Interest in the potential of demand response has risen by an increase in need for flexibility in energy systems. Residential demand response (RDR) is one of the potential sources of flexibility this initiative offers. Through an aggregator, households could offer the flexibility gained from altering their demand of certain appliances to the system. However, the uncertainty in their behaviour could limit the potential value of this source. Thus this thesis tries to gain insight into the reliability of household who are participating in an RDR program provided by an aggregator. To this end an agent-based model was designed and implemented. The model describes a RDR program where the aggregator scheduled the use of household semi-flexible appliances day-ahead. The conclusion describes how reliability in terms of time-varying capacity and schedule compliance varies for different households and system situations. ... Read more

Energy islands are emerging as a key tool in Europe's offshore wind expansion, acting as central hubs that collect electricity from multiple wind farms and redistribute it across borders. The Bornholm Energy Island (BEI), the world's first project of this kind, will connect Danish and German power grids through a hub located on the inhabited island of Bornholm. While BEI is driven primarily by national and European energy security interests, its local impact on Bornholm, a community of 40,000 residents with its own sustainability targets, is not guaranteed and depends on decisions that remain deeply uncertain. This thesis addresses the question: how does the integration of an energy island impact local energy targets, sustainability, security, and affordability, amidst the deep uncertainties of its development?

To answer this, a TIMES energy system model of Bornholm's electricity and district heating sectors was built and structured around the XLRM framework, mapping exogenous uncertainties and local policy levers identified through literature review and consultation with the Municipality of Bornholm and its utility operator, BEOF. Ten scenarios were constructed by systematically varying the Energy Island's local grid connection, the reliability of Bornholm's existing import cable to Sweden, onshore wind expansion, and waste heat integration, then evaluated using a structured scenario comparison.

Results show that the Energy Island does not accelerate Bornholm's decarbonization, which is already on track through electrification, but produces the most significant impact on energy security, eliminating dependence on the island's unreliable Sweden cable from 2035 onward, provided a local grid connection is secured. Affordability gains are concentrated at the utility level, with benefits reaching residents contingent on tariff design and electricity market structure. Without a local connection, Bornholm risks becoming a passive corridor for international power flows, hosting major infrastructure without capturing its benefits. Six policy recommendations are proposed, prioritizing the local grid connection, waste heat capture, and maintained local generation capacity as a hedge against project delays. The findings offer a replicable framework for energy peripheries navigating the tension between centrally driven infrastructure and local energy ambitions. ... Read more

Grid congestion has become a structural constraint on the Dutch energy transition, yet the flexible grid connection agreements introduced to facilitate Battery Energy Storage Systems (BESS) under congested conditions remain poorly understood as integrated institutional arrangements. While these agreements are intended to unlock network capacity through non-firm access, they also create an institutional paradox: the technologies best suited to alleviate congestion are increasingly constrained by access arrangements originally designed for passive network users. This thesis investigates how the institutional design of flexible connection agreements influences the financial viability, operational behaviour, and system alignment of utility-scale BESS in the Netherlands.

An exploratory sequential research design is adopted, combining qualitative institutional analysis with quantitative policy evaluation. First, 33 Dutch regulatory and stakeholder documents are analysed to identify recurring institutional frictions, which are synthesised into three structural tensions: temporal misalignment, asymmetric risk and value allocation, and a disconnect between operational control and financial responsibility. These tensions are subsequently operationalised as four contractual design properties: compensation structure, firmness, volume orientation, and directive control. Their effects are evaluated using a Mixed-Integer Linear Programming (MILP) Policy Testbed that simulates the operation of a 10 MW / 20 MWh utility-scale BESS across 200 independent Monte Carlo market realisations. The evaluation covers four existing Dutch flexible connection agreements, the Alternative Transport Right (ATR85), Capacity Limiting Contract (CBC), Capacity Steering Contract (CSC), and Time-Based Transport Right (TBTR), together with two prospective contract designs, Volume Bound and Firmness Gradient, developed to isolate the independent effects of individual contractual properties.

