The Netherlands aims for a climate-neutral energy system by 2050, targeting the deployment of up to 7 GW of new nuclear capacity to provide firm, low-carbon power alongside variable renewables. Integrating these baseload assets into a grid dominated by variable renewables creates profound physical and financial risks. Consequently, this thesis asks: what techno-economic boundary conditions are required for the economic viability of new nuclear capacity in the 2050 Dutch power system?

The research adopts a hybrid exploratory modelling methodology. First, a spatially explicit capacity expansion framework evaluates the cost-optimal configuration of the Dutch energy system and identifies local physical bottlenecks. Second, an hourly economic dispatch model evaluates the projected capacity mix incorporating the targeted 7 GW of nuclear power to determine specific wholesale revenues captured by the assets. Finally, a stochastic discounted cash flow model evaluates bankability for private investors and calculates required financial interventions.

From a central planner perspective, results demonstrate that the optimal level of unconstrained inland nuclear deployment is 10.4 GW, reducing annualised system costs by €816.7 million compared to a non-nuclear baseline. Enforcing the current political intention to locate this capacity at coastal nodes actively destroys this macroeconomic benefit, increasing total annual system costs by €250 million relative to a non-nuclear system, and resulting in a €328.7 million annual redispatch cost driven by existing grid congestion. Transitioning to the investor perspective reveals that price cannibalisation renders pure market financing unviable: whether operating as rigid baseload or flexible load-following, the assets face a net present value (NPV) deficit of approximately €14.7 billion and an expected internal rate of return (IRR) of 4.4%.

These findings demonstrate that forcing gigawatt-scale nuclear deployment into the future Dutch grid poses severe financial risks without specific interventions. To achieve economic viability, policymakers must satisfy three strict boundary conditions. First, an infrastructural boundary requires enabling alternative strategic locations to avoid congestion or executing extensive grid expansion to accommodate coastal generation. Second, a financial boundary requires state-backed financing mechanisms, specifically a regulated asset base (RAB) model paired with a two-way contract for difference (CfD), to compress the cost of capital during construction, protect against price cannibalisation, and structurally bridge the identified funding gap. Third, a project delivery boundary implies developers must adhere to rigorous construction management practices, such as strict standardisation and multi-unit deployment, to prevent severe cost overruns that historically affect Western megaprojects.

The core scientific contribution of this research lies in demonstrating the need for spatial explicitness in national energy modelling, alongside introducing a comprehensive methodological approach that integrates system-level assessments with investor-level metrics to prove the need for coordinated policy interventions aligning physical grid limits with market bankability. Future research should expand upon these findings by testing spatial configurations using detailed AC power flow models, evaluating alternative revenue streams like co-located hydrogen production, and elevating the assessment to a holistic social cost-benefit analysis that explicitly accounts for societal externalities. ... Read more

Long-term planning of power infrastructure is central to the energy transition, as new transmission capacity is needed to connect renewable-rich regions with centres of electricity demand. In Indonesia, this challenge is especially relevant because the national long-term electricity plan proposes a supergrid to connect the country's main islands and support decarbonisation until 2060. However, current planning mainly follows a single-path optimisation approach and does not explicitly account for the deeply uncertain effects of climate change. This is problematic because rising temperatures can simultaneously increase electricity demand and reduce the available generation capacity by derating.

This thesis investigates how deeply uncertain impacts of climate change affect Indonesia's long-term power infrastructure plans. It approaches the problem from a Decision Making under Deep Uncertainty perspective, specifically using Robust Decision Making. Rather than assessing whether the planned system performs well in an expected future, the study evaluates whether it remains adequate in many plausible futures, each representing unique combinations of these highly uncertain climate change impacts.

To do so, Calliope energy system models were developed for the Indonesian power system in 2034, 2045, and 2060, using data from Indonesia's state-owned electric utility company PT PLN, the 10-year Electricity Business Plan, the 2060 Long-Term Electricity Plan, and earlier modelling work on power system planning and the energy transition in Indonesia. These models were then used to stress-test different supergrid configurations under uncertain demand increases and capacity derating. Performance was assessed using lost-load hours, levelised system costs, emissions, and regional reserve margins.

