With the increasing transition to battery electric vehicles (EVs) and concerns about the capacity of the existing electrical infrastructure, the need for effective grid capacity management has become apparent. The V2X innovation, also known as bidirectional charging, has potential to enable EVs to interact with the electricity grid for various purposes, contributing to a sustainable and reliable transport and energy system. There are a variety of drivers and barriers to this innovation. The conducted literature review has highlighted a significant
issue regarding the drivers and barriers of Vehicle-to-Everything (V2X) technology. While various drivers and barriers have been identified, there is inconsistency in their level of aggregation, even within the same articles.

Furthermore, most of the literature takes a generic view of the V2X system, with only a few articles focusing on specific national contexts. In particular, there is a complete lack of scientific literature that specifically investigates the performance of V2X in the Dutch context. Also, very few studies consider the impact of configuration decisions on the drivers and barriers of the system. This research gap is critical to address and fill, as it provides essential insights into the effects of configuration decisions on the performance and innovation potential of a bidirectional charging system. Understanding these effects will enable a more targeted use of resources and facilitate the practical implementation of V2X systems. This thesis aims to explore different V2X system designs in a socio-technical context, analyse their socio-economic performance and innovation potential, and provide valuable insights for the successful implementation and adoption of V2X technologies in the Netherlands.

The severity of climate change impacts various aspects of human life today, with projections of up to six degrees of global warming by 2100 if significant changes are not adopted. (Intergovernmental Panel on Climate Change, 2021). Among others, the energy supply cannot be solely fossil-based but need to adopt alternative ways of generation, transportation, and storage. In response, politics world- wide stimulate the use of alternative energy sources, such as sustainable hydrogen. Hydrogen differs from conventional fossil fuels and gases. Since primary pollution arises from hydrogen production and the energy source to produce the hydrogen, additional metadata on the hydrogen is needed for suc- cessive parties in the hydrogen value chain. They depend on the truthfulness of the information for compliance with emission reduction targets and emission reporting obligations toward the public au- thorities. In the EU and worldwide, public authorities develop certification mechanisms to ensure the truthfulness of the information, ensuring the composition of the physical hydrogen. These certifications aid the information asymmetry between hydrogen sellers and buyers but are characterized by com- plicated administrative reporting efforts and regionally differing certification requirements. Questions remain on the GHG emissions accounting, the boundaries of the reporting obligation, and the tradeoff between certification rigor and administrative reporting burden for hydrogen producers (Abad & Dodds, 2020). Discrepancies among current certification standards lead to opacity, incom- patibility, and high auditing costs for hydrogen producers and public authorities. Addressing the problem of truthful information throughout hydrogen value chains can help to establish a green hydro- gen market, fostering green hydrogen production, and sustaining alternative energy supply in Europe (EU Commission, 2022a; IRENA & RMI, 2023; World Energy Council, 2022).
This thesis presents a blockchain-based artifact design for the European Union that addresses the re- quirements for reliable hydrogen certification, unifying European certification standards in one system while automating intensive reporting and certification processes. Design Science Research (DSR) helps to approach the research structurally. First, the complex hydrogen certification system is outlined, comprising the stakeholders, the institutional frame, and the technical certification processes. Second, the stakeholders contribute to the requirements engineering through semi-structured interviews. Third, a blockchain-IoT architecture framework is developed to translate the requirements of the hydrogen market into system design components. Fourth, the technical artifact is demonstrated in the complex hydrogen certification context. Last, expert interviews are conducted to evaluate the proposed design.
