros per kW relative to the KKM.
A robustness analysis followed, designed to assess whether model outcomes remain valid under extreme input conditions (edge cases). These edge cases were selected for the same assumptions as for the sensitivity analysis. The aim was to evaluate whether the KKM’s simplified relations hold up under stress. The results indicated that while the relationship itself remains observable, its quantitative implications (e.g., cost and deployment levels) vary substantially, suggesting that the relation needs to be interpreted as directional rather than predictive.
To further contextualise the findings, a comparative model analysis was conducted. This compared the r : e relationship in the KKM against other existing energy system models. A longlist was developed and refined to three studies: CE Delft, E-Bridge, and a NSWPH study. Extracted data confirmed that while each model uses different frameworks, a consistent structural trend in the r : e relation is present, supporting the underlying logic of the KKM, albeit under different boundary conditions.
Despite differences in geography, modelling scope, and sectoral integration, all three studies showed a similar acceleration in electrolyser deployment relative to renewable generation, particularly beyond 2040. This convergence across models suggests that the r : e relationship is a robust feature of future energy system dynamics, rather than an artefact of a specific model setup. It reinforces the validity of the KKM’s structural assumptions, even if absolute outcomes vary. As such, the r : e relation emerges as a valuable comparative indicator for system modellers and energy planners aiming to align infrastructure scaling with decarbonisation timelines.
In the discussion, the findings reveal that while the KKM offers a robust conceptual tool, its practical outputs are assumption-sensitive.. Key limitations include the use of a single weather year to simulate renewable variability, a strictly unidimensional approach to parameter varying, and the degree of certainty with which a particular impact can be attributed to an assumption in another model. These issues are particularly important for policymakers or investors relying on model outputs for long-term infrastructure decisions.
The conclusion confirms that the KKM captures a fundamental structural relationship between renewables and hydrogen capacity, which reappears when evaluating other models. However, the outputs of the KKM are highly dependent on assumption quality and scope, especially regarding solar share and the cost ratio between renewables and electrolysis. The research shows that integrating high-certainty simplifications and testing uncertain variables adds valuable depth. Therefore, the KKM proves useful for identifying strategic trends in the r : e relation. Future research should extend this work by incorporating power-to-heat, more detailed battery interaction, and policy scenarios to increase applicability in real-world system design.
...
The energy transition toward a carbon-neutral energy system presents complex challenges that require reliable system-level insights to guide investment and policy. Energy system models are essential tools in this context. They support planning by simulating interactions between technologies, markets, and policies under various future scenarios. Their strength lies in their ability to highlight structural system relationships and test the feasibility of different energy strategies.
At the foundation of these models lie assumptions and simplifications that define the internal logic of an energy system model. Importantly, a distinction must be made between assumptions (e.g., cost or efficiency parameters) and simplifications (e.g., ignoring demand fluctuations or omitting battery interaction). While simplifications make models tractable and transparent, they also risk overlooking key real-world constraints. This is why testing the impact of these assumptions and simplifications is critical: doing so ensures that model outcomes are robust and that their conclusions remain meaningful in practical applications.
Energy modelling simulates the operation and evolution of energy systems to support decision-making and policy planning. It helps simplify complex systems, forecast scenarios, and evaluate the effects of different strategies. While models are never perfectly accurate, their usefulness depends on data quality, transparent assumptions, and iterative refinement. These assumptions directly shape model credibility and must be rigorously tested to avoid the risk of unvalidated assumptions becoming accepted truths that undermine decision-making.
One such model is the Kramer and Koning Model (KKM), a stylised energy model developed to analyse the relationship between renewable electricity generation and hydrogen capacity. The KKM is appreciated for its simplicity and its capacity to clarify the fundamental relationship between renewable energy generation and electrolyser capacity - the r : e relationship. However, this simplicity raises the question of how sensitive its results are to added real-world complexities and how valid its outcomes remain. This study addresses that knowledge gap by investigating: "How Do Key Model Assumptions in the KKM Influence the Relationship Between Renewable Energy and Electrolysis Deployment?".
To evaluate the validity of KKM outcomes, this study introduces the Electrolyser Battery Balancing Model (EBBM) - a more detailed cost optimisation model operating under the same logic as the KKM, but with extensive additional parameters. The EBBM simulates hourly interactions between renewable supply, demand, electrolysers, and batteries. Developed in collaboration with Gasunie, a key player in the Dutch gas infrastructure and hydrogen transition, the EBBM is specifically designed to test real-world factors and find the cost-optimum interplay between renewable, electrolysis, and battery capacity. It is well-suited to validate the simplified relationships modelled by the KKM.
Firstly, a systematic identification of assumptions in the KKM was made. These were categorised as either explicit or implicit. Implicit assumptions were further divided into (1) real-world system simplifications (e.g., omitting compressors, conversion losses), and (2) wider context simplifications (e.g., sector coupling, market conditions). Based on their role in the model and feasibility for testing in the EBBM, a focused selection of assumptions was made, grouped into four categories: renewable energy, hydrogen, cost, and system simplifications. The eventual selection consisted of:
• Generation Mix;
• Electrolyser Efficiency;
• Electrolyser Limitations;
• Hydrogen Storage Cost;
• Cost Ratio between Renewables and Electrolysers;
• Neglect of Demand Fluctuations;
• Battery Interaction Exclusion;
• Demand Flexibility.
Moving on with the selected set of assumptions and simplifications, a sensitivity analysis was first conducted by incrementally reintroducing high-certainty system simplifications to the KKM base case. This included adding demand fluctuations, battery interaction, electrolyser efficiency curves, hydrogen storage cost and electrolyser limitations to create a new, more realistic base case. This updated case was then used to test the impact of four key parameters: electrolyser efficiency, demand flexibility, solar share, and the cost ratio between renewables an electrolysers. In each case, a high and low value was tested. These variations were used to assess how much each assumption shifts the r : e relationship, battery sizing (r : b), and total system cost (c).
Firstly, the incremental addition of complexities resulted in a flatter slope and lower overall system cost compared to the original KKM. Further results showed that parameters like solar share and cost ratio significantly affect infrastructure allocation between batteries and electrolysers, while demand flexibility and efficiency assumptions moderately shift total system cost and capacity sizing. The r : e relationship remained structurally linear in all cases but varied in slope and magnitude. Notably, the combination of battery interaction and electrolyser efficiency assumptions produced the largest cost savings, lowering total decarbonisation cost by several hundred euros per kW relative to the KKM.
A robustness analysis followed, designed to assess whether model outcomes remain valid under extreme input conditions (edge cases). These edge cases were selected for the same assumptions as for the sensitivity analysis. The aim was to evaluate whether the KKM’s simplified relations hold up under stress. The results indicated that while the relationship itself remains observable, its quantitative implications (e.g., cost and deployment levels) vary substantially, suggesting that the relation needs to be interpreted as directional rather than predictive.
To further contextualise the findings, a comparative model analysis was conducted. This compared the r : e relationship in the KKM against other existing energy system models. A longlist was developed and refined to three studies: CE Delft, E-Bridge, and a NSWPH study. Extracted data confirmed that while each model uses different frameworks, a consistent structural trend in the r : e relation is present, supporting the underlying logic of the KKM, albeit under different boundary conditions.
Despite differences in geography, modelling scope, and sectoral integration, all three studies showed a similar acceleration in electrolyser deployment relative to renewable generation, particularly beyond 2040. This convergence across models suggests that the r : e relationship is a robust feature of future energy system dynamics, rather than an artefact of a specific model setup. It reinforces the validity of the KKM’s structural assumptions, even if absolute outcomes vary. As such, the r : e relation emerges as a valuable comparative indicator for system modellers and energy planners aiming to align infrastructure scaling with decarbonisation timelines.
In the discussion, the findings reveal that while the KKM offers a robust conceptual tool, its practical outputs are assumption-sensitive.. Key limitations include the use of a single weather year to simulate renewable variability, a strictly unidimensional approach to parameter varying, and the degree of certainty with which a particular impact can be attributed to an assumption in another model. These issues are particularly important for policymakers or investors relying on model outputs for long-term infrastructure decisions.
