P.W.G. Bots
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
27 records found
1
The operational potential of an integrated flexibility market for Dutch system operations
A PROOF-OF-CONCEPT STUDY
A transmission system operator (TSO) is responsible for balancing (BA) and congestion management (CM) of the high voltage grid. BA matches supply and demand while CM ensures the save transport of electricity. For both functions the TSO uses procured upward or downward reserves from connected parties, called flexibility. Five flexibility markets exist: Frequency Containment Reserve (FCR), automatic Frequency Restoration Reserve (aFRR), manual Frequency Restoration Reserve directly activated (mFRRda), Reserve Other Purposes (ROP), and Capacity Restriction Contracts (CRCs). FCR, aFRR, and mFRRda are used for BA, while ROP and CRCs are used for CM. Each product, as these are called, has distinct bid requirements and renumeration schemes. Therefore, flexibility providers commit their capacity to specific products in advance according to their needs and capabilities. Consequently, a TSO lacks the ability to address potential flexibility shortfalls in one area by drawing on capacity reserved for another. Mismatches can arise, possibly leading to stressful situations for operators, higher costs, and even regional power outages. In addition, the various market options create complexities for participants potentially increasing the likelihood of inefficiencies and misallocations of flexibility resources.
To address this issue and improve system efficiency, this study explored the possible integration of these markets, combining two or more of the current products into a single flexible reserve product, which offers can be used universally. This provides TSOs with more leeway to mix and match, increasing the use of the available capacity, and simplifies the offering process for market participants. The study specifically analyzed a combination of the capacities currently offered through aFRR and ROP in context of the Dutch system operations. These products turned out to have notable similarities, including moderate response times, overlapping procurement timelines, and comparable system properties, which increases the likelihood that their capacities can be used universally. In addition, these products had the highest accessibility of historical data, which was important for the proposed quantitative analysis, an area identified as a significant gap in the existing literature. The simulation compared the current market design, where aFRR and ROP handle distinct BA and CM issues, with an integrated system where capacities are interchangeable. Performance was measured by problem-solving capacity, failure frequency, capacity usage for BA and CM, and costs. Historical data over five days was used, simulating various imbalance and congestion scenarios.
Results showed a significant improvement: BA and CM costs for the TSO dropped by 70% and 80%, respectively, thanks to better utilization of low-priced bids. The problem-solving capacity, especially in the upward balancing direction, rose by 360%, attributed to complementary reserves from aFRR and ROP. CM capacity usage increased by 11%, as low-priced aFRR bids overruled the increased effectivity of bids for CM. While integrating aFRR and ROP showed significant benefits, combining markets like FCR and CRC, which have specific characteristics, is less promising. A partial integration of more general markets, while keeping specialized products, appears more feasible and still offers flexibility for TSOs. Challenges remain, including the need for locational information in all bids and potential auction structure adjustments. Further research should explore the best auction designs and operational rules for integrated market designs. ...
To address this issue and improve system efficiency, this study explored the possible integration of these markets, combining two or more of the current products into a single flexible reserve product, which offers can be used universally. This provides TSOs with more leeway to mix and match, increasing the use of the available capacity, and simplifies the offering process for market participants. The study specifically analyzed a combination of the capacities currently offered through aFRR and ROP in context of the Dutch system operations. These products turned out to have notable similarities, including moderate response times, overlapping procurement timelines, and comparable system properties, which increases the likelihood that their capacities can be used universally. In addition, these products had the highest accessibility of historical data, which was important for the proposed quantitative analysis, an area identified as a significant gap in the existing literature. The simulation compared the current market design, where aFRR and ROP handle distinct BA and CM issues, with an integrated system where capacities are interchangeable. Performance was measured by problem-solving capacity, failure frequency, capacity usage for BA and CM, and costs. Historical data over five days was used, simulating various imbalance and congestion scenarios.
Results showed a significant improvement: BA and CM costs for the TSO dropped by 70% and 80%, respectively, thanks to better utilization of low-priced bids. The problem-solving capacity, especially in the upward balancing direction, rose by 360%, attributed to complementary reserves from aFRR and ROP. CM capacity usage increased by 11%, as low-priced aFRR bids overruled the increased effectivity of bids for CM. While integrating aFRR and ROP showed significant benefits, combining markets like FCR and CRC, which have specific characteristics, is less promising. A partial integration of more general markets, while keeping specialized products, appears more feasible and still offers flexibility for TSOs. Challenges remain, including the need for locational information in all bids and potential auction structure adjustments. Further research should explore the best auction designs and operational rules for integrated market designs. ...
A transmission system operator (TSO) is responsible for balancing (BA) and congestion management (CM) of the high voltage grid. BA matches supply and demand while CM ensures the save transport of electricity. For both functions the TSO uses procured upward or downward reserves from connected parties, called flexibility. Five flexibility markets exist: Frequency Containment Reserve (FCR), automatic Frequency Restoration Reserve (aFRR), manual Frequency Restoration Reserve directly activated (mFRRda), Reserve Other Purposes (ROP), and Capacity Restriction Contracts (CRCs). FCR, aFRR, and mFRRda are used for BA, while ROP and CRCs are used for CM. Each product, as these are called, has distinct bid requirements and renumeration schemes. Therefore, flexibility providers commit their capacity to specific products in advance according to their needs and capabilities. Consequently, a TSO lacks the ability to address potential flexibility shortfalls in one area by drawing on capacity reserved for another. Mismatches can arise, possibly leading to stressful situations for operators, higher costs, and even regional power outages. In addition, the various market options create complexities for participants potentially increasing the likelihood of inefficiencies and misallocations of flexibility resources.
To address this issue and improve system efficiency, this study explored the possible integration of these markets, combining two or more of the current products into a single flexible reserve product, which offers can be used universally. This provides TSOs with more leeway to mix and match, increasing the use of the available capacity, and simplifies the offering process for market participants. The study specifically analyzed a combination of the capacities currently offered through aFRR and ROP in context of the Dutch system operations. These products turned out to have notable similarities, including moderate response times, overlapping procurement timelines, and comparable system properties, which increases the likelihood that their capacities can be used universally. In addition, these products had the highest accessibility of historical data, which was important for the proposed quantitative analysis, an area identified as a significant gap in the existing literature. The simulation compared the current market design, where aFRR and ROP handle distinct BA and CM issues, with an integrated system where capacities are interchangeable. Performance was measured by problem-solving capacity, failure frequency, capacity usage for BA and CM, and costs. Historical data over five days was used, simulating various imbalance and congestion scenarios.
Results showed a significant improvement: BA and CM costs for the TSO dropped by 70% and 80%, respectively, thanks to better utilization of low-priced bids. The problem-solving capacity, especially in the upward balancing direction, rose by 360%, attributed to complementary reserves from aFRR and ROP. CM capacity usage increased by 11%, as low-priced aFRR bids overruled the increased effectivity of bids for CM. While integrating aFRR and ROP showed significant benefits, combining markets like FCR and CRC, which have specific characteristics, is less promising. A partial integration of more general markets, while keeping specialized products, appears more feasible and still offers flexibility for TSOs. Challenges remain, including the need for locational information in all bids and potential auction structure adjustments. Further research should explore the best auction designs and operational rules for integrated market designs.
To address this issue and improve system efficiency, this study explored the possible integration of these markets, combining two or more of the current products into a single flexible reserve product, which offers can be used universally. This provides TSOs with more leeway to mix and match, increasing the use of the available capacity, and simplifies the offering process for market participants. The study specifically analyzed a combination of the capacities currently offered through aFRR and ROP in context of the Dutch system operations. These products turned out to have notable similarities, including moderate response times, overlapping procurement timelines, and comparable system properties, which increases the likelihood that their capacities can be used universally. In addition, these products had the highest accessibility of historical data, which was important for the proposed quantitative analysis, an area identified as a significant gap in the existing literature. The simulation compared the current market design, where aFRR and ROP handle distinct BA and CM issues, with an integrated system where capacities are interchangeable. Performance was measured by problem-solving capacity, failure frequency, capacity usage for BA and CM, and costs. Historical data over five days was used, simulating various imbalance and congestion scenarios.
Results showed a significant improvement: BA and CM costs for the TSO dropped by 70% and 80%, respectively, thanks to better utilization of low-priced bids. The problem-solving capacity, especially in the upward balancing direction, rose by 360%, attributed to complementary reserves from aFRR and ROP. CM capacity usage increased by 11%, as low-priced aFRR bids overruled the increased effectivity of bids for CM. While integrating aFRR and ROP showed significant benefits, combining markets like FCR and CRC, which have specific characteristics, is less promising. A partial integration of more general markets, while keeping specialized products, appears more feasible and still offers flexibility for TSOs. Challenges remain, including the need for locational information in all bids and potential auction structure adjustments. Further research should explore the best auction designs and operational rules for integrated market designs.
A New Market Design For Balancing and Redispatch
Evaluation of New Market Design Options for Integrating Balancing and Redispatch in the Dutch Electricity Market
The integration of non-dispatchable renewable energy and the electrification of society pose significant challenges to maintaining grid stability and efficiency. Currently, balancing and redispatch services in the Dutch electricity market operate as two separate markets, each managed by the Transmission System Operator (TSO). However, this separation leads to inefficiencies such as conflicting price signals, competition for the same flexible capacity, and increased congestion. This thesis evaluates three new market design options aimed at integrating redispatch and balancing services: 1) Simultaneous co-optimization 2) Separate two-step optimization with net capacity use, and 3) Separate two-step optimization with gross capacity use. A simulation model was developed using a simplified small-scale electricity network, testing the impact of these market designs under different pricing approaches: Marginal Pricing (MP), Pay-As-Bid (PAB), and Locational Marginal Pricing (LMP). The results demonstrate that while co-optimization can reduce capacity requirements by 21%, up to 81% of this improvement can be achieved by integrating redispatch and balancing with net capacity usage, without co-optimization. The study concludes that net capacity usage offers a practical alternative to co-optimization, with significant cost and capacity improvements. Further research should explore the operational feasibility of these new designs and the impact of network scale on market performance.
...
The integration of non-dispatchable renewable energy and the electrification of society pose significant challenges to maintaining grid stability and efficiency. Currently, balancing and redispatch services in the Dutch electricity market operate as two separate markets, each managed by the Transmission System Operator (TSO). However, this separation leads to inefficiencies such as conflicting price signals, competition for the same flexible capacity, and increased congestion. This thesis evaluates three new market design options aimed at integrating redispatch and balancing services: 1) Simultaneous co-optimization 2) Separate two-step optimization with net capacity use, and 3) Separate two-step optimization with gross capacity use. A simulation model was developed using a simplified small-scale electricity network, testing the impact of these market designs under different pricing approaches: Marginal Pricing (MP), Pay-As-Bid (PAB), and Locational Marginal Pricing (LMP). The results demonstrate that while co-optimization can reduce capacity requirements by 21%, up to 81% of this improvement can be achieved by integrating redispatch and balancing with net capacity usage, without co-optimization. The study concludes that net capacity usage offers a practical alternative to co-optimization, with significant cost and capacity improvements. Further research should explore the operational feasibility of these new designs and the impact of network scale on market performance.
Reproducing the “Flip a Coin or Vote” experiment with GPT-3.5 and GPT-4o
An simulation study to the suitability of LLMs as participants in economic and behavioural experiments
The latest state-of-the-art large language models (LLMs) are implicit computational models of humans due to how they are trained and designed. This implies that LLMs can be used as participants in economic and behavioural experiments. However, their technical and ethical limitations have sparked ongoing debate about the usefulness of LLMs in experimental research. This study contributes to the research area by reproducing the “Flip a Coin or Vote” experiment conducted by Hoffmann & Renes (2021), with GPT-3.5 and GPT-4o as participants.
The findings indicate that GPT-3.5 (1) struggles to fully understand the rules of the experiment, especially in calculating payoffs; (2) has difficulties interpreting negative and positive valuations; (3) fails to match the percentage of rational choices observed in the lab results; and (4) demonstrate results that deviate too much with the lab results to be considered human-like.
In contrast, GPT-4o shows greater promise as a ‘good’ participant. GPT-4o understands the rules of the game correctly and demonstrates a much stronger ability to make rational choices, even matching, and sometimes surpassing, the percentage of rational choices made by human participants. However, in the second part of the experiment, GPT-4o's decision-making becomes "inhumanly" accurate. Moreover the results deviate too much with the lab results to be considered human-like.
Future research should be focused on how to steer the results of LLMs towards more human-like results, possibly through enhanced prompting techniques or further model training and fine-tuning. Furthermore, future research should focus on establishing best practices for developing a more standardised approach to conducting simulation research with LLMs. ...
The findings indicate that GPT-3.5 (1) struggles to fully understand the rules of the experiment, especially in calculating payoffs; (2) has difficulties interpreting negative and positive valuations; (3) fails to match the percentage of rational choices observed in the lab results; and (4) demonstrate results that deviate too much with the lab results to be considered human-like.
In contrast, GPT-4o shows greater promise as a ‘good’ participant. GPT-4o understands the rules of the game correctly and demonstrates a much stronger ability to make rational choices, even matching, and sometimes surpassing, the percentage of rational choices made by human participants. However, in the second part of the experiment, GPT-4o's decision-making becomes "inhumanly" accurate. Moreover the results deviate too much with the lab results to be considered human-like.
