As the share of solar and wind in the energy mix increases, the inherent intermittency of these energy sources pose a great challenge for the Dutch power grid. The occurrence of problems with congestion and balancing power supply and demand are becoming more common. Companies in the Netherlands show potential to implement means for smart use of energy to alleviate these kind of problems. However, the gap between academic solutions and the industry is often too large. This research presents a method to find the optimal design of an integrated energy system in the Netherlands. A use case at Picnic is used to apply the proposed method. A flexible load scheduling optimisation algorithm is presented to explore the financial benefit of an integrated energy system that combines a photovoltaic system, an energy storage system, a cold storage system and a fleet of electric vehicles. The financial performance of different system designs are compared. Results show that the optimised load schedules for the EV fleet and CS system achieve a 2.6% decrease of energy costs with respect to the benchmark of Picnic. A PV system turns out to be beneficial for every size that the grid connection allows. This research finds 600 square meters to be optimal in the case of Picnic. An energy storage system would make optimised load schedules obsolete due to its flexibility. An energy storage system with a capacity of 250 kWh is found to be optimal according to the performance analysis. However, significant limitations in the assumptions of the storage system advise against installing it. Further research is required to elaborate the different elements in the proposed model. Different markets are suggested to use as a basis for load scheduling and a broader set of system designs is suggested to analyse their performance.

During the next few decades, a significant increase in the use of intermittent renewable energy sources is expected in the Netherlands as well as a general increase in electricity consumption. Due to the increased demand as well as a more uncertain, volatile supply, substantial upgrades and redesign of the current Dutch electricity grids are needed. These upgrades will inevitably require large investments as the design and installation of electricity grids is costly. Additionally, the investments are lumpy and irreversible. It is therefore important that the investments will be made in such a way that the future grids will operate successfully, supplying consumers with the demanded electricity at sufficient quality with a low rate of interruptions. Recently, the Netherlands has been divided into 30 energy regions, which allows the Netherlands to work on its climate agreements both from a regional and from a national level. These regions will work on generation and consumption of electricity and heating as well as on the energy infrastructures needed to supply this energy. This aim of this research paper has been to create a method that can be used to design suitable electricity distribution networks for the energy regions in the Netherlands. One approach to designing electricity grid topologies is with the use of graph theory heuristics, which has shown to be a useful way of approaching the electricity design problem by discretising plots of land into a graph. The research paper has shown that by taking into account spatial constraints specific to the Dutch regions, more valid networks can be created. This further leads to increased implementability of the final networks in addition to a reduction in the possibility for unforeseen costs related to building on certain plots of land. The proposed method aims to minimise the investment costs of the future regional electricity distribution networks in the Netherlands, taking both cable lengths and capacities into account. A radiality constraint is applied, ensuring that the network is connected but does not contain any cycles. A flexible way of ensuring that the final networks do not overlap with unavailable land is thereafter applied and demonstrated. A heuristic method aiming to minimise network length is applied before assigning the required capacities to the network. An improvement procedure is performed in order to further reduce investment costs. The cost function is formulated as a non-linear function, incorporating the characteristic that savings can be made by combining lines in order to create a shorter, high-capacity network instead of a longer, low-capacity network. The proposed method has thereafter been verified with respect to the problem formulated and demonstrated using a case study on the energy region Goeree-Overflakkee. Experimental results have also been generated in order to assess the effectiveness of the method. In comparison to an alternative simultaneous topology and production optimisation, it has been found that the proposed method leads to a shorter final network that additionally leads to lower total investments costs.  

A distribution network design is of importance to systematically organize the flow of products from supply points to demand points. A distribution network is not only crucial before sales but also after sales because regardless of the initial quality of product, malfunctions occur. These malfunctions need to be repaired by replacing defective parts, which are delivered through after sales distribution network and all activities, carried out from the report of the malfunction until the product is up and running, are called after sales services. The replacement parts are called service parts or spare parts and this thesis project focuses on the distribution network design for delivery of these items. Customer surveys point out that high quality after sales services has become a criterion for selecting supplier companies. A shorter response time to failures and availability of service parts in the storage are given importance. Because after sales services became a field that companies can differentiate themselves from the competitors, practitioners have an increasing interest on establishing the optimal distribution network design to meet customer expectations while being cost-competitive. The researchers have addressed service parts distribution with a strong focus on inventory management. However, distribution network design involves more decisions such as number of facilities, facility locations, allocation of customer demand to these facilities, and storage capacities. In addition, the managerial aspects and strategies for service parts logistics in business case studies are derived from electronic, automotive and aviation industries. Considering these, there is a knowledge gap about distribution network design for service parts in healthcare industry. This thesis project aims at filling this gap by introducing a general mathematical model for distribution network optimization based on an empirical study, conducted in General Electric Healthcare service parts delivery network.