The results show that the planned infrastructure performs well from an optimisation perspective, with no lost-load hours in the baseline modelled periods. However, the exploratory analysis reveals that this conclusion becomes fragile under climate stress. Robustness is high in 2034, starts to depend on specific interconnection choices by 2045, and becomes strongly affected by demand increase and derating by 2060. The supergrid therefore acts both as a solution and as a source of vulnerability. Not only does it enable renewable integration, but it also makes system adequacy dependent on a limited number of critical transmission corridors. Reinforcing the Bali--Jawa Timur connection substantially improves robustness, showing that targeted reinforcements can be more valuable than simply maximising all supergrid capacities.

These findings suggest that Indonesia’s long-term power infrastructure plan can remain adequate in many plausible climate futures, but only if critical transmission corridors are identified, reinforced and built in time. The study demonstrates that stress-testing long-term infrastructure plans under deep uncertainty can reveal critical vulnerabilities that single-path optimisation approaches may overlook. ... Read more

The Dutch electricity grid is increasingly congested due to rapid electrification and the growing share of variable renewable energy. This congestion results from temporal mismatches between generation and demand, causing overloaded transmission lines and curtailment of renewable production. Policymakers and grid operators therefore require fast, regional insights into where and in which technologies to invest, accounting for transmission constraints and cost uncertainties. While TenneT currently relies on detailed planning tools such as PLEXOS and PowerFactory/PSSE, their long computation times limit rapid scenario exploration. This thesis investigates whether a simplified model built in the open-source software PyPSA can provide reliable quantitative insights for early-stage planning, particularly for offshore wind integration and electrolysers in the 2040 Koersvaste Middenweg scenario.

A PyPSA model of the Dutch electricity system was developed iteratively and validated against TenneT’s reference models. First, the Netherlands was represented as a single node to reproduce dispatch results comparable to PLEXOS. Second, the transmission network was added to incorporate power flows and grid constraints, with results compared to PowerFactory/PSSE load-flow outcomes. Finally, offshore wind farms and electrolysers were included to assess their system impacts. After validation, the EMA Workbench was used to explore investment cost uncertainty by varying key technology costs between −20% and +40%, identifying when cost-driven trade-offs between technologies occur.

Results show that large solar capacity is consistently cost-effective but requires flexibility options due to variability. In the single-node model, PyPSA favors batteries and gas plants for flexibility, whereas PLEXOS indicates a stronger role for offshore wind, likely due to more detailed cost representations. When transmission constraints are included, offshore wind becomes more attractive because its coastal location reduces congestion and long-distance transport to major industrial demand centers. Load-flow comparisons indicate that PyPSA captures overall flow patterns well: approximately 70–75% of flow directions match TenneT models, and congestion hotspots align with planned reinforcements.

Introducing electrolysers increases annual electricity consumption but does not raise peak demand, as they primarily operate during surplus, low-price periods. Consequently, total optimal generation capacity remains largely unchanged. Sensitivity analyses confirm that PyPSA responds to cost variations similarly to PLEXOS, increasing confidence in its use for scenario analysis. The EMA Workbench results reveal that the future system does not converge to a single optimal configuration; instead, multiple viable configurations balance costs, emissions, and flexibility differently. Gas plants reduce investment costs and peak prices but increase emissions, offshore wind lowers emissions but raises upfront costs, solar-battery systems require large capacities, and nuclear becomes competitive only under substantially lower costs.

Computation time is significantly reduced compared to detailed models. The most detailed PyPSA setup simulates a full year in about 1.5 hours, down from more than a day, and time-aggregation techniques can reduce runtime to minutes. This enables rapid exploratory analysis.