Future research should be focused on how to steer the results of LLMs towards more human-like results, possibly through enhanced prompting techniques or further model training and fine-tuning. Furthermore, future research should focus on establishing best practices for developing a more standardised approach to conducting simulation research with LLMs. ...
The latest state-of-the-art large language models (LLMs) are implicit computational models of humans due to how they are trained and designed. This implies that LLMs can be used as participants in economic and behavioural experiments. However, their technical and ethical limitations have sparked ongoing debate about the usefulness of LLMs in experimental research. This study contributes to the research area by reproducing the “Flip a Coin or Vote” experiment conducted by Hoffmann & Renes (2021), with GPT-3.5 and GPT-4o as participants.
The findings indicate that GPT-3.5 (1) struggles to fully understand the rules of the experiment, especially in calculating payoffs; (2) has difficulties interpreting negative and positive valuations; (3) fails to match the percentage of rational choices observed in the lab results; and (4) demonstrate results that deviate too much with the lab results to be considered human-like.
In contrast, GPT-4o shows greater promise as a ‘good’ participant. GPT-4o understands the rules of the game correctly and demonstrates a much stronger ability to make rational choices, even matching, and sometimes surpassing, the percentage of rational choices made by human participants. However, in the second part of the experiment, GPT-4o's decision-making becomes "inhumanly" accurate. Moreover the results deviate too much with the lab results to be considered human-like.
Future research should be focused on how to steer the results of LLMs towards more human-like results, possibly through enhanced prompting techniques or further model training and fine-tuning. Furthermore, future research should focus on establishing best practices for developing a more standardised approach to conducting simulation research with LLMs.
The findings indicate that GPT-3.5 (1) struggles to fully understand the rules of the experiment, especially in calculating payoffs; (2) has difficulties interpreting negative and positive valuations; (3) fails to match the percentage of rational choices observed in the lab results; and (4) demonstrate results that deviate too much with the lab results to be considered human-like.
In contrast, GPT-4o shows greater promise as a ‘good’ participant. GPT-4o understands the rules of the game correctly and demonstrates a much stronger ability to make rational choices, even matching, and sometimes surpassing, the percentage of rational choices made by human participants. However, in the second part of the experiment, GPT-4o's decision-making becomes "inhumanly" accurate. Moreover the results deviate too much with the lab results to be considered human-like.
Future research should be focused on how to steer the results of LLMs towards more human-like results, possibly through enhanced prompting techniques or further model training and fine-tuning. Furthermore, future research should focus on establishing best practices for developing a more standardised approach to conducting simulation research with LLMs.
Success Factors of an Energy Community in an Urban Area
A Mixed Method Approach
As electrification progresses, marked by the increasing adoption of heat pumps, electric vehicles (EVs), and electric cooking, household electricity consumption is surging. This rise is pushing low voltage electricity networks to their capacity limits. Expanding and enhancing the grid to be more adaptable and intelligent is crucial, yet such transformations require substantial investment and time and cannot be achieved overnight. A practical interim solution to this challenge involves optimizing electricity consumption within residential areas through the formation of energy communities. These communities allow households to collectively manage their energy use, especially during peak load periods, thereby reducing strain on the electricity network.
An energy community typically comprises a designated area or neighborhood where households aim to produce and consume electricity locally as much as possible. Additionally, these communities actively adjust their energy consumption patterns to avoid peak loads, significantly reducing the need for extensive investments in electrical infrastructure. To encourage participation, a demand response program is implemented that financially rewards households for adjusting their usage during peak times. This strategy not only facilitates more efficient grid management but also provides incentives for participating stakeholders.
This research presents a case study from the Sporenburg neighborhood in Amsterdam, where an energy community model is being tested. The study aims to identify the factors that contribute to the success of such communities. Sporenburg is an ideal case study due to its equipped smart meters and Amsterdam's ambitious climate goals, which necessitate a higher rate of electrification. Although currently stable, projections suggest that by 2050, Amsterdam’s electricity demand could increase threefold to fivefold compared to 2022 levels. The primary research question this study addresses is: What are the critical factors, categorized into distinct groups, that contribute to the success of the energy community?
The research is structured into three sections focusing on different aspects of the energy community: the technical system, organizational structure, and individual perspectives. It identifies critical success factors, differentiated into essential conditions for success and contributory factors. The study employs various analytical methods, including a technical analysis of Sporenburg, stakeholder analysis, and a survey. ...
An energy community typically comprises a designated area or neighborhood where households aim to produce and consume electricity locally as much as possible. Additionally, these communities actively adjust their energy consumption patterns to avoid peak loads, significantly reducing the need for extensive investments in electrical infrastructure. To encourage participation, a demand response program is implemented that financially rewards households for adjusting their usage during peak times. This strategy not only facilitates more efficient grid management but also provides incentives for participating stakeholders.
This research presents a case study from the Sporenburg neighborhood in Amsterdam, where an energy community model is being tested. The study aims to identify the factors that contribute to the success of such communities. Sporenburg is an ideal case study due to its equipped smart meters and Amsterdam's ambitious climate goals, which necessitate a higher rate of electrification. Although currently stable, projections suggest that by 2050, Amsterdam’s electricity demand could increase threefold to fivefold compared to 2022 levels. The primary research question this study addresses is: What are the critical factors, categorized into distinct groups, that contribute to the success of the energy community?
The research is structured into three sections focusing on different aspects of the energy community: the technical system, organizational structure, and individual perspectives. It identifies critical success factors, differentiated into essential conditions for success and contributory factors. The study employs various analytical methods, including a technical analysis of Sporenburg, stakeholder analysis, and a survey. ...
As electrification progresses, marked by the increasing adoption of heat pumps, electric vehicles (EVs), and electric cooking, household electricity consumption is surging. This rise is pushing low voltage electricity networks to their capacity limits. Expanding and enhancing the grid to be more adaptable and intelligent is crucial, yet such transformations require substantial investment and time and cannot be achieved overnight. A practical interim solution to this challenge involves optimizing electricity consumption within residential areas through the formation of energy communities. These communities allow households to collectively manage their energy use, especially during peak load periods, thereby reducing strain on the electricity network.
An energy community typically comprises a designated area or neighborhood where households aim to produce and consume electricity locally as much as possible. Additionally, these communities actively adjust their energy consumption patterns to avoid peak loads, significantly reducing the need for extensive investments in electrical infrastructure. To encourage participation, a demand response program is implemented that financially rewards households for adjusting their usage during peak times. This strategy not only facilitates more efficient grid management but also provides incentives for participating stakeholders.
This research presents a case study from the Sporenburg neighborhood in Amsterdam, where an energy community model is being tested. The study aims to identify the factors that contribute to the success of such communities. Sporenburg is an ideal case study due to its equipped smart meters and Amsterdam's ambitious climate goals, which necessitate a higher rate of electrification. Although currently stable, projections suggest that by 2050, Amsterdam’s electricity demand could increase threefold to fivefold compared to 2022 levels. The primary research question this study addresses is: What are the critical factors, categorized into distinct groups, that contribute to the success of the energy community?
The research is structured into three sections focusing on different aspects of the energy community: the technical system, organizational structure, and individual perspectives. It identifies critical success factors, differentiated into essential conditions for success and contributory factors. The study employs various analytical methods, including a technical analysis of Sporenburg, stakeholder analysis, and a survey.
An energy community typically comprises a designated area or neighborhood where households aim to produce and consume electricity locally as much as possible. Additionally, these communities actively adjust their energy consumption patterns to avoid peak loads, significantly reducing the need for extensive investments in electrical infrastructure. To encourage participation, a demand response program is implemented that financially rewards households for adjusting their usage during peak times. This strategy not only facilitates more efficient grid management but also provides incentives for participating stakeholders.
This research presents a case study from the Sporenburg neighborhood in Amsterdam, where an energy community model is being tested. The study aims to identify the factors that contribute to the success of such communities. Sporenburg is an ideal case study due to its equipped smart meters and Amsterdam's ambitious climate goals, which necessitate a higher rate of electrification. Although currently stable, projections suggest that by 2050, Amsterdam’s electricity demand could increase threefold to fivefold compared to 2022 levels. The primary research question this study addresses is: What are the critical factors, categorized into distinct groups, that contribute to the success of the energy community?
The research is structured into three sections focusing on different aspects of the energy community: the technical system, organizational structure, and individual perspectives. It identifies critical success factors, differentiated into essential conditions for success and contributory factors. The study employs various analytical methods, including a technical analysis of Sporenburg, stakeholder analysis, and a survey.
The Netherlands aims to have a carbon-neutral energy system by 2050. Hydrogen is seen as an important energy carrier that can contribute to this transition. Not only can hydrogen potentially decarbonise industry, transportation, and possibly agriculture and the built environment, but it can also be used to store electricity for longer periods. As more green hydrogen is produced over the years by electrolysis, the hydrogen demand and supply will increasingly diverge. Underground hydrogen storage can provide a solution for the imbalance between hydrogen demand and supply in both the short and long term.
For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems.
Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy.
The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen.
In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.
...
For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems.
Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy.
The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen.
In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.
...
The Netherlands aims to have a carbon-neutral energy system by 2050. Hydrogen is seen as an important energy carrier that can contribute to this transition. Not only can hydrogen potentially decarbonise industry, transportation, and possibly agriculture and the built environment, but it can also be used to store electricity for longer periods. As more green hydrogen is produced over the years by electrolysis, the hydrogen demand and supply will increasingly diverge. Underground hydrogen storage can provide a solution for the imbalance between hydrogen demand and supply in both the short and long term.
For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems.
Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy.
The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen.
In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.
For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems.
Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy.
The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen.
In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.
One of the biggest current challenges for Earth and humanity is climate change. To lower the impact of already existing effects and decrease potential future effects of rising temperatures, countries have decided on actions with the goal of keeping the temperature rise no higher than 1.5°C. One part of those actions is a transition in the energy system from polluting fossil fuels to renewable energy carriers, such as wind, solar, or hydropower.
In the Netherlands, hydropower is clearly lagging behind compared to other European coun- tries, for various reasons. Some pioneering companies are still trying to utilize the potential of hydropower they see in the large water system in the Netherlands. However, they are struggling to progress and deploy their solutions, as happened at the Bosscherveld project in the south of the Netherlands, Maastricht. The companies involved there claimed that stakeholder processes were the main causes for barriers that stalled project advancements.
Within energy transition projects, stakeholder management often causes problems, and the ac- tual factors that make an actor oppose or stall a project are multifaceted and hard to grasp. Additionally, there is a lack of tools and methods for a company working on technologies in this field to gain a thorough understanding of their stakeholders and translate that understanding into concrete strategic decisions on how to behave in such a project. With this research it was attempted to identify the most important stakeholder-related factors for a company to consider when working within an energy transition based project. This should add managerial guidance as well as the ability to assess the status of a project. For a company already working on the project or that joins it, they can estimate the condition the project is in and see what is missing for its success, improving the overall certainty for potential revenues.
With a mixed-framework approach consisting of the definition of the main structural compo- nents and policies of the technology innovation system (TIS) surrounding the SHP, a classic stakeholder analysis using a power-interest-grid, and semi-structured interviews to create an expert model for the small-scale hydropower (SHP) project has been applied. The first two areas were mainly used to get an understanding of the technology’s surrounding market and to make assumptions regarding the roles involved in the project. The interviews were aimed to fully understand the project’s processes and each stakeholder’s perceptions on barriers in those processes, so that an ideal process and the stakeholders’ views on most important factors for such a project could be derived.
With this approach it was possible to identify 25 stakeholder-related factors that are important to consider for a company working on such a project, as well as understanding their interconnec- tions and reasons for why they are important. The factors then could be classified into themes
covering Motivation, Purpose, Effective Teamwork, Investment, Entrepreneurial Activities, Base of Collaboration, and Macro-Environment and clustered into areas that build the Foundation of the Venture the factors that are Supporting Collaboration, and the ones that influ- ence and define the Stakeholder Interest. The first area consists of factors, that need to be present at the beginning of the project or need to be established within the starting phase such as resources, a business model, and trust. The factors to support the collaboration are fostering continuous interaction and general rules for how it should be worked together, such as planning and ownership. The factors of stakeholder interest, achieve a higher resolution of how a stakeholder’s interest is formed, and what should be looked at during the stakeholder analysis, to understand the actors positioning within and towards the project, like a stakeholder’s drive, their personal vision, or simply what they can gain from the project. This must be done on a continuous bases, since a stakeholder’s motivation and gains can change due to unforeseeable events or a change in for instance a country’s policy.
Therefore, it could be seen that not one or a few instances or factors could be identified that resulted in the halt of the project, but a variety of aspects combined hampered its progress.
Furthermore, it has been pointed out that the previously identified factors are only partly ad- dressed by an adapted TIS framework by Ortt and Kamp (2022), which has been applied at the end of the research, due to its claim that it had an improved managerial perspective for companies working in a niche environment, trying to bring their technology to a wide market diffusion.
The possibility of generalizing the conclusions regarding the crucial factors regarding stakeholder participation for a company working on an SHP project in the Netherlands are limited due to the specificity of the project and the limited number of stakeholders and perspectives that could be gathered. Therefore, it is suggested to investigate additional hydropower projects in the Netherlands, compare them with the Bosscherveld case, and see whether the concluded factors can capture the dynamics there as well or if further refinements are necessary. ...