Policy reforms in response to the crisis were initially expected to dampen future house price increases. Instead, the Dutch owner-occupied housing market has overheated not even five years after its recovery. As this could indicate a structural change in the housing demand of households, models were estimated using logistic regression analysis to examine how household mobility has changed following the crisis and position these changes in the context of macroeconomic and institutional developments. Based on the macroeconomic developments, the demand for owner-occupancy was expected to increase after the crisis. For one, the economy was gaining momentum rather than losing it, offering much better prospects for the near future. In addition, house prices were still relatively low and credit cheaper than ever, which greatly increased the accessibility of the owner-occupied sector for lower income households and offered interesting investment opportunities. Regarding institutional developments, the various reforms following the crisis are believed to cause an increase in the demand for affordable owner-occupied housing as a result of the tightened lending criteria in combination with the restricted accessibility of the social rental sector. In addition, the reforms are also expected to polarise households by widening the gap between the rental and owner-occupied sector through equity requirements. The results of the empirical analyses reveal a significant quantitative increase in demand with the intention to move nearly doubling from 21% in 2002 to 39% in 2015. The preference for homeownership itself, on the other hand, remained relatively stable. The most surprising development over time is the convergence of households from different income categories with respect to the intention to move, the preferred tenure, and desired purchase price. It is therefore believed that favourable macroeconomic conditions shortly after the crisis temporarily ameliorated the negative effects of the institutional changes. Further research is necessary to confirm whether the surge in the intention to move is merely a temporary effect of the crisis or an indication of a growing mismatch between housing demand and supply.

In the Netherlands, wind power has the highest potential for future development compared to alternatives such as solar PV and biomass. The intermittent nature of the wind power resource calls for novel approaches to supply and demand management, as well as power quality assurance. Energy storage technologies allow for the separation between power generation and power supply to the grid. The energy and power densities, efficiency and response times of secondary batteries make them highly suitable for utility scale application to wind farms. However, a knowledge gap exists on exact configurations of battery energy storage systems for wind farms within the existing power markets. This study proposes a hybrid power plant approach, combining an onshore wind farm with a battery energy storage system. Literature research identifies balance between power obligation and production as a main design criterium, along with the profitability of a configuration and operational strategy. A technical analysis establishes power fluctuation on multiple time scales as relevant wind power characteristics, and maximum depth of discharge, discharge rates and efficiency as important battery parameters. A detailed analysis of the Dutch power market structure identifies three accessible markets for trade: the Day Ahead Market, the primary reserve (FCR) and the secondary reserve (aFRR). For modelling and optimization purposes, FCR is not considered due to excessive uncertainties regarding bid acceptance and bid activation. The design phase of the study is charged with finding the optimal battery capacity for a given wind farm production, while assuring the obligation to the Day Ahead Market is met. A linear optimization model is developed with the objective to maximize the obtained revenue. It is shown that the optimal battery capacity scales with the power capacity of the wind farm. For a 60 MW wind farm, the optimal battery capacity lies within the range of 60 – 80 MWh. An operational model is designed based on the model of the plant established in the design phase, elaborated with a Model Predictive Control approach. Based on wind power generation forecasts, the algorithm is able to adjust the charge/discharge and imbalance settlement strategy continuously based on expected market price series. However, the low level of accuracy of the applied market price approximations lead to unprofitable results in all simulated cases. Market price approximations with increased accuracy, as well as reduction of battery installation costs may lead to profitable operation of a hybrid power plant.

