The brownfield container terminal at the Maasvlakte aims at realizing a zero-emission vehicle fleet to strive for its aim to be zero-emission by 2040. Therefore, the research question is as follows: “What are the consequences for costs and operational performance of implementing mature renewable energy carriers for AGVs at the container terminals of ECT before 2040?” Literature research is carried out to find the definition for energy carriers and to find out what possible energy carriers exist to transition a fossil fuel-based vehicle fleet into a zero-emission vehicle fleet. The list of possible energy carriers is reduced by a screening, consequently, resulting in the lithium-ion battery and hydrogen as possible candidates for the vehicle fleet of ECT. Consequently, discrete event simulation is carried out to find the operational performances of these two energy carriers. Afterward, a cost calculation is done and a possible phased transition is shown.

This thesis project investigates the applicability of different sustainable transport methods for delivering chemical products to the European customers and a decision-making business process is derived from that. So, a set of representative lanes are selected to be involved in this project and the hauliers responsible for those lanes are asked to provide available sustainable transport option for each lane, CO2 emission reduction by the options, cost and on-time delivery as affected KPIs by the options. At the discussion with senior sales managers this info is communicated, and it emerged that there are many other factors than the three main KPIs that determine the applicability of an option for the representative lanes. They are visualized in a decision-making business process that balances the KPIs for determining the applicability of an option for a lane. the design is then validated by applying it on representative lanes and comparing the result with haulier responses. It emerged that the design suggests the applicability of fuel change, payload increase and renewing trucks options for the lanes that have currently direct road transport method. The design results in 13.60% total emission reduction for lanes with any current transport method and 19% for the lanes with current direct road transport and 6% logistics cost increase with no effect on on-time delivery.

The need for water, energy, and food resources is central to human being. Demand of these resources will increase as global population increases. By 2050 it is expected that combination of population growth and economic development will lead to 50% increase of global water and food demand, while energy demand will be doubled. Addressing sustainable water, energy, and food consumption has also been emphasized in United Nations’ Sustainable Development Goals; among others, this applies to consumption at household level. Sustainable household consumption of water, energy, and food here implies not only the objective to improve the resource durability, but also to reduce environmental footprint. The unsustainable management of these resources will potentially harm the climate objective addressed in the Paris Agreement in which The Netherlands, among other countries, have been participating. Acknowledging the importance of household consumption on the environment, a joint research team funded by the National Science Foundation named INFEWS (Innovations at the Nexus of Food, Energy, and Water Systems) is commissioning this research project to investigate policy measures to sustainably reduce greenhouse gas emission concerning Dutch household food, energy, and water consumption sector. The main objective of this research is to obtain insight on the dynamics and interrelations of Dutch household water, energy, and food consumption; and to develop policy recommendations based on the insight toward reducing the direct and indirect greenhouse gas emissions (GHG) level associated with the household consumption over time in the Netherlands. To achieve this objective, this research mainly utilizes data-driven exploratory modeling approach in which the interrelations of water, energy, and food consumption system are modeled and simulated over time, while acknowledging the plausible parametric and structural uncertainties natural to the state of modeled subsystems. System Dynamics methodology is used to conceptualize the causal relations among the relevant subsystems and to computationally develop the model in a form of stock-flow diagram. Based on the developed model, the Exploratory Modeling and Analysis methodology is used to explore the model within the associated uncertainties. Through these methods, two types of policy frameworks can be respectively tested and explored. The first one consists of the policy approaches within household consumption domain. The second one consists of the plausible policy options outside of the household consumption domain which are relevant to improve the GHG indicator.