Concluding, blockchain-IoT can serve the requirements for interoperable, automated, and reliable green hydrogen certification while complying with EU regulations on sustainable hydrogen. How- ever, the technical design aspects required to fulfill requirements are premature and costly. Blockchain can serve as a solution, but the technological readiness of specific design aspects such as Zero- Knowledge-Proof (ZKP), Oracles, and Non-fungible tokens (NFT) induce tradeoffs between costs and the effectiveness of the design. Blockchain introduces a paradigm shift from central to decentral sys- tems, affecting technical architecture, governance, and institutions. Governance of the technological artifact is essential to guarantee a successful implementation in the market. Therefore, a decentral system maintenance council must align the physical hydrogen market with the digital blockchain infras- tructure and enforce mutual functionality. The alignment with institutions is considered to address com- pliance with regulatory green hydrogen standards and interoperability with multiple Voluntary Schemes. The current hydrogen market is characterized by institutional fragility affecting the confidence of green hydrogen producers. The artifact can ensure trust in the information, but institutions determine the rules of the certification game, whether virtual or physical. Moreover, the evaluation found that considering only the European market is insufficient. International trade scenarios would increase the impact of the artifact in complex internationally entangled hydrogen value chains. For example, hydrogen produc- ers outside the EU that comply with internationally accredited Voluntary Schemes could sell hydrogen in Europe. Hence, given the information trust issue in the hydrogen market, the artifact provides the first alternative to conventional centralized certification mechanisms benefiting researchers and practi-
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tioners in the blockchain application environment.
The thesis contributes socially, culturally, environmentally, and economically to society. The artifact can guide European policymakers to new decentralized methods of addressing the trustful information-sharing issue in the hydrogen certification market (social impact). Conventionally, certi- fication functions from top to bottom enforcing reporting to national authorities. Blockchain can rein- vent public-private cooperation by decentralizing control and tasks (cultural impact). Deploying the artifact can help to facilitate the EU’s plan to increase green hydrogen domestic production and im- port by 10 million tonnes by 2030 (EU Commission, 2023a). The blockchain artifact can guarantee environmental-benign hydrogen supply by ensuring trusted information on the emissions of hydrogen production (environmental impact). Lastly, the artifact can automate reporting processes for hydrogen producers and certification processes for public bodies and thus contribute to the economic capital of the EU. Public bodies and hydrogen buyers have enhanced trust in the information accompanying the hydrogen supply in the European market, and hydrogen producers have reduced market entrance bar- riers induced through administrative tasks (economic impact).
Methodologically, the thesis contributes to the green hydrogen certification economy: To the knowl- edge of this thesis’s author, the potential of blockchain technology as a tool to facilitate hydrogen certi- fication has not been analyzed yet. The thesis provides tangible design concepts for blockchain-based hydrogen certification systems. Scientific research and blockchain practitioners can develop upon this initial study. Secondly, partly outdated blockchain architecture modeling in combination with IoT infras- tructures is addressed. A framework is developed based on existing scientific research to serve the peculiarities of the hydrogen certification market, which can serve as an ontology for future blockchain designs in energy systems. Third, the socio-technical embedment of the technical blockchain design gives insights into adopting such complex, paradigm shift-inducing information systems in society. Last, the evaluation methods of DSR are addressed in the underlying research project. Interesting insights from practitioners with energy and blockchain backgrounds are discussed. These can serve as recom- mendations for future amendments or extensions of the design. Hence, the artifact can contribute to the theory of DSR and practical blockchain implementation research.
The research is limited to the hydrogen market of the EU and distribution via gas pipelines, neglect- ing navel and road transport. The study covers the first design cycle of the DSR approach. Adding successive cycles with the gradual inclusion of more industry experts, various use cases, and new in- stitutional changes can enhance the artifact’s viability for the hydrogen market. Furthermore, different evaluation parameters could be added, such as the tradeoff between technical optimization and the costs of such interventions. Other use cases could entail considerations of the artifact’s interoperability with hydrogen trade platforms, feasibility for different hydrogen trade scenarios (international trade, but also closed systems), and incorporation of additional requirements addressing hydrogen safety, hydro- gen facility construction, and financial incentives. These complexities can test the artifact’s applicability in the socio-technical context.