In the Netherlands, hydropower is clearly lagging behind compared to other European coun- tries, for various reasons. Some pioneering companies are still trying to utilize the potential of hydropower they see in the large water system in the Netherlands. However, they are struggling to progress and deploy their solutions, as happened at the Bosscherveld project in the south of the Netherlands, Maastricht. The companies involved there claimed that stakeholder processes were the main causes for barriers that stalled project advancements.
Within energy transition projects, stakeholder management often causes problems, and the ac- tual factors that make an actor oppose or stall a project are multifaceted and hard to grasp. Additionally, there is a lack of tools and methods for a company working on technologies in this field to gain a thorough understanding of their stakeholders and translate that understanding into concrete strategic decisions on how to behave in such a project. With this research it was attempted to identify the most important stakeholder-related factors for a company to consider when working within an energy transition based project. This should add managerial guidance as well as the ability to assess the status of a project. For a company already working on the project or that joins it, they can estimate the condition the project is in and see what is missing for its success, improving the overall certainty for potential revenues.
With a mixed-framework approach consisting of the definition of the main structural compo- nents and policies of the technology innovation system (TIS) surrounding the SHP, a classic stakeholder analysis using a power-interest-grid, and semi-structured interviews to create an expert model for the small-scale hydropower (SHP) project has been applied. The first two areas were mainly used to get an understanding of the technology’s surrounding market and to make assumptions regarding the roles involved in the project. The interviews were aimed to fully understand the project’s processes and each stakeholder’s perceptions on barriers in those processes, so that an ideal process and the stakeholders’ views on most important factors for such a project could be derived.
With this approach it was possible to identify 25 stakeholder-related factors that are important to consider for a company working on such a project, as well as understanding their interconnec- tions and reasons for why they are important. The factors then could be classified into themes
covering Motivation, Purpose, Effective Teamwork, Investment, Entrepreneurial Activities, Base of Collaboration, and Macro-Environment and clustered into areas that build the Foundation of the Venture the factors that are Supporting Collaboration, and the ones that influ- ence and define the Stakeholder Interest. The first area consists of factors, that need to be present at the beginning of the project or need to be established within the starting phase such as resources, a business model, and trust. The factors to support the collaboration are fostering continuous interaction and general rules for how it should be worked together, such as planning and ownership. The factors of stakeholder interest, achieve a higher resolution of how a stakeholder’s interest is formed, and what should be looked at during the stakeholder analysis, to understand the actors positioning within and towards the project, like a stakeholder’s drive, their personal vision, or simply what they can gain from the project. This must be done on a continuous bases, since a stakeholder’s motivation and gains can change due to unforeseeable events or a change in for instance a country’s policy.
Therefore, it could be seen that not one or a few instances or factors could be identified that resulted in the halt of the project, but a variety of aspects combined hampered its progress.
Furthermore, it has been pointed out that the previously identified factors are only partly ad- dressed by an adapted TIS framework by Ortt and Kamp (2022), which has been applied at the end of the research, due to its claim that it had an improved managerial perspective for companies working in a niche environment, trying to bring their technology to a wide market diffusion.
The possibility of generalizing the conclusions regarding the crucial factors regarding stakeholder participation for a company working on an SHP project in the Netherlands are limited due to the specificity of the project and the limited number of stakeholders and perspectives that could be gathered. Therefore, it is suggested to investigate additional hydropower projects in the Netherlands, compare them with the Bosscherveld case, and see whether the concluded factors can capture the dynamics there as well or if further refinements are necessary. ...
One of the biggest current challenges for Earth and humanity is climate change. To lower the impact of already existing effects and decrease potential future effects of rising temperatures, countries have decided on actions with the goal of keeping the temperature rise no higher than 1.5°C. One part of those actions is a transition in the energy system from polluting fossil fuels to renewable energy carriers, such as wind, solar, or hydropower.
In the Netherlands, hydropower is clearly lagging behind compared to other European coun- tries, for various reasons. Some pioneering companies are still trying to utilize the potential of hydropower they see in the large water system in the Netherlands. However, they are struggling to progress and deploy their solutions, as happened at the Bosscherveld project in the south of the Netherlands, Maastricht. The companies involved there claimed that stakeholder processes were the main causes for barriers that stalled project advancements.
Within energy transition projects, stakeholder management often causes problems, and the ac- tual factors that make an actor oppose or stall a project are multifaceted and hard to grasp. Additionally, there is a lack of tools and methods for a company working on technologies in this field to gain a thorough understanding of their stakeholders and translate that understanding into concrete strategic decisions on how to behave in such a project. With this research it was attempted to identify the most important stakeholder-related factors for a company to consider when working within an energy transition based project. This should add managerial guidance as well as the ability to assess the status of a project. For a company already working on the project or that joins it, they can estimate the condition the project is in and see what is missing for its success, improving the overall certainty for potential revenues.
With a mixed-framework approach consisting of the definition of the main structural compo- nents and policies of the technology innovation system (TIS) surrounding the SHP, a classic stakeholder analysis using a power-interest-grid, and semi-structured interviews to create an expert model for the small-scale hydropower (SHP) project has been applied. The first two areas were mainly used to get an understanding of the technology’s surrounding market and to make assumptions regarding the roles involved in the project. The interviews were aimed to fully understand the project’s processes and each stakeholder’s perceptions on barriers in those processes, so that an ideal process and the stakeholders’ views on most important factors for such a project could be derived.
With this approach it was possible to identify 25 stakeholder-related factors that are important to consider for a company working on such a project, as well as understanding their interconnec- tions and reasons for why they are important. The factors then could be classified into themes
covering Motivation, Purpose, Effective Teamwork, Investment, Entrepreneurial Activities, Base of Collaboration, and Macro-Environment and clustered into areas that build the Foundation of the Venture the factors that are Supporting Collaboration, and the ones that influ- ence and define the Stakeholder Interest. The first area consists of factors, that need to be present at the beginning of the project or need to be established within the starting phase such as resources, a business model, and trust. The factors to support the collaboration are fostering continuous interaction and general rules for how it should be worked together, such as planning and ownership. The factors of stakeholder interest, achieve a higher resolution of how a stakeholder’s interest is formed, and what should be looked at during the stakeholder analysis, to understand the actors positioning within and towards the project, like a stakeholder’s drive, their personal vision, or simply what they can gain from the project. This must be done on a continuous bases, since a stakeholder’s motivation and gains can change due to unforeseeable events or a change in for instance a country’s policy.
Therefore, it could be seen that not one or a few instances or factors could be identified that resulted in the halt of the project, but a variety of aspects combined hampered its progress.
Furthermore, it has been pointed out that the previously identified factors are only partly ad- dressed by an adapted TIS framework by Ortt and Kamp (2022), which has been applied at the end of the research, due to its claim that it had an improved managerial perspective for companies working in a niche environment, trying to bring their technology to a wide market diffusion.
The possibility of generalizing the conclusions regarding the crucial factors regarding stakeholder participation for a company working on an SHP project in the Netherlands are limited due to the specificity of the project and the limited number of stakeholders and perspectives that could be gathered. Therefore, it is suggested to investigate additional hydropower projects in the Netherlands, compare them with the Bosscherveld case, and see whether the concluded factors can capture the dynamics there as well or if further refinements are necessary.
In the Netherlands, hydropower is clearly lagging behind compared to other European coun- tries, for various reasons. Some pioneering companies are still trying to utilize the potential of hydropower they see in the large water system in the Netherlands. However, they are struggling to progress and deploy their solutions, as happened at the Bosscherveld project in the south of the Netherlands, Maastricht. The companies involved there claimed that stakeholder processes were the main causes for barriers that stalled project advancements.
Within energy transition projects, stakeholder management often causes problems, and the ac- tual factors that make an actor oppose or stall a project are multifaceted and hard to grasp. Additionally, there is a lack of tools and methods for a company working on technologies in this field to gain a thorough understanding of their stakeholders and translate that understanding into concrete strategic decisions on how to behave in such a project. With this research it was attempted to identify the most important stakeholder-related factors for a company to consider when working within an energy transition based project. This should add managerial guidance as well as the ability to assess the status of a project. For a company already working on the project or that joins it, they can estimate the condition the project is in and see what is missing for its success, improving the overall certainty for potential revenues.
With a mixed-framework approach consisting of the definition of the main structural compo- nents and policies of the technology innovation system (TIS) surrounding the SHP, a classic stakeholder analysis using a power-interest-grid, and semi-structured interviews to create an expert model for the small-scale hydropower (SHP) project has been applied. The first two areas were mainly used to get an understanding of the technology’s surrounding market and to make assumptions regarding the roles involved in the project. The interviews were aimed to fully understand the project’s processes and each stakeholder’s perceptions on barriers in those processes, so that an ideal process and the stakeholders’ views on most important factors for such a project could be derived.
With this approach it was possible to identify 25 stakeholder-related factors that are important to consider for a company working on such a project, as well as understanding their interconnec- tions and reasons for why they are important. The factors then could be classified into themes
covering Motivation, Purpose, Effective Teamwork, Investment, Entrepreneurial Activities, Base of Collaboration, and Macro-Environment and clustered into areas that build the Foundation of the Venture the factors that are Supporting Collaboration, and the ones that influ- ence and define the Stakeholder Interest. The first area consists of factors, that need to be present at the beginning of the project or need to be established within the starting phase such as resources, a business model, and trust. The factors to support the collaboration are fostering continuous interaction and general rules for how it should be worked together, such as planning and ownership. The factors of stakeholder interest, achieve a higher resolution of how a stakeholder’s interest is formed, and what should be looked at during the stakeholder analysis, to understand the actors positioning within and towards the project, like a stakeholder’s drive, their personal vision, or simply what they can gain from the project. This must be done on a continuous bases, since a stakeholder’s motivation and gains can change due to unforeseeable events or a change in for instance a country’s policy.
Therefore, it could be seen that not one or a few instances or factors could be identified that resulted in the halt of the project, but a variety of aspects combined hampered its progress.
Furthermore, it has been pointed out that the previously identified factors are only partly ad- dressed by an adapted TIS framework by Ortt and Kamp (2022), which has been applied at the end of the research, due to its claim that it had an improved managerial perspective for companies working in a niche environment, trying to bring their technology to a wide market diffusion.
The possibility of generalizing the conclusions regarding the crucial factors regarding stakeholder participation for a company working on an SHP project in the Netherlands are limited due to the specificity of the project and the limited number of stakeholders and perspectives that could be gathered. Therefore, it is suggested to investigate additional hydropower projects in the Netherlands, compare them with the Bosscherveld case, and see whether the concluded factors can capture the dynamics there as well or if further refinements are necessary.
The impact of the CBAM
Exploring the impact of the CBAM on promoting sustainable adjustments in the production process of semiconductor machines: A Malaysian case study in the semiconductor industry
This thesis aimed to investigate the potential of the EU’s carbon border adjustment mechanism (CBAM) to promote sustainable adjustments in the production process of semiconductor machines, considering uncertainties in carbon accounting. The research combines a review of existing literature, a case study analysis of a selected semiconductor company, and a policy analysis of the EU’s CBAM to provide insights into the ability of the CBAM to promote the use of sustainable materials in the semiconductor industry.
The literature review highlighted the significance of life cycle assessment (LCA) as a carbon accounting tool and its potential to assess the relevant embedded emissions for the CBAM. The review showed indeed that LCA could be useful to assess the first phases of the production of the raw materials. Additionally, the review showed that it is less useful as a decision-making tool due to the fact that decision-making regarding the CBAM requires the consideration of other parameters as well, not only the environmental ones. Additionally, the review revealed the novelty of the EU’s CBAM regarding the carbon accounting tools and the CBAM’s design. At the same time, it highlighted the relevance of this research project.
The statistical sensitivity analysis (SSA) conducted as an element of the case study showed that there exists a significant variation in the total embedded emissions of the semiconductor machine depending on the kind of steel and aluminium used. This highlighted the opportunity for improvements in the embedded emissions caused by the semiconductor machines. The SSA is also used to evaluate the cost-benefit analysis (CBA) of the implementation of green steel and aluminium in semiconductor machines. The CBA showed that a high reduction in embedded emissions is required to achieve a break-even level with the costs of sustainable steel and aluminium. However, this reduction also depends on the design and implementation of the CBAM. The stakeholder analysis highlighted the actors, transactions, frictions, and games caused by the implementation of the CBAM. A concluding policy analysis was conducted to present possible implementations of the CBAM. The analysis showed that under the current circumstances for the CBAM to be successful in promoting sustainable changes in the semiconductor machine, while considering the uncertainty in the carbon reporting process, an unrealistically
high carbon price is needed to break even. The analysis, however, indicated a bright spot; if global electricity production becomes less carbonintensive, the CBAM may work very well in promoting sustainable materials. Thus, this thesis concludes that for this case study the EU’s CBAM is not effective in promoting sustainable adjustments in semiconductor machines. However, this conclusion is obtained for this specific case with its specific extreme policy scenarios. Therefore, further research could be conducted in assessing other policy scenarios or carbon accounting tools and test how they evaluate the CBAM on its potential to promote the use of sustainable materials in other industries as well. ...