This study deals with the modelling of the future global energy demand in the transportation sector, how and in what extend this could be limited and which low carbon fuels and technologies could contribute to the mitigation of the energy demand. In the introduction of the current report, the greenhouse effect, the global warming and the climate change are defined. Then, the actions taken and the agreements made to deal with global warming and climate change are presented and the need for combating them is highlighted. After the introduction, a literature review is conducted in order to spot the knowledge gap and formulate the research questions. In the literature review, the ICCT’s and IEA’s studies are mainly discussed due to their high quality research and the big amount of published reports. More specifically, their models, scenarios, policies and results are discussed in detail in order to accurately define the knowledge gap and formulate the research questions. Thus, the goal and the main research question of this research is to answer how could the future global energy demand be mitigated and which low carbon fuels and technologies could significantly penetrate into the transportation sector. In the beginning of this research, the conceptual and the theoretical frameworks are presented in order to assist with the outline of the thesis and create the theoretical background for the development of the model. Then, in order for the research questions to be answered, a forecasting model calculating the transportation future energy demand by country, transport mode, technology and energy carrier throughout the period 2015-2050 is developed. The main features that distinguish this model from models used in similar studies are the strong focus on the diffusion of low carbon fuels and technologies, the use of a different diffusion model (the Bass s-curve) and the country by country with one-year time increments approach. After the model is verified and validated through comparison with similar studies and a sensitivity analysis, results are presented for two different scenarios. The first scenario is called Current Policies scenario and aims to show a potential pathway of the future global energy demand in the transportation sector that could happen if no more policies are applied after 2020 and the second scenario is called Accelerated Policies scenario and its target is to represent a pathway that could happen if new policies are adopted and stricter implementation is applied. The results of the two scenarios show that it is possible to achieve a bending of the energy demand and a diffusion enhancement of low carbon fuels and technologies if new and stricter policies that motivate technology improvements and fossil vehicles ban are applied. In particular, the results indicate that the implementation of new and stricter policies, which could lead to efficiency improvements, and to more effective diffusion of low carbon fuels and technologies could achieve reduction of the energy demand after 2029. Moreover, according to the results, electricity is expected to dominate in the transportation sector, while biofuels, hydrogen and ammonia are also expected to be highly used. However, without further policy action, the global energy demand is expected to follow a constantly increasing trend in the future while the penetration of low carbon fuels is expected to be significantly lower.

Many regions around the world are facing water shortage, low water quality, unsustainable overuse or other problems that may even cause conflict among different actors. These water resources are typical examples of common-pool resources. Leaders seem to play a crucial role in the emergence of community-based management of resources and overcoming the collective action problem. The main purpose of this study is to find the mechanism behind the emergence of leaders in the process of starting collective action. I synthesized leadership studies from different domains and created Leadership for Community Development (LCD) theory. Based on LCD, leadership emerges from influencing interactions that are done to share a vision and make other community members dedicated to it. In this process, leaders gather the requirements of collective action, like support or political capital. Central to this idea is the innovation process, which has four phases of idea generation, idea elaboration, idea promotion, and idea adoption. The idea in the innovation process is the same as the vision. According to this theory, first, there might be problematizing leaders who challenge the current state and give attention to a problem through influencing interactions. Dissatisfaction with the current state can lead to idea generation. In the idea elaboration phase, the idea creator will try to elaborate and gather support for the idea among his or her close connections. If this is successful and enough support is received, dedicated actors will start promoting the idea by influencing others. In the idea promotion phase, enabling leadership emerges when actors promote their own vision and echoing leadership emerges when convinced actors advocate the vision of their influencers. Enabling leadership and echoing leadership form up effective leadership, which is the indispensable influencing effort of actors for reaching the next phase. Idea adoption or implementation, which is in the domain of management, starts when requirements such as political and intellectual capitals are enough. This theory is further supported by a case study and interview. I created an agent-based model based on the LCD theory. The model was a successful proof of concept for the LCD theory and how the identified types of leadership emerge through the process of initiating collective action. I used the model to identify the most significant factors on the role of leaders and the initiation of collective action. The social network proved to be the most important factor. Based on these findings, I emphasized on the importance of focusing on networks and building connections in the communities. Moreover, I have suggested using these findings to diagnose the lack of collective action in communities and to use this diagnosis in the process of facilitating collective action in communities. I proposed many recommendations for future steps in this study. The top priority recommendations are a call for case studies to be done to further validate the LCD theory and to perform group experiments to increase understanding of the role of the social network in collective action problems.