Society is facing a huge challenge in switching the energy sectors dependence on fossil fuels into an energy sector using mostly renewable energy sources. The switch towards using more sustainable energy sources is known as the energy transition. The goal of the energy transition is to lower the greenhouse gas (GHG) emissions emitted by the energy sector. Lowering the GHG emissions helps society limit the global warming caused by GHG [3]. 17.5 % of the global energy usage comes from the energy use in buildings [50]. It is thus very important that the energy use in buildings transitions towards using more sustainable energy sources. One of the renewable energy sources that is ought promising in the energy transition for energy use in buildings is geothermal energy [3]. Geothermal energy is energy that is captured in reservoirs of hot water in the earth’s crust. The hot water captured in the hot water pockets is pumped to the surface, to use it in spatial heating. The return pipe returns the cooled water to the geothermal well, where it can heat up again over a certain period of time [63] [23]. In some cases, geothermal energy is applied using a district heating network. A district heating network is an example of a system that provides heating and/or cooling capacities to a group of buildings [65]. A district heating network is a network of pipelines that transport the hot water from the geothermal well to the buildings in the district. A geothermal well in combination with a district heating network is developed in Delft [27]. The district heating network will deliver energy to the TU Delft campus, two neighborhoods in Delft and industry at the Schieweg in Delft [28]. Besides the district heating network in Delft, it is expected that district heating networks will be applied more often to accelerate the energy transition. Yun-Chao and Chen (2012) concluded that most optimization techniques optimize the whole system with its components. Less optimization techniques are applied to the sole components. Besides the fact that most optimization methods optimize the system as a whole, most optimization objectives only include optimizing the cost of the system. Also, effective optimization techniques are required as optimizing large graphs may be computationally time consuming [36]. In literature there are also clear signals that state that the trade-off between thermal comfort, and efficiency with respect to cost has to be tackled [53]. In this research, optimizing district heating networks for cost is compared to optimizing district heating to maximize thermal comfort or efficiency. In this research two models are developed: a model that calculates the cost of the district heating network, and a model that calculates the thermal losses of the district heating network. Both models are applied to a district heating networks that is developed in a street network. Furthermore, multiple heuristics are applied to come up with better district heating networks. The optimization technique is tested on 100 small, randomly generated district heating networks. After that, the district heating network in Delft is optimized. The differences in cost, efficiency, etc. will be evaluated. Besides, the performances of the district heating networks are evaluated by introducing energy deficits under different conditions. Optimizing the district heating networks for cost led to a very consistent result: When compared to their individual starting point, the district heating networks became cheaper and more efficient. A moderate-strong correlation is found between the the increase in efficiency and the decrease in cost while optimizing the district heating networks. In contrast to that, the networks that maximize efficiency are much more expensive than their cost optimized alternative, while the increase in efficiency is in most cases moderate. However, there are rare cases where the efficiency is increased much at a moderate increase in cost. This phenomenon is also found in Delft. Given the result that the efficient district heating network also performed much better than the cheapest alternative during energy deficits, in this research it is shown that choosing an objective function has a very large impact on the characteristics of the network. Therefore it is shown that for future district heating network optimization, it is important to trade off cost against efficiency.

The decarbonization of economies around the world is crucial for reducing the impact of human-induced climate change. Many proposed means to achieve this decarbonization like the electrification of various sectors or the introduction of ‘new’ forms of energy such as hydrogen and carbon capture and storage require existing energy infrastructures to be expended or entirely new energy infrastructures to be created. Since energy networks are capital intensive, minimizing their construction costs is essential for their realization. Previous works studying the cost minimization of energy networks often neglect that their construction costs can be influenced by geographical areas such as mountain ranges, existing infrastructures or zoning rules. Using methods borrowed from graph theory and geometrical computing, a method has been developed that is able to find costoptimal energy infrastructures in a spatial context of geographic regions with different costs for constructing power cables or pipelines through them. The method models these geographical areas as triangles on which borders a user-defined number of points are uniformly placed representing potential entry and exit points for pipelines or power cables. Experimenting with 144 randomly generated routing problems has shown that, on average, increasing the number of these placed border points results in a reduction of the total network costs. Although, this effect flattens when more than 7 of these points are placed. The method is applied to two offshore electricity networks in the Dutch North sea. These cases demonstrate the method’s strength in identifying trade-offs between energy networks’ investment costs and their (negative) influence on their spatial surroundings. Additionally, the method’s outcomes are well-suited to be used as a springboard for dialogue in energy infrastructure decision-making processes. Further research could focus on the extension of the model, by including existing connections, or the improvement of the model, by implementing a different method for modelling geographical areas that has a higher accuracy and is computationally faster.