The conclusion confirms that the KKM captures a fundamental structural relationship between renewables and hydrogen capacity, which reappears when evaluating other models. However, the outputs of the KKM are highly dependent on assumption quality and scope, especially regarding solar share and the cost ratio between renewables and electrolysis. The research shows that integrating high-certainty simplifications and testing uncertain variables adds valuable depth. Therefore, the KKM proves useful for identifying strategic trends in the r : e relation. Future research should extend this work by incorporating power-to-heat, more detailed battery interaction, and policy scenarios to increase applicability in real-world system design.
At the foundation of these models lie assumptions and simplifications that define the internal logic of an energy system model. Importantly, a distinction must be made between assumptions (e.g., cost or efficiency parameters) and simplifications (e.g., ignoring demand fluctuations or omitting battery interaction). While simplifications make models tractable and transparent, they also risk overlooking key real-world constraints. This is why testing the impact of these assumptions and simplifications is critical: doing so ensures that model outcomes are robust and that their conclusions remain meaningful in practical applications.
Energy modelling simulates the operation and evolution of energy systems to support decision-making and policy planning. It helps simplify complex systems, forecast scenarios, and evaluate the effects of different strategies. While models are never perfectly accurate, their usefulness depends on data quality, transparent assumptions, and iterative refinement. These assumptions directly shape model credibility and must be rigorously tested to avoid the risk of unvalidated assumptions becoming accepted truths that undermine decision-making.
One such model is the Kramer and Koning Model (KKM), a stylised energy model developed to analyse the relationship between renewable electricity generation and hydrogen capacity. The KKM is appreciated for its simplicity and its capacity to clarify the fundamental relationship between renewable energy generation and electrolyser capacity - the r : e relationship. However, this simplicity raises the question of how sensitive its results are to added real-world complexities and how valid its outcomes remain. This study addresses that knowledge gap by investigating: "How Do Key Model Assumptions in the KKM Influence the Relationship Between Renewable Energy and Electrolysis Deployment?".
To evaluate the validity of KKM outcomes, this study introduces the Electrolyser Battery Balancing Model (EBBM) - a more detailed cost optimisation model operating under the same logic as the KKM, but with extensive additional parameters. The EBBM simulates hourly interactions between renewable supply, demand, electrolysers, and batteries. Developed in collaboration with Gasunie, a key player in the Dutch gas infrastructure and hydrogen transition, the EBBM is specifically designed to test real-world factors and find the cost-optimum interplay between renewable, electrolysis, and battery capacity. It is well-suited to validate the simplified relationships modelled by the KKM.
Firstly, a systematic identification of assumptions in the KKM was made. These were categorised as either explicit or implicit. Implicit assumptions were further divided into (1) real-world system simplifications (e.g., omitting compressors, conversion losses), and (2) wider context simplifications (e.g., sector coupling, market conditions). Based on their role in the model and feasibility for testing in the EBBM, a focused selection of assumptions was made, grouped into four categories: renewable energy, hydrogen, cost, and system simplifications. The eventual selection consisted of:
• Generation Mix;
• Electrolyser Efficiency;
• Electrolyser Limitations;
• Hydrogen Storage Cost;
• Cost Ratio between Renewables and Electrolysers;
• Neglect of Demand Fluctuations;
• Battery Interaction Exclusion;
• Demand Flexibility.
Moving on with the selected set of assumptions and simplifications, a sensitivity analysis was first conducted by incrementally reintroducing high-certainty system simplifications to the KKM base case. This included adding demand fluctuations, battery interaction, electrolyser efficiency curves, hydrogen storage cost and electrolyser limitations to create a new, more realistic base case. This updated case was then used to test the impact of four key parameters: electrolyser efficiency, demand flexibility, solar share, and the cost ratio between renewables an electrolysers. In each case, a high and low value was tested. These variations were used to assess how much each assumption shifts the r : e relationship, battery sizing (r : b), and total system cost (c).
Firstly, the incremental addition of complexities resulted in a flatter slope and lower overall system cost compared to the original KKM. Further results showed that parameters like solar share and cost ratio significantly affect infrastructure allocation between batteries and electrolysers, while demand flexibility and efficiency assumptions moderately shift total system cost and capacity sizing. The r : e relationship remained structurally linear in all cases but varied in slope and magnitude. Notably, the combination of battery interaction and electrolyser efficiency assumptions produced the largest cost savings, lowering total decarbonisation cost by several hundred euros per kW relative to the KKM.
A robustness analysis followed, designed to assess whether model outcomes remain valid under extreme input conditions (edge cases). These edge cases were selected for the same assumptions as for the sensitivity analysis. The aim was to evaluate whether the KKM’s simplified relations hold up under stress. The results indicated that while the relationship itself remains observable, its quantitative implications (e.g., cost and deployment levels) vary substantially, suggesting that the relation needs to be interpreted as directional rather than predictive.
To further contextualise the findings, a comparative model analysis was conducted. This compared the r : e relationship in the KKM against other existing energy system models. A longlist was developed and refined to three studies: CE Delft, E-Bridge, and a NSWPH study. Extracted data confirmed that while each model uses different frameworks, a consistent structural trend in the r : e relation is present, supporting the underlying logic of the KKM, albeit under different boundary conditions.
Despite differences in geography, modelling scope, and sectoral integration, all three studies showed a similar acceleration in electrolyser deployment relative to renewable generation, particularly beyond 2040. This convergence across models suggests that the r : e relationship is a robust feature of future energy system dynamics, rather than an artefact of a specific model setup. It reinforces the validity of the KKM’s structural assumptions, even if absolute outcomes vary. As such, the r : e relation emerges as a valuable comparative indicator for system modellers and energy planners aiming to align infrastructure scaling with decarbonisation timelines.
In the discussion, the findings reveal that while the KKM offers a robust conceptual tool, its practical outputs are assumption-sensitive.. Key limitations include the use of a single weather year to simulate renewable variability, a strictly unidimensional approach to parameter varying, and the degree of certainty with which a particular impact can be attributed to an assumption in another model. These issues are particularly important for policymakers or investors relying on model outputs for long-term infrastructure decisions.
The conclusion confirms that the KKM captures a fundamental structural relationship between renewables and hydrogen capacity, which reappears when evaluating other models. However, the outputs of the KKM are highly dependent on assumption quality and scope, especially regarding solar share and the cost ratio between renewables and electrolysis. The research shows that integrating high-certainty simplifications and testing uncertain variables adds valuable depth. Therefore, the KKM proves useful for identifying strategic trends in the r : e relation. Future research should extend this work by incorporating power-to-heat, more detailed battery interaction, and policy scenarios to increase applicability in real-world system design.
The Impact of Bidding Zone Reconfiguration
A Flow-Based Analysis of the Netherlands and Germany on Market Efficiency, Congestion and Distributional Effects
The increasing congestion on the electricity grid in the Netherlands and Germany, combined with delays in grid reinforcement, has raised questions about the adequacy of the current zonal pricing model. As renewable energy integration grows, the assumption of unconstrained internal grids no longer holds, and redispatching has become more frequent and costly. These challenges have renewed interest in reconfiguring bidding zones to better reflect physical constraints and improve market signals. However, existing studies often focus on national contexts and overlook system-wide interactions across interconnected markets.
This thesis investigates how bidding zone reconfigurations in the Netherlands and Germany affect electricity market outcomes within a flow-based market coupling context. Using the Exact Projection method within a Lagrangian relaxation and dynamic programming framework, a flow-based market coupling model was developed to simulate various reconfiguration scenarios based on proposals from ACER's Bidding Zone Review 2. These include a North South split in each country individually and a simultaneous split in both.
A key contribution of this research is the development of a multi-dimensional assessment framework that evaluates congestion, market efficiency, and distributional effects across the entire system. The analysis shows that a Dutch split has limited system-wide impacts, while a German split significantly alters market dynamics, exposing grid bottlenecks, reducing internal redispatch, increasing redispatch needs in the Netherlands, and shifting price patterns. A simultaneous split amplifies these effects, primarily driven by the German configuration.