In order for the Netherlands to reach CO2 neutrality by 2050, large investments in zero emission technologies are needed. These investments would comprise out of renewable energy generation, higher energy efficiency alternatives and electrification of end uses. Although climate mitigation has become an ever growing societal concern since the ratification of "het klimaatakkoord", progress in the industrial sector has been seriously lagging in the Netherlands. In 2021 the reduction of CO2 emissions in the Netherlands stagnated and the emissions of the industrial sector actually slightly rose. So too in the Port of Rotterdam, where the chlorine cluster is not showing any significant CO2 reductions. There are several potential explanations why there is a gap between what should be invested and what actually is invested. They define the gap as, ’the apparent reality that some technologies that would pay off for adopters are, nevertheless, not adopted’. So, why do decision makers under invest in zero emission technologies? The explanations of fall into two broad categories: •Market barriers •Behavioural barriers Following the problem situation, this thesis focused on researching to what extent market & behavioural barriers contribute to the investment gap in the Port of Rotterdam Chlorine cluster, by incorporating both market and behavioural barriers in a quantitative investment model. The incorporation of these barriers in the model result in a range of investment types, some of them non-optimal and varying in perspectives on valuing the future. By simulating technology adoption under the assumption of this range of varying configurations of market and behavioural barriers, it is possible to determine the effect of those two categories of barriers on technology adoption at the PoR chlorine cluster. The obtained insights of this research feed into the larger study of quantitative decision models for the industrial sector. Where the following main research question is answered: What is the effect of market and behavioural barriers on zero emission technology adoption at the PoR chlorine cluster? To answer the main research question, the model represents the PoR chlorine cluster on a highly detailed level and bases it's technical system's configuration on a thorough plant-process-product & zero-emission technology inventarisation. These two inventarisations give the current and possible future configuration of the PoR chlorine cluster's technical system. Consequently, this makes it possible to explicitly model the technology stock at the chlorine cluster and determine technology adoption on an asset level. The market & behavioural barriers are represented by 8 evaluation types. These evaluation types represent 8 varying configurations of the market & behavioural barriers. Consequently, a scenario analysis was conducted with the model. The scenario analysis resulted in transition pathways, total CO2 emissions and total cash flows between 2022-2050. The model results show that the incorporation of market and behavioural barriers lead to postponed adoption of zero emission technology adoption. This is reflected by the lower number of years that these alternatives are installed between 2022 and 2050. The lower adoption lead to an increase of 288% total CO2 emissions between 2022-2050, compared to the optimal solution. Underneath, the key findings from the model results are presented…

After the start of the COVID19 pandemic, many countries decided to develop digital contact tracing applications to assist in contact tracing. This aimed to warn people who were potentially infected as early as possible and stop them from spreading the virus further and bringing down the total amount of new infections. Even though there was some proof of the potential effectiveness of applications such as these, they had never been deployed at scale before the pandemic and there was no standardised and shared vision on how to best implement these applications, meaning that countries that wanted to include digital contact tracing as part of their COVID19 strategies needed to design and develop an application themselves...

As heating forms a large share of the primary energy consumption in the Netherlands, district heating networks are regarded as an effective means to address the emissions- and renewable energy targets. In the Province of Zuid-Holland, the aim is to construct a large-scale regional open heat network that should create a more sustainable built environment. Since open heat networks are a novel phenomenon, challenges arise that relate to the organisation of this market, how heat should be priced and more specifically how to deal with the unique characteristics of transmission losses with heat. In this thesis, evaluate several conventional pricing mechanisms are evaluated and an alternative pricing mechanism including a transmission loss allocation method is developed by means of an algorithm, to investigate if it improves the market performance. This alternative pricing mechanism succeeds in allocating the transmission losses to loads on the basis of fairness, but overall fails to improve the wholesale market performance compared to other pricing mechanisms. The allocation method can be improved, depending on the desirable market outcomes, by adapting several assumptions and procedures in the algorithm.

This research has compared three different market mechanisms in a quantitative way. The influences of these market mechanisms on the heating system were investigated for CO2 emissions, consumer price and producer surplus. In addition, the influences of investment decisions and price and consumption growth scenarios were tested. The research showed that the end-to-end market mechanism is the least uncertain in the future and is only influenced by consumption growth. In addition, the construction of a large source of residual heat can moderate this effect. The wholesale market is strongly influenced by price scenarios. This effect can be moderated by the construction of the pipeline through the Midden, which connects two different heating systems. Finally, there is the single buyer market. This is influenced by both scenario variables, while no investment decisions affect it. Further research must be done into the latter market mechanism. And the models need to be extended to more specific markets because this research has used archetypes. Keywords: District heating network, Market mechanism, Single-buyer,