The Dutch government described its vision to achieve significant reductions in greenhouse emissions in the climate goals of 2030 and 2050. The energy infrastructure in the Netherlands will be a critical factor in achieving these goals. As an extension to the stated importance, understanding the current energy infrastructure is equally significant. Models often form the base of understanding such complex systems. Unfortunately, the modelling environment of the Dutch energy infrastructure is fragmented. There exists no one model that is able to give comprehensive oversight in order to provide policy makers with the information needed to facilitate key energy policy decisions. However, there is a variety of models present that each clarify their own piece of the puzzle. Therefore it is the ambition of the TU Delft and partners to create a multi-model infrastructure that is able to couple existing energy models in order to facilitate comprehensive energy policy creation. This thesis is part of the research needed in order to achieve this higher goal.

The main conclusion of the conducted research is that the problem described in the main research question can be answered by using a coupling process based on audits, comprised of questions aimed at detecting issues and checking the effectivity of the means to solve the issues. These audits have proven successful in completing two different multi-resolution multi-modelling coupling case studies. The process entails (for a two-model coupling) a separate model audit for each model, leading to a coupling audit using both models and finally to the realisation of the coupling itself. Adherance to the process standardises the way couplings are created to a degree. Because of this, model audits done for one coupling could for example be re-used at a later date for another one. This provides value over old coupling methods, which were often done individually in an ad-hoc manner.

What exactly causes the emergence of the attitude-behaviour gap, and to what extent, is not fully understood. Previous academic research has shown that many barriers to adoption remain, and that difficulty lies in the heterogeneity of consumers. Through the use of a simulation model this thesis explores the emergence of the attitude-behaviour gap by analyzing the decision-making behaviour of heterogenous Dutch households, parametrized by real-world survey data. Ultimately, to close the gap by lowering the barriers to adoption with policy interventions. An agent-based simulation model (ABM) has been developed to address the main research question: To which extent can different psychological factors influence the emergence of the attitude-behaviour gap in household energy consumption, and what policy interventions can be employed to close the gap?

The long time horizon of sustainable energy developments is often associated with all sorts of issues that are caused by changes in values. The public attitude can be affected by these changes and potential risks in the lack of social acceptance could occur, which always lead to public oppositions to the implementation of the sustainable energy system in a city district. Integrating social acceptance into the long-term planning of energy projects is necessary to lower the risks. However, it is hard to assess the future acceptance of energy consumers in different sustainable energy options. Also, different stakeholders always have different opinions and concerns on the public accepted solutions, which causes conflicts in negotiation in the planning and decision-making phases. This research employed a participatory modelling approach to forecast the possible future developments in the district heating network in Amsterdam Southeast concerning the value changes. This method also helps stakeholders to understand the design requirements of the more public accepted sustainable energy system in the future.

The national government of the Netherlands has mandated heat transition to the municipality as part of the global execution of climate agreement. However, despite many guides has been offered, municipal heat-transition policy is a tricky matter that the municipal policy-makers struggle to grasp. To support the systematic municipal heat transition policy-making process, we propose the usage of a Decision Support System (DSS) that serves the whole cycle of policy-making. However, there is a knowledge gap of an integrated DSS framework with both policy target profiling and policy system (policy cycle) that is a context-specific for the municipal heat transition policy-making in the Netherlands. Therefore, this thesis study was conducted to answer the research question: "what decision support system framework can systematically assist municipalities in the heat transition policy-making process?" To answer this question, a refined Heat Transition Policy Making (HeTPoM) DSS framework is made based on a functional visual design method to cater to the needs of DSS creation in the context of municipal heat transition policy-making in the Netherlands. The refined HeTPoM DSS framework is designed to support policy-makers to create a systematic municipal heat transition policy by supporting the process of policy design, assessment, and evaluation. Furthermore, the refined HeTPoM DSS framework can potentially be used as a base to create a computerised municipal heat transition policy-making DSS. This then leads into two types of future research recommendations that need to be done in series. First is the HeTPoM DSS framework components mechanism study (i.e. the interaction between municipal heat transition influencing factors and the policy-making process). Which then can be followed by digital architecture of the DSS system to support the development of municipal heat transition DSS.