The literature review highlighted the significance of life cycle assessment (LCA) as a carbon accounting tool and its potential to assess the relevant embedded emissions for the CBAM. The review showed indeed that LCA could be useful to assess the first phases of the production of the raw materials. Additionally, the review showed that it is less useful as a decision-making tool due to the fact that decision-making regarding the CBAM requires the consideration of other parameters as well, not only the environmental ones. Additionally, the review revealed the novelty of the EU’s CBAM regarding the carbon accounting tools and the CBAM’s design. At the same time, it highlighted the relevance of this research project.
The statistical sensitivity analysis (SSA) conducted as an element of the case study showed that there exists a significant variation in the total embedded emissions of the semiconductor machine depending on the kind of steel and aluminium used. This highlighted the opportunity for improvements in the embedded emissions caused by the semiconductor machines. The SSA is also used to evaluate the cost-benefit analysis (CBA) of the implementation of green steel and aluminium in semiconductor machines. The CBA showed that a high reduction in embedded emissions is required to achieve a break-even level with the costs of sustainable steel and aluminium. However, this reduction also depends on the design and implementation of the CBAM. The stakeholder analysis highlighted the actors, transactions, frictions, and games caused by the implementation of the CBAM. A concluding policy analysis was conducted to present possible implementations of the CBAM. The analysis showed that under the current circumstances for the CBAM to be successful in promoting sustainable changes in the semiconductor machine, while considering the uncertainty in the carbon reporting process, an unrealistically
high carbon price is needed to break even. The analysis, however, indicated a bright spot; if global electricity production becomes less carbonintensive, the CBAM may work very well in promoting sustainable materials. Thus, this thesis concludes that for this case study the EU’s CBAM is not effective in promoting sustainable adjustments in semiconductor machines. However, this conclusion is obtained for this specific case with its specific extreme policy scenarios. Therefore, further research could be conducted in assessing other policy scenarios or carbon accounting tools and test how they evaluate the CBAM on its potential to promote the use of sustainable materials in other industries as well. ...
This thesis aimed to investigate the potential of the EU’s carbon border adjustment mechanism (CBAM) to promote sustainable adjustments in the production process of semiconductor machines, considering uncertainties in carbon accounting. The research combines a review of existing literature, a case study analysis of a selected semiconductor company, and a policy analysis of the EU’s CBAM to provide insights into the ability of the CBAM to promote the use of sustainable materials in the semiconductor industry.
The literature review highlighted the significance of life cycle assessment (LCA) as a carbon accounting tool and its potential to assess the relevant embedded emissions for the CBAM. The review showed indeed that LCA could be useful to assess the first phases of the production of the raw materials. Additionally, the review showed that it is less useful as a decision-making tool due to the fact that decision-making regarding the CBAM requires the consideration of other parameters as well, not only the environmental ones. Additionally, the review revealed the novelty of the EU’s CBAM regarding the carbon accounting tools and the CBAM’s design. At the same time, it highlighted the relevance of this research project.
The statistical sensitivity analysis (SSA) conducted as an element of the case study showed that there exists a significant variation in the total embedded emissions of the semiconductor machine depending on the kind of steel and aluminium used. This highlighted the opportunity for improvements in the embedded emissions caused by the semiconductor machines. The SSA is also used to evaluate the cost-benefit analysis (CBA) of the implementation of green steel and aluminium in semiconductor machines. The CBA showed that a high reduction in embedded emissions is required to achieve a break-even level with the costs of sustainable steel and aluminium. However, this reduction also depends on the design and implementation of the CBAM. The stakeholder analysis highlighted the actors, transactions, frictions, and games caused by the implementation of the CBAM. A concluding policy analysis was conducted to present possible implementations of the CBAM. The analysis showed that under the current circumstances for the CBAM to be successful in promoting sustainable changes in the semiconductor machine, while considering the uncertainty in the carbon reporting process, an unrealistically
high carbon price is needed to break even. The analysis, however, indicated a bright spot; if global electricity production becomes less carbonintensive, the CBAM may work very well in promoting sustainable materials. Thus, this thesis concludes that for this case study the EU’s CBAM is not effective in promoting sustainable adjustments in semiconductor machines. However, this conclusion is obtained for this specific case with its specific extreme policy scenarios. Therefore, further research could be conducted in assessing other policy scenarios or carbon accounting tools and test how they evaluate the CBAM on its potential to promote the use of sustainable materials in other industries as well.
The literature review highlighted the significance of life cycle assessment (LCA) as a carbon accounting tool and its potential to assess the relevant embedded emissions for the CBAM. The review showed indeed that LCA could be useful to assess the first phases of the production of the raw materials. Additionally, the review showed that it is less useful as a decision-making tool due to the fact that decision-making regarding the CBAM requires the consideration of other parameters as well, not only the environmental ones. Additionally, the review revealed the novelty of the EU’s CBAM regarding the carbon accounting tools and the CBAM’s design. At the same time, it highlighted the relevance of this research project.
The statistical sensitivity analysis (SSA) conducted as an element of the case study showed that there exists a significant variation in the total embedded emissions of the semiconductor machine depending on the kind of steel and aluminium used. This highlighted the opportunity for improvements in the embedded emissions caused by the semiconductor machines. The SSA is also used to evaluate the cost-benefit analysis (CBA) of the implementation of green steel and aluminium in semiconductor machines. The CBA showed that a high reduction in embedded emissions is required to achieve a break-even level with the costs of sustainable steel and aluminium. However, this reduction also depends on the design and implementation of the CBAM. The stakeholder analysis highlighted the actors, transactions, frictions, and games caused by the implementation of the CBAM. A concluding policy analysis was conducted to present possible implementations of the CBAM. The analysis showed that under the current circumstances for the CBAM to be successful in promoting sustainable changes in the semiconductor machine, while considering the uncertainty in the carbon reporting process, an unrealistically
high carbon price is needed to break even. The analysis, however, indicated a bright spot; if global electricity production becomes less carbonintensive, the CBAM may work very well in promoting sustainable materials. Thus, this thesis concludes that for this case study the EU’s CBAM is not effective in promoting sustainable adjustments in semiconductor machines. However, this conclusion is obtained for this specific case with its specific extreme policy scenarios. Therefore, further research could be conducted in assessing other policy scenarios or carbon accounting tools and test how they evaluate the CBAM on its potential to promote the use of sustainable materials in other industries as well.
Adoption and implementation of AI-driven Clinical Decision Support Systems in Cancer Care
A Case Study on Mammaprint in the Dutch Healthcare System using an Institutional Actor Analysis
The Dutch healthcare system is facing increasing pressures on accessibility, quality, and affordability, making it unsustainable in its current form. This is particularly evident in cancer care, where rising incidence and treatment costs threaten timely and effective care. To maintain sustainable cancer care, innovations proven to improve outcomes must be quickly adopted. AI-driven Clinical Decision Support Systems (AI-CDSS) are often cited as promising tools to support adequate, patient-centered cancer care, reducing over-treatment and enhancing quality of life. Despite extensive research on AI-CDSS development, adoption and implementation remain limited, especially in the Netherlands. This thesis investigates why adoption of AI-CDSS in cancer care fails, using Mammaprint, a molecular diagnostic AI-CDSS for breast cancer, as a case study.
The research finds that the primary barrier to adoption is an institutional void in reimbursement by basic health insurance. Institutional voids refer to the absence or inadequacy of supportive structures, regulations, and frameworks. In the case of Mammaprint, the reimbursement process relies on the “state of science and practice” (SWP) criterion, which assesses whether clinical utility—demonstrated health benefits for the patient—is proven. Since no definitive requirements exist for diagnostic AI-CDSS, the SWP criterion is open to interpretation. Disagreements between policy analysts and medical specialists emerged regarding burden of proof, study design, and the trade-off between quality of life and survival, highlighting the institutional ambiguity that hinders adoption.
The study further reveals that the use of AI per se is not a determining factor in reimbursement. AI-CDSS adoption pathways vary depending on technology type (molecular diagnostics vs. image analysis) and use case (in-hospital vs. screening). Similar institutional voids were identified for other AI-CDSS, emphasizing that these challenges are not unique to Mammaprint. Broader contextual factors also affect adoption, including limited hospital-based development resources, regulatory hurdles for certification, and additional evidence requirements for marketing. Comparisons with other countries reveal significant differences between predominantly public European healthcare systems and private U.S. systems.
This thesis applies a case study methodology using interviews and grey literature. Eighteen interviews with nineteen stakeholders, including policy analysts and medical specialists, were analyzed through an institutional actor analysis framework. This framework maps formal and informal institutions, identifies key actors and interactions, and assesses their power and interests. Findings were categorized using a sequential framework of device adoption phases, allowing structured analysis of barriers across the innovation lifecycle.
The research identifies actionable recommendations for Mammaprint and similar AI-CDSS. Consensus is needed on the required burden of proof, appropriate study designs to establish clinical utility, and ethical trade-offs between quality of life and survival. Temporary admission policies can facilitate data collection while maintaining accessibility. Furthermore, clarifying reimbursement pathways for molecular diagnostics and image analysis-based AI-CDSS, alongside developing a strategic vision for AI-CDSS in Dutch cancer care, is essential to future-proof the system. Without a clear vision and strategy, AI-CDSS adoption risks being impeded, potentially undermining sustainability and quality of cancer care.
In conclusion, institutional voids in reimbursement are the main factor stalling adoption of AI-CDSS in Dutch cancer care. Addressing these gaps through consensus building, clearer frameworks, and strategic planning can enhance the uptake of AI innovations, contributing to a more sustainable, patient-centered healthcare system. ...
The research finds that the primary barrier to adoption is an institutional void in reimbursement by basic health insurance. Institutional voids refer to the absence or inadequacy of supportive structures, regulations, and frameworks. In the case of Mammaprint, the reimbursement process relies on the “state of science and practice” (SWP) criterion, which assesses whether clinical utility—demonstrated health benefits for the patient—is proven. Since no definitive requirements exist for diagnostic AI-CDSS, the SWP criterion is open to interpretation. Disagreements between policy analysts and medical specialists emerged regarding burden of proof, study design, and the trade-off between quality of life and survival, highlighting the institutional ambiguity that hinders adoption.
The study further reveals that the use of AI per se is not a determining factor in reimbursement. AI-CDSS adoption pathways vary depending on technology type (molecular diagnostics vs. image analysis) and use case (in-hospital vs. screening). Similar institutional voids were identified for other AI-CDSS, emphasizing that these challenges are not unique to Mammaprint. Broader contextual factors also affect adoption, including limited hospital-based development resources, regulatory hurdles for certification, and additional evidence requirements for marketing. Comparisons with other countries reveal significant differences between predominantly public European healthcare systems and private U.S. systems.
This thesis applies a case study methodology using interviews and grey literature. Eighteen interviews with nineteen stakeholders, including policy analysts and medical specialists, were analyzed through an institutional actor analysis framework. This framework maps formal and informal institutions, identifies key actors and interactions, and assesses their power and interests. Findings were categorized using a sequential framework of device adoption phases, allowing structured analysis of barriers across the innovation lifecycle.
The research identifies actionable recommendations for Mammaprint and similar AI-CDSS. Consensus is needed on the required burden of proof, appropriate study designs to establish clinical utility, and ethical trade-offs between quality of life and survival. Temporary admission policies can facilitate data collection while maintaining accessibility. Furthermore, clarifying reimbursement pathways for molecular diagnostics and image analysis-based AI-CDSS, alongside developing a strategic vision for AI-CDSS in Dutch cancer care, is essential to future-proof the system. Without a clear vision and strategy, AI-CDSS adoption risks being impeded, potentially undermining sustainability and quality of cancer care.
In conclusion, institutional voids in reimbursement are the main factor stalling adoption of AI-CDSS in Dutch cancer care. Addressing these gaps through consensus building, clearer frameworks, and strategic planning can enhance the uptake of AI innovations, contributing to a more sustainable, patient-centered healthcare system. ...
The Dutch healthcare system is facing increasing pressures on accessibility, quality, and affordability, making it unsustainable in its current form. This is particularly evident in cancer care, where rising incidence and treatment costs threaten timely and effective care. To maintain sustainable cancer care, innovations proven to improve outcomes must be quickly adopted. AI-driven Clinical Decision Support Systems (AI-CDSS) are often cited as promising tools to support adequate, patient-centered cancer care, reducing over-treatment and enhancing quality of life. Despite extensive research on AI-CDSS development, adoption and implementation remain limited, especially in the Netherlands. This thesis investigates why adoption of AI-CDSS in cancer care fails, using Mammaprint, a molecular diagnostic AI-CDSS for breast cancer, as a case study.
The research finds that the primary barrier to adoption is an institutional void in reimbursement by basic health insurance. Institutional voids refer to the absence or inadequacy of supportive structures, regulations, and frameworks. In the case of Mammaprint, the reimbursement process relies on the “state of science and practice” (SWP) criterion, which assesses whether clinical utility—demonstrated health benefits for the patient—is proven. Since no definitive requirements exist for diagnostic AI-CDSS, the SWP criterion is open to interpretation. Disagreements between policy analysts and medical specialists emerged regarding burden of proof, study design, and the trade-off between quality of life and survival, highlighting the institutional ambiguity that hinders adoption.