The transition to a carbon-neutral power system has become a global imperative, ever more driven due to recent events such as the energy crisis resulting from the Ukraine invasion. The Netherlands, devoted to the large-scale development of offshore wind and solar PV, see great potential in the use of hydrogen as a gas replacing energy carrier. A knowledge gap has been identified on what this role of electrolysis will be in the future energy system and more specifically, in what quantities electricity can and should be converted to hydrogen. This research aims to address these uncertainties and provide insights into the prudent capacity for green electrolytic hydrogen production in the Netherlands by 2030 and 2040. To this end, a model of the combined electricity and hydrogen system of the Netherlands is constructed in Linny-R, an executable graphical representation language for Mixed Integer Linear Programming (MILP). A vast array of output data from forecasting scenarios is gathered and consolidated to establish the spectrum of input variables. By systematically assessing the uncertainty, based on these ranges of input variables, and the impact, based on a sensitivity analysis, an experiment design is composed to encompass the most complete reflection of all uncertainties within computation limits. Numerous experiments are then conducted to generate outcomes, determine prudent capacity, identify influential uncertainties, and evaluate the impact of large-scale hydrogen storage on electrolysis potential. The research results demonstrate that the potential for electrolysis increases from 8-12 GW in 2030 to 8-44 GW in 2040, indicating the cruciality of flexibility for the Dutch energy system in the coming years. The most prudent capacities for 2030 and 2040 are identified as 12 GW and 38 GW, respectively, with 9 GW being identified as the no-regret capacity to be installed by 2030. Therefore, the research recommends stimulating additional investments beyond the initial targets of 3-4 GW for 2030 to foster the Dutch hydrogen economy. Moreover, there is substantial risk of overinvestment associated with the prudent capacities, especially as a result of the growing uncertainty in 2040. Deploying additional flexible system capacity to prevent electrolysis from acting as a loss of load mitigation measure can lower the prudent capacity and reduce the risk of overinvestment. Furthermore, policy measures should focus on stimulating influential uncertainties such as hydrogen demand, peak residual load and hydrogen storage. Every additional 100GWh of underground hydrogen storage capacity enables 500MW of electrolysis potential. Moreover, the last few gigawatts of the prudent capacities will primarily serve as peak production plants with limited operational hours and margins starting at 11.00 EURO/kg required to recoup the investments. Further research opportunities include incorporating geographical and grid aspects into the model, exploring profitable conditions for investments in the last few GW of electrolysis capacity, investigating the role of large-scale hydrogen storage, and studying the potential of electrolysis beyond national borders in the North Sea interconnected electricity generation hub.

An important process for navigating the uncertainties associated with the energy transition is power system planning. Energy system models are considered to be useful tools for supporting the power system planning process but are limited in several ways. Firstly, energy system models often have a high computational burden, limiting the possibility of conducting a large range of experiments. Secondly, to reduce their computational burden some energy system models have limited temporal detail, resulting in overestimation of VRE capacity in the system, underestimation of VRE curtailment, and undervaluation of flexible resources. Thirdly, many energy system models are considered to be ‘black boxes’ due to their high complexity and limited transparency. To address these problems related to models for power system planning, the Operation and Planning Model (OPM) has been developed in this study. The OPM is a myopic electricity system cost-optimisation model with a high level of temporal detail and a relatively low computational burden to allow for exploring the development of a national energy system, focussing on the interplay between demand, supply, and storage of electricity. The OPM has been applied to a reference case and two targeted experiments on the development of the Dutch electricity system from 2030 to 2050. The reference case showed that in case natural gas-fired power plants with carbon capture and storage are included in the OPM, the electricity system becomes highly dependent on this technology for the provision of electricity and flexibility. If this technology is not included in the OPM, the maximum number of additional generation and storage units per year is shown to have a major impact on system development. In case this factor is sufficiently high to not limit investments, the system becomes mainly dependent on onshore wind for the provision of electricity and energy storage in underground hydrogen storage facilities for flexibility. In case this factor is limited to a lower level, the system develops a more balanced technology mix. Multiple recommendations are made for future research continuing on the work presented in this report. 1. Applying the OPM to more experiments can result in more in-depth insights into factors influencing the development path of the Dutch electricity system. This could entail analysing scenarios with variations of the costs for the different technologies to assess under which cost levels the system develops a favourable or unfavourable technology mix. 2. The spatial scope of the OPM can be expanded to assess the importance of cross-border trade for ensuring system reliability and gaining insights into the co-evolution of interconnected electricity systems. 3. The demand side is assumed to be inflexible in the OPM. Including demand side flexibility can result in a more accurate view of the electricity system's development between 2030 and 2050. 4. The used time series data is based on a single year. An alternative approach is suggested in which time series data from multiple years is used which would better represent that perfect forecasting of capacity factors and electricity demand for a future year is not possible.