Overall, the study concludes that PyPSA is suitable for supporting early-stage planning of the Dutch transmission system, especially under uncertainty. The future electricity system appears robust but highly sensitive to technology costs, implying that policymakers should prepare flexible strategies that remain effective across multiple possible development pathways rather than relying on a single optimal scenario. ... Read more

The Dutch heating transition aims to accelerate the shift away from heating with natural gas toward low-carbon alternatives. Although national climate policy provides longterm emission reduction targets, heating transition pathways are often assessed using
aggregated data, which do not show distributional impacts across households. This thesis addresses this gap by examining how neighborhood-scale heating systems can be designed to achieve decarbonization goals while incorporating dimensions of energy poverty. A community-level energy system optimization model was developed using Calliope, allowing for detailed representation of household heterogeneity, technology choices, cost-sharing structures, and equity-based objective functions.
The model incorporates three aspects of energy poverty: affordability, insulation quality, and the ability to participate. Seven scenarios are evaluated, combining individual or shared investment structures, cost or equity objective functions, and the presence or absence of a CO2 constraint. Household-level heat demand, income, and housing characteristics were used as inputs, alongside technology parameters and constraints. Outputs include total household costs, installed technology capacities, emissions and energy burden.
The scenario comparison shows several findings. First, shared heating systems consistently reduce total system costs relative to individual systems while achieving identical emission levels under a CO2 constraint. These benefits are strongest for apartment households, whose smaller demands enable more efficient use of shared technologies. Second, only the lowest-cost scenario without emission constraints meets the affordability criteria of the Dutch Wgiw. While electrified heating reduces annual energy bills due to higher efficiency, the associated investments are too high to be recovered over thirty years when strict emission limits are imposed. Third, stringent emission constraints increase total system costs, up to 2.5 times higher than the gas baseline. This shows that there is a strong trade-off between large emission reduction and costs. Moderate emission reductions can achieved at lower or moderately higher costs than the baseline. Sensitivity analyses highlight that energy price variations affect costs and emissions less than proportionally, indicating that the system configurations are relatively robust. The equity-based objective functions, designed to minimize income-weighted costs, have only marginal influence on outcomes. This is because optimal configurations under cost minimization already lie close to the equity-weighted optimum, and in shared systems a significant share of the costs are allocated using fixed fractions based on demand, leaving limited flexibility for redistribution. These findings explain why no single scenario fully satisfies affordability, equity, and emission reduction goals simultaneously.
Overall, the results show that achieving both low-carbon and socially equitable heatiing outcomes requires more than optimal system design. In particular, insulation upgrades, collective investment structures, targeted subsidies for low-income households, and careful alignmentwith local grid constraints are essential for reducing energy poverty while meeting emission targets. ... Read more

Net congestion is becoming a more pressing issue in the Netherlands, partly due to the increased penetration of renewable energy generation sources in the Dutch energy mix. The implementation of residential microgrids into the hundreds of planned Energy Hubs in the Netherlands might alleviate the pressure on the main utility grid through local production, consumption and storage. However, when PV systems have period of low production and the BESSs are depleted, the microgrids would all rely on the main utility grid for electricity supply, potentially only extending the limits needed to be reached by the main grid. Conversely, if all microgrids would have a high PV production and storage would be full, electricity might be exported at such a high rate that the main utility grid is challenged heavily. These situations form the basis of the research objective of this thesis.

A literature review has indicated the need for further research on Dutch residential microgrids, while using historical weather condition data for prolonged periods of time. In addition a clear quantitative definition for the performance of the microgrid. Combining the research objective with the knowledge gaps, the following main research question has been formulated:

How does the performance of residential microgrids in the Netherlands vary under different testing conditions?

By varying load patterns, weather condition data longevity, BESS types, and microgrid sizes throughout the various sub-questions, different scenarios have been developed to assess the performance of the microgrid on different performance metrics. The metrics that different scenarios are scored on are: cumulative deficit, import period duration, import and export power, and import and export ramp rates. Taking on the modelling approach has allowed for answering the sub-questions, filling the knowledge gaps, and achieving the research objective. Using the python library GSEE, PV system production has been estimated and compared against different load patterns (household load patterns with gas heating versus household load patterns with heat pump). Weather data for different time periods have been obtained from the European Commission’s PVGIS, SARAH-3, and ERA5 datasets.