The findings highlight the importance of capturing interdependencies and cross-zonal dynamics. Without a multi-dimensional and system-wide perspective, critical outcomes remain hidden. This research bridges technical and societal aspects of market design, offering insights that support informed decision-making aligned with the goals of the energy transition.
...
This thesis investigates how bidding zone reconfigurations in the Netherlands and Germany affect electricity market outcomes within a flow-based market coupling context. Using the Exact Projection method within a Lagrangian relaxation and dynamic programming framework, a flow-based market coupling model was developed to simulate various reconfiguration scenarios based on proposals from ACER's Bidding Zone Review 2. These include a North South split in each country individually and a simultaneous split in both.
A key contribution of this research is the development of a multi-dimensional assessment framework that evaluates congestion, market efficiency, and distributional effects across the entire system. The analysis shows that a Dutch split has limited system-wide impacts, while a German split significantly alters market dynamics, exposing grid bottlenecks, reducing internal redispatch, increasing redispatch needs in the Netherlands, and shifting price patterns. A simultaneous split amplifies these effects, primarily driven by the German configuration.
The findings highlight the importance of capturing interdependencies and cross-zonal dynamics. Without a multi-dimensional and system-wide perspective, critical outcomes remain hidden. This research bridges technical and societal aspects of market design, offering insights that support informed decision-making aligned with the goals of the energy transition.
...
The increasing congestion on the electricity grid in the Netherlands and Germany, combined with delays in grid reinforcement, has raised questions about the adequacy of the current zonal pricing model. As renewable energy integration grows, the assumption of unconstrained internal grids no longer holds, and redispatching has become more frequent and costly. These challenges have renewed interest in reconfiguring bidding zones to better reflect physical constraints and improve market signals. However, existing studies often focus on national contexts and overlook system-wide interactions across interconnected markets.
This thesis investigates how bidding zone reconfigurations in the Netherlands and Germany affect electricity market outcomes within a flow-based market coupling context. Using the Exact Projection method within a Lagrangian relaxation and dynamic programming framework, a flow-based market coupling model was developed to simulate various reconfiguration scenarios based on proposals from ACER's Bidding Zone Review 2. These include a North South split in each country individually and a simultaneous split in both.
A key contribution of this research is the development of a multi-dimensional assessment framework that evaluates congestion, market efficiency, and distributional effects across the entire system. The analysis shows that a Dutch split has limited system-wide impacts, while a German split significantly alters market dynamics, exposing grid bottlenecks, reducing internal redispatch, increasing redispatch needs in the Netherlands, and shifting price patterns. A simultaneous split amplifies these effects, primarily driven by the German configuration.
The findings highlight the importance of capturing interdependencies and cross-zonal dynamics. Without a multi-dimensional and system-wide perspective, critical outcomes remain hidden. This research bridges technical and societal aspects of market design, offering insights that support informed decision-making aligned with the goals of the energy transition.
This thesis investigates how bidding zone reconfigurations in the Netherlands and Germany affect electricity market outcomes within a flow-based market coupling context. Using the Exact Projection method within a Lagrangian relaxation and dynamic programming framework, a flow-based market coupling model was developed to simulate various reconfiguration scenarios based on proposals from ACER's Bidding Zone Review 2. These include a North South split in each country individually and a simultaneous split in both.
A key contribution of this research is the development of a multi-dimensional assessment framework that evaluates congestion, market efficiency, and distributional effects across the entire system. The analysis shows that a Dutch split has limited system-wide impacts, while a German split significantly alters market dynamics, exposing grid bottlenecks, reducing internal redispatch, increasing redispatch needs in the Netherlands, and shifting price patterns. A simultaneous split amplifies these effects, primarily driven by the German configuration.
The findings highlight the importance of capturing interdependencies and cross-zonal dynamics. Without a multi-dimensional and system-wide perspective, critical outcomes remain hidden. This research bridges technical and societal aspects of market design, offering insights that support informed decision-making aligned with the goals of the energy transition.
Governing collective heat in Amsterdam
Lessons learned from a case study comparison of 5th generation district heating and cooling
To reach the goal of becoming a natural gas-free city, the municipality of Amsterdam has to move towards more sustainable heat sources. Unlike the electricity- and gas market, there are no European guidelines for heat. This raises the question of how to govern sustainable, collective heat. This master’s thesis explores how the municipality of Amsterdam currently governs her heat grids, how the upcoming legislations might affect this, and what lessons could be learned from the way two case studies governed their collective heat grids. This research is done through a document analysis, and a case comparison through interviews. The interviews insights showed that the municipality of Amsterdam currently struggles with creating a feasible business case, a lack of trust from stakeholders (mainly residents, and weak management tools. The WCW and WGIW will most likely not change anything about this. The cases of Mijnwater and Clyde Gateway showed how there is still a knowledge imbalance regarding 5th generation district heating and cooling. These cases proved there’s a need for strong leadership, clear communication, and a need to start looking at heat differently (as a service instead of a commodity). The research results in a synthesis with recommendations for the municipality of Amsterdam on how to govern collective heat.
...
To reach the goal of becoming a natural gas-free city, the municipality of Amsterdam has to move towards more sustainable heat sources. Unlike the electricity- and gas market, there are no European guidelines for heat. This raises the question of how to govern sustainable, collective heat. This master’s thesis explores how the municipality of Amsterdam currently governs her heat grids, how the upcoming legislations might affect this, and what lessons could be learned from the way two case studies governed their collective heat grids. This research is done through a document analysis, and a case comparison through interviews. The interviews insights showed that the municipality of Amsterdam currently struggles with creating a feasible business case, a lack of trust from stakeholders (mainly residents, and weak management tools. The WCW and WGIW will most likely not change anything about this. The cases of Mijnwater and Clyde Gateway showed how there is still a knowledge imbalance regarding 5th generation district heating and cooling. These cases proved there’s a need for strong leadership, clear communication, and a need to start looking at heat differently (as a service instead of a commodity). The research results in a synthesis with recommendations for the municipality of Amsterdam on how to govern collective heat.
Tax Energy, Fuel the Future
A Quantitative Modelling Approach to Assess Policy Strategies for Sustainable Transformation of the Dutch Non-Residential Building Stock
The decarbonisation of Dutch non-residential service buildings is vital for achieving national climate goals. Yet current policies have primarily focused on new constructions, leaving the existing building stock—responsible for a significant share of emissions—underregulated. Financial, organisational, and behavioural barriers delay or prevent timely investments in sustainable heating. Since major heating system replacements typically occur only once or twice before 2050, failure to act during these moments risks locking in carbon-intensive technologies for decades. Targeted policy measures are therefore needed to steer building owners toward low-carbon alternatives at these critical decision points.
This study investigates how selected policy interventions can accelerate CO₂ emission reductions in the Dutch non-residential service sector, assessed through their effectiveness, cost-effectiveness, and distributional impacts. The CEKER techno-economic model, developed by CE Delft and originally tailored to the residential sector, was adapted to reflect the heterogeneity of the non-residential building stock in terms of function, size, and energy demand. The model simulates rational, cost-minimising investment decisions made at natural heating system replacement moments. Twelve policy scenarios were analysed, including increases in the energy tax on natural gas, mandatory energy label standards, and a ban on gas-based heating systems, both as stand-alone measures and in combined scenarios. All scenarios were tested across three electricity price futures to assess policy robustness.
Results show that combined interventions—particularly higher gas taxes paired with mandatory insulation standards—achieve the highest CO₂ reductions (up to 1.2 Mt). Economic measures alone are more cost-effective per tonne of CO₂, but less effective overall. Smaller buildings (≤500 m²) and assembly buildings exhibit high abatement potential but face relatively high retrofit costs.
Policy recommendations include gradually increasing the energy tax on natural gas, mandating a minimum energy label (e.g., Label D by 2030–2035), and offering targeted financial support for small buildings (≤500 m²) and assembly buildings. These results represent a techno-economic upper bound, assuming fully rational investment behaviour without non-financial barriers. As such, they provide a benchmark for evaluating the environmental impact, economic efficiency, and fairness of alternative policy strategies. ...