The study further reveals that the use of AI per se is not a determining factor in reimbursement. AI-CDSS adoption pathways vary depending on technology type (molecular diagnostics vs. image analysis) and use case (in-hospital vs. screening). Similar institutional voids were identified for other AI-CDSS, emphasizing that these challenges are not unique to Mammaprint. Broader contextual factors also affect adoption, including limited hospital-based development resources, regulatory hurdles for certification, and additional evidence requirements for marketing. Comparisons with other countries reveal significant differences between predominantly public European healthcare systems and private U.S. systems.
This thesis applies a case study methodology using interviews and grey literature. Eighteen interviews with nineteen stakeholders, including policy analysts and medical specialists, were analyzed through an institutional actor analysis framework. This framework maps formal and informal institutions, identifies key actors and interactions, and assesses their power and interests. Findings were categorized using a sequential framework of device adoption phases, allowing structured analysis of barriers across the innovation lifecycle.
The research identifies actionable recommendations for Mammaprint and similar AI-CDSS. Consensus is needed on the required burden of proof, appropriate study designs to establish clinical utility, and ethical trade-offs between quality of life and survival. Temporary admission policies can facilitate data collection while maintaining accessibility. Furthermore, clarifying reimbursement pathways for molecular diagnostics and image analysis-based AI-CDSS, alongside developing a strategic vision for AI-CDSS in Dutch cancer care, is essential to future-proof the system. Without a clear vision and strategy, AI-CDSS adoption risks being impeded, potentially undermining sustainability and quality of cancer care.
In conclusion, institutional voids in reimbursement are the main factor stalling adoption of AI-CDSS in Dutch cancer care. Addressing these gaps through consensus building, clearer frameworks, and strategic planning can enhance the uptake of AI innovations, contributing to a more sustainable, patient-centered healthcare system.
The research finds that the primary barrier to adoption is an institutional void in reimbursement by basic health insurance. Institutional voids refer to the absence or inadequacy of supportive structures, regulations, and frameworks. In the case of Mammaprint, the reimbursement process relies on the “state of science and practice” (SWP) criterion, which assesses whether clinical utility—demonstrated health benefits for the patient—is proven. Since no definitive requirements exist for diagnostic AI-CDSS, the SWP criterion is open to interpretation. Disagreements between policy analysts and medical specialists emerged regarding burden of proof, study design, and the trade-off between quality of life and survival, highlighting the institutional ambiguity that hinders adoption.
The study further reveals that the use of AI per se is not a determining factor in reimbursement. AI-CDSS adoption pathways vary depending on technology type (molecular diagnostics vs. image analysis) and use case (in-hospital vs. screening). Similar institutional voids were identified for other AI-CDSS, emphasizing that these challenges are not unique to Mammaprint. Broader contextual factors also affect adoption, including limited hospital-based development resources, regulatory hurdles for certification, and additional evidence requirements for marketing. Comparisons with other countries reveal significant differences between predominantly public European healthcare systems and private U.S. systems.
This thesis applies a case study methodology using interviews and grey literature. Eighteen interviews with nineteen stakeholders, including policy analysts and medical specialists, were analyzed through an institutional actor analysis framework. This framework maps formal and informal institutions, identifies key actors and interactions, and assesses their power and interests. Findings were categorized using a sequential framework of device adoption phases, allowing structured analysis of barriers across the innovation lifecycle.
The research identifies actionable recommendations for Mammaprint and similar AI-CDSS. Consensus is needed on the required burden of proof, appropriate study designs to establish clinical utility, and ethical trade-offs between quality of life and survival. Temporary admission policies can facilitate data collection while maintaining accessibility. Furthermore, clarifying reimbursement pathways for molecular diagnostics and image analysis-based AI-CDSS, alongside developing a strategic vision for AI-CDSS in Dutch cancer care, is essential to future-proof the system. Without a clear vision and strategy, AI-CDSS adoption risks being impeded, potentially undermining sustainability and quality of cancer care.
In conclusion, institutional voids in reimbursement are the main factor stalling adoption of AI-CDSS in Dutch cancer care. Addressing these gaps through consensus building, clearer frameworks, and strategic planning can enhance the uptake of AI innovations, contributing to a more sustainable, patient-centered healthcare system.
Cost-Optimal Transition of the Dutch Electricity System From 2030 to 2050
Development of a Myopic Electricity System Optimisation Model
An important process for navigating the uncertainties associated with the energy transition is power system planning. Energy system models are considered to be useful tools for supporting the power system planning process but are limited in several ways. Firstly, energy system models often have a high computational burden, limiting the possibility of conducting a large range of experiments. Secondly, to reduce their computational burden some energy system models have limited temporal detail, resulting in overestimation of VRE capacity in the system, underestimation of VRE curtailment, and undervaluation of flexible resources. Thirdly, many energy system models are considered to be ‘black boxes’ due to their high complexity and limited transparency. To address these problems related to models for power system planning, the Operation and Planning Model (OPM) has been developed in this study. The OPM is a myopic electricity system cost-optimisation model with a high level of temporal detail and a relatively low computational burden to allow for exploring the development of a national energy system, focussing on the interplay between demand, supply, and storage of electricity.
The OPM has been applied to a reference case and two targeted experiments on the development of the Dutch electricity system from 2030 to 2050. The reference case showed that in case natural gas-fired power plants with carbon capture and storage are included in the OPM, the electricity system becomes highly dependent on this technology for the provision of electricity and flexibility. If this technology is not included in the OPM, the maximum number of additional generation and storage units per year is shown to have a major impact on system development. In case this factor is sufficiently high to not limit investments, the system becomes mainly dependent on onshore wind for the provision of electricity and energy storage in underground hydrogen storage facilities for flexibility. In case this factor is limited to a lower level, the system develops a more balanced technology mix. Multiple recommendations are made for future research continuing on the work presented in this report.
1. Applying the OPM to more experiments can result in more in-depth insights into factors influencing the development path of the Dutch electricity system. This could entail analysing scenarios with variations of the costs for the different technologies to assess under which cost levels the system develops a favourable or unfavourable technology mix.
2. The spatial scope of the OPM can be expanded to assess the importance of cross-border trade for ensuring system reliability and gaining insights into the co-evolution of interconnected electricity systems.
3. The demand side is assumed to be inflexible in the OPM. Including demand side flexibility can result in a more accurate view of the electricity system's development between 2030 and 2050.
4. The used time series data is based on a single year. An alternative approach is suggested in which time series data from multiple years is used which would better represent that perfect forecasting of capacity factors and electricity demand for a future year is not possible. ...
The OPM has been applied to a reference case and two targeted experiments on the development of the Dutch electricity system from 2030 to 2050. The reference case showed that in case natural gas-fired power plants with carbon capture and storage are included in the OPM, the electricity system becomes highly dependent on this technology for the provision of electricity and flexibility. If this technology is not included in the OPM, the maximum number of additional generation and storage units per year is shown to have a major impact on system development. In case this factor is sufficiently high to not limit investments, the system becomes mainly dependent on onshore wind for the provision of electricity and energy storage in underground hydrogen storage facilities for flexibility. In case this factor is limited to a lower level, the system develops a more balanced technology mix. Multiple recommendations are made for future research continuing on the work presented in this report.
1. Applying the OPM to more experiments can result in more in-depth insights into factors influencing the development path of the Dutch electricity system. This could entail analysing scenarios with variations of the costs for the different technologies to assess under which cost levels the system develops a favourable or unfavourable technology mix.
2. The spatial scope of the OPM can be expanded to assess the importance of cross-border trade for ensuring system reliability and gaining insights into the co-evolution of interconnected electricity systems.
3. The demand side is assumed to be inflexible in the OPM. Including demand side flexibility can result in a more accurate view of the electricity system's development between 2030 and 2050.
4. The used time series data is based on a single year. An alternative approach is suggested in which time series data from multiple years is used which would better represent that perfect forecasting of capacity factors and electricity demand for a future year is not possible. ...
An important process for navigating the uncertainties associated with the energy transition is power system planning. Energy system models are considered to be useful tools for supporting the power system planning process but are limited in several ways. Firstly, energy system models often have a high computational burden, limiting the possibility of conducting a large range of experiments. Secondly, to reduce their computational burden some energy system models have limited temporal detail, resulting in overestimation of VRE capacity in the system, underestimation of VRE curtailment, and undervaluation of flexible resources. Thirdly, many energy system models are considered to be ‘black boxes’ due to their high complexity and limited transparency. To address these problems related to models for power system planning, the Operation and Planning Model (OPM) has been developed in this study. The OPM is a myopic electricity system cost-optimisation model with a high level of temporal detail and a relatively low computational burden to allow for exploring the development of a national energy system, focussing on the interplay between demand, supply, and storage of electricity.
The OPM has been applied to a reference case and two targeted experiments on the development of the Dutch electricity system from 2030 to 2050. The reference case showed that in case natural gas-fired power plants with carbon capture and storage are included in the OPM, the electricity system becomes highly dependent on this technology for the provision of electricity and flexibility. If this technology is not included in the OPM, the maximum number of additional generation and storage units per year is shown to have a major impact on system development. In case this factor is sufficiently high to not limit investments, the system becomes mainly dependent on onshore wind for the provision of electricity and energy storage in underground hydrogen storage facilities for flexibility. In case this factor is limited to a lower level, the system develops a more balanced technology mix. Multiple recommendations are made for future research continuing on the work presented in this report.
1. Applying the OPM to more experiments can result in more in-depth insights into factors influencing the development path of the Dutch electricity system. This could entail analysing scenarios with variations of the costs for the different technologies to assess under which cost levels the system develops a favourable or unfavourable technology mix.
2. The spatial scope of the OPM can be expanded to assess the importance of cross-border trade for ensuring system reliability and gaining insights into the co-evolution of interconnected electricity systems.
3. The demand side is assumed to be inflexible in the OPM. Including demand side flexibility can result in a more accurate view of the electricity system's development between 2030 and 2050.
4. The used time series data is based on a single year. An alternative approach is suggested in which time series data from multiple years is used which would better represent that perfect forecasting of capacity factors and electricity demand for a future year is not possible.
The OPM has been applied to a reference case and two targeted experiments on the development of the Dutch electricity system from 2030 to 2050. The reference case showed that in case natural gas-fired power plants with carbon capture and storage are included in the OPM, the electricity system becomes highly dependent on this technology for the provision of electricity and flexibility. If this technology is not included in the OPM, the maximum number of additional generation and storage units per year is shown to have a major impact on system development. In case this factor is sufficiently high to not limit investments, the system becomes mainly dependent on onshore wind for the provision of electricity and energy storage in underground hydrogen storage facilities for flexibility. In case this factor is limited to a lower level, the system develops a more balanced technology mix. Multiple recommendations are made for future research continuing on the work presented in this report.
1. Applying the OPM to more experiments can result in more in-depth insights into factors influencing the development path of the Dutch electricity system. This could entail analysing scenarios with variations of the costs for the different technologies to assess under which cost levels the system develops a favourable or unfavourable technology mix.
2. The spatial scope of the OPM can be expanded to assess the importance of cross-border trade for ensuring system reliability and gaining insights into the co-evolution of interconnected electricity systems.
3. The demand side is assumed to be inflexible in the OPM. Including demand side flexibility can result in a more accurate view of the electricity system's development between 2030 and 2050.
4. The used time series data is based on a single year. An alternative approach is suggested in which time series data from multiple years is used which would better represent that perfect forecasting of capacity factors and electricity demand for a future year is not possible.
Design of a Decision-making Business Process for Transport Method Selection of Shell Chemicals
A Process Balancing Decarbonization, Cost and On-time Delivery Key Performance Indicators in Transporting Chemicals to the European Customers
This thesis project investigates the applicability of different sustainable transport methods for delivering chemical products to the European customers and a decision-making business process is derived from that. So, a set of representative lanes are selected to be involved in this project and the hauliers responsible for those lanes are asked to provide available sustainable transport option for each lane, CO2 emission reduction by the options, cost and on-time delivery as affected KPIs by the options. At the discussion with senior sales managers this info is communicated, and it emerged that there are many other factors than the three main KPIs that determine the applicability of an option for the representative lanes. They are visualized in a decision-making business process that balances the KPIs for determining the applicability of an option for a lane. the design is then validated by applying it on representative lanes and comparing the result with haulier responses. It emerged that the design suggests the applicability of fuel change, payload increase and renewing trucks options for the lanes that have currently direct road transport method. The design results in 13.60% total emission reduction for lanes with any current transport method and 19% for the lanes with current direct road transport and 6% logistics cost increase with no effect on on-time delivery.
...
This thesis project investigates the applicability of different sustainable transport methods for delivering chemical products to the European customers and a decision-making business process is derived from that. So, a set of representative lanes are selected to be involved in this project and the hauliers responsible for those lanes are asked to provide available sustainable transport option for each lane, CO2 emission reduction by the options, cost and on-time delivery as affected KPIs by the options. At the discussion with senior sales managers this info is communicated, and it emerged that there are many other factors than the three main KPIs that determine the applicability of an option for the representative lanes. They are visualized in a decision-making business process that balances the KPIs for determining the applicability of an option for a lane. the design is then validated by applying it on representative lanes and comparing the result with haulier responses. It emerged that the design suggests the applicability of fuel change, payload increase and renewing trucks options for the lanes that have currently direct road transport method. The design results in 13.60% total emission reduction for lanes with any current transport method and 19% for the lanes with current direct road transport and 6% logistics cost increase with no effect on on-time delivery.
