The transition to renewable energy is vital for the Netherlands to meet the National Climate Agreement's goal of a 95% reduction in CO2 emissions by 2050. Residential energy, accounting for 20% of the nation's total, is a key area for local municipalities, which are crucial in implementing decarbonisation measures. Solar photovoltaics (PV) have rapidly emerged as a leading technology, achieving the government's target of 7 TWh from small onshore projects by 2022, eight years ahead of schedule.

However, the rapid adoption of solar PV presents several challenges for policymakers, particularly at the municipal level. These challenges include grid integration, financing, and ensuring equitable access to solar technology. Addressing these issues requires a deep understanding of the factors influencing solar PV adoption and the dynamics at play within different municipalities. Inverse modelling presents a methodology to explore these complexities. This technique involves working backwards from observed outcomes to identify and understand the underlying processes and parameters that generated those results. The application of inverse modelling is, however, very novel. Therefore, this thesis aims to lay the groundwork for future applications of inverse modelling by exploring the factors that influence solar PV adoption in Dutch municipalities. To achieve this goal, the following research question has been formulated:

How can inverse modelling contribute to uncovering plausible explanations for residential solar PV adoption dynamics in Dutch municipalities?

The study employs inverse modelling combined with agent-based modelling to analyse adoption patterns across different municipalities, utilising Random Search and Bayesian Search algorithms. Findings indicate that social factors are often more influential than economic incentives in driving solar PV adoption, emphasising the role of community engagement and peer effects.

These insights underscore the potential of inverse modelling to reveal the complex interplay of factors influencing solar PV adoption. This study demonstrates that inverse modelling can effectively identify patterns and key determinants within municipal data. The research suggests that future efforts should refine this approach, considering multiple agent-based models and alternative methodologies to enhance robustness and accuracy, paving the way for more comprehensive and accurate research in the future.

In conclusion, inverse modelling provides a powerful tool for uncovering the dynamics of solar PV adoption in Dutch municipalities by providing a systematic approach to understanding the underlying factors of the system and its intentions. Through IM, the key determinants that drive solar PV adoption can be inferred by analysing observed patterns and behaviours within the municipalities. This approach is additionally powerful because it does not rely solely on pre-existing theories or assumptions, as with forward modelling. Instead, it derives insights directly from the data.

To prevent the worst dangers of climate change, the EU has committed to net zero by fully decarbonizing the European energy sector by 2050. While most energy sectors can be decarbonized by electrification, some “hard-to-abate” sectors need other solutions to lower their emission output. The EU has chosen green hydrogen to replace fossil fuels in such sectors. However, despite many advantages, it is expensive to produce and thus not competitive in the energy market. Therefore, the EU has created the Hydrogen Strategy as a central policy package to optimally implement green hydrogen within the energy sector. The finding of naturally occurring hydrogen, known as white hydrogen, could revolutionize the EU transition towards hydrogen as its production cost could be very low. However, as much is unknown about what effects white hydrogen could have on energy markets, it creates uncertainty for the EU and the Hydrogen Strategy. Modeling future scenarios can reduce the uncertainty for policymakers. While green hydrogen has been studied with a modeling approach, no such literature exists on white hydrogen. Thus, this study aimed to fill this knowledge gap by modeling white hydrogen to increase insights into the white hydrogen system and how it might affect the future energy system. During this study, a Decision Making under Deep Uncertainty approach was used to analyze the effects and uncertainties of white hydrogen on the global energy system and EU hydrogen policymaking. First, a system dynamics model was made to model global energy markets, including all relevant hydrogen. This model was tested with various experiments under uncertainty. Then, multiple scenarios were developed to match the possible trajectories of a white hydrogen market. The system behavior within the model was analyzed using the Exploratory Modeling Analyses method to determine the effects of white hydrogen on the energy system. Finally, the EU hydrogen policies were analyzed in the context of the scenarios, and recommendations were made to the EU on handling the uncertainty surrounding white hydrogen. The system analysis showed that the global energy system is heavily impacted by white hydrogen in scenarios where the white hydrogen market takes off. In specific scenarios, the price of white hydrogen may directly compete with gas prices, leading to a surge in demand. As the prices of other types of hydrogen decrease, they will also see an increase in demand, resulting in a net positive effect for all hydrogen types. However, despite rising demand, the production of white hydrogen may not keep up, leading to a lower overall usage of hydrogen. This supply-demand gap could cause significant shortages of hydrogen, which will exacerbate the existing energy shortages caused by carbon taxing and result in an overall decrease in energy usage. The results showed that the EU can only achieve its renewable hydrogen goals by 2030 and 2050 if the white hydrogen market grows substantially. An increase in the share of white and green hydrogen will lead to greater use of renewable energy sources. This, combined with a decrease in energy demand, will achieve net zero emissions more frequently and earlier in cases where the white hydrogen market grows. While EU hydrogen policies contribute to these goals, their impact alone is not enough to achieve them. However, the white hydrogen market works well with these policies, increasing total hydrogen demand. Furthermore, results showed that the policies aimed at reducing hydrogen prices significantly impact all scenarios, with the hydrogen bank policy being particularly effective. Yet, if hydrogen becomes too expensive, the sudden increase in green hydrogen prices could harm the green hydrogen market. The results suggest that developing the white hydrogen market is crucial for achieving renewable hydrogen goals, and policies that drive down hydrogen prices are also important in reaching these objectives. ii iii The main scientific contribution of this study lies in the modeling of white hydrogen within a dynamic system, which allows asserting the impact of white hydrogen on the energy system under multiple scenarios. The insights gained from it serve as a first indication of how the white hydrogen market may develop and how it can contribute to the adaptation of hydrogen. This study could function as a wake-up call for the EU and other governmental bodies to take white hydrogen seriously and implement timely policies to maximize its potential, enabling a more optimal transition towards renewable hydrogen and benefiting society. Overall, this study showed that in most scenarios, white hydrogen has a significant impact on the energy system. In general, white hydrogen market growth will stimulate the hydrogen market, resulting in a higher share of renewables in the EU energy mix, increasing the chances of the EU reaching net zero. The timely adoption of white hydrogen policies could ease EU efforts to transition towards clean hydrogen. However, the EU should not bet on one horse and should continue its efforts to