Simulating the different scenarios has provided insights into the effects of different testing conditions on the performance of the microgrid. The factor with the highest impact on the performance was the load pattern, with the addition of a high-impact load in the form of a heat pump to be the scenarios requiring the highest capacity from the main utility grid. BESS type generally also impact the results, with community batteries proving to be successful in reducing the peak import power and ramp rate when compared to home batteries that are used for individual households. Microgrid size only impacts the performance results in a minimal matter.

Analysing the system over a 42-year period has proven to be highly useful in redetermining the upper limits required to be handled by the microgrid and main utility grid, when compared to the singular year (TMY) scenarios. In all scenarios and performance metrics, analysing the system over this prolonged period of time has given new insights into system boundaries. It would, therefore, be highly recommended for future studies on the performance of (Dutch residential) microgrids to take this multidecade perspective and prevent underestimation of the limits the microgrid system is subjected to.

These results have largely been validated by existing academic literature, but are still subject to numerous limitations. This limitations include, but are not limited to, missing values in datasets, low temporal resolution, and the exclusion of the role of monetary costs. Further recommendations would be to focus government policies and subsidies mainly on the demand of Dutch households, as electrification in Dutch households is increasing rapidly. High-impact loads, such as a heat pump, drastically increases the maximum burden the main utility grid has to carry and with the slow development of grid expansion, the electricity grid can not keep up with the additional load. In addition, the Dutch home battery market is still in its infancy stage, but is developing rapidly. To bear the fruits of community batteries, the Dutch government is advised to act quickly and start with the implementation of community atteries in the planned Energy Hubs.

Understanding the interplay between different microgrid components and methods for analysis is vital for successful implementation of the Dutch Energy Hubs and alleviation of the main utility grid. This forms one of the largest challenges of the upcoming decade in the Dutch energy sector. For a full understanding, the results need to be placed in the context of the socio-technical environment. Dutch government instances will need to adjust regulations to incentivise dynamic pricing structures, rethink the cost allocations to allow a fair distribution of costs among households, and setting up a regulatory framework for the emerging Energy Hubs and communities, while also account for behavioural and cultural barriers to smooth implementation of microgrids into the Dutch Energy Hubs. As these Energy Hubs are still in the infancy stage, the Dutch government still has the opportunity to guide the standards, policies, and market mechanisms that will underpin scalable, community-driven Energy Hubs - ensuring they enhance grid stability, foster public trust, and accelerate the transition to a low-carbon energy system. ... Read more

Green hydrogen is increasingly recognised as an essential component for achieving global climate neutrality, offering a versatile and sustainable alternative to fossil fuels in hard-to-abate sectors such as industry, transport, and energy storage. Among the production pathways, water electrolysis using renewable electricity stands out as the most scalable and environmentally viable method. However, a major barrier to large-scale adoption is the uncertainty surrounding future electrolyser costs and deployment trajectories. Most existing cost projections rely on deterministic approaches that provide single-point estimates without explicitly addressing uncertainty, often leading to overly conservative cost decline assumptions and potentially misguided policy and investment decisions.

To address these shortcomings, this thesis develops a probabilistic, empirically grounded framework to forecast both the deployment and capital expenditure (CAPEX) of alkaline electrolysis cells (AEC) and proton exchange membrane (PEM) electrolysers. The framework integrates a logistic S-curve model to simulate technology deployment and a stochastic implementation of Wright’s Law to model cost reductions as a function of cumulative capacity, explicitly incorporating uncertainty through Monte Carlo simulations. Hindcasting validation is used to assess the robustness and predictive performance of the framework, ensuring alignment with historical trends.