This study investigates how selected policy interventions can accelerate CO₂ emission reductions in the Dutch non-residential service sector, assessed through their effectiveness, cost-effectiveness, and distributional impacts. The CEKER techno-economic model, developed by CE Delft and originally tailored to the residential sector, was adapted to reflect the heterogeneity of the non-residential building stock in terms of function, size, and energy demand. The model simulates rational, cost-minimising investment decisions made at natural heating system replacement moments. Twelve policy scenarios were analysed, including increases in the energy tax on natural gas, mandatory energy label standards, and a ban on gas-based heating systems, both as stand-alone measures and in combined scenarios. All scenarios were tested across three electricity price futures to assess policy robustness.
Results show that combined interventions—particularly higher gas taxes paired with mandatory insulation standards—achieve the highest CO₂ reductions (up to 1.2 Mt). Economic measures alone are more cost-effective per tonne of CO₂, but less effective overall. Smaller buildings (≤500 m²) and assembly buildings exhibit high abatement potential but face relatively high retrofit costs.
Policy recommendations include gradually increasing the energy tax on natural gas, mandating a minimum energy label (e.g., Label D by 2030–2035), and offering targeted financial support for small buildings (≤500 m²) and assembly buildings. These results represent a techno-economic upper bound, assuming fully rational investment behaviour without non-financial barriers. As such, they provide a benchmark for evaluating the environmental impact, economic efficiency, and fairness of alternative policy strategies. ...
The decarbonisation of Dutch non-residential service buildings is vital for achieving national climate goals. Yet current policies have primarily focused on new constructions, leaving the existing building stock—responsible for a significant share of emissions—underregulated. Financial, organisational, and behavioural barriers delay or prevent timely investments in sustainable heating. Since major heating system replacements typically occur only once or twice before 2050, failure to act during these moments risks locking in carbon-intensive technologies for decades. Targeted policy measures are therefore needed to steer building owners toward low-carbon alternatives at these critical decision points.
This study investigates how selected policy interventions can accelerate CO₂ emission reductions in the Dutch non-residential service sector, assessed through their effectiveness, cost-effectiveness, and distributional impacts. The CEKER techno-economic model, developed by CE Delft and originally tailored to the residential sector, was adapted to reflect the heterogeneity of the non-residential building stock in terms of function, size, and energy demand. The model simulates rational, cost-minimising investment decisions made at natural heating system replacement moments. Twelve policy scenarios were analysed, including increases in the energy tax on natural gas, mandatory energy label standards, and a ban on gas-based heating systems, both as stand-alone measures and in combined scenarios. All scenarios were tested across three electricity price futures to assess policy robustness.
Results show that combined interventions—particularly higher gas taxes paired with mandatory insulation standards—achieve the highest CO₂ reductions (up to 1.2 Mt). Economic measures alone are more cost-effective per tonne of CO₂, but less effective overall. Smaller buildings (≤500 m²) and assembly buildings exhibit high abatement potential but face relatively high retrofit costs.
Policy recommendations include gradually increasing the energy tax on natural gas, mandating a minimum energy label (e.g., Label D by 2030–2035), and offering targeted financial support for small buildings (≤500 m²) and assembly buildings. These results represent a techno-economic upper bound, assuming fully rational investment behaviour without non-financial barriers. As such, they provide a benchmark for evaluating the environmental impact, economic efficiency, and fairness of alternative policy strategies.
This study investigates how selected policy interventions can accelerate CO₂ emission reductions in the Dutch non-residential service sector, assessed through their effectiveness, cost-effectiveness, and distributional impacts. The CEKER techno-economic model, developed by CE Delft and originally tailored to the residential sector, was adapted to reflect the heterogeneity of the non-residential building stock in terms of function, size, and energy demand. The model simulates rational, cost-minimising investment decisions made at natural heating system replacement moments. Twelve policy scenarios were analysed, including increases in the energy tax on natural gas, mandatory energy label standards, and a ban on gas-based heating systems, both as stand-alone measures and in combined scenarios. All scenarios were tested across three electricity price futures to assess policy robustness.
Results show that combined interventions—particularly higher gas taxes paired with mandatory insulation standards—achieve the highest CO₂ reductions (up to 1.2 Mt). Economic measures alone are more cost-effective per tonne of CO₂, but less effective overall. Smaller buildings (≤500 m²) and assembly buildings exhibit high abatement potential but face relatively high retrofit costs.
Policy recommendations include gradually increasing the energy tax on natural gas, mandating a minimum energy label (e.g., Label D by 2030–2035), and offering targeted financial support for small buildings (≤500 m²) and assembly buildings. These results represent a techno-economic upper bound, assuming fully rational investment behaviour without non-financial barriers. As such, they provide a benchmark for evaluating the environmental impact, economic efficiency, and fairness of alternative policy strategies.
Evaluating investment performance under uncertainty for Battolyser Systems
A Value Driver Tree-based simulation model
The rapid integration of renewable energy into the electricity grid, driven by climate goals, has led to an urgent need for greater system flexibility to manage variability in supply and demand. This transition offers new opportunities for hydrogen technologies, particularly in the Netherlands, where large-scale deployment of green hydrogen is central to national energy ambitions. However, despite these ambitions, many investments remain hindered due to ongoing technical, financial and regulatory uncertainties, which hinder progress and threaten to slow down the broader energy transition.
One promising innovation is Battolyser Systems, a dual-function technology that combines battery storage with electrolytic hydrogen production. Its ability to dynamically switch between energy storage and conversion makes it a valuable asset for grid flexibility. Nevertheless, the market uptake is limited by systemic barriers, underscoring the need for robust, uncertainty-based decision-making frameworks to support early-stage investments.
This research addresses this need by developing a simulation model based on a value driver tree (VDT) to evaluate the investment performance of Battolyser Systems under uncertainty in the Dutch green hydrogen market. The central research question is: How can a simulation model based on a value driver tree be designed and applied to the investment performance of Battolyser Systems under uncertainty?
The VDT framework is used as a visual and causal tool to decompose the economic added value (EVA) into its drivers: revenues, costs and capital input. The approach explicitly links technical parameters and policy instruments to investment performance, allowing for a structured analysis under uncertain conditions. After identifying the most important value drivers through literature research and stakeholder analysis, five primary uncertainties were selected for further modelling: electricity price, hydrogen price, unit capital costs, operating hours and system efficiency.
These drivers were formalised in a computational model using Monte Carlo simulation, yielding probabilistic distributions of EVA outcomes. Sensitivity and entropic analyses were performed to assess which parameters most strongly influence investment viability and where vulnerability to uncertainty is greatest. Baseline results indicate a negative EVA under current assumptions, indicating limited financial viability. However, the results show that policy factors, in particular hydrogen price and operating hours, have the greatest influence on shifting outcomes towards profitability.
The findings demonstrate that VDT simulation is a valuable method to capture the techno-economic complexity in energy innovations at an early stage. It allows for transparently tracing causal paths from technical inputs to financial outcomes and supports the exploration of risks and robustness in uncertain futures. Nevertheless, the scope of the model is limited by the availability of empirical data, in particular for new technologies. Moreover, institutional and behavioural dynamics, such as regulatory evolution and stakeholder strategies, have not yet been integrated.
In conclusion, this study provides a structured, simulation-based approach for evaluating investments in emerging hydrogen technologies. The VDT model improves decision-making by linking technical feasibility to financial feasibility under uncertainty. Future extensions should integrate dynamic institutional modelling and broader sustainability metrics to better inform adaptive policy design and systemic innovation in the energy transition. ...
One promising innovation is Battolyser Systems, a dual-function technology that combines battery storage with electrolytic hydrogen production. Its ability to dynamically switch between energy storage and conversion makes it a valuable asset for grid flexibility. Nevertheless, the market uptake is limited by systemic barriers, underscoring the need for robust, uncertainty-based decision-making frameworks to support early-stage investments.
This research addresses this need by developing a simulation model based on a value driver tree (VDT) to evaluate the investment performance of Battolyser Systems under uncertainty in the Dutch green hydrogen market. The central research question is: How can a simulation model based on a value driver tree be designed and applied to the investment performance of Battolyser Systems under uncertainty?