Increased variable renewable energy sources penetration in The Netherlands require more flexibility that could be provided by distributed energy resources located at small end-users combined with demand response. Aggregators could act as intermediary entities to exploit the flexibility potential of small end-users and create value for Distribution System Operators (DSOs) by delivering electric flexibility services that support congestion management. Aggregator Eneco CrowdNett is planning to commission a pool of households which will have a combined system of solar-PV and residential Battery Energy Storage (BES) units. Next to optimizing the household’s self-consumption, these systems will jointly be able to deliver power to the grid. Therefore, the system as designed by Eneco CrowdNett is used as a case-study during this research. This research aims to provide a comprehensive understanding of how the value of an Aggregator’s electric flexibility services can be determined for DSOs, without jeopardizing the business case for the Aggregator and the CrowdNett participants themselves. A tool that simulates the behavior of residential BES units connected to the LV distribution grid is developed and tested, followed by a reflection on its usability for other cases. The system under review in this study is framed as a Unit Commitment Problem (UCP), in which the generation units are represented by CrowdNett’s residential BES units that primarily deliver local service, but can also deliver congestion management services through load shifting. Linear Programming (LP) is considered a useful method to solve the UCP, which is implemented in the modelling software Linny-R. It can be concluded the value of Eneco CrowdNett’s electric flexibility services for DSOs can not be determined by exclusive using a Unit Commitment model with a LP solver to replicate the behavior of residential BES units located at the LV distribution grid. The model rather provides a tool to generally assess whether an Aggregator is able to reduce congestions in a given distribution network without jeopardizing her own business case and that of the end-users owning the BES units. The model should be supplied with input data regarding residential demand profiles, PV production profiles and the power capacity of the LV substation before the model output can be compared with the expected costs of grid reinforcements on a case-by-case basis to determine the value for DSOs.

Following the climate goals of the Dutch government, a tremendous amount of vRES (variable Renewable Energy Sources) will be built. These energy sources will produce large amounts of power, which must travel great distances on the transmission grid. The Dutch TSO, TenneT, is responsible for the transmission grid's design, construction, and maintenance by European and Dutch law. The planning for transmission grids has become more complex since the liberalization of power markets. Because of this complexity, the planned expansions are published every two years. In energy systems, the ever-changing context of the system makes the possible future states of the system uncertain. In this research, we 1. identify the relevant uncertainties for the transmission system, 2. Identify the most vulnerable lines under these uncertainties, and 3. Look at the effect of the delays on the performance of the overall power system. This gives insights into how the transmission system must evolve to maintain the grid's performance. Not only more power from vRES will be generated, but coal plants will also be phased out, and the demand for other energy sources (fuel, gas, etc.) will be electrified. More power flow will lead to congestion on the current grid. The Dutch Transmission System Operator (TSO) uses congestion management to alleviate acute congestion and expansion planning to prevent future congestion. To decide whether grid expansion should be used, Production Cost Models (PCMs) are used. To account for uncertainties in a system, the concept of deep uncertainty can be employed to map the uncertainty of a system. Exploratory modeling uses this concept to understand the consequences of deep uncertainty. Experimental modeling and analysis could thus be used to understand the effects of vRES on the power grid. Direct Current Optimal Power Flow (DCOPF) is used for the formulation of the Production Cost Model (PCM) for the power network modeling. The chosen exploratory modeling framework is a simplified form of the XLRM framework, in which the policies in the model (L) is not altered. The data used as input for the model is selected from a wide range of sources, following the format of the DCOPF formulation. The tool used to build and simulate the DCOPF model is Scenario Analysis Interface for Energy Systems (SAInt) software. The EMA workbench is used to perform exploratory modeling and analysis. Two times 1,000 experiments for a week period have been run with these tools, one set in January and one set in June. To assess which lines in the transmission network might be susceptible to congestion, we analyzed the experiments' outcomes with a congestion-seeking algorithm. The adopted methodology has been verified using a 5-node model. The results of these experiments are divided into two types of outcomes: the general system outcomes and the line outcomes; the latter has been split up based on the urgency of capacity expansion. The analysis tools by the EMA workbench were used to analyze the results of the experiments, especially the Patient Rule Induction Method (PRIM) algorithm. The experiments showed significant differences in congestion between June and January...

The energy system is a complex socio-technical system, characterised by a multitude of stakeholders, different goals, long-term investments, and large scale sub systems with long lifetimes. The government of the Netherlands aims to support the transition of the energy system of the Netherlands towards a sustainable system by implementing policy measures. Recently, companies have voiced their concerns on the fluctuating effects of policy measures in the Netherlands and have asked for a more long term vision of policy measures. This long term vision can be (partly) provided by analysing the interference in the energy system. Another component of a policy strategy is that to develop and implement a long term policy strategy, multiple governmental organisation have to collaborate. The policy measures are designed by different ministries within the Netherlands. In order to implement the coherent policy strategy, collaboration between these ministries is essential. This research proposes a methodology to identify five types of interference and to analyse the effects of the identified interference on the Dutch energy system. This categorization of interference allows for a discussion on the unavoidable nature of interference. Moreover, it shows which types of interference can prove to be beneficial at certain times. A conceptual theoretical framework for factors that influence interorganizational collaboration between public parties has been constructed based on literature, and is tested and validated within the case study. The research into interorganizational collaboration also includes a focus on the relations between the identified factors. The relations can provide insight into the more complex system view of the factors that influence interorganizational collaboration between public parties. Both researches are based on a view of the world in systems, and for both is searched for links between sub-systems. Finding the link between the technical energy system and the collaborations required in policy making was based on this system perspective. These two topics are invariably linked together. The integrations in the energy system increases the need for collaboration, whereas collaboration can increase (the effectiveness of) the integrations in the energy system.