Power system planning is a strenuous exercise for both people and computers. As carbon goals and geopolit- ical complexities intensify, the number of scenarios for electricity system development is limitless. In search of robust policy and profitable investments, policymakers and investment managers rely on long-term elec- tricity system simulation models. Those seeking to simulate myopic investment behaviour in systems reliant on intermittent generation, batteries and seasonal storage then encounter a computational barrier. The sole model detailed enough to conduct such simulations – MIDO (Myopic Investment Detailed Operational) – takes over 12 hours to run a 20-year scenario. Exploring avenues to cut back computational time is key to un- locking the potential to meaningful scenario analyses. Also, due to its novelty, many of its assumptions and modeling choices were not validated with stakeholders. Research into methodologies to reduce the computational burden at an acceptable compromise to model behaviour thus holds merit.
To this end, this methodological thesis explores myopic optimisation, developing the MODO methodology: Myopic Optimisation, with Detailed Operational analysis of system performance. We have realised an 85% improvement in computational efficiency with a sufficient degree of accuracy for it to be considered as an alternative to MIDO. The essence of our approach is the replacement of an iterative investment loop with a single optimisation. In doing so, we transfer insights from the field of agent-based modeling into the field of myopic optimisation, potentially enabling the revitalisation of a research area which has seen little activity in recent years.
MODO has been implemented as a modular toolkit in Linny-R, in such a way that users need not have any technical modeling knowledge to be able to construct their own system and/ or analyse their own scenarios. The implementation in Linny-R greatly enhances model transparency and usability.
We create a projection for the state of the Dutch electricity system by 2030 and use MODO to simulate the transition path from 2030-2050. Preliminary results suggest the significant potential of both seasonal (hy- drogen) and battery (lithium-ion) storage to lower electricity prices below 2030 levels, bringing down annual consumer spending to EUR 15 bn annually in a scenario with mid-range commodity price forecasts. Addi- tionally, results affirm the functionality of MODO in more complex electricity systems.
We identify three exciting avenues for future research. The risk of contemporary investment decisions is largely mitigated through long-term contracting, such as bilateral power purchase agreements or forward ENDEX markets. Ideally, academic work would converge to the point where a first model simulates a range of potential portfolio choices based on long-term contracting, after which - based on risk appetite - the upside potential of offering excess capacity on spot markets is assessed with a methodology such as MODO. However, as the data on such long-term contracting is often kept secret, research into ways to approximate the data or work with it using ’black-box’ models could prove fruitful. Secondly, researchers could choose to build upon this work by leveraging it to conduct large-scale exploratory analyses. The case studies in this work illustrate the potential of MODO, but only provide a preliminary insight into system development. Whilst model devel- opment has been informed by a stakeholder consultation, the scenarios analysed have not. This is simplified by the highly accessible nature of MODO’s implementation in Linny-R. Lastly, we suspect that qualitative re- search into the empirical applicability of power system simulation models might prove invaluable. Research that treats decision-makers and policy analysts as clients to discover their modeling needs could result in a roadmap for the participatory development of power simulation models in order to better foster their adop- tion, and that of their findings. ...
Power system planning is a strenuous exercise for both people and computers. As carbon goals and geopolit- ical complexities intensify, the number of scenarios for electricity system development is limitless. In search of robust policy and profitable investments, policymakers and investment managers rely on long-term elec- tricity system simulation models. Those seeking to simulate myopic investment behaviour in systems reliant on intermittent generation, batteries and seasonal storage then encounter a computational barrier. The sole model detailed enough to conduct such simulations – MIDO (Myopic Investment Detailed Operational) – takes over 12 hours to run a 20-year scenario. Exploring avenues to cut back computational time is key to un- locking the potential to meaningful scenario analyses. Also, due to its novelty, many of its assumptions and modeling choices were not validated with stakeholders. Research into methodologies to reduce the computational burden at an acceptable compromise to model behaviour thus holds merit.
To this end, this methodological thesis explores myopic optimisation, developing the MODO methodology: Myopic Optimisation, with Detailed Operational analysis of system performance. We have realised an 85% improvement in computational efficiency with a sufficient degree of accuracy for it to be considered as an alternative to MIDO. The essence of our approach is the replacement of an iterative investment loop with a single optimisation. In doing so, we transfer insights from the field of agent-based modeling into the field of myopic optimisation, potentially enabling the revitalisation of a research area which has seen little activity in recent years.
MODO has been implemented as a modular toolkit in Linny-R, in such a way that users need not have any technical modeling knowledge to be able to construct their own system and/ or analyse their own scenarios. The implementation in Linny-R greatly enhances model transparency and usability.
We create a projection for the state of the Dutch electricity system by 2030 and use MODO to simulate the transition path from 2030-2050. Preliminary results suggest the significant potential of both seasonal (hy- drogen) and battery (lithium-ion) storage to lower electricity prices below 2030 levels, bringing down annual consumer spending to EUR 15 bn annually in a scenario with mid-range commodity price forecasts. Addi- tionally, results affirm the functionality of MODO in more complex electricity systems.
We identify three exciting avenues for future research. The risk of contemporary investment decisions is largely mitigated through long-term contracting, such as bilateral power purchase agreements or forward ENDEX markets. Ideally, academic work would converge to the point where a first model simulates a range of potential portfolio choices based on long-term contracting, after which - based on risk appetite - the upside potential of offering excess capacity on spot markets is assessed with a methodology such as MODO. However, as the data on such long-term contracting is often kept secret, research into ways to approximate the data or work with it using ’black-box’ models could prove fruitful. Secondly, researchers could choose to build upon this work by leveraging it to conduct large-scale exploratory analyses. The case studies in this work illustrate the potential of MODO, but only provide a preliminary insight into system development. Whilst model devel- opment has been informed by a stakeholder consultation, the scenarios analysed have not. This is simplified by the highly accessible nature of MODO’s implementation in Linny-R. Lastly, we suspect that qualitative re- search into the empirical applicability of power system simulation models might prove invaluable. Research that treats decision-makers and policy analysts as clients to discover their modeling needs could result in a roadmap for the participatory development of power simulation models in order to better foster their adop- tion, and that of their findings.
Master thesis
(2022)
-
N.F. van der Kooij, S. Hinrichs-Krapels, C.P. van Beers, P.W.G. Bots, Jelle Schuitemaker
Currently, medical technologies in Sub-Saharan Africa (SSA) are not yet sufficient to provide robust healthcare systems. This is not due to it being unavailable, but due to a large portion of present technologies being non-functional. This non-functionality also causes a lack of adoption of medical technologies. Through a T-shaped case study (e.g. a multiple case study with one case worked out in-depth), current barriers in adoption are uncovered. These are then integrated into an actionable framework that can be used by organizations implementing medical technologies in SSA to uncover barriers applicable to them, as well as proposed mitigating strategies to overcome these barriers. Themes included affecting adoption in this thesis are: maintenance, training, organizational and behavioral change. Barriers discovered can be divided into three categories. The first being resource-barriers, covering time, money, human and materials. Secondly, institutional barriers, covering policy, trust and the need for breaking habits. The last barrier category covers communication between client and organization. The framework offers the possibility to find applicable barriers based upon characteristics of an implementation situation. The implementation situation herein is medical equipment (ME) being implemented by an organization (O) into a client organization (CO). Using the knowledge on barriers in an early stage provides opportunity for the organization to overcome these barriers by informed analysis of mitigating strategies that they could implement.
...
Currently, medical technologies in Sub-Saharan Africa (SSA) are not yet sufficient to provide robust healthcare systems. This is not due to it being unavailable, but due to a large portion of present technologies being non-functional. This non-functionality also causes a lack of adoption of medical technologies. Through a T-shaped case study (e.g. a multiple case study with one case worked out in-depth), current barriers in adoption are uncovered. These are then integrated into an actionable framework that can be used by organizations implementing medical technologies in SSA to uncover barriers applicable to them, as well as proposed mitigating strategies to overcome these barriers. Themes included affecting adoption in this thesis are: maintenance, training, organizational and behavioral change. Barriers discovered can be divided into three categories. The first being resource-barriers, covering time, money, human and materials. Secondly, institutional barriers, covering policy, trust and the need for breaking habits. The last barrier category covers communication between client and organization. The framework offers the possibility to find applicable barriers based upon characteristics of an implementation situation. The implementation situation herein is medical equipment (ME) being implemented by an organization (O) into a client organization (CO). Using the knowledge on barriers in an early stage provides opportunity for the organization to overcome these barriers by informed analysis of mitigating strategies that they could implement.
Institutions and contact tracing applications
Comparing design processes in France, Germany and the Netherlands
After the start of the COVID19 pandemic, many countries decided to develop digital contact tracing applications to assist in contact tracing. This aimed to warn people who were potentially infected as early as possible and stop them from spreading the virus further and bringing down the total amount of new infections. Even though there was some proof of the potential effectiveness of applications such as these, they had never been deployed at scale before the pandemic and there was no standardised and shared vision on how to best implement these applications, meaning that countries that wanted to include digital contact tracing as part of their COVID19 strategies needed to design and develop an application themselves...
...
After the start of the COVID19 pandemic, many countries decided to develop digital contact tracing applications to assist in contact tracing. This aimed to warn people who were potentially infected as early as possible and stop them from spreading the virus further and bringing down the total amount of new infections. Even though there was some proof of the potential effectiveness of applications such as these, they had never been deployed at scale before the pandemic and there was no standardised and shared vision on how to best implement these applications, meaning that countries that wanted to include digital contact tracing as part of their COVID19 strategies needed to design and develop an application themselves...
As global climate change concerns intensify, the European Union aims towards the decarbonization of its Member States. Over the last decade, hydrogen has received increasing attention as a vital element to achieve this decarbonization. However, the diffusion of hydrogen in the European Union is critically dependent on the emergence of a pipeline transmission infrastructure. This research aims towards the generation of a robust European hydrogen infrastructure between 2030 and 2050. To do so, a novel methodology combining Exploratory Modelling and Analysis (EMA) and graph theory is proposed. The essence of this methodology is to generate a network that performs well over a wide range of plausible futures. Results of this research highlight that a sizeable European hydrogen network may emerge towards 2050 with Italy and Germany at its core. In 2030, multiple relatively small networks may arise around Italy, Germany and the Benelux. These components can be expected to grow in size by 2040 but nevertheless stay disconnected. As of 2050, a cohesive European network spanning from the Balkan in the East to France in the West may emerge, connecting the previously disconnected networks.
...
As global climate change concerns intensify, the European Union aims towards the decarbonization of its Member States. Over the last decade, hydrogen has received increasing attention as a vital element to achieve this decarbonization. However, the diffusion of hydrogen in the European Union is critically dependent on the emergence of a pipeline transmission infrastructure. This research aims towards the generation of a robust European hydrogen infrastructure between 2030 and 2050. To do so, a novel methodology combining Exploratory Modelling and Analysis (EMA) and graph theory is proposed. The essence of this methodology is to generate a network that performs well over a wide range of plausible futures. Results of this research highlight that a sizeable European hydrogen network may emerge towards 2050 with Italy and Germany at its core. In 2030, multiple relatively small networks may arise around Italy, Germany and the Benelux. These components can be expected to grow in size by 2040 but nevertheless stay disconnected. As of 2050, a cohesive European network spanning from the Balkan in the East to France in the West may emerge, connecting the previously disconnected networks.
In an energy-only market, private investors play a crucial role in realizing the security of supply. However, several specific characteristics inherent to his market design cause concern about whether this market design is suitable for system adequacy. Moreover, due to policy goals aimed at working towards low-carbon energy systems, these concerns are increased. In this light, many countries have implemented a capacity remuneration mechanism to increase the stability of the security-of-supply in their energy system. While earlier research has been conducted on this topic, we observe two knowledge gaps: 1) from a theoretical perspective, there is debate on the effectiveness of capacity remuneration mechanisms, especially on the role of seasonal storage, 2) from a methodological perspective, we find that there are no models that are equipped to consider myopic investments while having a high enough detailed operational model also to consider investments in (seasonal) storage and the impact of extreme weather scenarios. This research aims to fill these knowledge gaps by presenting a proof-of-concept of a myopic investment detailed operational (MIDO) model. In our thesis, we focus on system adequacy. However, by developing the MIDO model, we hope to enable researchers to analyze any problem related to long-term energy system development with a multi-time scale nature. At the center of the MIDO model are three sub-models: the investment decision model (ID), the future price (FP) model, and the present price model (PP). The first step in this loop is the present-price model, which generates highly accurate information on the operation of a market. After running a year in the PP model, information on the performance of all assets in a market is sent to the ID model. With this data, the ID model performs two tasks: invest based on limited information in an iterative greedy process and dismantle assets losing money. The ID model gets the information on the performance of investment assets under consideration from the FP model. The FP model generates less reliable information than the PP, but it does so very fast. After the investment decisions are made, this information is transferred to the PP model, and the loop starts from the beginning. In this way, a market is simulated with investment cycles, delayed responses, uncertainty, and risk upon investment decisions. This process enables the comparison of different forms of capacity remuneration mechanisms and the effect on the security-of-supply in an isolated energy system. We explore two distinctly different futures within our thesis to see if there is any added value in a capacity market in any of these futures. Moreover, within these futures, we analyze how a capacity market can help withstand an extreme-weather scenario. From this analysis, we find that a capacity market has added value in all scenarios, from the point of system adequacy but also a total consumer cost perspective. However, because we only have explored two futures, we do not argue that a capacity market will always positively impact the system adequacy within an energy system. Instead, we argue against the notion that seasonal storage on its own is always enough to reduce all shortages created in an energy-only market and disagree with the idea that seasonal storage makes capacity markets redundant. We identify two avenues for future research. First, researchers interested in generating more and better results should utilize the MIDO model to explore more future scenarios and compare different forms of capacity mechanisms. Second, research aimed at improving the MIDO model should focus on limiting the computational time generated by the investment loop and seek to expand on the model’s functionalities.