Predictive maintenance (PdM) is one of the promising technologies coming along with the fourth industrial revolution being pushed by disruptive technologies like Internet of Things (IoT), Artificial Intelligence (AI), robotics and Augmented and Virtual Reality (AR/VR). Adopting PdM potentially allows companies to reduce equipment downtime, increase the safety of their processes, increase revenue and develop additional business models. Although the promises of the technology are extensive, the successful adoption rate of this technology is still relatively slow. This is stemming from PdM’s multi-disciplinary nature and “hype” that over-promised its ease of implementation. Organizations are now starting to understand what is needed for efficient implementation, and this helps to manage the expectations about this technology. The fundamental problems highlighted in this research are the complexity, unclear vision, lack of knowledge and know-how in adopting AI predictive maintenance technologies inside an organization. According to Bain & Company’s survey companies in the industrial sector indicated that implementing IoT inside their organization proved to be more complicated than anticipated (Schallehn, Schorling, Bowen, & Straehle, 2019). There is a knowledge gap in the scientific literature, where a lack of best practice methods in terms of predictive maintenance implementation can be identified. Based on the problem highlighted and knowledge gap, the main research question was formulated: “How to facilitate the adoption of Artificial Intelligence-based predictive maintenance technology in the manufacturing industry?“. This study follows a phase-wise approach to obtain the research results. In the first phase, a literature study is conducted to identify the current situation about PdM, what information is available about the factors affecting this technology’s adoption and where is the knowledge gap to be filled. Selected factors to focus on with this research are discussed and agreed upon with the researcher and supervisors. In the second phase, the development of the best practices checklist is commenced. The centrepiece of this phase and the research project overall is the set of semi-structured interviews with 11 industry experts with extensive domain knowledge about predictive maintenance to collect best practices in PdM implementation. The insights gathered from the interviews are analysed in-detail in multiple iterations and then that filtered, aggregated information is used to develop the predictive maintenance project reference checklist. In the third phase, expert panel evaluates the practical applicability, generalizability and the validity of the constructed PdM checklist. Efficient implementation of PdM inside the organization could face numerous barriers and difficulties. Most of these barriers related to technologies using big data could be divided into three categories: technical, organizational and people related (S. Li, Peng, & Xing, 2019). Addressing all of these barriers in those 3 major categories would be unwise since that would not provide sufficient depth of analysis for each one of them. Selection of barriers is based on 3 criteria: the barriers must be relevant and applicable to the adoption of the PdM technologies; there should be a noticeable knowledge gap about how to overcome the barriers; the barriers must be complex enough (affecting multiple layers and stakeholders of the organizations) to fit with the Management of Technology multidisciplinary problem-solving perspective. Based on the information from scientific literature and consultancy reports on PdM, 3 relevant barriers to be focused on are chosen: business case building for PdM; trust in AI-based PdM (lack of trust in big data analytical results) and data management for PdM (the challenge of collecting the data, utilizing it and making sense of it). The interviews with the industry experts revealed valuable insights about predictive maintenance adoption, factors affecting the implementation and best practices that other companies have followed during the process of PdM realization. The most notable best practice that all the interviewees mentioned was involving all the relevant stakeholders early on. In addition, taking small steps, maintaining PdM platforms, celebrating small successes, showing a broad picture and providing a range for PdM business case were outlined. Furthermore, key factors that emerged from the conducted interviews influencing PdM adoption are delineated and summarized in this research project. These are useful for both practitioners and academic personnel who have an interest in this domain and want to gain further understanding of the dynamics surrounding predictive maintenance projects. This research project developed best practices reference checklist for predictive maintenance project implementation that supports organizations on high-level in adopting this novel technology by illustrating and bringing awareness to best practices that other organizations have been following during PdM implementation. This reference checklist is constructed to be a holistic, high-level PdM project support tool for the stakeholders proceeding with predictive maintenance implementation for the first time. This means that a detailed analysis of separate nuances is not sought after since that would misalign with the goal of being a wholesome, comprehendible overview of PdM project implementation checklist. Having a clear, structured and holistic perspective allows stakeholders to conveniently follow this checklist commencing and during predictive maintenance projects without being overwhelmed by excessively detailed information. This best practice checklist based on empirical study comprises a five-phase approach where the enablers and barriers in each phase are mentioned and suggestions on how to deal with them are outlined. These 5 phases are as follows: concept, feasibility, data, PdM algorithm development and operation phase. Furthermore, high-level, structured steps in each phase are laid out to support and offer recommendations to organizations with their PdM activities. In the end of each phase, an overview of best practices and barriers is delineated to recapitulate. In the concluding section of this best practices checklist, a compact, five-page adaptation of this reference checklist is devised for a quick overview of this constructed PdM project support medium and it is advisable to resort back to phases in the checklist itself if the more detailed explanation is needed. This compact version is meant for practitioners in the industry who have strict time limitations and wish to receive information quickly in a condensed format. To the best of our knowledge, such kind of high-level compact overview to assess PdM projects was not existing in the scientific literature. This research project directly investigates and provides a best practices checklist to fill this gap. In addition, this research provided design improvement ideas for different stakeholders to incorporate in their processes/products to facilitate better adoption of PdM. Trust factors affecting the implementation process of predictive maintenance are also outlined, helping companies to better communicate with their clients and internal organization about the benefits and usefulness of PdM. The developed research output has been preliminarily validated and evaluated by the expert panel that concluded that this best practice checklist indeed supports organizations in adopting predictive maintenance technologies. Furthermore, it was agreed that the output is clear and understandable with a well-structured approach. Coming from the high-level nature of this research, experts agreed that this research is generalizable to other industries. Main recommendations (for future research) include validating the best practice checklist in practice with multiple organizations inside the industry to correlate usage of this approach and success factor of implementing PdM. Furthermore, the development of additional support tools and frameworks to facilitate efficient implementation of predictive maintenance technologies would yield increased adoption rates of the technology. This research highlighted important factors contributing to the adoption of predictive maintenance technologies from organizational, people and technology perspectives. This helps to create more awareness about what is needed to consider for better adoption of this technology. Furthermore, a high-level structured overview of best practices checklist supporting PdM implementation is contributed to the scientific and practical domain, filling the previously outlined gap in the literature. In addition, coming from the analysed literature, this research complements the scientific literature on the topic of predictive maintenance by providing original content and additional awareness to the overall academic context regarding the dynamics of this technology’s adoption.