This thesis aimed to investigate the potential of the EU’s carbon border adjustment mechanism (CBAM) to promote sustainable adjustments in the production process of semiconductor machines, considering uncertainties in carbon accounting. The research combines a review of existing literature, a case study analysis of a selected semiconductor company, and a policy analysis of the EU’s CBAM to provide insights into the ability of the CBAM to promote the use of sustainable materials in the semiconductor industry. The literature review highlighted the significance of life cycle assessment (LCA) as a carbon accounting tool and its potential to assess the relevant embedded emissions for the CBAM. The review showed indeed that LCA could be useful to assess the first phases of the production of the raw materials. Additionally, the review showed that it is less useful as a decision-making tool due to the fact that decision-making regarding the CBAM requires the consideration of other parameters as well, not only the environmental ones. Additionally, the review revealed the novelty of the EU’s CBAM regarding the carbon accounting tools and the CBAM’s design. At the same time, it highlighted the relevance of this research project. The statistical sensitivity analysis (SSA) conducted as an element of the case study showed that there exists a significant variation in the total embedded emissions of the semiconductor machine depending on the kind of steel and aluminium used. This highlighted the opportunity for improvements in the embedded emissions caused by the semiconductor machines. The SSA is also used to evaluate the cost-benefit analysis (CBA) of the implementation of green steel and aluminium in semiconductor machines. The CBA showed that a high reduction in embedded emissions is required to achieve a break-even level with the costs of sustainable steel and aluminium. However, this reduction also depends on the design and implementation of the CBAM. The stakeholder analysis highlighted the actors, transactions, frictions, and games caused by the implementation of the CBAM. A concluding policy analysis was conducted to present possible implementations of the CBAM. The analysis showed that under the current circumstances for the CBAM to be successful in promoting sustainable changes in the semiconductor machine, while considering the uncertainty in the carbon reporting process, an unrealistically high carbon price is needed to break even. The analysis, however, indicated a bright spot; if global electricity production becomes less carbonintensive, the CBAM may work very well in promoting sustainable materials. Thus, this thesis concludes that for this case study the EU’s CBAM is not effective in promoting sustainable adjustments in semiconductor machines. However, this conclusion is obtained for this specific case with its specific extreme policy scenarios. Therefore, further research could be conducted in assessing other policy scenarios or carbon accounting tools and test how they evaluate the CBAM on its potential to promote the use of sustainable materials in other industries as well.