The results demonstrate that the principal barrier to battery bankability lies upstream of contract design itself. The high-voltage transport tariff alone absorbs approximately 70% of gross market revenues, rendering the unconstrained benchmark structurally non-bankable. Among the evaluated agreements, only the Capacity Limiting Contract consistently restores financial viability. However, it achieves this through a compensation coverage ratio of up to 1,596% of demonstrated operational risk, indicating that bankability is restored through financial transfers sufficiently large to offset the underlying tariff burden rather than through proportionate risk compensation. By contrast, fixed tariff-discount arrangements, particularly the Time-Based Transport Right, remain financially unviable because rigid transport windows conflict with high-value market opportunities.

Beyond financial performance, no single contractual design property simultaneously optimises all evaluation dimensions. Directive control, implemented through the Capacity Steering Contract, delivers the strongest congestion alignment but depends critically on accurate DSO forecasting and activation timing. Volume-oriented arrangements reduce battery degradation and extend operational lifetime, while their contribution to congestion management is regime dependent, improving outcomes in urban, demand-driven networks but potentially worsening them in rural, generation-driven systems. Overall, the findings demonstrate that the effectiveness of flexible connection agreements is determined by their underlying design properties rather than by contractual labels alone. More fundamentally, they indicate that existing agreements are compensating for structural shortcomings in the Dutch grid tariff architecture, suggesting that the greatest opportunity for future reform lies in grid-access pricing rather than incremental contract redesign. ... Read more

This research examines how perceived hassle and habit strength influence households’ willingness to shift electric vehicle home charging away from peak hours. Empirical behavioural data were integrated with a probabilistic allocation of home chargers in a behaviour-informed agent-based model of a congestion-prone Dutch distribution network. Results reveal a mismatch between charger ownership and behavioural willingness: an unweighted willingness of 39.3% overestimated the realised, population-weighted flexibility of 28.6%. The findings show that residential charging flexibility is shaped jointly by household behaviour, infrastructure access and household heterogeneity. Automation may unlock additional flexibility by reducing perceived hassle. ... Read more

Dutch greenhouses rely on natural gas-fired combined heat and power (CHP) systems that supply their electricity, heat, and CO\textsubscript{2} while providing flexibility to the grid. As the sector moves away from natural gas, this research investigates how an energy system integrating spectral-filtering agriphotovoltaic (APV) modules can support this transition, and what its impact is on grid support, self-sufficiency, and economic performance.

A sustainable multi-commodity energy hub combining spectral-filtering APV, a battery, geothermal heat, thermal storage, a heat pump, and direct air capture was modelled in Python for Power System Analysis (PyPSA) for a 5.2 ha greenhouse at Koppert Cress in Monster, the Netherlands. The system was built up in three steps: an electrical system with APV and a battery energy storage system (BESS), a heating system adding geothermal heat, thermal storage, and a heat pump, and the full energy hub adding direct air capture with liquid CO\textsubscript{2} storage. Each configuration was evaluated on three key performance indicators mirroring the CHP's functions: Economic Performance, Self-Sufficiency, and Grid Support, the latter based on the imbalance settlement data of the Dutch grid operator. Evaluation was done using Modelling to Generate Alternatives with 5\% and 10\% cost slack, a rolling-horizon optimisation, and a sensitivity analysis on the geothermal capacity and the grid import limit.

The hub can supply the greenhouse's electricity, heat, and CO\textsubscript{2} demand without natural gas. The optimised battery raises the simple self-consumption of the APV electricity from 20.8\% to 90.2\%. Self-sufficiency reaches 16.4\% at the cost-optimal dispatch and 27.0\% within a 5\% cost increase, while the weighted grid alignment can be improved by 34.7\% within the same budget. Economic performance is the main barrier: the APV alone does not pay back within its lifetime, although the combination with the battery pays back in 19.2 years. The hub can therefore take over the CHP's core functions, but mainly when its grid support is actively valued. While these results reflect a single greenhouse, crop, and modelled year, they provide a quantified indication of how sustainable multi-commodity hubs can replace the CHP. ... Read more

Decision making under deep uncertainty relies on simulation models to explore how complex systems may behave across a wide range of uncertain conditions. Scenario discovery is an important method within the field of deep uncertainty. It generates large ensembles of model runs, classifies these runs into decision relevant outcome classes, and then uses rule induction to identify the input space regions associated with those classes. In this way, scenario discovery helps analysts understand under which uncertain conditions problematic outcomes occur. However, the method usually focuses on the relation between model inputs and model outcomes, while the structure of the model remains outside the analysis.