The city of Rotterdam must find a way to transition away from natural gas for heating the built environment before 2050. Among the alternatives for future spatial heating is district heating, as residual heat is abundantly available in the Rotterdam harbour. Currently, a small part of the Rotterdam South area is covered by the district heating network (DHN). There is room for expansion of the existing DHN, but the available residual capacity is limited. Also, Rotterdam South has a mixed buildings stock, which could allow for the possibility of exploring a cascaded DHN. However, it is unclear what the future network should look like that fits the criteria of stakeholders best. Twelve possible configurations are tested and evaluated using a minimal cost network model. The objective is to minimize overall network cost and connect all the demand in Rotterdam South. The best configuration is the network where all main collection points are supplied by high temperature heat and where additional capacity is added on top of the existing transport network. The building stock mix in Rotterdam South is not optimal for cascading, which results in higher costs for a connection and a lower overall connectivity.

Energy system planners and decision makers rely on Energy System Optimization Models in assisting long-term decisions that ensure robust real-world energy system designs that deliver the energy transition goal. Optimization usually provides a single 'optimal' outcome which misrepresents the underlying uncertainties and the large set of possible futures. In this research a method is proposed with which model-owners can be provided insight into the impact of uncertainties on (Energy System Design) Optimization Model outcomes by producing insights regarding the model behavior across model runs under uncertainty. The proposed method consists of three steps: 1) Uncertainty Characterization, 2) Exploratory Modelling, and 3) Results Analysis. The proposed method is applicable to Energy System Design Optimization Models specifically, and to Design Optimization Models in general. This general applicability is constrained to (Energy System) Design Optimization Models where the 'optimized' outcome, the design, can be formatted as a (high dimensional) vector which contains the value for all potential design components, including the zero values, in a fixed ordering over all experiment runs. When applied to an existing Design Optimization Model, this method should help to answer two questions: ‘How do the (Energy System) Designs vary resulting from underlying uncertainties?’; ‘What (Energy System) Design trade-offs are driven by which underlying uncertainties?’. Step 1) consists of the identification of uncertain model-parameters and the assignment of a mathematical representation of their uncertainty. In step 2) the characterized uncertainties are integrated into the model under analysis with the Exploratory Modeling and Analysis (EMA) Workbench. Each exploratory modelling case is composed of n model simulations which results in n experiments. Each experiment represents a unique set of uncertain parameter value combinations and of outcomes containing the model behavior resulting from that specific experiment input space. To answer the first question, clusters of experiments resulting in similar energy system design are identified with a novel approach of cosine distance-based agglomerative hierarchical clustering with complete linkage. To characterize the cluster designs, the total outcome is aggregated to case-design specifics and related back to the underlying uncertainty input with subspace partitioning. The second question is answered with sensitivity analysis and subspace partitioning techniques to characterize design elements that are of interest and to identify trade-offs between these design elements resulting from the underlying uncertainties. As a proof of concept of the applicability and functionalities of the proposed method, it is applied to an Energy System Optimization Model that aims to aid decision-making regarding integrated energy system design and operation in urban areas. This revealed that model-owners can use the insights to: identify model specifications (or the lack thereof) that are determining for the model output, and possibly take measures to limit these effects, and to offer their clients (possibly decision-makers) strategy advice.