The shifting global climate patterns present an imminent threat to human existence. Addressing this critical issue necessitates decisive action in multiple sectors, including the mobility service, to mitigate climate change. As the world seeks cleaner alternatives, hydrogen has emerged as a promising solu- tion. TotalEnergies, a prominent energy company, has invested substantially in hydrogen technology to facilitate the transition toward sustainability. Although commendable, it is essential to recognize that the current environment remains a high-risk prototype phase. The successful establishment of hydrogen infrastructure and mobility services demands meticulous planning, robust technological advancements, and comprehensive risk assessment.
Thus, there is a need to comprehend the potential areas of development within a high-risk prototype environment by harnessing the company’s extensive 40 years of experience in Reliability, Availability, Maintainability, and Safety (RAMS) analysis. This need entails conducting a comprehensive and systematic RAMS analysis specifically tailored to the unique challenges and requirements of hydrogen refueling stations. However, a notable gap exists in the existing literature, as there is a lack of established guidelines or published research that specifically addresses the intricacies of RAMS analysis for the stations. This knowledge gap poses a significant burden in effectively assessing and mitigating risks, ensuring optimal performance, and fostering the safe and reliable operation of hydrogen refueling infrastructure. In order to address this critical knowledge gap, the main research question has been formulated as follows:
Research Question – Main: How can a systematic RAMS study-based approach be developed to improve the availability of hydrogen refueling stations by leveraging conventional RAMS methodological frameworks?
An extensive literature study was conducted to address the main research question. The aim of that part was to gather insights and identify best practices from existing research from other industries. As a result of the literature study, a Systematic Techno-Economic RAMS Analysis (STRAMSA) framework was developed. The STRAMSA framework introduces significant contributions to RAMS analysis for hydrogen refueling stations. It integrates system engineering, RAMS analysis, and techno-economic analysis to maximize the availability of system design. The framework establishes a strong link between RAMS analysis and techno-economic evaluation, facilitating informed decision-making by considering financial aspects. In techno economic analysis, it incorporates novel elements such as inflation, learning-by doing effect (in terms of market growth), and deflation rates for hydrogen. Moreover, the framework separates maintenance costs from operational costs to facilitate targeted improvements for the operation. This part also quantifies safety by assessing risks in monetary terms for accidents/fatality occurrence. Lastly, its iterative nature allows continuous improvement and adjustment throughout the design and operation process. Overall, the STRAMSA framework provides a comprehensive approach for analyzing the reliability, availability, maintainability, and safety aspects of systems.
To evaluate the applicability and effectiveness of the STRAMSA framework, the Total Energies Breda Hydrogen Refueling Station was selected as a case study. The steps outlined in the framework were applied to this specific station. Throughout the process, an iterative approach was adopted, allowing for adjustments to the framework as necessary.
After applying the STRAMSA framework to the Breda Hydrogen Refueling Station, the Net Present Value (NPV) was calculated as 1,815,202.69 €. The analysis also revealed the uncertainty of attaining a positive NPV, with a 11.18%. Further investigation into the sources of uncertainty identified market-related parameters as the dominant contributors to the variance. Specifically, parameters such as ”Market Growth Rate,” ”Hydrogen Price,” and ”Hydrogen Sale” significantly influenced the uncertainty surrounding the NPV. These findings underscore the importance of a coordinated policy approach that encourages investments in both the hydrogen market and hydrogen infrastructure. Such an approach is crucial for the rapid adoption of hydrogen technology and the development of a robust hydrogen economy. In addition to market parameters, from the operational perspective, the dispenser is identified as a decisive contributor to the uncertainty that requires closer examination.
As a further theoretical research, exploration the applicability of the STRAMSA framework for multi-case studies can be conducted. Initially, the framework can be applied to hydrogen refueling stations as a potential case study, but its feasibility can also be assessed for other industries. Furthermore, the impact of a design configuration change can be evaluated for the case at stake. One potential modification to consider is the addition of a supplementary High-Pressure (HP) compressor, as this particular component has been identified as the least reliable subsystem based on the RAMS analysis. Conducting such assessments can provide valuable insights into the effectiveness and adaptability of the STRAMSA framework in different scenarios and improve the design of hydrogen refueling stations.