The VDT framework is used as a visual and causal tool to decompose the economic added value (EVA) into its drivers: revenues, costs and capital input. The approach explicitly links technical parameters and policy instruments to investment performance, allowing for a structured analysis under uncertain conditions. After identifying the most important value drivers through literature research and stakeholder analysis, five primary uncertainties were selected for further modelling: electricity price, hydrogen price, unit capital costs, operating hours and system efficiency.
These drivers were formalised in a computational model using Monte Carlo simulation, yielding probabilistic distributions of EVA outcomes. Sensitivity and entropic analyses were performed to assess which parameters most strongly influence investment viability and where vulnerability to uncertainty is greatest. Baseline results indicate a negative EVA under current assumptions, indicating limited financial viability. However, the results show that policy factors, in particular hydrogen price and operating hours, have the greatest influence on shifting outcomes towards profitability.
The findings demonstrate that VDT simulation is a valuable method to capture the techno-economic complexity in energy innovations at an early stage. It allows for transparently tracing causal paths from technical inputs to financial outcomes and supports the exploration of risks and robustness in uncertain futures. Nevertheless, the scope of the model is limited by the availability of empirical data, in particular for new technologies. Moreover, institutional and behavioural dynamics, such as regulatory evolution and stakeholder strategies, have not yet been integrated.
In conclusion, this study provides a structured, simulation-based approach for evaluating investments in emerging hydrogen technologies. The VDT model improves decision-making by linking technical feasibility to financial feasibility under uncertainty. Future extensions should integrate dynamic institutional modelling and broader sustainability metrics to better inform adaptive policy design and systemic innovation in the energy transition. ...
The rapid integration of renewable energy into the electricity grid, driven by climate goals, has led to an urgent need for greater system flexibility to manage variability in supply and demand. This transition offers new opportunities for hydrogen technologies, particularly in the Netherlands, where large-scale deployment of green hydrogen is central to national energy ambitions. However, despite these ambitions, many investments remain hindered due to ongoing technical, financial and regulatory uncertainties, which hinder progress and threaten to slow down the broader energy transition.
One promising innovation is Battolyser Systems, a dual-function technology that combines battery storage with electrolytic hydrogen production. Its ability to dynamically switch between energy storage and conversion makes it a valuable asset for grid flexibility. Nevertheless, the market uptake is limited by systemic barriers, underscoring the need for robust, uncertainty-based decision-making frameworks to support early-stage investments.
This research addresses this need by developing a simulation model based on a value driver tree (VDT) to evaluate the investment performance of Battolyser Systems under uncertainty in the Dutch green hydrogen market. The central research question is: How can a simulation model based on a value driver tree be designed and applied to the investment performance of Battolyser Systems under uncertainty?
The VDT framework is used as a visual and causal tool to decompose the economic added value (EVA) into its drivers: revenues, costs and capital input. The approach explicitly links technical parameters and policy instruments to investment performance, allowing for a structured analysis under uncertain conditions. After identifying the most important value drivers through literature research and stakeholder analysis, five primary uncertainties were selected for further modelling: electricity price, hydrogen price, unit capital costs, operating hours and system efficiency.
These drivers were formalised in a computational model using Monte Carlo simulation, yielding probabilistic distributions of EVA outcomes. Sensitivity and entropic analyses were performed to assess which parameters most strongly influence investment viability and where vulnerability to uncertainty is greatest. Baseline results indicate a negative EVA under current assumptions, indicating limited financial viability. However, the results show that policy factors, in particular hydrogen price and operating hours, have the greatest influence on shifting outcomes towards profitability.
The findings demonstrate that VDT simulation is a valuable method to capture the techno-economic complexity in energy innovations at an early stage. It allows for transparently tracing causal paths from technical inputs to financial outcomes and supports the exploration of risks and robustness in uncertain futures. Nevertheless, the scope of the model is limited by the availability of empirical data, in particular for new technologies. Moreover, institutional and behavioural dynamics, such as regulatory evolution and stakeholder strategies, have not yet been integrated.
In conclusion, this study provides a structured, simulation-based approach for evaluating investments in emerging hydrogen technologies. The VDT model improves decision-making by linking technical feasibility to financial feasibility under uncertainty. Future extensions should integrate dynamic institutional modelling and broader sustainability metrics to better inform adaptive policy design and systemic innovation in the energy transition.
One promising innovation is Battolyser Systems, a dual-function technology that combines battery storage with electrolytic hydrogen production. Its ability to dynamically switch between energy storage and conversion makes it a valuable asset for grid flexibility. Nevertheless, the market uptake is limited by systemic barriers, underscoring the need for robust, uncertainty-based decision-making frameworks to support early-stage investments.
This research addresses this need by developing a simulation model based on a value driver tree (VDT) to evaluate the investment performance of Battolyser Systems under uncertainty in the Dutch green hydrogen market. The central research question is: How can a simulation model based on a value driver tree be designed and applied to the investment performance of Battolyser Systems under uncertainty?
The VDT framework is used as a visual and causal tool to decompose the economic added value (EVA) into its drivers: revenues, costs and capital input. The approach explicitly links technical parameters and policy instruments to investment performance, allowing for a structured analysis under uncertain conditions. After identifying the most important value drivers through literature research and stakeholder analysis, five primary uncertainties were selected for further modelling: electricity price, hydrogen price, unit capital costs, operating hours and system efficiency.
These drivers were formalised in a computational model using Monte Carlo simulation, yielding probabilistic distributions of EVA outcomes. Sensitivity and entropic analyses were performed to assess which parameters most strongly influence investment viability and where vulnerability to uncertainty is greatest. Baseline results indicate a negative EVA under current assumptions, indicating limited financial viability. However, the results show that policy factors, in particular hydrogen price and operating hours, have the greatest influence on shifting outcomes towards profitability.
The findings demonstrate that VDT simulation is a valuable method to capture the techno-economic complexity in energy innovations at an early stage. It allows for transparently tracing causal paths from technical inputs to financial outcomes and supports the exploration of risks and robustness in uncertain futures. Nevertheless, the scope of the model is limited by the availability of empirical data, in particular for new technologies. Moreover, institutional and behavioural dynamics, such as regulatory evolution and stakeholder strategies, have not yet been integrated.
In conclusion, this study provides a structured, simulation-based approach for evaluating investments in emerging hydrogen technologies. The VDT model improves decision-making by linking technical feasibility to financial feasibility under uncertainty. Future extensions should integrate dynamic institutional modelling and broader sustainability metrics to better inform adaptive policy design and systemic innovation in the energy transition.
Balancing Data Centre Growth and Sustainability
Mapping the Future of Dutch Data Centres Integrating in the Energy Sector
Driven by a growing digital demand and the global race for AI dominance, data centres are an essential infrastructure but face significant challenges in the Netherlands regarding energy consumption, grid capacity, and social acceptance. This thesis explores what insights can support the development of policy instruments aimed at facilitating an acceptable role for data centres as intermediaries in the future Dutch energy system. With a qualitative approach, based on stakeholder interviews and events, pattern modelling and social acceptance frameworks are used to map the complex sociotechnical system and identify stakeholder interactions.
The outlined complex pattern model provided a basis to develop desired future scenarios, and seven different roles data centres can obtain to contribute to this pathway. However, in realising these roles, obstacles are faced. To tackle these barriers, to provide acceptable intermediate roles for data centres in the energy system, eight policy incentives are determined and prioritized based on their technical, institutional or economic domain.
This research has contributed to uncovering further opportunities for the Netherlands, which lies in boosting cooperation to realise higher energy efficiency for data centres, energy flexibility to tackle grid congestion, setting up an infrastructure for heat utilization and distribution, and exploring decentralisation.
Even though, some limitations on research constraints like time and access to stakeholders influenced outcomes. The research clearly indicates what areas require attention to further develop a sustainable energy sector within the Netherlands, which is a crucial step, even if a detailed plan for how these policy incentives should be implemented requires further research.
...
The outlined complex pattern model provided a basis to develop desired future scenarios, and seven different roles data centres can obtain to contribute to this pathway. However, in realising these roles, obstacles are faced. To tackle these barriers, to provide acceptable intermediate roles for data centres in the energy system, eight policy incentives are determined and prioritized based on their technical, institutional or economic domain.