Hydrogen is considered to be a promising replacement of fossil fuel-based energy for the future energy supply. The possibilities to use hydrogen are extensive; hydrogen can provide high temperatures for industrial processes, produce electricity, heat buildings and be a fuel for the mobility sector without releasing carbon. In order to implement hydrogen transition in the current economy, a hydrogen infrastructure needs to be established. Prior research has pointed out that after certain alterations, the current natural gas infrastructure can transport hydrogen. The natural gas infrastructure in the Netherlands is extensive, and the capacity is big enough to satisfy the Dutch hydrogen need. Additionally, considerable costs can be saved if the natural gas network is used. The costs to adapt natural gas pipelines to transport hydrogen is ten times lower than the costs of constructing new hydrogen pipelines. The common approach for the design of the new hydrogen infrastructure is optimisation. However, prior research in regard to network evolution has indicated that infrastructure evolution is characterised by path-dependency, lock-ins and network effects. These factors are neutralised in the current optimisation methods. It is reasonable to presume that these factors of network evolution will also have an impact on the network transition that is based on an existing network. The first reason to assume this is that the development of the hydrogen network is estimated to take 30 years, in which other developments are likely to occur. Second, investments that are made, are locked in the new infrastructures, as expenses made cannot be spent again in a different manner. Furthermore, the investments made determine future options for investment. Research into the transition of one network, which is based on an existing network, remains uncharted. This thesis will evaluate the effect of different tactics and strategies on the transition of a network from fulfilling one purpose, distributing natural gas, to another, distributing hydrogen while taking path dependency into account. An agent-based model that applies rule-based behaviour is constructed to answer the main research question, which reads as follows: How do different transition strategies for the transition of a natural gas infrastructure to a (partial) hydrogen infrastructure perform over time? To create this agent-based network, a representation of a network was made with several components, so-called nodes and edges. Nodes are the entry or exit points of the network, the production sites of gas (either natural gas or hydrogen), heavy industry, energy generators or the points where the gas is converted from the transmission network to the distribution network. All nodes have a utility score based on the type of node, and the distance of the node to the closest hydrogen point in the network. This allows for the calculation of the utility score of a specific edge. Edges are the connection between the nodes and represent existing pipelines, potential new pipelines or temporary connections in de form of tanks. The effect of four tactical choices on the transition behaviour of the network is tested in regard to the costs of the transition, the volume hydrogen that is delivered to the network and the volume hydrogen that is exported. The following tactical choices are evaluated: -Prioritise the network transition on local optimisation criteria, -Including new pipe to be constructed in the excising graph, -Prioritising the export of both hydrogen and natural gas, -Allocate the available budget over time in different patterns. Based on the results of experimenting with the tactics, the following four strategies are formed: - Minimise cost, prioritise the export of both hydrogen and natural gas, - Minimise cost, no prioritisation of the export of hydrogen and natural gas, - Maximise hydrogen delivery, prioritise the export of both hydrogen and natural gas, - Maximise hydrogen delivery, no prioritisation of the export of hydrogen and natural gas. These strategies are applied to the random network developed for this thesis, and on topologies based on the Netherlands, Belgium and the United Kingdom. The results show that the strategies focusing on the minimisation of costs structurally have lower expenses than the strategies that maximise hydrogen delivery. However, in the case of the random starting topology and the topology based on Belgium, this is always at the expense of the hydrogen delivery as these strategies cause lock-ins. Prioritising the export of hydrogen and natural gas delays the developments of lock-ins and is therefore not only beneficial for the hydrogen export, but also for the volume of hydrogen delivered in the system. The topologies based on the Netherlands and the United Kingdom are less susceptible to lock-ins. There are situations in the topologies based on the Netherlands and the United Kingdom where the same volume of hydrogen is delivered in the strategies based on maximising hydrogen delivery. In these cases, minimising costs is the optimal strategy. In other situations, the hydrogen delivery in the strategies based on minimising costs is lower. In that case, a trade-off needs to be made between the hydrogen delivery and costs. \newline The experiments in this thesis have led to the seven insights that should be considered in the realisation of a hydrogen infrastructure. 