This study examines the relative importance of contract attributes in the context of Vehicle-to-Grid (V2G) contracts by means of a choice experiment. The experiment was conducted with 67 Dutch car drivers, including both EV drivers and non-EV drivers. They were asked to choose between two V2G contracts with different contract attributes and an option “no V2G contract”. The contract attributes had varying levels of remuneration, guaranteed minimum driving range, and required plug-in time during weekdays and weekends. The data collected was analyzed using a Multinomial Logit model (MNL) to estimate the utility function of the V2G contracts and to identify the most important attributes for the respondents. Besides, an estimation could be made on the preference of a V2G contract over no contract at all. The results showed that, surprisingly, the attribute remuneration had a relatively low importance coefficient and did not have a significant impact on the perceived utility of the respondents. On the other hand, it could be proved that consumers perceive different utility during weekdays and weekends, preferring more flexibility in the weekends. Guaranteed minimum driving range resulted to be the most important contract attribute. The results show that there is a relatively high willingness to participate in V2G contract both among EV-drivers as potential future EV-drivers. The results can be used for policymakers and aggregator companies to design more effectively V2G contracts and to promote the adoption of EVs in a more sustainable way enhancing the energy transition. According to this study the V2G system results to be profitable for the aggregator for various scenarios and really promising. Some application possibilities are suggested in this research, and from them could be concluded that there can be achieved satisfaction for all stakeholders involved.

Carbon dioxide can be used during concrete production, which leads to stronger concrete as well as a sequestration method for CO2. While these technologies are developed quite far, they are currently not being used within society nor is there any systemic overview of the system and a reason why these technologies are not used. This thesis performs a Technological Innovation System analysis to investigate the Dutch concrete system and find barriers to the transition from conventional to carbon-cured concrete. The three located barriers are knowledge exchange, guidance of the search and the formation of markets. Intervention tools are presented to decrease the barriers and stimulate the technology within the system to enable further growth.

Increased pace of developments strain the ability of policy makers to be timely and sufficiently informed. While there are already sufficient methods available for gauging what plays a role, topic modelling is a novel method that has the potential to be deployed at high speed with low effort. Values play an important role as it shapes policy making and in turn affects stakeholders. A semi-supervised topic modelling method called correlation explanation (CorEx) was used for this purpose as it allows steering the model to find aforementioned values. Green hydrogen is used as a case study where topic modelling is used to find values in scientific literature on this subject. Green hydrogen is envisioned to play a more important role in the context of decarbonisation. Such a transition has a major influence on society engaging business and households alike. Three different value sets are used reflecting different perspectives. These perspectives focus on corporate values, public values and the context in which hydrogen is discussed respectively. It was found that using topic modelling to identify values is highly constrained by its given inputs and processed outputs however. A methodological framework is therefore proposed. It suggests how topic modelling can be conducted for the purpose of identifying values playing a role in a certain domain. This framework consists of five components which are value definition, corpus selection, language processing, topic modelling and result interpretation. Utilising this framework helps with structuring the topic modelling procedure and identify bottlenecks in result quality.

Intermittent renewable energy supply changes in time and is uncertain, and needs to be balanced (i.e. matched) with demand at all times, which requires system flexibility. Congested distribution and transmission grids in the Netherlands and Germany highlights the importance of providing system flexibility. Residential, commercial and industrial consumers can install their own renewable energy sources + energy storage to maximize self-consumption of renewable energy, to reduce electricity bills, reduce demand charges, to provide backup power and to provide grid balancing services. However, the deployment of behind-the-meter storage systems comes with major barriers, which are the main focus of this study. The research consists of a literature study, an analysis of behind-the-meter storage systems in the Netherlands and Germany and 10 expert interviews. Moreover, the Y-factor method, a method that allows to easily visualize, in once, which barriers are affecting what technologies, is used to score the identified barriers on stationary battery systems and electric vehicles enabled to bi-directional charge. The main conclusion is that 13 factors hamper the deployment of behind-the-meter storage systems, categorized in cost & financing barriers, technical barriers, market & regulatory barriers, multi-stakeholder complexity and behavioral barriers. Moreover, all combinations of technologies, sectors and countries face significant barriers for the deployment of these storage technologies. There can be concluded that cost & financing barriers play a major role for the deployment of residential stationary battery systems in the Netherlands as well as a lack of signals for self-consumption. Additionally, required investment costs and resource constraints play a significant barrier for commercial and industrial consumers in Germany. Moreover, the barriers: technology uncertainty, lack of communication protocols, dependency on other actors and a lack of a clear regulatory framework hamper the deployment of V2G technology significantly in both sectors in Germany.