This research has contributed to uncovering further opportunities for the Netherlands, which lies in boosting cooperation to realise higher energy efficiency for data centres, energy flexibility to tackle grid congestion, setting up an infrastructure for heat utilization and distribution, and exploring decentralisation.
Even though, some limitations on research constraints like time and access to stakeholders influenced outcomes. The research clearly indicates what areas require attention to further develop a sustainable energy sector within the Netherlands, which is a crucial step, even if a detailed plan for how these policy incentives should be implemented requires further research.
...
Driven by a growing digital demand and the global race for AI dominance, data centres are an essential infrastructure but face significant challenges in the Netherlands regarding energy consumption, grid capacity, and social acceptance. This thesis explores what insights can support the development of policy instruments aimed at facilitating an acceptable role for data centres as intermediaries in the future Dutch energy system. With a qualitative approach, based on stakeholder interviews and events, pattern modelling and social acceptance frameworks are used to map the complex sociotechnical system and identify stakeholder interactions.
The outlined complex pattern model provided a basis to develop desired future scenarios, and seven different roles data centres can obtain to contribute to this pathway. However, in realising these roles, obstacles are faced. To tackle these barriers, to provide acceptable intermediate roles for data centres in the energy system, eight policy incentives are determined and prioritized based on their technical, institutional or economic domain.
This research has contributed to uncovering further opportunities for the Netherlands, which lies in boosting cooperation to realise higher energy efficiency for data centres, energy flexibility to tackle grid congestion, setting up an infrastructure for heat utilization and distribution, and exploring decentralisation.
Even though, some limitations on research constraints like time and access to stakeholders influenced outcomes. The research clearly indicates what areas require attention to further develop a sustainable energy sector within the Netherlands, which is a crucial step, even if a detailed plan for how these policy incentives should be implemented requires further research.
The outlined complex pattern model provided a basis to develop desired future scenarios, and seven different roles data centres can obtain to contribute to this pathway. However, in realising these roles, obstacles are faced. To tackle these barriers, to provide acceptable intermediate roles for data centres in the energy system, eight policy incentives are determined and prioritized based on their technical, institutional or economic domain.
This research has contributed to uncovering further opportunities for the Netherlands, which lies in boosting cooperation to realise higher energy efficiency for data centres, energy flexibility to tackle grid congestion, setting up an infrastructure for heat utilization and distribution, and exploring decentralisation.
Even though, some limitations on research constraints like time and access to stakeholders influenced outcomes. The research clearly indicates what areas require attention to further develop a sustainable energy sector within the Netherlands, which is a crucial step, even if a detailed plan for how these policy incentives should be implemented requires further research.
Towards Sustainable Heating
The Impact of Integrating District Heating Networks with Electricity Systems
Master thesis
(2025)
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B.A.R. Kempkes, L.J. de Vries, A.F. Correlje, Barthold Schroot, Timme van Melle, Raymond Godderij
The Dutch heating sector, responsible for 41% of national energy use, remains heavily reliant on fossil fuels. In light of climate goals, this thesis explores how integrating district heating systems with the electricity grid, particularly using geothermal energy and high-temperature aquifer thermal energy storage (HT-ATES), can reduce operational costs and improve flexibility and sustainability. Using a techno-economic optimization model, a real-world case study of Delft was analyzed across four scenarios: fixed and dynamic electricity pricing, grid constraints, and grid reinforcement. The results show that dynamic pricing enhances system responsiveness and storage value, while thermal storage proves value in shifting demand, reducing gas reliance, and improving grid stability. Grid limitations significantly impact electric heating, but storage offers a viable short-term mitigation. A trade-off is evident between capital costs, system resilience, and emissions: larger systems reduce CO₂ but require higher investment.
...
The Dutch heating sector, responsible for 41% of national energy use, remains heavily reliant on fossil fuels. In light of climate goals, this thesis explores how integrating district heating systems with the electricity grid, particularly using geothermal energy and high-temperature aquifer thermal energy storage (HT-ATES), can reduce operational costs and improve flexibility and sustainability. Using a techno-economic optimization model, a real-world case study of Delft was analyzed across four scenarios: fixed and dynamic electricity pricing, grid constraints, and grid reinforcement. The results show that dynamic pricing enhances system responsiveness and storage value, while thermal storage proves value in shifting demand, reducing gas reliance, and improving grid stability. Grid limitations significantly impact electric heating, but storage offers a viable short-term mitigation. A trade-off is evident between capital costs, system resilience, and emissions: larger systems reduce CO₂ but require higher investment.
Decision-Making in PJM’s Interconnection Process
An Agent-Based Modeling Approach to ERIS Adoption
PJM Interconnection, the largest regional transmission organization in the United States, is facing mounting pressure to accelerate the interconnection of new energy generation resources to meet load while maintaining system reliability and minimizing rate payer impacts. The interconnection process- the procedure through which new generation projects get connected to the existing transmission grid is a key bottleneck in the clean energy transition. Over the past decade the queue of projects waiting to get interconnected in PJM has grown to greater than the total installed capacity in PJM. Each year the average time in queue has increased, as have the costs assigned to projects for network upgrades required to connect them to the grid. Meanwhile, while the MW of new generation resources coming online has decreased each year, load continues to increase and the grid is becoming capacity constrained, one symptom of which is the capacity market clearing with record small margins the last two years. In the face of large and increasing queue sizes and wait times, interconnection reforms are receiving increased attention from policy makers, advocates, and academics. One such reform is wider adoption of Energy Resource Interconnection Service (ERIS) agreements....
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PJM Interconnection, the largest regional transmission organization in the United States, is facing mounting pressure to accelerate the interconnection of new energy generation resources to meet load while maintaining system reliability and minimizing rate payer impacts. The interconnection process- the procedure through which new generation projects get connected to the existing transmission grid is a key bottleneck in the clean energy transition. Over the past decade the queue of projects waiting to get interconnected in PJM has grown to greater than the total installed capacity in PJM. Each year the average time in queue has increased, as have the costs assigned to projects for network upgrades required to connect them to the grid. Meanwhile, while the MW of new generation resources coming online has decreased each year, load continues to increase and the grid is becoming capacity constrained, one symptom of which is the capacity market clearing with record small margins the last two years. In the face of large and increasing queue sizes and wait times, interconnection reforms are receiving increased attention from policy makers, advocates, and academics. One such reform is wider adoption of Energy Resource Interconnection Service (ERIS) agreements....
Clean Energy Supply for All
Towards an Equitable Energy Transition - Achieving Energy Justice in the Global South with a Case Study on South Africa
The global energy transition is an urgent challenge driven by the need to combat climate change and meet the growing energy demands of an expanding population. As existing energy systems rely heavily on fossil fuels, a rapid shift toward cleaner, more sustainable alternatives is essential. Energy justice ensures that the benefits and burdens of this transition are distributed equitably. However, in the Global South, historical exploitation, economic constraints, and systemic inequalities worsen vulnerability to climate change and hinder progress toward sustainable energy solutions. Much of the current energy justice literature is Western-centric, often overlooking the Global South and focusing on a narrow definition of the concept. Furthermore, while existing studies often concentrate on qualitative analysis, quantitative measures of energy justice are underdeveloped. A robust methodology to quantify inclusive energy justice is essential, as the lack of such an index can hinder effective policy-making for equitable transitions. This thesis employs a mixed-methods approach, combining exploratory research with empirical data from a case study in South Africa, to develop new theoretical insights and a methodology for creating an energy justice index that integrates restorative justice into existing frameworks.
The research begins with a comprehensive review of existing knowledge, including theoretical frameworks and key principles to establish a foundation for further exploration of energy justice. This review focuses on the current state of energy justice, and critically examines the traditional three-tenet framework of distributional, procedural and recognition justice, as well as key decision-making principles. Recent critiques reveal that these frameworks are often too narrow, generalised, human- centred, and Western-centric. In response, this research integrates restorative justice as a key component, focusing on addressing past harms and preventing future damage to individuals, communities, and the environment as a whole. An ethical analysis using both Western and non-Western perspectives has been conducted to develop a more inclusive and diverse framework for energy justice. This approach offers a more holistic view of global ethical values in energy justice by combining the focus on individual rights from Western liberalism with the communitarian and relational values from non-Western philosophies.