...
In an energy-only market, private investors play a crucial role in realizing the security of supply. However, several specific characteristics inherent to his market design cause concern about whether this market design is suitable for system adequacy. Moreover, due to policy goals aimed at working towards low-carbon energy systems, these concerns are increased. In this light, many countries have implemented a capacity remuneration mechanism to increase the stability of the security-of-supply in their energy system. While earlier research has been conducted on this topic, we observe two knowledge gaps: 1) from a theoretical perspective, there is debate on the effectiveness of capacity remuneration mechanisms, especially on the role of seasonal storage, 2) from a methodological perspective, we find that there are no models that are equipped to consider myopic investments while having a high enough detailed operational model also to consider investments in (seasonal) storage and the impact of extreme weather scenarios. This research aims to fill these knowledge gaps by presenting a proof-of-concept of a myopic investment detailed operational (MIDO) model. In our thesis, we focus on system adequacy. However, by developing the MIDO model, we hope to enable researchers to analyze any problem related to long-term energy system development with a multi-time scale nature. At the center of the MIDO model are three sub-models: the investment decision model (ID), the future price (FP) model, and the present price model (PP). The first step in this loop is the present-price model, which generates highly accurate information on the operation of a market. After running a year in the PP model, information on the performance of all assets in a market is sent to the ID model. With this data, the ID model performs two tasks: invest based on limited information in an iterative greedy process and dismantle assets losing money. The ID model gets the information on the performance of investment assets under consideration from the FP model. The FP model generates less reliable information than the PP, but it does so very fast. After the investment decisions are made, this information is transferred to the PP model, and the loop starts from the beginning. In this way, a market is simulated with investment cycles, delayed responses, uncertainty, and risk upon investment decisions. This process enables the comparison of different forms of capacity remuneration mechanisms and the effect on the security-of-supply in an isolated energy system. We explore two distinctly different futures within our thesis to see if there is any added value in a capacity market in any of these futures. Moreover, within these futures, we analyze how a capacity market can help withstand an extreme-weather scenario. From this analysis, we find that a capacity market has added value in all scenarios, from the point of system adequacy but also a total consumer cost perspective. However, because we only have explored two futures, we do not argue that a capacity market will always positively impact the system adequacy within an energy system. Instead, we argue against the notion that seasonal storage on its own is always enough to reduce all shortages created in an energy-only market and disagree with the idea that seasonal storage makes capacity markets redundant. We identify two avenues for future research. First, researchers interested in generating more and better results should utilize the MIDO model to explore more future scenarios and compare different forms of capacity mechanisms. Second, research aimed at improving the MIDO model should focus on limiting the computational time generated by the investment loop and seek to expand on the model’s functionalities.
Institutional interactions in climate adaptation of interdependent transport infrastructures
The case surrounding the Port of Rotterdam
Transport infrastructures that connect ports to the hinterland are important enablers of economic growth and development in the region. Therefore, infrastructure owners and governments on different levels are engaging in climate adaptation to adapt transport infrastructures to actual or expected climatic hazards. Existing research on the institutional dimension of climate adaptation had exclusively focussed on trying to understand whether existing institutions allowed and encouraged actors to develop and realize adaptation strategies, and as a result, enhance the adaptive capacity of society. However, the connectivity and interdependencies between institutions that guide actors in their decision-making had not been studied. This research therefore improved and applied the Institutional Network Analysis (INA) method to understand how the various public and private parties interact with each other for climate adaptation of transport infrastructures connected to the Port of Rotterdam.
...
Transport infrastructures that connect ports to the hinterland are important enablers of economic growth and development in the region. Therefore, infrastructure owners and governments on different levels are engaging in climate adaptation to adapt transport infrastructures to actual or expected climatic hazards. Existing research on the institutional dimension of climate adaptation had exclusively focussed on trying to understand whether existing institutions allowed and encouraged actors to develop and realize adaptation strategies, and as a result, enhance the adaptive capacity of society. However, the connectivity and interdependencies between institutions that guide actors in their decision-making had not been studied. This research therefore improved and applied the Institutional Network Analysis (INA) method to understand how the various public and private parties interact with each other for climate adaptation of transport infrastructures connected to the Port of Rotterdam.
Multi-value comparison for (raw) materials and innovations in the growing media sector
A Multi-Criteria Decision Analysis comparing alternatives to contribute towards the use of more sustainable materials in the growing media sector considering all the business constraints, applied to a selection of materials from growing media company Kekkilä-BVB
The increase in the global population and the shift towards a healthier diet is leading to several global challenges such as the growing demand for safe and healthy food and therefore also for growing media. The growing media sector is looking for sustainable alternative raw materials, but at the same time the global population needs to be provided of food. The aim of this research is to contribute to the use of more sustainable materials in the European growing media sector, considering all the business constraints. There is looked at what developments and analytical tools can contribute to decision-making in the growing media sector in its transition to a sustainable sector. A system analysis on the basis of a multi-level perspective and a Multi-Criteria Decision Analysis have been carried out. An expert panel was asked to indicate their preferences for the selection criteria. The results of the system analysis show that the following developments are important in the transition to a sustainable growing media sector; Circular economy, Data-driven horticulture, Microbial horticulture and Increasing legislation. In the MCDA, four groups of alternatives have been defined: (1) white peat, perlite and woodfibre. (2) Accretio, mineral wool, foam and black peat. (3) Bark. (4) (standardized) Compost and the coir materials. The scenarios show that in the future the results will be very robust. However, the importance of peat will decrease and other materials will score better. The growing demand for growing media requires to focus on the low scores of the MCDA and the utilisation of current and future developments in order to make all raw materials attractive for use in the substrate sector. In addition, the sector will have to cooperate with all those involved in order to operate more sustainably. This will minimise the importance of peat and ensure that the sector can continue to provide the global food demand in a sustainable manner.
...
The increase in the global population and the shift towards a healthier diet is leading to several global challenges such as the growing demand for safe and healthy food and therefore also for growing media. The growing media sector is looking for sustainable alternative raw materials, but at the same time the global population needs to be provided of food. The aim of this research is to contribute to the use of more sustainable materials in the European growing media sector, considering all the business constraints. There is looked at what developments and analytical tools can contribute to decision-making in the growing media sector in its transition to a sustainable sector. A system analysis on the basis of a multi-level perspective and a Multi-Criteria Decision Analysis have been carried out. An expert panel was asked to indicate their preferences for the selection criteria. The results of the system analysis show that the following developments are important in the transition to a sustainable growing media sector; Circular economy, Data-driven horticulture, Microbial horticulture and Increasing legislation. In the MCDA, four groups of alternatives have been defined: (1) white peat, perlite and woodfibre. (2) Accretio, mineral wool, foam and black peat. (3) Bark. (4) (standardized) Compost and the coir materials. The scenarios show that in the future the results will be very robust. However, the importance of peat will decrease and other materials will score better. The growing demand for growing media requires to focus on the low scores of the MCDA and the utilisation of current and future developments in order to make all raw materials attractive for use in the substrate sector. In addition, the sector will have to cooperate with all those involved in order to operate more sustainably. This will minimise the importance of peat and ensure that the sector can continue to provide the global food demand in a sustainable manner.
In order to achieve the goal of sustainability, industries will have to make a transition to renewable energy and a more circular production. This requires substantial investments as well as constructive cooperation between the various companies in industrial clusters. The problem addressed in this thesis is to find ways of determining optimal investment paths for industrial clusters under specific constraints: the investments should contribute to sustainability (e.g., reduce emissions), provide a positive return for a cluster as a whole, and allow for a distribution of costs and benefits (e.g., through contracts) such that the companies that make the investment have strong incentives for cooperation.
The idea that underlies this thesis is to represent industrial clusters as networks of processes that are owned and operated by different companies, and interdependent via their input-output flows. Such representation should then facilitate analysis from an industrial ecosystem perspective, and identification of potential investment options that would improve the cluster’s sustainability. From a game theory perspective, these investment options can then be seen as potential moves, and the companies as players. Assuming a time horizon (e.g., somewhere between 10 and 40 years), any combination of investments in that period constitutes a strategy, while for each company the difference in cumulative cash flow for that company with/without these investments constitutes the players’ payoffs. Analysis of such a multi-company investment game will reveal rational strategies. We have elaborated and tested this idea by using the Linny-R modelling language and its associated MILP optimisation tool that is being developed at TU Delft first to represent and analyse a variety of simple process configurations with only one or two investment options. Conducting this first series of small-scale simulation experiments, and verifying their outcomes has demonstrated the feasibility of our approach. Subsequently, we have applied the same approach to a realistic, albeit simplified and stylised, industrial cluster that comprises three companies. After identifying a set of potential investment options, we have used the resulting Linny R model to conduct a second series of experiments to simulate and analyse solitary investment strategies per company, a cluster-wide cooperative strategy, as well as competitive strategies with various contractual arrangements. The results of this study show that we can indeed use Linny-R as a modelling language and simulation tool to represent and analyse investment decisions as multi-actor games, which then allow us to infer and evaluate cooperative as well as competitive investment strategies. This study has several limitations. We did not investigate the scalability of the method. We experimented with a relatively small and simplified cluster; upscaling to a cluster with 10 or 100 times more processes could become computationally infeasible. Another limitation is that we did not test in practice whether models in the Linny-R notation will indeed effectively support communication and negotiation between companies. Thirdly, the set of categories of investment options that we have identified is not exhaustive. Our recommendations for future research hence are to explore the computational limits using a state-of-the-art commercial solver, and to conduct real-world case studies, meanwhile extending and refining the categories of investment options that can be instrumental in furthering the transition towards more sustainable industrial clusters. ...
The idea that underlies this thesis is to represent industrial clusters as networks of processes that are owned and operated by different companies, and interdependent via their input-output flows. Such representation should then facilitate analysis from an industrial ecosystem perspective, and identification of potential investment options that would improve the cluster’s sustainability. From a game theory perspective, these investment options can then be seen as potential moves, and the companies as players. Assuming a time horizon (e.g., somewhere between 10 and 40 years), any combination of investments in that period constitutes a strategy, while for each company the difference in cumulative cash flow for that company with/without these investments constitutes the players’ payoffs. Analysis of such a multi-company investment game will reveal rational strategies. We have elaborated and tested this idea by using the Linny-R modelling language and its associated MILP optimisation tool that is being developed at TU Delft first to represent and analyse a variety of simple process configurations with only one or two investment options. Conducting this first series of small-scale simulation experiments, and verifying their outcomes has demonstrated the feasibility of our approach. Subsequently, we have applied the same approach to a realistic, albeit simplified and stylised, industrial cluster that comprises three companies. After identifying a set of potential investment options, we have used the resulting Linny R model to conduct a second series of experiments to simulate and analyse solitary investment strategies per company, a cluster-wide cooperative strategy, as well as competitive strategies with various contractual arrangements. The results of this study show that we can indeed use Linny-R as a modelling language and simulation tool to represent and analyse investment decisions as multi-actor games, which then allow us to infer and evaluate cooperative as well as competitive investment strategies. This study has several limitations. We did not investigate the scalability of the method. We experimented with a relatively small and simplified cluster; upscaling to a cluster with 10 or 100 times more processes could become computationally infeasible. Another limitation is that we did not test in practice whether models in the Linny-R notation will indeed effectively support communication and negotiation between companies. Thirdly, the set of categories of investment options that we have identified is not exhaustive. Our recommendations for future research hence are to explore the computational limits using a state-of-the-art commercial solver, and to conduct real-world case studies, meanwhile extending and refining the categories of investment options that can be instrumental in furthering the transition towards more sustainable industrial clusters. ...
In order to achieve the goal of sustainability, industries will have to make a transition to renewable energy and a more circular production. This requires substantial investments as well as constructive cooperation between the various companies in industrial clusters. The problem addressed in this thesis is to find ways of determining optimal investment paths for industrial clusters under specific constraints: the investments should contribute to sustainability (e.g., reduce emissions), provide a positive return for a cluster as a whole, and allow for a distribution of costs and benefits (e.g., through contracts) such that the companies that make the investment have strong incentives for cooperation.