Intermittent generation behaviour of wind and solar power units requires Transmission System Operators (TSOs) to activate balancing power to maintain a stable grid frequency. With the current strong growth in installed renewable energy capacity in The Netherlands, it is therefore expected that the volumes of activated balancing power will grow accordingly. In the Dutch balancing power market system, the activation of balancing power is remunerated against the highest activated balancing power bid in the respective time-block. Without a proportional increase in affordable balancing power bids, the economic costs of grid balancing in The Netherlands are expected to increase as a result. Imbalance power prices for positive balancing power, the counterbalancing of net shortages of power supply, may see a particularly strong rise, as the opportunity to quickly increase grid frequency places influential restrictions on the operation status of suitable power assets. The financial opportunities inherent to this trend have gained the attention of non-traditional suppliers of technical positive balancing power potential, amongst which the energy-intensive industry and greenhouse farmers. However, limited insight into the exact value potential of market participation, and contradictory developments in the German market are currently preventing these parties to invest in unlocking their technical balancing power potential. A limited supply of new balancing power bids will further increase the economic costs of grid balancing. This study therefore sets out to identify a method to quantitatively determine the profitability potential of providing positive balancing power to the Dutch balancing power market to an individual market participant. A case study is conducted at AkzoNobel, whose sizeable chlorine plant possesses significant balancing power potential. It is found that a Unit Commitment Model based on a Mixed Integer Linear Programming solver can be used to successfully replicate the market environment. To simulate market profitability to AkzoNobel over time, it should be supplied with input data of the future development of balancing power activation, the merit order of balancing power bids, and balancing power capabilities of the AkzoNobel plant. Whilst the balancing power capabilities of the plant can be relatively simply and accurately modelled into a Unit Commitment Model, the same does not hold for the future activation of balancing power and respective prices in the merit order of balancing power bids. On the one hand, the future development of the various factors that comprise these sub-systems is characterized by significant uncertainty, as power system dynamics are notoriously hard to predict. On the other, both are the emergent behaviour from complex systems of interacting power system agents and assets. Their actions result from a large number of factors that may vary strongly on a short time scale. This causes difficulties in obtaining accurate projections of their behaviour on the required minute-by-minute time scale. The activation of a high balancing power bid price in general also implies a strong grid frequency deviation. This implies that the profitability of providing balancing power is particularly sensitive to deviations in high imbalance power prices. As imbalance power prices are primarily determined by the specific height, volatility and frequency of high balancing power activation peaks relative to the height of prices in the merit order for those respective fifteen-minute time blocks, the choice of a method to obtain the input data of balancing power activation and the merit order must be based on a clear substantiation of their accuracy. Various approaches to obtain data projections with the required accuracy have been assessed, for which the following conclusions are drawn. To determine the development of the future activation of balancing power bids it is advised to evaluate a combined method of statistical convolution of probability distributions and hidden Markov chains. However, this method must be preceded by significant additional research on critical complexities and uncertain developments in the balancing power market, for which it may not be possible to draw definite conclusions. To obtain scenarios of the various potential developments in the volume and price elements of balancing power bids in the merit order it is suggested to use a stochastic modelling approach, such as Monte Carlo simulation, to evaluate the range of different compositions the merit order may take over time. It will, however, be difficult to strategize on the results, as the potential range of states of bid prices and volumes placed by individual agents with balancing power technologies are very wide. These methods must be synthesized into an approach to determine balancing power market profitability based on the above combination of methods. Projections of the development of the key determinants of profitability in the balancing power market will inherently be associated with strong uncertainty. Considering the sensitivity of model output to inaccuracies it is very questionable whether the effort of further research weighs up against the benefits. It is concluded that it is not deemed feasible to obtain quantitative long-term indications of balancing power market profitability to an individual market participant. As a result, it is not advised to proceed with the execution of the suggested methods. It appears that alternative approaches to ensure the future affordability of balancing the power grid are more worthwhile pursuing. It is instead advised to focus continued research on public incentive schemes to attract non-conventional suppliers of positive balancing power to provide their technical potential to the market if the market fails to find an economic optimum. Continued research could also focus on enhanced regulatory designs to reduce total balancing power requirements.

In an energy-only market, private investors play a crucial role in realizing the security of supply. However, several specific characteristics inherent to his market design cause concern about whether this market design is suitable for system adequacy. Moreover, due to policy goals aimed at working towards low-carbon energy systems, these concerns are increased. In this light, many countries have implemented a capacity remuneration mechanism to increase the stability of the security-of-supply in their energy system. While earlier research has been conducted on this topic, we observe two knowledge gaps: 1) from a theoretical perspective, there is debate on the effectiveness of capacity remuneration mechanisms, especially on the role of seasonal storage, 2) from a methodological perspective, we find that there are no models that are equipped to consider myopic investments while having a high enough detailed operational model also to consider investments in (seasonal) storage and the impact of extreme weather scenarios. This research aims to fill these knowledge gaps by presenting a proof-of-concept of a myopic investment detailed operational (MIDO) model. In our thesis, we focus on system adequacy. However, by developing the MIDO model, we hope to enable researchers to analyze any problem related to long-term energy system development with a multi-time scale nature. At the center of the MIDO model are three sub-models: the investment decision model (ID), the future price (FP) model, and the present price model (PP). The first step in this loop is the present-price model, which generates highly accurate information on the operation of a market. After running a year in the PP model, information on the performance of all assets in a market is sent to the ID model. With this data, the ID model performs two tasks: invest based on limited information in an iterative greedy process and dismantle assets losing money. The ID model gets the information on the performance of investment assets under consideration from the FP model. The FP model generates less reliable information than the PP, but it does so very fast. After the investment decisions are made, this information is transferred to the PP model, and the loop starts from the beginning. In this way, a market is simulated with investment cycles, delayed responses, uncertainty, and risk upon investment decisions. This process enables the comparison of different forms of capacity remuneration mechanisms and the effect on the security-of-supply in an isolated energy system. We explore two distinctly different futures within our thesis to see if there is any added value in a capacity market in any of these futures. Moreover, within these futures, we analyze how a capacity market can help withstand an extreme-weather scenario. From this analysis, we find that a capacity market has added value in all scenarios, from the point of system adequacy but also a total consumer cost perspective. However, because we only have explored two futures, we do not argue that a capacity market will always positively impact the system adequacy within an energy system. Instead, we argue against the notion that seasonal storage on its own is always enough to reduce all shortages created in an energy-only market and disagree with the idea that seasonal storage makes capacity markets redundant. We identify two avenues for future research. First, researchers interested in generating more and better results should utilize the MIDO model to explore more future scenarios and compare different forms of capacity mechanisms. Second, research aimed at improving the MIDO model should focus on limiting the computational time generated by the investment loop and seek to expand on the model’s functionalities.