In the second research phase, the study examined institutional frameworks and governance structures essential for implementing and scaling up sustainable energy in the Global South. With the ethical dimensions of energy justice thoroughly explored from the first research phase, this part transitions to a practical examination of how these frameworks and structures can support an equitable energy transition. Using the Original Institutional Economics (OIE) approach, this research highlights that achieving a just energy transition requires more than just technological innovation; it requires transformative shifts in institutional structures and cultural norms. The OIE framework provides valuable insights into how values embedded within social, political, economic, and cultural contexts impact the effectiveness of energy transition strategies. Additionally, the study integrates the Williamson framework, incorporating universal human rights law as a key layer. Recognised by international and national courts, this legal foundation strengthens the understanding of how governance structures in the Global South can support justice principles within energy governance...
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The research begins with a comprehensive review of existing knowledge, including theoretical frameworks and key principles to establish a foundation for further exploration of energy justice. This review focuses on the current state of energy justice, and critically examines the traditional three-tenet framework of distributional, procedural and recognition justice, as well as key decision-making principles. Recent critiques reveal that these frameworks are often too narrow, generalised, human- centred, and Western-centric. In response, this research integrates restorative justice as a key component, focusing on addressing past harms and preventing future damage to individuals, communities, and the environment as a whole. An ethical analysis using both Western and non-Western perspectives has been conducted to develop a more inclusive and diverse framework for energy justice. This approach offers a more holistic view of global ethical values in energy justice by combining the focus on individual rights from Western liberalism with the communitarian and relational values from non-Western philosophies.
In the second research phase, the study examined institutional frameworks and governance structures essential for implementing and scaling up sustainable energy in the Global South. With the ethical dimensions of energy justice thoroughly explored from the first research phase, this part transitions to a practical examination of how these frameworks and structures can support an equitable energy transition. Using the Original Institutional Economics (OIE) approach, this research highlights that achieving a just energy transition requires more than just technological innovation; it requires transformative shifts in institutional structures and cultural norms. The OIE framework provides valuable insights into how values embedded within social, political, economic, and cultural contexts impact the effectiveness of energy transition strategies. Additionally, the study integrates the Williamson framework, incorporating universal human rights law as a key layer. Recognised by international and national courts, this legal foundation strengthens the understanding of how governance structures in the Global South can support justice principles within energy governance...
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The global energy transition is an urgent challenge driven by the need to combat climate change and meet the growing energy demands of an expanding population. As existing energy systems rely heavily on fossil fuels, a rapid shift toward cleaner, more sustainable alternatives is essential. Energy justice ensures that the benefits and burdens of this transition are distributed equitably. However, in the Global South, historical exploitation, economic constraints, and systemic inequalities worsen vulnerability to climate change and hinder progress toward sustainable energy solutions. Much of the current energy justice literature is Western-centric, often overlooking the Global South and focusing on a narrow definition of the concept. Furthermore, while existing studies often concentrate on qualitative analysis, quantitative measures of energy justice are underdeveloped. A robust methodology to quantify inclusive energy justice is essential, as the lack of such an index can hinder effective policy-making for equitable transitions. This thesis employs a mixed-methods approach, combining exploratory research with empirical data from a case study in South Africa, to develop new theoretical insights and a methodology for creating an energy justice index that integrates restorative justice into existing frameworks.
The research begins with a comprehensive review of existing knowledge, including theoretical frameworks and key principles to establish a foundation for further exploration of energy justice. This review focuses on the current state of energy justice, and critically examines the traditional three-tenet framework of distributional, procedural and recognition justice, as well as key decision-making principles. Recent critiques reveal that these frameworks are often too narrow, generalised, human- centred, and Western-centric. In response, this research integrates restorative justice as a key component, focusing on addressing past harms and preventing future damage to individuals, communities, and the environment as a whole. An ethical analysis using both Western and non-Western perspectives has been conducted to develop a more inclusive and diverse framework for energy justice. This approach offers a more holistic view of global ethical values in energy justice by combining the focus on individual rights from Western liberalism with the communitarian and relational values from non-Western philosophies.
In the second research phase, the study examined institutional frameworks and governance structures essential for implementing and scaling up sustainable energy in the Global South. With the ethical dimensions of energy justice thoroughly explored from the first research phase, this part transitions to a practical examination of how these frameworks and structures can support an equitable energy transition. Using the Original Institutional Economics (OIE) approach, this research highlights that achieving a just energy transition requires more than just technological innovation; it requires transformative shifts in institutional structures and cultural norms. The OIE framework provides valuable insights into how values embedded within social, political, economic, and cultural contexts impact the effectiveness of energy transition strategies. Additionally, the study integrates the Williamson framework, incorporating universal human rights law as a key layer. Recognised by international and national courts, this legal foundation strengthens the understanding of how governance structures in the Global South can support justice principles within energy governance...
The research begins with a comprehensive review of existing knowledge, including theoretical frameworks and key principles to establish a foundation for further exploration of energy justice. This review focuses on the current state of energy justice, and critically examines the traditional three-tenet framework of distributional, procedural and recognition justice, as well as key decision-making principles. Recent critiques reveal that these frameworks are often too narrow, generalised, human- centred, and Western-centric. In response, this research integrates restorative justice as a key component, focusing on addressing past harms and preventing future damage to individuals, communities, and the environment as a whole. An ethical analysis using both Western and non-Western perspectives has been conducted to develop a more inclusive and diverse framework for energy justice. This approach offers a more holistic view of global ethical values in energy justice by combining the focus on individual rights from Western liberalism with the communitarian and relational values from non-Western philosophies.
In the second research phase, the study examined institutional frameworks and governance structures essential for implementing and scaling up sustainable energy in the Global South. With the ethical dimensions of energy justice thoroughly explored from the first research phase, this part transitions to a practical examination of how these frameworks and structures can support an equitable energy transition. Using the Original Institutional Economics (OIE) approach, this research highlights that achieving a just energy transition requires more than just technological innovation; it requires transformative shifts in institutional structures and cultural norms. The OIE framework provides valuable insights into how values embedded within social, political, economic, and cultural contexts impact the effectiveness of energy transition strategies. Additionally, the study integrates the Williamson framework, incorporating universal human rights law as a key layer. Recognised by international and national courts, this legal foundation strengthens the understanding of how governance structures in the Global South can support justice principles within energy governance...
Prioritising Congestion Mitigation Agents
An Institutional Analysis of Contract Negotiations for Implementing a New Queue Management Approach in the Dutch Distribution Grid
The Dutch electricity grid is experiencing significant congestion due to the increased integration of renewable energy sources and the rising electrification of consumption, leading to prolonged queues for grid connections. In response, the Authority for Consumers and Markets (ACM) has introduced a queue management approach that prioritizes market participants based on societal value, with the highest priority given to Congestion Mitigation Agents (CMAs). CMAs are entities that, by receiving transport capacity, contribute to increasing the overall available transport capacity for other users. This research investigates how Distribution System Operators (DSOs) can negotiate conditions with CMAs to ensure effective congestion mitigation and improved queue management.
Using the Institutional Analysis and Development (IAD) framework, this study analyzes the contract negotiations between DSOs and CMAs, focusing on how these negotiations are shaped by grid congestion conditions, financial considerations, regulatory context, and stakeholder attitudes. The research is informed by interviews with DSO and industry experts, data analysis of transport capacity usage during congestion, and a review of relevant regulations.
Key findings reveal that the physical and material conditions influencing contract negotiations highlight the complexity of exchanging transport capacity and congestion mitigation services. The study identifies two types of CMAs—CMA-f for feed-in congestion and CMA-c for consumption congestion—and examines their distinct negotiation challenges. The research also explores the attributes of stakeholders involved in the negotiations, including financial incentives and experiences with battery technology, which significantly impact negotiation dynamics. The study identifies four key variables in the contract negotiations: the power and duration of CMS provided by CMAs, the precision of availability, the coordination of activation, and the allocation of transport capacity outside congestion peaks. The study concludes that the outcomes of these negotiations are influenced by the types of market parties, supply and demand dynamics, and transport cost structures, with significant implications for fairness, sustainability, and grid stability.