Freight transport is a crucial component of our society and economy. However, achieving sustainability in this sector is complex, due to a lack of incentives among stakeholders. The heavy-duty road transport sector is struggling to meet European emission standards. To address this issue, a research study examined the impact of combining Battery Electric Trucks (BETs), Hydrogen trucks, and Bio-Liquefied Natural Gas (LNG) trucks on a network level. The study used freight simulation and refuelling location optimization through K-means to determine the cost and emissions of the system under different conditions. The results showed that Bio-LNG trucks and BETs are viable fuel options for sustainable freight transport, with Bio-LNG being immediately usable and BETs showing promise for future technological development. The study further revealed that BETs are a cost-effective and low-emission choice for short- and long-haul freight trips, particularly when using Mega Watt chargers and larger battery capacities. The research contributes to understanding the complexities of transitioning to greener freight transport and provides insights into policy implications. Future research could explore the broader social and environmental impacts of these alternative fuel options.

To meet the goal of the European Union of becoming climate neutral in 2050, the Netherlands and North West Europe will integrate more and more variable renewable energy sources (VRE) into their power system. The uncertain weather conditions on which the VRE are dependent, will increase the need for a more flexible power system. An important measurement to control the balance of supply and demand in a power system with a lot of variable renewable energy is curtailing the excess power of renewable energy. However, the large amount of renewable energy that is currently integrated will result in an increase in lost power through curtailment. This increase in lost power can result in renewable energy projects (current and future) not getting the expected economic benefits, which can result in less investments in these projects. To prevent this, it is necessary to reduce this curtailment by either storing the otherwise lost power with batteries or by using it through sector coupling possibilities such as power-to-heat, power-to-gas, or power-to-mobility. The aim of this study is to research the potential of sector coupling options and batteries in utilizing the lost power from VRE curtailment by means of the IRENA FlexTool. In order to reach the objective of this study, two scenarios were used on two scales, the Netherlands and North West Europe and were modeled with the IRENA FlexTool. In order to compare the different sector coupling possibilities and batteries, an optimal design of experiments was used. The results of the sector coupling and batteries in this design of experiment were compared on their levelized cost of energy (LCOE) and the loss of load and curtailment of the power system. The study showed that the IRENA FlexTool is better suited to model smaller energy systems to gain a quick insight into the flexibility issues of loss of load and curtailment, instead of modelling larger complex energy systems. The modelling results of the IRENA FlexTool shows that the costs for utilizing the lost power of curtailment and avoiding loss of load differs for the Netherlands and North West Europe. For North West Europe, batteries have the lowest LCOE when almost all lost power trough curtailment is used. Hydrogen provides a good solution for the loss of load. Therefore, both battery storage and hydrogen could be a good substitution for natural gas in NWEU. For the Netherlands hydrogen storage is the option with the lowest LCOE when all lost power through curtailment is used and loss of load is avoided. Therefore, hydrogen could provide a good substitution for natural gas in the Netherlands. The difference in LCOE can be explained through the allocation of the power use of batteries and sector coupling, but this is not included in this study. Future research should focus on the interaction between the different options. This could provide useful results on how to efficiently allocate and use battery storage and sector coupling, when they are used in combination with each other.