As the Earth's temperature rises, it is critical that countries address and adapt to climate change. The European Union (EU) is committed to achieving carbon neutrality by 2050 (European Commission, 2018).The cornerstone of the EU’s climate policy to combat climate change is the EU Emission Trading System (ETS). It is the world’s first major compliance carbon market and remains the biggest one. Currently, the focus of the EU is to strengthen the market of the EU ETS for the next decade and beyond (European Commission, 2022-a). An important development in this phase is the recognition of global carbon markets to reduce global GHG emissions effectively. Three main challenges of ETSs are identified in this research: lack of system compatibility to link with other ETSs, security issues, and (manual) monitoring of transactions. To realize the full potential of ETSs, the added value of a blockchain-based architecture to support the carbon trading market and counteract the current deficiencies is explored. Although the reviewed literature is useful to identify the possibilities of blockchain solutions to address the current challenges in ETSs, it is not clear how the current EU compliance market could allow extension by providing a large, transparent, verifiable, interoperable, and robust carbon system. Research about blockchain-based EU ETSs would contribute so that the development of global carbon markets could happen. This results in the following main research question: What blockchain-based design can be used by the European Union to improve the technical design of the EU ETS while allowing for the extension to other Emission Trading Systems?. For this research project, a Design Science Research (DSR) approach is taken to ensure a discipline-oriented creation of a successful design that addresses the challenges in the EU ETS. This research proposes a blockchain-based solution that is able to cover the core functions of the system while actively improving system compatibility, preventing security issues and non-transparency, and enable automatized monitoring of transactions. It is concluded that the proposed design is able to improve the current system by enabling extension to other ETSs, automatizing manual processes, providing data security through encryption, and providing transparency in the narket and trade of emission allowances. Additionally, this research provides a blockchain governance framework to align policy and stakeholder interests with governance and technical blockchain control points in the emission trading sector. Besides contributing to closing a gap in a growing research field, the use of the architecture offers a technological solution in which systems can be integrated with each other without having to throw away the existing principles. This is beneficial because these existing principles have been developed after a great amount of learnings, investments, and experience. Also, the design enables more authorities to join a system that is already running while also able to have a say in the governance. This could lead to convergence of market mechanisms and prices where as a result, a more efficient and unified carbon market will emerge.

In order for the Netherlands to reach CO2 neutrality by 2050, large investments in zero emission technologies are needed. These investments would comprise out of renewable energy generation, higher energy efficiency alternatives and electrification of end uses. Although climate mitigation has become an ever growing societal concern since the ratification of "het klimaatakkoord", progress in the industrial sector has been seriously lagging in the Netherlands. In 2021 the reduction of CO2 emissions in the Netherlands stagnated and the emissions of the industrial sector actually slightly rose. So too in the Port of Rotterdam, where the chlorine cluster is not showing any significant CO2 reductions. There are several potential explanations why there is a gap between what should be invested and what actually is invested. They define the gap as, ’the apparent reality that some technologies that would pay off for adopters are, nevertheless, not adopted’. So, why do decision makers under invest in zero emission technologies? The explanations of fall into two broad categories:
•Market barriers
•Behavioural barriers

Following the problem situation, this thesis focused on researching to what extent market & behavioural barriers contribute to the investment gap in the Port of Rotterdam Chlorine cluster, by incorporating both market and behavioural barriers in a quantitative investment model. The incorporation of these barriers in the model result in a range of investment types, some of them non-optimal and varying in perspectives on valuing the future. By simulating technology adoption under the assumption of this range of varying configurations of market and behavioural barriers, it is possible to determine the effect of those two categories of barriers on technology adoption at the PoR chlorine cluster. The obtained insights of this research feed into the larger study of quantitative decision models for the industrial sector. Where the following main research question is answered:
What is the effect of market and behavioural barriers on zero emission technology adoption at the PoR chlorine cluster?

To answer the main research question, the model represents the PoR chlorine cluster on a highly detailed level and bases it's technical system's configuration on a thorough plant-process-product & zero-emission technology inventarisation. These two inventarisations give the current and possible future configuration of the PoR chlorine cluster's technical system. Consequently, this makes it possible to explicitly model the technology stock at the chlorine cluster and determine technology adoption on an asset level. The market & behavioural barriers are represented by 8 evaluation types. These evaluation types represent 8 varying configurations of the market & behavioural barriers. Consequently, a scenario analysis was conducted with the model.
The scenario analysis resulted in transition pathways, total CO2 emissions and total cash flows between 2022-2050. The model results show that the incorporation of market and behavioural barriers lead to postponed adoption of zero emission technology adoption. This is reflected by the lower number of years that these alternatives are installed between 2022 and 2050. The lower adoption lead to an increase of 288% total CO2 emissions between 2022-2050, compared to the optimal solution. Underneath, the key findings from the model results are presented…

The urgency for decarbonization of the energy grid is ever increasing and fossil energy sources must be replaced by sustainable sources of energy to prevent detrimental levels of global warming. Renewable energy sources wind and solar show potential and contribute to decarbonization but are intermitted and require storage solutions that are exposed to further constrictions and costs. Nuclear fusion is regarded as the most promising new energy source since scientists first learned about it and in theory poses the ideal properties to meet the needs of the world's future energy system. Despite fusion’s potential as the ideal energy source for innovating and decarbonizing the energy system, the reality is that 70 years of research and development have not yet resulted in fusion energy on the grid. Overpromises by fusion scientists since the seventies have resulted in scepticism and cynicism towards fusion development with the longstanding quip that “Fusion energy is thirty years away…and always will be”. Nevertheless, 2021 saw many important breakthroughs and achievements in fusion research which resulted in it being titled “The best year in the history of fusion development” and led to revived optimism and claims that fusion development is moving from the lab to the grid and commercialization is closer than ever before.