This creates a tension for System Dynamics models. System Dynamics is a white-box modeling approach in which behavior is understood as the result of its model structure. Growth, decline, oscillation, and collapse are not treated as isolated output patterns, but as consequences of stocks, flows, delays, and feedback loops. When scenario discovery is applied to System Dynamics models without considering this structure, part of the potential of the model is lost.

The recently developed Loops That Matter method may help address this gap. Loops That Matter calculates loop scores that indicate how feedback loops contribute to the model behavior over time. This formalizes the quest to explain how model behavior is related to model structure. While feedback loops are commonly used to explain model behavior, such explanation is usually limited to a small number of exemplar runs and is rarely embedded in large uncertainty analyses.
This thesis therefore investigates how Loops That Matter can be integrated into scenario discovery to relate decision relevant model outcomes to the feedback structure of System Dynamics models.

This research develops four approaches. The first approach adds loop scores after a conventional scenario discovery workflow. The second approach uses a single loop score directly in the classification step. The third approach extends this logic by applying time series clustering to multiple loop scores. The fourth approach transforms dominant loop sequences into a network trie. These approaches are tested on two models, a small epidemic model and a more complex energy model. The epidemic model provides a straightforward relation between structure and behavior. The energy model provides more uncertain parameters, more feedback loops, and more diverse behavior.

The results show that Loops That Matter can meaningfully extend scenario discovery when the relation between feedback structure and model behavior is clear. In the epidemic model, loop scores helped explain the differences between mild, moderate, and severe outbreak classes. The dominant loop sequence strips showed how outbreak severity was related to the timing and duration of feedback loop dominance. In addition, clustering the R1 Infection loop score produced classes that could be related back to outbreak behavior and input space. This demonstrates that loop scores can be used both as an ex post interpretation of the model structure, and as a meaningful input to the classification step itself.

The results for the energy model were less successful. Multivariate clustering of two loop scores did not produce classes that were clearly distinct in the model outcomes or well separated in the input space. Similarly, the network trie did not provide a useful classification workflow for scenario discovery. However, the trie did provide an important insight. Namely, that the energy model generated many different dominant loop sequences across the ensemble. Similar behavioral patterns within a key model outcome were also associated with diverse loop dominancy sequences. This suggests that the model exhibits dynamic equifinality, where similar outcomes can arise through different shapes of the feedback loop structure.

The main conclusion is therefore twofold. Loops That Matter can be integrated into scenario discovery, but its usefulness depends on the clarity of the relation between input parameters, and model outcomes. A main contribution lies in the explanatory power of feedback loops after a scenario discovery workflow, as they can help with explaining why classes differ. Their use as a direct basis for classification is more uncertain, especially in larger models. The direct identification of dynamic equifinality is also novel. Previous work has only guessed at its existence, but this research exposes it, and discusses its impact on model based decision support. The thesis contributes a methodological bridge between scenario discovery and structural explanation in System Dynamics, while also showing that equifinality can limit the extent to which model structure can be used to explain decision relevant model behavior. ... Read more

Steel manufacturers are under increasing pressure to reduce emissions and operate more sustainably. This transition requires strategic decisions based on reliable, consistent, and traceable data. This thesis investigates how large industrial companies like Tata Steel Nederland (TSN) can improve data governance to support that goal. The study focuses on improving reporting and decision-making processes that rely on shared data across departments.