The large-scale integration of variable renewable energy sources (VRES) brings about challenges related to the stochastic characteristics in supply. In this light, a recent and promising approach which has received increasing attention is that of multi-energy systems (MES). A MES is defined as an energy system whereby different energy sectors interact with each other, and builds on the notion of considering the optimization of the whole energy system instead of focussing on specific energy sectors. The increase in substitutions between different energy carriers offer increases in overall efficiency and system flexibility, compared to non-integrated energy system (NIES). This thesis presents a MES investment model. The model is able to systematically find the cost-optimal investment portfolio for meeting three types of demand (electricity, heating, and hydrogen). The outcomes consist of the capacities and locations of generation and conversion technologies, as well as electricity and hydrogen network lines and storage technologies, simultaneously, whilst adhering to balance and RES target constraints. The model is an extension of the current cost-optimal energy investment Greenfield Renewables Investment Model (GRIM) and allows the incorporating of spatial characteristics of VRES supply, leading to a more realistic representation of the real energy system. Furthermore, the model is able to account for limitations on maximum available land for onshore wind and solar photovoltaics (PV). A case study was conducted on the energy system of the Netherlands. Fifteen scenarios were run, for increasing RES targets, different levels of sector integration and land-use restriction for solar and onshore. The results show that increasing sector integration can lead to a cost reduction of 25% in a fully renewable scenario. Increasing connections between electricity, heating and hydrogen sectors provides system flexibility, resulting in a decrease in generation, network, and storage investment. Furthermore, the results highlight the importance of considering the spatial components: both limitations on land-use and spatial-temporal VRES supply profiles have significant impact on the optimal solution. For the Netherlands, imposing land-use constraints resulted in a decrease in market share from onshore and an increase in solar, since the maximum amount of available land for onshore wind is reached, when including three types of demands. The academic relevance of this thesis is found in the modelling approach developed. In addition, this thesis builds on existing energy planning literature by providing a case study of the future energy system of the Netherlands. Future research should analyse different objectives and include additional generation technologies.

In this thesis a two-stage model is proposed, combining the idea of a two-stage model from Anjos and Vieira [1] with a gradient descent approach, much like proposed in Sikaroudi and Shahanaghi [42], in order to solve the facility layout problem for problems consisting of 8 and 12 departments . This method was chosen after analyzing the criteria for a vegetable processing factory and combining this with a literature research to previous methods applied to solve these "facility layout problems". The gradient descent approach uses the partial derivatives of a multi-variable objective, the flow-cost, in order to get a vector which is the direction of greatest descent. Computing this vector for all departments and then moving them creates an iterative improving loop. In addition to the gradient descent approach a swapping procedure and a shooting procedure is introduced in order to reduce the effect of random starting position. From the result of the first-stage model, relative location constraints are extracted to be used in the second-stage model, a linear constraint programming solver capable of running optimizations by google or-tools [36], to greatly reduce the solution space. The proposed method was tested on two toy-problems from Tam and Li [44] and compared to results existing in literature. Additionally, the effect of including a first-stage model was tested as well. When looking directly at the results, the proposed method is consistent and shows good results. Especially the inclusion of the first-stage model helps finding better solutions. Compared to other results found in the literature, the method shows slightly less good results based purely on the objective value. However, the final layouts found in this thesis are more compact.

Societal concern is growing regarding the effect fossil energy use has on our planet and societies. Many promising sustainable energy solutions, like hydrogen, carbon capture and storage, and district heating, require long-distance distribution and transmission through networked infrastructure. Often, existing infrastructure can be repurposed to reduce overall network costs. Examples are the repurposing of natural gas infrastructure for hydrogen transport or the repurposing of oil pipelines for transport of CO2. This repurposing of existing infrastructure is expected to have a major influence on future network layouts, which uncovers an interesting research question. What is a suitable network optimisation approach that aids in the design of costeffective future network layouts, taking into account repurposing of existing infrastructure?. Reviewing the available literature on network optimisation modelling in relation to repurposing of existing infrastructure, we find that Geometric Graph Theoretical methods are the most promising approach to the problem at hand. Mixed Integer (Non-)linear programming approaches are computationally heavy, and do not provide the flexibility necessary to gain quick insights into design implications. Agent Based models provide more than enough flexibility, but no suitable methodology for integrating repurposing was found. A network optimisation model by Heijnen et al. (2020) is selected for adaption because of its already integrated existing pipeline functionality. Limitations of the existing model are that it does not take into account costs related to the repurposing of existing infrastructure, and that it can not work with cycles in the network. These cycles, defined by a multitude of paths between two points, are necessary because existing infrastructure often contains cycles, and the optimal addition of new infrastructure can include cycles that contain existing infrastructure. To address these limitations, first, repurposing costs were integrated into the objective function of the model. A novel variable called the repurposing cost coefficient is used to allow model users to input the cost of repurposing relative to the building of new pipelines. Three novel heuristics are also introduced. The network simplex repurposing heuristic creates optimal network layouts for a single moment in time and allows for cycles in the network to be created if this is the most optimal layout. When investigating supply and demand patterns that differentiate over time, the heuristic is supplemented by the timesteps merged heuristic that combines the networks created for each separate moment in time into one network. Finally, the joined edges heuristic removes redundancies from the network to create a cost optimised network layout. The model was unit tested by applying it to small experiments, and the basic functionalities of the model were verified...