The idea that underlies this thesis is to represent industrial clusters as networks of processes that are owned and operated by different companies, and interdependent via their input-output flows. Such representation should then facilitate analysis from an industrial ecosystem perspective, and identification of potential investment options that would improve the cluster’s sustainability. From a game theory perspective, these investment options can then be seen as potential moves, and the companies as players. Assuming a time horizon (e.g., somewhere between 10 and 40 years), any combination of investments in that period constitutes a strategy, while for each company the difference in cumulative cash flow for that company with/without these investments constitutes the players’ payoffs. Analysis of such a multi-company investment game will reveal rational strategies. We have elaborated and tested this idea by using the Linny-R modelling language and its associated MILP optimisation tool that is being developed at TU Delft first to represent and analyse a variety of simple process configurations with only one or two investment options. Conducting this first series of small-scale simulation experiments, and verifying their outcomes has demonstrated the feasibility of our approach. Subsequently, we have applied the same approach to a realistic, albeit simplified and stylised, industrial cluster that comprises three companies. After identifying a set of potential investment options, we have used the resulting Linny R model to conduct a second series of experiments to simulate and analyse solitary investment strategies per company, a cluster-wide cooperative strategy, as well as competitive strategies with various contractual arrangements. The results of this study show that we can indeed use Linny-R as a modelling language and simulation tool to represent and analyse investment decisions as multi-actor games, which then allow us to infer and evaluate cooperative as well as competitive investment strategies. This study has several limitations. We did not investigate the scalability of the method. We experimented with a relatively small and simplified cluster; upscaling to a cluster with 10 or 100 times more processes could become computationally infeasible. Another limitation is that we did not test in practice whether models in the Linny-R notation will indeed effectively support communication and negotiation between companies. Thirdly, the set of categories of investment options that we have identified is not exhaustive. Our recommendations for future research hence are to explore the computational limits using a state-of-the-art commercial solver, and to conduct real-world case studies, meanwhile extending and refining the categories of investment options that can be instrumental in furthering the transition towards more sustainable industrial clusters.
The idea that underlies this thesis is to represent industrial clusters as networks of processes that are owned and operated by different companies, and interdependent via their input-output flows. Such representation should then facilitate analysis from an industrial ecosystem perspective, and identification of potential investment options that would improve the cluster’s sustainability. From a game theory perspective, these investment options can then be seen as potential moves, and the companies as players. Assuming a time horizon (e.g., somewhere between 10 and 40 years), any combination of investments in that period constitutes a strategy, while for each company the difference in cumulative cash flow for that company with/without these investments constitutes the players’ payoffs. Analysis of such a multi-company investment game will reveal rational strategies. We have elaborated and tested this idea by using the Linny-R modelling language and its associated MILP optimisation tool that is being developed at TU Delft first to represent and analyse a variety of simple process configurations with only one or two investment options. Conducting this first series of small-scale simulation experiments, and verifying their outcomes has demonstrated the feasibility of our approach. Subsequently, we have applied the same approach to a realistic, albeit simplified and stylised, industrial cluster that comprises three companies. After identifying a set of potential investment options, we have used the resulting Linny R model to conduct a second series of experiments to simulate and analyse solitary investment strategies per company, a cluster-wide cooperative strategy, as well as competitive strategies with various contractual arrangements. The results of this study show that we can indeed use Linny-R as a modelling language and simulation tool to represent and analyse investment decisions as multi-actor games, which then allow us to infer and evaluate cooperative as well as competitive investment strategies. This study has several limitations. We did not investigate the scalability of the method. We experimented with a relatively small and simplified cluster; upscaling to a cluster with 10 or 100 times more processes could become computationally infeasible. Another limitation is that we did not test in practice whether models in the Linny-R notation will indeed effectively support communication and negotiation between companies. Thirdly, the set of categories of investment options that we have identified is not exhaustive. Our recommendations for future research hence are to explore the computational limits using a state-of-the-art commercial solver, and to conduct real-world case studies, meanwhile extending and refining the categories of investment options that can be instrumental in furthering the transition towards more sustainable industrial clusters.
Emergence of leadership in communities
A study on how leadership emerges through an innovation process for solving community problems
Master thesis
(2019)
-
Mohammadhosein Hamedtavasoli, Pieter Bots, Amineh Ghorbani, Pieter van der Zaag
Many regions around the world are facing water shortage, low water quality, unsustainable overuse or other problems that may even cause conflict among different actors. These water resources are typical examples of common-pool resources. Leaders seem to play a crucial role in the emergence of community-based management of resources and overcoming the collective action problem. The main purpose of this study is to find the mechanism behind the emergence of leaders in the process of starting collective action.
I synthesized leadership studies from different domains and created Leadership for Community Development (LCD) theory. Based on LCD, leadership emerges from influencing interactions that are done to share a vision and make other community members dedicated to it. In this process, leaders gather the requirements of collective action, like support or political capital. Central to this idea is the innovation process, which has four phases of idea generation, idea elaboration, idea promotion, and idea adoption. The idea in the innovation process is the same as the vision.
According to this theory, first, there might be problematizing leaders who challenge the current state and give attention to a problem through influencing interactions. Dissatisfaction with the current state can lead to idea generation. In the idea elaboration phase, the idea creator will try to elaborate and gather support for the idea among his or her close connections. If this is successful and enough support is received, dedicated actors will start promoting the idea by influencing others. In the idea promotion phase, enabling leadership emerges when actors promote their own vision and echoing leadership emerges when convinced actors advocate the vision of their influencers. Enabling leadership and echoing leadership form up effective leadership, which is the indispensable influencing effort of actors for reaching the next phase. Idea adoption or implementation, which is in the domain of management, starts when requirements such as political and intellectual capitals are enough. This theory is further supported by a case study and interview.
I created an agent-based model based on the LCD theory. The model was a successful proof of concept for the LCD theory and how the identified types of leadership emerge through the process of initiating collective action. I used the model to identify the most significant factors on the role of leaders and the initiation of collective action. The social network proved to be the most important factor.
Based on these findings, I emphasized on the importance of focusing on networks and building connections in the communities. Moreover, I have suggested using these findings to diagnose the lack of collective action in communities and to use this diagnosis in the process of facilitating collective action in communities. I proposed many recommendations for future steps in this study. The top priority recommendations are a call for case studies to be done to further validate the LCD theory and to perform group experiments to increase understanding of the role of the social network in collective action problems.
...
I synthesized leadership studies from different domains and created Leadership for Community Development (LCD) theory. Based on LCD, leadership emerges from influencing interactions that are done to share a vision and make other community members dedicated to it. In this process, leaders gather the requirements of collective action, like support or political capital. Central to this idea is the innovation process, which has four phases of idea generation, idea elaboration, idea promotion, and idea adoption. The idea in the innovation process is the same as the vision.
According to this theory, first, there might be problematizing leaders who challenge the current state and give attention to a problem through influencing interactions. Dissatisfaction with the current state can lead to idea generation. In the idea elaboration phase, the idea creator will try to elaborate and gather support for the idea among his or her close connections. If this is successful and enough support is received, dedicated actors will start promoting the idea by influencing others. In the idea promotion phase, enabling leadership emerges when actors promote their own vision and echoing leadership emerges when convinced actors advocate the vision of their influencers. Enabling leadership and echoing leadership form up effective leadership, which is the indispensable influencing effort of actors for reaching the next phase. Idea adoption or implementation, which is in the domain of management, starts when requirements such as political and intellectual capitals are enough. This theory is further supported by a case study and interview.
I created an agent-based model based on the LCD theory. The model was a successful proof of concept for the LCD theory and how the identified types of leadership emerge through the process of initiating collective action. I used the model to identify the most significant factors on the role of leaders and the initiation of collective action. The social network proved to be the most important factor.
Based on these findings, I emphasized on the importance of focusing on networks and building connections in the communities. Moreover, I have suggested using these findings to diagnose the lack of collective action in communities and to use this diagnosis in the process of facilitating collective action in communities. I proposed many recommendations for future steps in this study. The top priority recommendations are a call for case studies to be done to further validate the LCD theory and to perform group experiments to increase understanding of the role of the social network in collective action problems.
...
Many regions around the world are facing water shortage, low water quality, unsustainable overuse or other problems that may even cause conflict among different actors. These water resources are typical examples of common-pool resources. Leaders seem to play a crucial role in the emergence of community-based management of resources and overcoming the collective action problem. The main purpose of this study is to find the mechanism behind the emergence of leaders in the process of starting collective action.
I synthesized leadership studies from different domains and created Leadership for Community Development (LCD) theory. Based on LCD, leadership emerges from influencing interactions that are done to share a vision and make other community members dedicated to it. In this process, leaders gather the requirements of collective action, like support or political capital. Central to this idea is the innovation process, which has four phases of idea generation, idea elaboration, idea promotion, and idea adoption. The idea in the innovation process is the same as the vision.
According to this theory, first, there might be problematizing leaders who challenge the current state and give attention to a problem through influencing interactions. Dissatisfaction with the current state can lead to idea generation. In the idea elaboration phase, the idea creator will try to elaborate and gather support for the idea among his or her close connections. If this is successful and enough support is received, dedicated actors will start promoting the idea by influencing others. In the idea promotion phase, enabling leadership emerges when actors promote their own vision and echoing leadership emerges when convinced actors advocate the vision of their influencers. Enabling leadership and echoing leadership form up effective leadership, which is the indispensable influencing effort of actors for reaching the next phase. Idea adoption or implementation, which is in the domain of management, starts when requirements such as political and intellectual capitals are enough. This theory is further supported by a case study and interview.
I created an agent-based model based on the LCD theory. The model was a successful proof of concept for the LCD theory and how the identified types of leadership emerge through the process of initiating collective action. I used the model to identify the most significant factors on the role of leaders and the initiation of collective action. The social network proved to be the most important factor.
Based on these findings, I emphasized on the importance of focusing on networks and building connections in the communities. Moreover, I have suggested using these findings to diagnose the lack of collective action in communities and to use this diagnosis in the process of facilitating collective action in communities. I proposed many recommendations for future steps in this study. The top priority recommendations are a call for case studies to be done to further validate the LCD theory and to perform group experiments to increase understanding of the role of the social network in collective action problems.
I synthesized leadership studies from different domains and created Leadership for Community Development (LCD) theory. Based on LCD, leadership emerges from influencing interactions that are done to share a vision and make other community members dedicated to it. In this process, leaders gather the requirements of collective action, like support or political capital. Central to this idea is the innovation process, which has four phases of idea generation, idea elaboration, idea promotion, and idea adoption. The idea in the innovation process is the same as the vision.
According to this theory, first, there might be problematizing leaders who challenge the current state and give attention to a problem through influencing interactions. Dissatisfaction with the current state can lead to idea generation. In the idea elaboration phase, the idea creator will try to elaborate and gather support for the idea among his or her close connections. If this is successful and enough support is received, dedicated actors will start promoting the idea by influencing others. In the idea promotion phase, enabling leadership emerges when actors promote their own vision and echoing leadership emerges when convinced actors advocate the vision of their influencers. Enabling leadership and echoing leadership form up effective leadership, which is the indispensable influencing effort of actors for reaching the next phase. Idea adoption or implementation, which is in the domain of management, starts when requirements such as political and intellectual capitals are enough. This theory is further supported by a case study and interview.
I created an agent-based model based on the LCD theory. The model was a successful proof of concept for the LCD theory and how the identified types of leadership emerge through the process of initiating collective action. I used the model to identify the most significant factors on the role of leaders and the initiation of collective action. The social network proved to be the most important factor.
Based on these findings, I emphasized on the importance of focusing on networks and building connections in the communities. Moreover, I have suggested using these findings to diagnose the lack of collective action in communities and to use this diagnosis in the process of facilitating collective action in communities. I proposed many recommendations for future steps in this study. The top priority recommendations are a call for case studies to be done to further validate the LCD theory and to perform group experiments to increase understanding of the role of the social network in collective action problems.
Comparing the Performance of different Market Structures for Regional Heat Networks
A simulation study into the impact of fuel prices, consumption growth and investment decisions
Master thesis
(2019)
-
wilan Hartwig, Zofia Lukszo, Pieter Bots, Rob Stikkelman, Thomas van de Sande, Jan Keenan
This research has compared three different market mechanisms in a quantitative way. The influences of these market mechanisms on the heating system were investigated for CO2 emissions, consumer price and producer surplus. In addition, the influences of investment decisions and price and consumption growth scenarios were tested. The research showed that the end-to-end market mechanism is the least uncertain in the future and is only influenced by consumption growth. In addition, the construction of a large source of residual heat can moderate this effect. The wholesale market is strongly influenced by price scenarios. This effect can be moderated by the construction of the pipeline through the Midden, which connects two different heating systems. Finally, there is the single buyer market. This is influenced by both scenario variables, while no investment decisions affect it. Further research must be done into the latter market mechanism. And the models need to be extended to more specific markets because this research has used archetypes. Keywords: District heating network, Market mechanism, Single-buyer,
...
This research has compared three different market mechanisms in a quantitative way. The influences of these market mechanisms on the heating system were investigated for CO2 emissions, consumer price and producer surplus. In addition, the influences of investment decisions and price and consumption growth scenarios were tested. The research showed that the end-to-end market mechanism is the least uncertain in the future and is only influenced by consumption growth. In addition, the construction of a large source of residual heat can moderate this effect. The wholesale market is strongly influenced by price scenarios. This effect can be moderated by the construction of the pipeline through the Midden, which connects two different heating systems. Finally, there is the single buyer market. This is influenced by both scenario variables, while no investment decisions affect it. Further research must be done into the latter market mechanism. And the models need to be extended to more specific markets because this research has used archetypes. Keywords: District heating network, Market mechanism, Single-buyer,