In order to truly understand the process of commercializing fusion energy, the barriers need to be known and understood so that these can be addressed specifically, and the process can be accelerated. Adding to that is, that there are also numerous different technical approaches to fusion, each with their own characteristics. However, currently comprehensive knowledge of these barriers is missing. Information is highly scattered as it is focusses on specific topics of fusion development, mostly the scientific or technical barriers. Adding to that is that most information is on separate technologies or specific experiments. As a result, there is a severe lack of knowledge: it is unknown what all the barriers towards commercialization are, how these barriers differ in severity and how they differ amongst the numerous fusion technologies. In an attempt to tackle this knowledge gap and enable a better understanding of the fusion development, the objective of this research was to develop a comprehensive list of barriers and subsequently study and assess this list for the different approaches to fusion with the intention of increasing the understanding of the pathway for commercialization of fusion energy.

Before starting this endeavour, a conceptual analysis was performed to define the concepts “barrier” and “commercialization”. Using these definitions an extensive literature study was performed, alongside 23 semi-structured interviews with almost all the leading fusion institutes and companies. Using predefined selection criteria to deal with the vast amounts of information, a list of fifteen relevant barriers was identified. The barriers described in literature were complemented and extended by empirical experiences and practical examples obtained in the interviews, resulting in a manageable but comprehensive list of fusion barriers towards commercialization, including several barriers that have received very little attention to date.

In an attempt to gain further insight into these barriers and research how these are different for the various approaches to fusion, a methodology was developed to assess the barriers in a standardized way. Based on the principles of the Y-factor method developed by (Chappin et al., 2020), a customized framework was developed for the commercialization of fusion energy technologies. Each barrier was concisely described and subsequently the identified barriers were organized into five categories: Technology, Operation, Cost & Financing, Governance and Engineering. The framework assesses the barriers on a tripartite scale, scoring a value of 0 indicating no barrier, 1; indicating a potential barrier and 2; indicating a significant barrier. For every barrier the scoring criteria were detailed to allow for accurate scoring.

The five most developed and pursued technical approaches to fusion (Tokamak, Spherical Tokamak, Stellarator, Field Reversed Configuration and Inertial Confinement Fusion) were assessed using the designed framework. This was done by three sperate expert interviews. These respondents were selected because they all had high expertise of both fusion energy and experience within the fusion industry and hence contribute to the validity of the research. Analysis of the results lead to numerous interesting findings

• Barriers generally apply to all technologies: Although the difference between the fusion technologies were identified and acknowledged by the various respondents, this did not result in notable differences in the scoring of these technologies. Instead, most barriers apply in a similar severity for all technologies.

• Experts disagree on fundamental barriers: Two of the respondents disagreed strongly on the scoring of several barriers, such as “Plasma physics”, “Radiation shielding” and “Energy production”. The fact that these respondents both have a PHD in plasma physics, demonstrates the uncertainty of fusion development and underlines the complexity and difficulty of predicting the pathway of fusion technologies. In this particular case the differences mostly originated from the reasoning of the respondents; one argued more from a theoretical point of view while the other purely looked at results to date, exposing that the framework can be interpreted differently by different respondents.

• Barriers have a strong time element: The abovementioned disagreements can be partially explained by time. The application of the framework exposed that nearly all barriers are characterized by a strong time dependency and that the barrier value is heavily dependent on the timeframe it is evaluated in. Fusion technology is still under development and while an active effort was made to describe the scoring criteria as closely as possible during the synthesis of the framework, the time dependency and the interpretability that comes with it could not be eliminated

• Hierarchy within barriers: The application of the framework also exposed a certain degree of hierarchy within the barriers and found that there was an order of urgency within the barrier categories. A clear and logical pathway could be observed; firstly the “Technology” barriers must be resolved, afterwards the category of “Operation” barriers become most urgent and finally the “Engineering” category. This was substantiated by the scores as these categories received the highest scores. The remaining categories “Governance” and “Cost & Financing” are present throughout the entire innovation pathway.

All in all, the research has three main contributions. The first contribution is the identification of a comprehensive list of fifteen barriers that is validated by experts, can be used to assess all fusion technologies and captures the complete commercialization pathway. Secondly, the developed framework is the first tool that can be used to uniformly assess these barriers and compare them amongst different technologies. Finally, application of the framework increased understanding of the time-dependency and hierarchy of the barriers. Despite the limited value of the quantitative output, the qualitative findings have certainly increased understanding of the barriers and complexity of fusion energy development and showed that the use of the method can enable insightful discussions.

