This thesis presents a thorough exploration of mid-term storage strategies aimed at enhancing flexibility in hybrid offshore power systems that integrate wind and solar energy. A multi-criteria analysis was employed to identify the most suitable storage technologies for medium-term applications, resulting in the selection of Compressed Air Energy Storage (CAES), Lithium-ion batteries, and Lead-acid batteries as the top three candidates.
A specific study area, already equipped with offshore wind turbines, was selected for this research. Weather data were obtained and analyzed to reduce power mismatches and their associated costs. A multi-objective optimization approach was applied, focusing on minimizing the hourly loss of load and total capital costs associated with the hybrid renewable energy system.
In the base case scenario examined, which comprised 100% wind power, CAES emerged as the storage technology with the lowest total capital cost. However, by varying the proportions of offshore wind and solar energy, it was found that adjusting the mix to 80% offshore wind and 20% solar led to a significant reduction in the annual loss of load, thus reducing the total capital cost of the renewable hybrid system.

The agricultural sector holds the potential to integrate a resilient and renewable energy system due to high penetration of renewable energy sources. This study addresses the obstacles towards this transition. The first obstacle is the Dutch electricity grid. As of 2024, the greatest bottleneck in the Dutch energy transition is the lagging grid capacity. Instead of relying on grid power, matching local generation and demand of energy provides an elegant alternative. The second obstacle is that on-farm activity is powered mainly by diesel, since the majority of the demand is from tractor activities. To these two ends, this report investigates two primary aims: first, to provide an overview of the hourly and seasonal energy demand for dairy and open-field farms; second, to evaluate the feasibility of decarbonizing on-farm energy systems given the current grid capacity. The first aim was achieved by identifying the main energy-consuming activities in both dairy and open-field farming, revealing that dairy farming has consistent day profiles throughout the year, whereas open-field farming shows significant variability depending on crop needs and practices. The activities constituting most of the electricity demand on dairy farms have been found to be 1) milking, 2) milk cooling, 3) water pumping and 4) lighting. Feeding and grassland management (fertilizing and mowing) have been identified as the main diesel-consuming activities. For open-field farming, electricity is consumed largely for crop storage in the form of cooling or ventilation, or a combination. This contribution is small compared to the diesel consumption for field operations (mainly for tillage).
The second aim involved accurate modeling of various photovoltaic (PV) system architectures with TNO’s BIGEYE software and linear programming (LP) optimisation using Open Energy Modeling Framework (OEMOF) to assess their impact on the feasibility of decarbonization. The findings suggest that while the choice of PV systems can dampen the mismatch peaks by several percentages, the type of farm and its specific practices play the most significant role. For open-field farms, minimal crop storage requirements and sowing/harvesting periods, such as broad beans and wheat, that align with PV yield profiles are more suitable for achieving self-sufficiency. Short-term forms of energy storage, shifting loads and clipping maximum PV power are measures that directly enhance self-sufficiency and decarbonization without additional grid reinforcement. The highest self-sufficiency rates reach to 82%. In the discussion, the potential contribution of long-term storage needed to decarbonize the farm types with high mismatch levels is discussed. The report concludes with an outlook for further studies to enhance the reliability of energy demand data and suggests actionable and effective alternatives to grid reinforcement for the successful electrification and decarbonization of the agricultural sector. The most straightforward and pressing step in further research is broadening the scope to matching mismatch profiles with other prosuming systems, such as other farmers, towns and businesses, forming resilient energy communities

This report focuses on raw material requirements for self-sufficient, carbon-neutral European energy systems. It addresses the need to ensure that the transition to a low-carbon economy in Europe is realistic, feasible, and sustainable. Previous studies have often overlooked the integration of material requirements in optimized designs considering a sector-coupled energy system, or have only considered a single configuration without exploring trade-offs in other equally feasible pathways.