1. The characteristics of a network are important. Best practices in one infrastructure should not be copied without any further consideration. 2. Purely adapting the excising network does not lead to the best outcome, and therefore the option for constructing new pipes on some critical points should be considered. The construction of new pipes helps to overcome lock-ins and therefore has a positive effect on the system outcome. 3. It is best to invest maximally according to the availed budget, the maximal capacity of the system and the foreseen future. With this, the system can benefit the longest from these investments and changes to the network. 4. Be reluctant about the network transition to certain geographic areas where the contribution is limited to only a small part of the network. 5. It is wise to determine minimal thresholds for the performance of the system to ensure that the system does not minimise costs at the expense of other key performance indicators. 6. Prioritise the flow of export and import of natural gas and hydrogen through the country. Not only does the country financially benefit from an export corridor, there are also positive effects for the network as this export corridor ensures an available hydrogen connection throughout the country. 7. Specific for the topology based on the Netherlands and the United Kingdom; there are situations where the strategy that minimises costs reaches the same hydrogen delivery as the strategy that maximises the hydrogen delivery. This reinforces the first insight. The specific situation and location of nodes should be reviewed in order to determine the optimal strategy. There are some limitations to the model created in this thesis. First, the local optimisation is done based on the utility of a pipe. This utility has a direct connection to the utility of the nodes it is connected to. Calculating the pipe utility as the added gain for the whole system would strengthen this model’s approach. Second, the average betweenness centrality and closeness centrality does not show a relation with the effectiveness of the tactics and strategies. This is probably because centrality measures are calculated for the whole system and not for the flows of hydrogen and/or natural gas. It is recommended to recalculate the two centrality measures, taking the gas flows into account, and observe whether there is a relation that can be used as a predictor for the effect of tactics and strategies. In this thesis, a system-level approach with a step for step transition is used. Network evolutionary elements liken path-dependency, lock-ins and network effects were taken into account. Including these elements of network transition, led to seven insights regarding the process of (network) evolution, compared to overall system optimisation. These seven insights should be considered when formulating an approach for the realisation of a hydrogen infrastructure.

Edge computing can deliver substantial value to the general idea of the Internet of Things (IoT). However, there is a myriad of potential IoT applications for edge computing. Stakeholders are left with uncertainty about how the business potential of edge computing for these IoT applications can be identified. This research contributes in solving this, by designing a business model tool that can be used to identify the business model potential of edge computing for distinct IoT application areas, based on business model viability and feasibility. Through the Design Science Research Methodology (DSRM), the tool has been designed, demonstrated, and evaluated. Based on the STOF ontology, and supplemented by the theoretical domains of business ecosystems and platform theory, nine generic variables have been identified to explain business model viability and feasibility. These generic variables have in turn been contextualized towards the edge computing domain, in terms of 45 contextual input variables. This is the first research that unfolds these business model variables for edge computing.