The increase in the global population and the shift towards a healthier diet is leading to several global challenges such as the growing demand for safe and healthy food and therefore also for growing media. The growing media sector is looking for sustainable alternative raw materials, but at the same time the global population needs to be provided of food. The aim of this research is to contribute to the use of more sustainable materials in the European growing media sector, considering all the business constraints. There is looked at what developments and analytical tools can contribute to decision-making in the growing media sector in its transition to a sustainable sector. A system analysis on the basis of a multi-level perspective and a Multi-Criteria Decision Analysis have been carried out. An expert panel was asked to indicate their preferences for the selection criteria. The results of the system analysis show that the following developments are important in the transition to a sustainable growing media sector; Circular economy, Data-driven horticulture, Microbial horticulture and Increasing legislation. In the MCDA, four groups of alternatives have been defined: (1) white peat, perlite and woodfibre. (2) Accretio, mineral wool, foam and black peat. (3) Bark. (4) (standardized) Compost and the coir materials. The scenarios show that in the future the results will be very robust. However, the importance of peat will decrease and other materials will score better. The growing demand for growing media requires to focus on the low scores of the MCDA and the utilisation of current and future developments in order to make all raw materials attractive for use in the substrate sector. In addition, the sector will have to cooperate with all those involved in order to operate more sustainably. This will minimise the importance of peat and ensure that the sector can continue to provide the global food demand in a sustainable manner.

Transport infrastructures that connect ports to the hinterland are important enablers of economic growth and development in the region. Therefore, infrastructure owners and governments on different levels are engaging in climate adaptation to adapt transport infrastructures to actual or expected climatic hazards. Existing research on the institutional dimension of climate adaptation had exclusively focussed on trying to understand whether existing institutions allowed and encouraged actors to develop and realize adaptation strategies, and as a result, enhance the adaptive capacity of society. However, the connectivity and interdependencies between institutions that guide actors in their decision-making had not been studied. This research therefore improved and applied the Institutional Network Analysis (INA) method to understand how the various public and private parties interact with each other for climate adaptation of transport infrastructures connected to the Port of Rotterdam.

The Netherlands aims to have a carbon-neutral energy system by 2050. Hydrogen is seen as an important energy carrier that can contribute to this transition. Not only can hydrogen potentially decarbonise industry, transportation, and possibly agriculture and the built environment, but it can also be used to store electricity for longer periods. As more green hydrogen is produced over the years by electrolysis, the hydrogen demand and supply will increasingly diverge. Underground hydrogen storage can provide a solution for the imbalance between hydrogen demand and supply in both the short and long term. For this study, a model has been constructed to simulate the Dutch electricity and hydrogen market in 2040. Four different energy systems have been modelled, differing mainly in the extent to which the Netherlands is self-sufficient in their electricity and hydrogen demand. The model is constructed in Linny-R: a graphical modelling tool specifically designed for the formulation of Mixed Integer Linear Programming (MILP) problems, particularly for Unit Commitment (UC) problems. Firstly, using the model, the required underground hydrogen storage capacity in 2040 in the four different energy systems has been determined. The performance of different storage configurations, consisting of a certain number of short-cyclic salt caverns in addition to a certain policy for seasonal storage in gas fields, has been compared, and it has been concluded that only when a large installed capacity of solar and wind energy is placed in the Netherlands, the preference is for a mid-seasonal storage policy. In the other energy systems, the preference was for a low-seasonal storage policy. The second part of the research focuses on the financial feasibility of underground hydrogen storage facilities. The average system costs and profits have been mapped out. For the most plausible energy system, it has been determined that the storage facilities in 2040, when they have to generate their income purely based on market prices, are not profitable. To recoup the investment in salt caverns, the government will need to subsidise 0.26 €/kg of hydrogen. In addition to the insight that subsidisation will be needed to kickstart investments in underground hydrogen storage, some other important insights for policymakers have emerged. The installed capacity of hydrogen turbines has a significant effect on both the operational system costs of the entire energy system and the economic feasibility of the storage facilities. Furthermore, it has been found that in an energy system already dominated by green electricity and green hydrogen, adding additional storage facilities does not result in further CO2 reduction. As long as the carbon capture rate is limited to 90%, approximately 10,000 kton of CO2 will still be emitted annually. To become fully carbon-neutral, the Dutch government must therefore consider negative emission measures such as reforestation and ecosystem restoration.