To overcome these limitations, this report evaluates the material requirements of hundreds of radically different energy configurations that would allow Europe to become energy self-sufficient and carbon- neutral by 2050. The solutions were generated with the Euro-Calliope framework using an extension of the modeling-to-generate-alternatives approach, creating spatially explicit practically optimal results (SPORES). This approach broadens the solution space and explores energy configurations that are within 10% of the cost-optimal solution.

The results reveal that future energy configurations will be inherently material-intensive, primarily due to the large-scale deployment of power technologies and electric vehicles. In contrast, technologies such as infrastructure expansion and heating systems pose minimal challenges regarding resource consumption. The findings confirm that equally feasible energy system designs can have significantly different CRM demands, with some configurations more likely to face supply-chain bottlenecks for materials like lithium, cobalt, and nickel. Trade-offs emerge between specific CRMs and energy system options. For example, high electrification of the transport sector requires nearly double the amount of CRMs compared to configurations with greater biofuel utilization. However, reducing the number of EVs significantly limits flexibility in energy configurations, pushing Europe toward an energy system design that maximizes biofuel.
Nevertheless, this research identifies key strategies that may help mitigate CRM demand in electric vehicles. In the next 15 to 20 years, recycling could become a significant alternative to mining for meeting a substantial share of raw material needs. This report estimates that end-of-life battery recycling rates could decrease the need for newly mined materials like lithium, cobalt, and nickel by more than half. However, in the short-term, the availability of these minerals will be insufficient for recycling to become a practical solution. Furthermore, technical and economic barriers currently limit the potential of recycling and the complete shift to battery technologies that do not rely on critical raw materials. This provides actionable guidance for integrating circular economy efforts into energy policy.

Future research would benefit from adopting a more dynamic approach to better capture future material requirements. This can be done by incorporating potential improvements in material intensities, a wider range of sub-technologies, and their evolving market shares. Furthermore, exploring alternative energy configurations and examining how changes in constraints, such as self-sufficiency or moving further away from the cost-optimal solution, affect system design and material demand would be beneficial. Finally, material constraints could be included directly in energy models by limiting CRM demand, which would allow the assessment of feasible energy configurations.

Hydropower is currently the largest renewable energy generation method worldwide, being the third overall after coal and natural gas, and providing 15% of global electricity. However, generation data regarding hydropower is scarce. If available, it mostly exists at national and annual level. Only limited generation data is available at plant scale. For research on power grid decarbonization, electricity grid expansion development and electricity grid optimization, more data on hydropower generation is wanted at plant scale. Hydropower generation differs significantly throughout the year following weather patterns, which means monthly generation data would be beneficial for research. To fill in data gaps, two models were created for the prediction of hydropower, using plant capacity, discharge, and reservoir area as predictor variables for the monthly model. For the yearly model, reservoir area was not included in the final model. A linear mixed-effects regression model and a mixed-effects random forest model were fitted and compared to the Hydro Plant Generation Estimation Model. The models were created using data from the United States (US) and used for predictions with hydropower plants from the US and the European Union (EU). The median KGE for the monthly LMER model was -0.08 in the US. For the monthly MERF model, the median KGE was 0.12 in the US. In the EU the models were evaluated at an annual time step due to data limitations, resulting in the LMER model scoring better (-0.16) than the MERF model (-0.68) on median KGE. The prediction errors of the annual US model were comparable to the Hydro Plant Generation Estimation Model. Discharge and plant capacity were found to be important predictor variables, followed by reservoir area for the monthly model. The models were able to predict at plant scale in data-scarce regions and at a monthly time step, although they can produce large outliers. A purpose for the model could be to not use it at plant scale but at a larger scale, as the median KGE scores were around zero, showing that predictions over multiple HPPs are usable.

The aim of this thesis project is to investigate how different PV-deployment strategies can help reinforce energy security and reduce GHG emissions in Ukraine. The author addresses several sub-questions related to energy security, the current status of Ukraine's electricity and heat sector, and the potentials and yields of renewable energy sources in Ukraine. To answer this question, the author uses literature review and simulations using optimization software. Three different deployment strategies are considered: utility-scale PV systems, distributed PV systems, and a combination of both. The simulations are based on hourly data for solar irradiance, temperature, wind speed, and load demand for 2018 in Ukraine. The results are evaluated based on several key performance indicators (KPIs) linked to energy security such as import dependency, diversity, as well as GHG emissions reduction potential.