The thesis begins with a literature review that highlights key gaps in current research. Eight core data governance criteria were derived from the challenges to structure the findings: data lifecycle management, interoperability, data classification, data security & compliance, data unit responsibility, version control, data storage, and performance monitoring. These criteria were drawn from academic literature and refined based on the steel industry context. While the steel industry has used data to optimize processes and monitor performance for decades, little attention has been paid to how this data is governed. Studies often focus on real-time production data but overlook the governance of supporting documents for sustainability disclosures, regulatory reports, and investment proposals. The role of data governance in aligning technical, regulatory, and operational reporting remains underexplored. Especially with new regulations like the Corporate Sustainability Reporting Directive, the need for structured, transparent data governance has become urgent.

To explore this, the study uses a qualitative case study at TSN’s Transformation Office. This team coordinates strategic reporting, including the Green Book, a report used to secure investment for the transition to green steel. Data was collected through interviews, document analysis, and observations during a five-month internship. The analysis showed that governance practices at TSN are often inconsistent, with unclear document versions, missing responsibilities, and scattered file storage. Most issues are not technical, but organizational.

To address these challenges, the thesis presents practical recommendations for each of the eight criteria. These include using consistent versioning, labeling files with classification levels, assigning responsibility for key data elements, and creating shared templates. The recommendations are designed to work within TSN’s existing systems and routines. They aim to enhance clarity, coordination, and accountability across teams without requiring the implementation of major new technologies. Importantly, the recommendations emphasize that lasting change depends on three key factors: top-down prioritization, practical bottom-up training, and alignment among the eight data governance criteria.

To test the recommendations, a focus group was held at the end of the study. One recommendation per criterion was discussed. Most were confirmed as useful, especially when aligned with team behavior and leadership support. Six out of eight recommendations were seen as transferable to other industries with similar data challenges. However, data unit responsibility and performance monitoring were not confirmed as broadly applicable.

This thesis contributes to the growing conversation about how data governance can support sustainability in heavy industry. It shows that better governance improves data quality, which in turn strengthens strategic reporting and decision-making. While the recommendations are tailored to TSN, many of the insights also apply to other data-intensive organizations. The findings highlight that successful change depends not only on tools and frameworks but on leadership, shared responsibilities, and practical skills.
... Read more

The energy transition requires greater flexibility from households, especially as the energy system becomes increasingly decentralised and the Netherlands moves towards phasing out the net metering scheme. Load shifting, using appliances during periods of solar production, is a relevant way in which households with solar panels can contribute. However, many do not adopt this behaviour consistently yet.

This study examines how perceived hassle acts as a behavioural barrier to load shifting among Dutch households with solar panels. It explores how different types of hassle affect load shifting behaviour, with a focus on dishwasher and washing machine use. Also, it studies what shapes the perception of hassle. Hereby, it distinguishes between contextual factors, arising from an individual’s environment, and internal factors, linked to an individual’s characteristics. To guide the analysis, a conceptual model was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT), using the effort expectancy construct to position hassle perception. The model was extended with internal and contextual factors as antecedents, and habits were added as an internal factor using insights from habit theory.

The study conducted a structured survey combining closed- and open-ended questions among Dutch households with solar panels. The results show that hassle is a measurable and particularly relevant behavioural barrier. Specifically, the hassle factors described as PV monitoring effort, manual planning effort, decision uncertainty, and overloaded machine inconvenience were linked to lower levels of load shifting. Qualitative responses further highlighted the role of family-related hassle, hygiene-related hassle and technical hassle in load shifting behaviour. Furthermore, the findings highlight how family dynamics, personal flexibility, spillover effects of hassle, and habitual routines shape the perception of hassle.

The insights suggest that interventions should focus on improving energy engagement, not only by improving access to user-friendly monitoring tools but also through campaigns to increase familiarity with solar systems. Most importantly, interventions should focus on supporting households in integrating load shifting into daily routines. Future research could build on this study by examining other appliances, such as electric vehicle chargers, exploring how family dynamics shape hassle perception, and using longitudinal methods to better understand how hassle develops over time.
... Read more

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. ... Read more

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-
ii
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. ... Read more

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. ... Read more

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?
... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more