It should be noted that in spite of continuous attempts at safeguarding the validity of the research, there are a number of limitations that should be taken into account when interpreting the research and its results. The development of the scoring criteria is subjective and can be interpreted differently by different respondents, despite the effort to formulate these with a high accuracy and clarity. Simultaneously, because the barrier definitions and scoring criteria are newly designed, these are also constrained by the perception and interpretation of the researcher. Lastly it is important to note that the application of the designed framework was limited to only 3 respondents and the outcomes are therefore based on a small sample size. The overall results of applying and scoring the framework is greatly determined by the individual views and can’t be generalized.

By the year 2050, the Dutch government aims to have transitioned away from natural gas and provide heating to all residential dwellings using more sustainable technologies. Heat pumps (both all-electric and hybrid models) are expected to play a large role in fulfilling this heating demand. However, because heat pumps require electricity in order to provide heating, a rise in the number of installed heat pumps is expected to have a significant effect on the residential electricity demand. This poses an issue during times of peak demand and low renewable power availability, leaving no choice but to increase reserves and ramping needs from fossil-fueled conventional power assets. Luckily, when provided with the right control strategy, heat pumps are able to flexibly draw electricity from the power grid, lowering the electricity required during peak hours. As a result, it is both possible that an increase of installed heat pumps in the Netherlands can either aggravate the flexibility problem or provide a key role in solving it.

The extent to which heat pumps could contribute to providing this demand response is not only dependent on the technical demand-response characteristics of the heat pumps. Rather, it is determined by both the willingness of households to adopt the heat pump and the way users react to the proposed control strategy. Using econometric techniques, the master thesis research project quantifies these behavioral dimensions into a model estimating the electrical flexibility heat pumps will be able to either require or provide in the Netherlands in 2050. In the research, several consumer typologies are defined distinguishing between consumer user groups and consumer adoption groups. The research finds that an additional 1.6 GW of flexibility can be provided by heat pumps in 2050 should policy be developed that targets consumer adoption behavior. Consumer user behavior has less of an influence on the average demand response potential of heat pumps, however could be an area of focus should a frequent demand response be desired.

The Dutch heating transition involves changing the heating systems of eight million buildings to a sustainable alternative by 2050. Many heating system technologies are available, but deciding which systems are cheapest for all these buildings is a difficult question to answer. Local policymakers are increasingly making use of heating transition models that estimate the feasibility and costs of systems in municipal neighbourhoods. The applicability of these models is limited by the degree of uncertainty about the future as well as the complexity in communicating the model results to policymakers. Sensitivity Analysis (SA) is a tool with which the most influential model uncertainties can be identified, quantified and communicated. So far, limited energy transition model studies have extensively used this method. A case study of SA on the CEGOIA heating transition model was performed to fill this gap and evaluate SA’s value. CEGOIA calculates the costs of a variety of heating systems and optimizes the allocation of scarce energy carriers such as green gas and hydrogen to find the lowest societal costs. Sensitivities of eight heating system options were analysed in different archetypical neighbourhood contexts using Fractional Factorial analysis, the Method of Morris and the Sobol’ Method. Out of an initial set of 953 parameters, a subset of less than a dozen highly influential variables – consistent between neighbourhoods of different physical characteristics – was identified for each heating system option. High sensitivities indicate that changing the value of a parameter leads to a large change in total costs. These sets, therefore, describe exactly what uncertainties are crucial to evaluating what heating system is the cheapest possible solution. Variables in these sets include, but are not limited to, the price and infrastructure costs of electricity and gas, heating installation costs and insulation costs. Interviews with other heating transition model owners further illustrated that the use of systematic SA as done in this analysis is not the norm. Besides results and insights from the CEGOIA SA, further applications for SA in heating transition modelling is postulated to be able to improve the modelling process, as well as better, understand complex model dynamics. One recommendation is, therefore, to include SA as part of the toolkit for the large heating transition models currently being used in the Netherlands. The main barrier for doing so with CEGOIA is the computational time of the model, which limited the number of parameters that could be evaluated as well as the SA techniques that could be used. Still, a more systematic analysis of sensitivities in heating transition models will provide insights that ultimately aid Dutch policymakers in making robust decisions.

Climate change and global warming are some of the greatest challenges the world is currently facing. This challenge has structural impacts on the natural environment as well as on human well-being. As a response to the concerns regarding climate change, the Netherlands started a renewable energy transition. Nevertheless, the realisation of the Dutch energy transition is far from being achieved. Changes in energy behaviour such as the adoption of energy-efficient technologies will help to achieve the Dutch energy transition goals. Businesses and policymakers in the energy sector can accelerate this process. To do so it is vital for them to know the preferences of the energy consumers. Stated choice experiments are a useful method to measure choice behaviour and consumer preferences. The method uses experiments with hypothetical choice situations in which the respondents are asked to choose between multiple options. However, it is the question of whether this method is useful for measuring energy consumer preferences. Prior academic research showed that energy consumer behaviour strongly depends on situational and contextual factors. Context-dependent stated choice experiments can serve as a solution because they include a stated choice context related to the choice situations. In this research, the contexts will