The author finds that a combination of utility-scale and distributed PV systems can provide the most effective solution for reinforcing energy security while also reducing GHG emissions in Ukraine. The simulations also show that improvements in grid infrastructure is crucial for achieving these results.
Overall, the author concludes that deploying both utility-scale and distributed PV systems can provide a cost-effective way to reinforce energy security while also reducing GHG emissions in Ukraine.

A method to help renewables flourish is mitigating the variability that is inherent in natural resources. To do so, we explore the intricacies of the relationship between airborne wind (AWE) and solar energy to uncover the possibilities of future energy-generating hybrid power plants (HPP). Which leads to steering away from fossil fuel reliance, while increasing the dependability of renewable technologies.

The resources were investigated at one primary test site, where anomalies and trends were uncovered. By tracking the solar radiation and wind speed over time, the complementarity of the two is studied. When the Pearson correlation coefficients are negative, a non-variable energy generation capacity can be found leading to less intermittency in energy stock. These results are expanded to evaluate other locations in Europe, identifying the main contributing factors of a successful hybrid set-up. The case study location was Marseille based on pre-analysis of solar and wind availability.

Using resource correlation, energy output, and location data, the model developed to assess the location feasibility of HPPs found that most areas are not suited for annual generation situations, but are more successful on a quarterly basis. The HPP setup would allow the dependency on fossil fuels and storage options to decrease while having a flexible implementation option meaning it is a viable option for off-grid / remote locations and urban areas to help lighten the grid load.

The model created can be further developed into an HPP site map, to help further identify areas that would benefit from more renewable options without as many drawbacks. Overall, this research leads to a method for reaching 2050 climate goals by identifying HPP potential on a variable time basis.

The electrification of all sectors is essential to achieve carbon neutrality by 2050. In terms of mobility, many nations around the globe envisage a zero-emission fleet resulting in a massive deployment of Electric Vehicles (EV). Nevertheless, this goes hand-in-hand with some challenges related to congestion and voltage problems in power grids caused by inadequate grid capacity. When grid reinforcement cannot keep up with the increasing demand for electricity, both in terms of planning and excessive financial means, smarter, more efficient solutions should solve the problem.

This thesis aims at providing an optimal strategy for consumers’ flexibility distribution by smartly charging EVs in Low Voltage (LV) grids. In that way, grid reinforcement should be reduced to a minimum. In order to do so, the main contribution of this work is covered by the development of a smart, optimal charging model that tries to comply with grid constraints including voltage and congestion boundaries. Mathematical (near) real-time optimisation is used together with the receding horizon optimisation principle. The work of this thesis relates to a typical urban LV grid in the Netherlands.

First, the required level of grid reinforcement was investigated by means of quantifying the voltage and congestion problems in the LV grid. This was done by applying uncontrolled charging scenarios up to 2050 for both winter and summer.

After investigating the uncontrolled scenarios, the need for an optimal smart charging strategy became apparent. This was developed afterwards. The results showed that in 2050, 94.5% of line congestion and 100% of transformer congestion and voltage problems could be bypassed with smart charging only. This was achieved by implementing grid constraints at the most vulnerable locations in the power grid.
Consequently, the need for grid reinforcement could be almost completely avoided till at least 2050.

Lastly, this work distinguishes itself from others by extensively reflecting on the integration of three concepts: a novel recently developed tariff structure, the effect of bidirectional charging, as well as the necessity for grid constraints. This allows summarising the benefits for all stakeholders including the distribution system operator, the charge point operator and EV owners. The final conclusion can be made that including these three concepts results in one of the most favourable investigated strategies for all three parties together.