The Indonesian government has set a target to increase new and renewable energy sources in the energy mix by 23% in 2030 and 31% in 2050, which are to be met by investing in hydropower and geothermal energy capacity mainly. However, Indonesia has abundant other renewable energy potentials, which are largely untapped. The Java-Madura-Bali (Jamali) system is Indonesia's largest electricity system. Integration of variable renewable energy resources requires flexibility options such as grid expansion and short- and long-term electricity storage. This research aims to fill the knowledge gap in the literature on the effect that different carbon emission reduction limits have on the Jamali power system design in 2050. This was done by studying the potential of various promising renewable energy technologies (solar photovoltaics, on- and offshore wind, Ocean Thermal Energy Conversion (OTEC), geothermal and hydropower) in combination with short- and long-term storage (lithium-ion batteries and hydrogen storage) and grid expansions to mitigate renewable variability. For this purpose a techno-economic model was developed that optimizes operation and capacities of generation, storage and network simultaneously. The model simulates the system dynamics in the Jamali system in 2050 and was implemented in Python for Power System Analysis (PyPSA). It was found that there is an exponential relationship between system costs and carbon emission reductions in the Jamali power system. Additionally, only moderate system cost increases were found up to 80% emission reductions compared to the reference scenario with no emission mitigation efforts. At higher carbon reduction scenarios the solar capacities reach their maximum installable capacities. As a result, system cost increase exponentially due to the need for OTEC and offshore wind capacities. The high costs increases by offshore wind can be explained by the uniform costs and coarse resolution of the model. In low carbon scenarios high battery capacities were found and network expansion is limited. It is concluded that the Jamali network cannot smooth the variability of wind throughout the power system, therefore, without storage capacities the system cost almost double. On the other hand, the network remains important to transport electricity from rich renewable regions to large demand centers. Based on the results three recommendations were proposed related to the reconsideration of the energy targets for 2050, strategy to achieve the targets and present policies.

This research explores the integration of electric vehicles (EVs) into the logistics of bulk-liquid transportation, specifically within Heineken Netherlands’ operations, using a two-echelon network. This study is pivotal as it aligns with global moves towards zero-emission regulations and sustainability in logistics. The primary question it addresses is optimizing a logistic network and truck operations for bulk liquid delivery by transitioning to EVs within a two-echelon framework, ensuring efficiency while adhering to sustainability and regulatory demands. The investigation employs a sequential exploratory strategy, beginning with qualitative analysis to identify core challenges and opportunities, followed by quantitative methods to refine network design and decision-making. Advanced clustering techniques such as the center of gravity, p-median, and k-means are utilized to determine optimal depot locations, essential for overcoming the operational range and charging limits of EVs. This approach aids in developing a logistics network that is both operationally efficient and environmentally sustainable. A significant portion of the study focuses on vehicle routing within the two-echelon location-routing model. It considers critical factors like the limited range of EVs, multi-compartment transport requirements for bulk liquids, and specific customer delivery windows. The model integrates these elements to optimize vehicle routes for efficiency and regulatory compliance, illustrating its practical use through the two-echelon multi-compartment electric vehicle routing problem with time windows (2E-MCEVRPTW). The practical application of this research is demonstrated in a case study of Heineken Netherlands, highlighting the logistical complexities of transitioning to an EV fleet for beer distribution. The study examines operational challenges such as vehicle range and product diversity management, proving the viability and effectiveness of the proposed models. Results discussion reveals that strategic network design using the center of gravity method significantly enhances kilometer savings and operational efficiencies. However, the benefits diminish with additional hubs, indicating an optimal hub number exists. While transshipment costs pose a significant challenge, outweighing the kilometer savings, potential cost reductions through increased reefer capacity and reduced transshipment times are identified, pointing to possible areas for improvement. The study concludes that the two-step optimization process, integrating network design and vehicle routing, effectively addresses the research question. It not only shows the potential of EVs in transforming logistics but also underscores the economic and operational challenges of adopting a two-echelon network. The findings lay a groundwork for future innovations in sustainable logistics, though they caution the need for tailored solutions across different operational contexts and suggest further research into computational strategies and customer clustering for enhanced route optimization.