Global Earth Systems Integrated Assessment Models are used by policymakers to understand the complex interactions between anthropogenic activity, including energy use, and its environmental impacts. However, they are computationally expensive, so spatiotemporal resolution is kept low as a trade-off. Generally, the smallest geopolitical component is a large nation. Yet, much of energy transition planning occurs at subnational levels. Downscaling is the process of turning low resolution model outputs into higher resolution ones and has been used extensively in hydrological and weather modelling. Since downscaling in the energy sector is nascent, this presentation reviews the trade-offs between statistical downscaling methods within the energy domain using ten newly developed criteria. Highlighting such trade-offs can better equip policymakers as they craft energy transition policies. These criteria fit within three main categories: replicability, coherence to the parent model (the IAM), and handling of energy-specific insights. The linear downscaling method was highly replicable and, though coupled to the parent model, assumes that all its constituent regions are homogeneous and distributes outputs blindly. It performs well to introduce technologies that do not exist yet, but does not consider geographic to resource use here. The convergence method similarly struggles with geospatial limitations, but obfuscates energy sector nuances described by the original model. It is less replicable than the linear method. Modifying these approaches could resolve some of their issues, but they will likely never be useful for serious policymaking. Two other methods described in the literature are also discussed that could overcome the limitations of these two approaches, though they are not implemented here. Downscaling energy systems still holds some promise, though significant research is required to integrate technological innovation and diffusion considerations to the methods.

District heating is gaining popularity and can serve as an alternative for the use of natural gas to provide heat to residential areas. District heating systems can make use of heat sources that are often locally distracted and would have otherwise been wasted. Data centers could act as low temperature heat sources by recovering residual heat for district heating purposes. However, integration issues arise on various levels. This research aimed to provide insights into how data center waste heat in Amsterdam can be integrated into the district heating network.

The district heating concept are explained by investigating the technical, economic, environmental, and institutional concepts. District heating markets of the Netherlands, Sweden, and Denmark were compared, based on the type of markets (regulated or deregulated), the pricing structures, the degree of market opening, and the ownership structure. Next, a system engineering approach was developed to find and test opportunities to integrate data center waste heat into the district heating network of Amsterdam. The approach could offer support in the feasibility phase before making development decisions for a district heating network case. The network layered approach that was applied for the case of Amsterdam was used as the starting point for a pilot project in Amsterdam, modeled in the simulation tool EnergyPRO

The investigation into possible integration opportunities has resulted in a two-step approach: Creating a theoretical understanding of the district heating system for a specific case, and also looking into a practical case study by using a simulation tool. The decision-making and waste heat integration will simply not be ready in a day. Still, a clear vision of the functionality and interdependencies of all system components support steps towards successful integration.

Motivations for sustainability are initiating an energy transition that is changing the European energy domain. The transition effectuated the adaptation of large volumes of wind and solar based generation capacity. The intermittent power-output of these Variable Renewable Energy Sources challenges the balancing operation of the electricity network in particular. Despite the availability of different solutions like storage, smart applications and infrastructure substitution, large investments in transmission capacity are inevitable. While the need for additional transmission capacity is evident, the realization of transmission capacity has become increasingly complex due to the uncertainty surrounding the future landscape in which this expansion would take place. The many possible pathways towards a sustainable future make it increasingly difficult to predict the development of generation and load profiles and thereby complicate the identification of capacity requirements within the electricity network. This raises the need for new approaches that address the high degree of uncertainty present within the electricity domain. Literature describes the framework of Decision Making under Deep Uncertainty as an alternative approach to addressing the role of uncertainty in Transmission Expansion Planning. In contrast to traditional scenario planning approaches, this approach focuses on the computational evaluation of large numbers of scenarios that are sampled from a constrained uncertainty space. The idea is to inform decision making by exploring the uncertainty space and identifying conditions under which certain outcomes occur. Consequently, decision makers are aware of the conditions under which interventions might succeed or fail and are therefor able to design strategies that perform in different futures. The potential of the framework of Decision Making under Deep Uncertainty in the context of Transmission Expansion Planning is explored through a proof-of-concept approach that focuses on Transmission Expansion Planning in the context of The Netherlands. In this approach a simplified integrated market simulation and network model are used to explore the effects of different quantities of wind and solar based generation capacity on the required transmission capacity within the electricity network. Instead of using merely three traditional scenarios, this thesis has evaluated and analyzed 20,000 different scenarios. The results of these analyses have been reviewed by domain experts during two workshop sessions. These sessions established that approaches to Decision Making under Deep Uncertainty could provide useful insights in relation to model sensitivity, the reduction of dimensional complexity of the uncertainty space and the development of scenarios that describe areas within the uncertainty space. The sessions furthermore established that the application of Decision Making under Deep Uncertainty in relation to Transmission Expansion Planning requires further development in order to become a viable alternative to traditional scenario planning in a corporate environment. The application of Decision Making under Deep Uncertainty approaches within the context of Transmission Expansion Planning provides a unique opportunity to make the uncertainty space more visible for Transmission System Operators. The approach provides the building blocks to design adaptive investment strategies which in turn are geared towards facilitating the energy transition in a robust manner.