The results reveal that achieving the IEA Net Zero Emissions (NZE) 2050 targets would require average annual growth rates of 46% for AEC and 49% for PEM technologies, significantly higher than historical growth trends of around 39%. Even under a more conservative industrial-use scenario, focusing only on sectors such as refining, ammonia, and methanol production, substantial acceleration remains necessary, with required growth rates of 41% for AEC and 45% for PEM. These findings highlight the immense scale of the deployment challenge and the value of explicitly addressing uncertainty when evaluating policy pathways.

Cost forecasts demonstrate a clear divergence between the two technologies. AEC shows a positive experience exponent, suggesting a negative learning rate, implying potential cost increases with expanded deployment. This trend is not statistically significant (p = 0.41), and the model exhibits low explanatory power (R2 = 0.04), indicating that historical data do not support a strong cost-deployment relationship for AEC. In contrast, PEM electrolysers display a statistically significant learning rate of 3.3% (p < 0.01), with an experience exponent of –0.0480 and a higher model fit (R2 = 0.62). Under the reference scenario, AEC median CAPEX is projected to rise to 1,921 EUR/kW by 2030 and 2,076 EUR/kW by 2050, with a wide interquartile range reflecting large uncertainties. For PEM, median CAPEX declines to 1,800 EUR/kW by 2030 and further to 1,533 EUR/kW by 2050, with more pronounced cost reductions under high deployment scenarios.

This transparent and modular framework not only improves cost forecasting for electrolysers but is also adaptable to other emerging energy technologies facing similar learning and deployment uncertainties. By relying on openly available data and explicitly quantifying uncertainty, it provides a robust foundation for analysts, policymakers, and scenario developers. Moreover, its structure makes it suitable for integration into Integrated Assessment Models (IAMs), supporting more realistic and adaptive long-term energy transition planning. Future research should focus on expanding its application across technologies, refining empirical learning rates, and assessing policy impacts within comprehensive system-level analyses, ultimately enabling more confident and informed decisions towards a net-zero future.
The data and the code are available at: https://doi.org/10.4121/82988dc7-099b-45e2-81e2-3850cee1b940 ... Read more

This research investigates how to improve collaboration between multinational enterprises (MNEs) and local public clients in transmission and distribution (TD) projects, which are essential for enabling Europe’s energy transition. Currently, there is growing pressure on transmission and distribution infrastructure to support the energy transition process. Even though the importance of this collaboration is stated, there is a lack of research on this type of collaboration and in this particular construction sector. Therefore, the main research question is formed: ”How can collaboration between divisions within the same multinational enterprise and local public clients in the transmission and distribution projects be improved to accelerate energy transition?”

To answer the main research question, three subsequent questions are developed to explore the characteristics of this collaboration, the key challenges, and the best practices. This research utilized literature review, semi-structured interviews to gather data, ensuring both theoretical depth and practical relevance, and a focus group to validate the findings. The literature review was conducted in order to define the characteristics of this collaboration, discover the challenges, and best practices.

The research identified 36 collaboration challenges in total by combining insights from literature and interviews with nine main categories. Of these 36, 25 challenges across eight categories were derived from the literature, while 11 new challenges and one additional category emerged through empirical study. However, only 27 were found to be relevant in TD context. Using a multi-criteria decision-making (MCDM) analysis, five key challenges were identified: conflicting working culture, expectation misalignment, language barriers, communication style, and different software/programs. The best practices to address these challenges were found through literature review and interviews, then refined into a tailored framework of actionable recommendations.