Small islands that support urban communities provide a unique opportunity to study the urban water cycle, its energy needs, and possible links to renewable energy. The aim of this paper is to explore to what extent an island’s urban water cycle and the renewable electricity production system required to satisfy the urban water cycle’s demand can become sustainable using Caye Chapel (Belize) as a study case. For this research, the water-energy system is the urban water cycle and the renewable electricity production for the urban water cycle. Twelve alternatives were proposed for the water-energy system. The different alternatives are divided among those that consider the reuse of wastewater, rainwater harvesting, and the use of wind turbines, PV panels, or both for the renewable electricity production. Then, those alternatives were optimized to produce the minimum water demand shortage, minimum amount of treated water that is not reused, and renewable electricity shortage. Later, the optimized alternatives are evaluated using multi-criteria decision analysis (MCDA). It was observed that the alternatives that only consider one renewable source for the electricity generation and do not consider the reuse of wastewater, are outperformed by the alternatives that consider more than one renewable source and reuse the treated wastewater.

Climate Change continues to pose a considerable threat to the well-being of people and economies. Today, to avoid catastrophic and irreversible damage, decision-makers and policy advisers need to explore possible scenarios and enact mitigation and adaptation policies to curb the rise of global temperatures within the thresholds set by the Paris Agreement. However, avoiding a 1.5-degree warming seems already out of hand, and the last Conference of Parties in Glasgow (COP26) sparked a contentious debate surrounding the role of coal. Representatives rely on climate reports and models to understand the problem, including integrated assessment models that aim to encompass the whole process straightforwardly and transparently. One example is the RICE-2010 model developed by the 2018 Nobel Prize winner in economics William Nordhaus, used by the IPCC and known for its simplicity. However, the model does not include an explicit formulation of energy. This renders it hard to explore scenarios and policy questions directly tied to the diversification of the energy mix, a topic that has gained considerable attention with the Energy Crisis sparked by the Russian Invasion of Ukraine. Therefore, this thesis attempts to introduce energy intensity and carbon intensity to the model by decomposing the Emission Output Ratio. These parameters will allow the user to explore the drivers behind decarbonisation, whether it is related to an improvement in the energy efficiency of processes or a greener energy mix. The selected approach yielded surprising insights, such as the poor documentation and data quality of the RICE model, the over-simplistic design choices behind emissions and decarbonisation, and the under-representation of carbon intensity. These outcomes have highlighted potential, underestimations of future temperature rise, limited policy testing potential and a lack of transparency in data, methodology, and reproducibility.

Sales are the backbone for a company, sales forecasting plays an important role in organizational decision-making, and improving the sales forecasting performance has been an intention for managers. Sales forecasting is a management function that is affected by various organizational factors. The aim of this research is to validate a Sales Forecasting Management (SFM) framework to improve the sales forecasting performance in a high-tech industry and study the impact of forecasting on the organization’s business performance. The organizational factors such as sales compensation structure, product life-cycle stage of the company, information logistics, and cross-functional communication of the SFM framework are validated in the context of the high-tech industry. Further, the framework is quantified by developing an appropriate sales forecasting model. Both quantitative and qualitative forecasting methods are studied and the most appropriate technique with high accuracy is chosen. Finally, the effect of sales forecasting performance on a specific business outcome – End of Service Benefits (EOSB) is quantitatively measured. The EOSB is the gratuity payment received by the employees of the company when they are terminated or resigning from the job. It is a crucial operational expenditure that is proportional to the salaries earned by the sales employees and the accuracy in EOSB forecasts has an impact on the company’s stock price and investment decisions. The results of the research show that the accuracy in sales forecasts has a significant impact on the End of Service Benefits forecast. The research also finds that the SFM framework has to be adjusted to include the sales pipeline stages as an important organizational factor that influences sales forecasting management. Thus, the thesis provides an interesting insight to include the sales pipeline stages in the framework to improve the sales forecasting credibility. Finally, the SFM framework is evaluated, the impact of sales forecasting performance on a business outcome (EOSB) is quantitatively measured using a sales forecasting simulation model and the conclusions are derived.