One of the tools that is being equipped in the fight against global warming is greenhouse gas accounting. This thesis studies what factors standard setters can use to influence the probability of widespread adoption for a newly proposed standardized measurement and calculation methodology for corporate greenhouse gas inventories. A theoretical framework of factors that determine the adoption of quality standards was first established which contains 31 success-determining factors, divided over 6 categories. The weights of importance of each of the identified factors for the proposed standard were determined through interviews with experts in the field of greenhouse gas accounting. The interviews were structured by means of a survey, which was constructed based on the Best-Worst Method (BWM). This multi-criteria decision-making method was then used to determine the relative weights of importance for each of the factors. The results demonstrated great importance for the composition of the alliance and the involvement of stakeholders in the standardization process of a standardized calculation and measurement methodology for corporate greenhouse gas inventories.

By analysing existing agent-based model (ABM) charging behaviour formalisations, this study evaluates the impact of a new static band-width network tariff structure on charging demand. Additionally, this approach enables a comparative study to reflect upon the susceptibility and value of the ABM method. Based on the simulation results, this study shows how steering EV charging behaviour by implementing a static bandwidth tariff structure will lead to lower demand peaks. However, the results also show how the increased simultaneity among charging sessions are expected to mit-igate the decreasing impact of the intervention. Furthermore, this study signifies how interpreting and scoping of a complex system can lead to significantly different model dynamics.

Reducing GHG emissions has become a widely publicized topic to halt future effects of global warming. In an effort to accelerate the energy transition a group of policy-maker from McKinsey developed a tool named marginal abatement cost curve (MACC) capable of illustrating the relationship between the cost-effectiveness of different abatement options and the total amount of GHG abated (Bockel et al, 2011). Even though the MACC has become popular for government reports and environmental analysis of abatement options through their abatement cost it lacks to analyze the options beyond the financial perspective. In 2016, Chappin published the introduction of the Y-factor method with the aim of solving the why factors that were hampering the pursual of implementing the abatement options. The method relies on the use of grading each abatement option in a scale of 0 to 2 through 12 socio-technical factors that are divided into four categories: multi-actor complexities, physical embeddedness, behavior and the cost & financing. This new method is a more robust approach than the MACC and it helps in providing new insights across different categories. The Y-factor is a relatively new method that has been furtherly assessed by Arensman (2018), Cheung (2018), and Soana (2018). This master thesis follows in the method of the Y-factor and goes a step beyond in proving its reliability when applied to a case-study, in this case applying the Y-factor for Mexico. The main research question is What emission abatement curve can capture the complexity of reducing GHG emissions in Mexico? For the construction of the emission abatement curve this research focuses on 20 abatement options that are relevant for the country. These options were selected through a process to provide diversity in the sector and reflect the reality of the biggest GHG emissions contributors of the country (energy creation, transport sector). Through a preliminary scoring based on literature review including government reports, scientific and news articles a preliminary Y-curve was constructed. The validation of the emission abatement curve was provided by contacting different experts in the country. This validation relied on interviews made to provide insights of the current situation of Mexico further understand what is hampering the implementation of the abatement options. To remove subjectivity for the validation each of the abatement option was graded by 2 or 3 experts’ interviews reducing personal bias and increasing result accuracy. The validated Y-curve results had interesting insights when comparing to the initial MACC developed by the US government for the low emission development program in Mexico and presented by Rebolledo et al (2016). The energy sector had the highest scoring abatement options on average which included renewable options such as Geothermal, Wind-Energy, Small Hydroelectric while also including fossil-based options that have become a priority for the new government administration Coal CCS for new plants. From all the options the highest ranked option according to the Y-score was the Coal CCS retrofit with a score of 22 out of 24. This means that this is the least convenient 6 option to be pursued according to the grading across the four categories. It was also interesting to note how the transport sector with options such as modal shift freight transport, transport policy changes, hybrid and electric vehicles, among others conform a highly diverse group each with different goals and widely different factors that obstruct its pursual. A key area is how the transport sector is dependent on a lot of different actors for any structural change or policy implementation meaning that these options are some of the most difficult to follow specially in the cities. Results of the Forestry & Agriculture cluster can be misleading if only the McKinsey data is available given the peculiarities of how the plot areas of land are owned in the country making it a unique situation that is hard to conceive in different countries. A general link between Mexico’s current affairs and the abatement options selected are explained to provide valuable information on the country and possible pitfalls when dealing with similarities in other countries. At the end of the report a recapitulation of the process and the main steps of the thesis are provided, as well as, concluding remarks for each chapter are mentioned to highlight the most important aspects of them. Valuable suggestions given by the interviewees on how to improve the Y-factor method have been highlighted, as well as the limitations of the study and how it can be improved. The societal and academic relevance of the project, as well as the limitations of the study are addressed and given an opinion of the added value of using the Y-factor for future research purposes is given. Concluding, the Y-factor approach adds value to decision makers and serves its main purpose of understanding the factors that hamper the abatement option implementation while also helps in unravel the complexity associated with such abatement options to a better overall understanding.