In the light of the global transition away from fossil fuels, a multitude of solutions are implemented in the Netherlands. A transition towards electrification of appliances is part of these solutions. Usage of heat and mobility is increasingly electrified. This trend is expected to increase over the upcoming years. This electrification will have an impact on the electricity demand and will impact the electricity grid. Furthermore, a transition towards a more decentralized electricity production can be noticed. The introduction of high quantities of these renewables will have major consequences for the low voltage grid. The integration of these transitions in the distribution grid is one of the main challenges for a distribution system operator. However, the transition towards electrical appliances might also possess opportunities to create smart solutions to solve for higher electrical demand and loads within the electricity grid. The batteries of electric vehicles might be utilized to aid the functioning of the grid. The introduction of this charging technique might defer necessary investments for grid reinforcement within the distribution system based on the introduction of higher electricity demand.

The increased usage of Electric Vehicles and Renewable Energy Sources causes issues regarding the balancing of the electricity grid. To avoid investment costs, Distribution System Operators desire flexibility solutions. One of these flexibility solutions is the usage of the battery of the Electric Vehicle as an electricity source. This concept is known as Vehicle to Grid (V2G). However, providing V2G services might cause discomfort for the Electric Vehicle user. A contract can be used to compensate the user for the experienced discomfort. Literature on these contracts is lacking and the behaviour of EV user to these contracts is unknown. This study aims to close this gap. Data is collected by means of a web survey and evaluated with a multinomial logit model. It is shown that the difference in expected demand for price- and volume-based contracts is minimal. In addition, three contract elements can solely increase demand for V2G but require high levels. More value is created when a combination of these three contract elements is used. It would be valuable to understand how and where V2G can provide value. To do so, the results of this study can be used as input for a dynamic model that evaluates day-to-day electricity supply and demand.

Many societal, environmental and technological challenges can be characterized as wicked problems by virtue of being difficult to understand, define and solve. examples include sustainable management and consumption of resources, resilient technical infrastructure or curbing plastic pollution of the oceans. One method of tackling such wicked problems is the use of computer-aided modelling and simulation. Model-based decision support is a growing discipline involving the use of computer models of complex systems to explore, understand and manage them. A core concept in model-based decision support is scenario discovery. In scenario discovery, a model’s inputs and outputs are related to understand under which conditions policy-relevant outputs may occur. In a first step, a diverse set of inputs is used to generate a variety of outputs. In a second step, the subset of decision-relevant outputs is identified among the outputs
through some external criterion, such as a threshold value. Finally, the inputs which generated those outputs of interest are identified, and a generative rule set is induced which usefully predicts under which conditions an input will generate an output meeting the external criterion. This rule set bounds an input subspace of interest, from which (most of) the outputs of interest originate. While scenario discovery performs adequately for quasi-linear and simple models, it is not well suited to behaviorally complex, nonlinear models. This is both because external criteria are hard to define for complex model behaviors, and also because there are often significant interactions and dependencies between model inputs, which current rule induction algorithms have trouble identifying.......

Today’s energy system has originally been developed based on central energy production and a passive consumer, whose interests have to be represented by the energy suppliers and distribution system operators. However, the industry is quickly changing. The share of renewable energy is growing, and consumers are increasingly involved in the production of energy. As a result, the share of intermittent energy sources increases and so does the need for flexibility in the energy industry. Therefore, it is questioned whether the current system in the energy industry still fits with today’s developments.
To allow for flexible demand and supply of energy, digital technology for communication between computers or devices and electricity providers and consumers will become necessary. This will help to balance production and consumption at each time resolution, without high costs and unnecessary bothering of consumers. Blockchain is a technology that could potentially serve as a solution for a new energy industry system. Blockchain enables direct and reliable transactions of assets, between every party willing to do so, without the need for an intermediary or central party in control...