Finally, the study offers policy recommendations to optimize CMA implementation, including reforms to transport cost structures, enhancing transparency, and improving coordination between DSOs and market participants. Future research should further explore the optimization of social welfare through CMA implementation and the coordination between Transmission System Operators (TSOs) and DSOs. ...
Using the Institutional Analysis and Development (IAD) framework, this study analyzes the contract negotiations between DSOs and CMAs, focusing on how these negotiations are shaped by grid congestion conditions, financial considerations, regulatory context, and stakeholder attitudes. The research is informed by interviews with DSO and industry experts, data analysis of transport capacity usage during congestion, and a review of relevant regulations.
Key findings reveal that the physical and material conditions influencing contract negotiations highlight the complexity of exchanging transport capacity and congestion mitigation services. The study identifies two types of CMAs—CMA-f for feed-in congestion and CMA-c for consumption congestion—and examines their distinct negotiation challenges. The research also explores the attributes of stakeholders involved in the negotiations, including financial incentives and experiences with battery technology, which significantly impact negotiation dynamics. The study identifies four key variables in the contract negotiations: the power and duration of CMS provided by CMAs, the precision of availability, the coordination of activation, and the allocation of transport capacity outside congestion peaks. The study concludes that the outcomes of these negotiations are influenced by the types of market parties, supply and demand dynamics, and transport cost structures, with significant implications for fairness, sustainability, and grid stability.
Finally, the study offers policy recommendations to optimize CMA implementation, including reforms to transport cost structures, enhancing transparency, and improving coordination between DSOs and market participants. Future research should further explore the optimization of social welfare through CMA implementation and the coordination between Transmission System Operators (TSOs) and DSOs. ...
The Dutch electricity grid is experiencing significant congestion due to the increased integration of renewable energy sources and the rising electrification of consumption, leading to prolonged queues for grid connections. In response, the Authority for Consumers and Markets (ACM) has introduced a queue management approach that prioritizes market participants based on societal value, with the highest priority given to Congestion Mitigation Agents (CMAs). CMAs are entities that, by receiving transport capacity, contribute to increasing the overall available transport capacity for other users. This research investigates how Distribution System Operators (DSOs) can negotiate conditions with CMAs to ensure effective congestion mitigation and improved queue management.
Using the Institutional Analysis and Development (IAD) framework, this study analyzes the contract negotiations between DSOs and CMAs, focusing on how these negotiations are shaped by grid congestion conditions, financial considerations, regulatory context, and stakeholder attitudes. The research is informed by interviews with DSO and industry experts, data analysis of transport capacity usage during congestion, and a review of relevant regulations.
Key findings reveal that the physical and material conditions influencing contract negotiations highlight the complexity of exchanging transport capacity and congestion mitigation services. The study identifies two types of CMAs—CMA-f for feed-in congestion and CMA-c for consumption congestion—and examines their distinct negotiation challenges. The research also explores the attributes of stakeholders involved in the negotiations, including financial incentives and experiences with battery technology, which significantly impact negotiation dynamics. The study identifies four key variables in the contract negotiations: the power and duration of CMS provided by CMAs, the precision of availability, the coordination of activation, and the allocation of transport capacity outside congestion peaks. The study concludes that the outcomes of these negotiations are influenced by the types of market parties, supply and demand dynamics, and transport cost structures, with significant implications for fairness, sustainability, and grid stability.
Finally, the study offers policy recommendations to optimize CMA implementation, including reforms to transport cost structures, enhancing transparency, and improving coordination between DSOs and market participants. Future research should further explore the optimization of social welfare through CMA implementation and the coordination between Transmission System Operators (TSOs) and DSOs.
Using the Institutional Analysis and Development (IAD) framework, this study analyzes the contract negotiations between DSOs and CMAs, focusing on how these negotiations are shaped by grid congestion conditions, financial considerations, regulatory context, and stakeholder attitudes. The research is informed by interviews with DSO and industry experts, data analysis of transport capacity usage during congestion, and a review of relevant regulations.
Key findings reveal that the physical and material conditions influencing contract negotiations highlight the complexity of exchanging transport capacity and congestion mitigation services. The study identifies two types of CMAs—CMA-f for feed-in congestion and CMA-c for consumption congestion—and examines their distinct negotiation challenges. The research also explores the attributes of stakeholders involved in the negotiations, including financial incentives and experiences with battery technology, which significantly impact negotiation dynamics. The study identifies four key variables in the contract negotiations: the power and duration of CMS provided by CMAs, the precision of availability, the coordination of activation, and the allocation of transport capacity outside congestion peaks. The study concludes that the outcomes of these negotiations are influenced by the types of market parties, supply and demand dynamics, and transport cost structures, with significant implications for fairness, sustainability, and grid stability.
Finally, the study offers policy recommendations to optimize CMA implementation, including reforms to transport cost structures, enhancing transparency, and improving coordination between DSOs and market participants. Future research should further explore the optimization of social welfare through CMA implementation and the coordination between Transmission System Operators (TSOs) and DSOs.
Enhancing Tender and Supply Contract Designs for a Robust Offshore Wind Industry
Improving the coordination between government, offshore wind farm developers, and wind turbine manufacturers in the North Sea
Offshore wind energy plays a key role in the renewable energy transition but faces significant obstacles due to increased project costs and complexities associated with supply chain bottlenecks. These challenges are compounded by inadequate tender and contract designs, which fail to effectively address macroeconomic challenges and supply chain constraints. Ensuring proper allocation of benefits, costs, and risks among key stakeholders is essential for mitigating these issues and achieving successful project development. This study employs an institutional analysis followed by a stylized quantitative model to represent the bargaining dynamics between developers and turbine suppliers. The research systematically examines key transactions and interactions among stakeholders—governments, developers, and turbine manufacturers. It evaluates how these entities manage financial risks and benefits and analyzes the adequacy of current tender frameworks in mature offshore wind markets, including the Netherlands, Denmark, Germany, and the United Kingdom. Through this analysis, the study proposes five key recommendations: linking tender roadmaps to decarbonization targets, aligning non-price and price criteria across markets, extending construction timelines and supporting design flexibility, prioritizing advanced-stage projects and balancing incentives, and shifting from financial offers to government equity participation. These recommendations aim to create a more robust framework for offshore wind development. Future research should include comparative analyses of tender mechanisms across various global markets to account for regional differences. Additionally, examining vertical integration within the offshore wind value chain—such as the acquisition of partners or infrastructure development by developers—could offer valuable insights into reducing coordination challenges. Incorporating a broader range of supply chain partners into the research will further enhance understanding of industry dynamics and inform more effective practices.
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Offshore wind energy plays a key role in the renewable energy transition but faces significant obstacles due to increased project costs and complexities associated with supply chain bottlenecks. These challenges are compounded by inadequate tender and contract designs, which fail to effectively address macroeconomic challenges and supply chain constraints. Ensuring proper allocation of benefits, costs, and risks among key stakeholders is essential for mitigating these issues and achieving successful project development. This study employs an institutional analysis followed by a stylized quantitative model to represent the bargaining dynamics between developers and turbine suppliers. The research systematically examines key transactions and interactions among stakeholders—governments, developers, and turbine manufacturers. It evaluates how these entities manage financial risks and benefits and analyzes the adequacy of current tender frameworks in mature offshore wind markets, including the Netherlands, Denmark, Germany, and the United Kingdom. Through this analysis, the study proposes five key recommendations: linking tender roadmaps to decarbonization targets, aligning non-price and price criteria across markets, extending construction timelines and supporting design flexibility, prioritizing advanced-stage projects and balancing incentives, and shifting from financial offers to government equity participation. These recommendations aim to create a more robust framework for offshore wind development. Future research should include comparative analyses of tender mechanisms across various global markets to account for regional differences. Additionally, examining vertical integration within the offshore wind value chain—such as the acquisition of partners or infrastructure development by developers—could offer valuable insights into reducing coordination challenges. Incorporating a broader range of supply chain partners into the research will further enhance understanding of industry dynamics and inform more effective practices.