The framework is designed to be directly applicable and testable in real-world setting, since it provides concrete recommendations and clear explanations to address challenges arising between MNEs and public clients in TD projects. The aim of this framework is not only to enhance the collaboration, but also to encourage stakeholders to collaborate in future projects and strengthen their relationship. With more effective collaboration, TD infrastructure can be developed efficiently, which in turn has a direct impact on the energy transition by enabling transportation renewable energy to end users and accelerating the energy transition. ... Read more

The European Union’s transition to climate neutrality faces growing risks of underdelivery from supply-side technologies. This study investigates how demand reduction and circularity strategies can relieve pressure on the European energy system and how these benefits trade off against macroeconomic impacts. Six demand- and circularity-focused scenarios are developed and implemented in the sector-coupled European energy system optimisation model (ESOM) ZEN-garden to quantify how such strategies reshape Europe’s cost-optimal transition to net zero. An Input–Output analysis is applied ex post to assess their effects on European GDP.

Our findings show that demand reduction and circularity strategies can meaningfully relieve system pressure in a transition pathway consistent with achieving the 1.5 °C target. However, their impacts differ markedly. Strategies targeting transport demand, particularly through compact urban form and modal shift, provide substantial system-wide relief with comparatively small macroeconomic impacts. Consumer behavioural demand reductions in electricity, heat and mobility deliver the strongest reduction in energy-system pressure, lowering cumulative electricity generation needs and overall system costs by more than 19% over 2022–2050, but should be pursued with caution due to the sizeable GDP losses they induce. Circularity measures in steel, cement and chemicals exert limited system-wide effects but significantly relieve pressure within these hard-to-abate sectors. Yet, circular material-efficiency practices should also be pursued with caution as they can trigger notable macroeconomic contractions. Efficiency improvements offer modest relief with negligible GDP impacts.

Furthermore, the results indicate a time-critical sequencing for demand reduction and circularity strategies to be the most effective in reducing energy system pressure: early-action priorities should be measures that relieve pressure on the electricity and urban-transport systems, alongside preparing the expansion of industrial circularity options to maximise industrial pressure relief later in the transition. Furthermore, demand and circularity strategies most effectively mitigate energy system cost escalation during the 2030s, when stock turnover drives major investments.

The findings provide quantitative guidance for policymakers and decision makers in prioritising demand-side and circularity measures that most effectively support the energy transition whilst safeguarding macroeconomic stability.
... Read more

This thesis explores the integration of flexibility measures in the European energy system. The thesis uses an integrated assessment model to simulate the integration of flexibility based on parameters from an energy system model with a high spatial and temporal scope.

The key objectives include evaluating different flexibility technologies' roles in enhancing future energy systems' reliability and resilience.
The foundation of the thesis is identifying and evaluating the most promising storage technologies. Thereafter, the storage technologies are placed into the context of energy modeling, highlighting their strengths, weaknesses, and ability to be modeled.

The research uses the WITCH (World Induced Technical Change Hybrid) model. However, this research is based only on the region of Europe. The WITCH model can run simulations under different climate policy scenarios, including the business-as-usual (BAU) and carbon tax (ctax) pathways. Variables and parameters such as flexibility measures and associated costs are modeled to reflect future energy system configurations based on pre-run cost-optimal configurations from the Calliope framework.

The thesis results show that the main flexibility measures from the literature are storage, grid expansion, demand response, and sector coupling. These measures can enhance the energy system's integration of variable renewable energy sources. Climate policies, i.e., carbon taxes, enable higher levels of VRE and, therefore, flexibility measures, resulting in lower emissions and more efficient energy systems. Fundamentally, the results show a different approach to flexibility than that utilized in long-term models. Using aggregated parameters from energy systems models' pre-run configuration is a novel method of informing other models. This coupling method is effective when the variables of the two models can be harmonized.
The thesis discussion raises areas for future research. The main discussion point is the effectiveness of using pre-run optimization results. The 2030 and 2050-based data provide the energy system's transitional nature. However, extracting insight for a purely transitional model like WITCH proved challenging. Furthermore, the impact of scaling the data from Calliope to match the WITCH data ranges needs further investigation. Lastly, the implications of the elasticity of substitution between the individual flexibility measures, e.g., between storage capacity expansion and transmission grid expansion. ... Read more

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. ... Read more

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 ... Read more

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. ... Read more

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. ... Read more

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.
... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more