The ongoing electrification of the Dutch energy system, driven by ambitious 2050 climate targets, is causing increasing congestion in medium- and low-voltage electricity networks. A key challenge lies in the mismatch between rapidly increasing renewable electricity generation and the limited capacity of the grid infrastructure. This imbalance not only delays the energy transition but also obstructs economic growth, as businesses and consumers struggle to connect new renewable projects to the grid. Residential Battery Energy Storage Systems (R-BESS) are considered a key flexibility solution to mitigate this problem. However, if operated purely for market profit, especially through arbitrage and participation in the imbalance market, R-BESS can unintentionally exacerbate grid congestion. This thesis investigates how congestion-neutral operation of R-BESS can be implemented to ensure their contribution to a stable and resilient grid. This thesis investigates how the concept of congestion-neutral operation, a relatively new design approach that aims to prevent residential batteries from contributing to peak grid load, can be applied to the integration of R-BESS. While R-BESS are often deployed to maximize economic returns through market participation, this research focuses on the impact of this implementation and investigates operational strategies that explicitly avoid increasing grid congestion. The central research question addressed is: How can congestion-neutral R-BESS be integrated into Dutch electricity networks to mitigate net congestion while balancing techno-economic trade-offs? A mixed-method approach was applied. The qualitative phase involved literature review and interviews with involved actors (distribution system operators, aggregators, and policy advisors) to identify key technical and institutional design elements for congestion-neutral R-BESS. The quantitative phase implemented a Mixed-Integer Linear Programming model in PyPSA to simulate R-BESS behavior at aggregator level under various market scenarios. The model was applied to a real Dutch medium-voltage substation (MSR), with scenario analyses for 2024 and 2030, supported by sensitivity and robustness testing on weather variability and system parameters. Results show that uncoordinated R-BESS operation substantially increases congestion, particularly in the imbalance market. The outcomes are summarized in Table 1. Congestion-neutral strategies, in particular time constraints, significantly reduced negative grid impacts. For example, applying time constraints reduced new congestion events by half for day-ahead and imbalance 2024 scenarios. However, mitigation periods also decreased with more than 25% for both scenarios. In addition, time-constraint operation preserved economic viability in the day-ahead market: total system costs increased by only 0.9% (2024) and 1.3% (2030), when combining battery trading with self-consumption (”value stacking”). However, for the imbalance market, total system costs increased by 3.47% and 5.09% under time-constraint operation. The study highlights key trade-offs between grid impact and business model profitability. It also identifies limitations, including single-node focus and perfect foresight assumptions.
Practical recommendations include enabling congestion-neutral R-BESS through flexible constraints, distinguishing day-ahead and imbalance market, and clear coordination between DSOs and aggregators. Future research should extend to multinode grids, explore dynamic real-time signals, and further integrate market design for congestion-neutral flexibility services. All results of this thesis are open source. The complete model developed for this research is publicly available at: https://github.com/Tanneheemsbergen/pypsa-NL2025

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

The Netherlands faces strong congestion on the distribution grid, which makes it a good setting to study practical congestion management for medium voltage users. This thesis examines contract based capacity limitation with day ahead notice, and develops a plant level optimisation framework for a real wastewater treatment plant. The model represents the site’s energy assets, gas plant, a biogas tank, photovoltaic generation and, later on, the addition of a battery. It also includes real Dutch tariffs, metering and verification rules, and the contract logic. The method uses two variants built on the same core. A perfect foresight set up provides clear upper bound benchmarks and supports design screening. A rolling horizon set up mirrors day ahead operation with forecast error. Experiments focus on a demanding stress week with Monte Carlo call schedules, and include sensitivity tests on prices, demand and tariffs. We evaluate feasibility, operational cost, and the compensation needed to cover the additional costs of participating in the program.
Results showthat reliable delivery under contract based capacity limitation is possible with out changing core processes, by coordinating on site energy assets and, where appropriate, adding a modest battery sized to the typical call profile. Under imperfect information, fea sibility depends mainly on the asset state at the start of a call and on forecast quality. We see that simple operating rules help manage this risk. The economic analysis indicates that asset based flexibility can be delivered at activation prices lower than those linked to production shifting or shedding, as long as compensation covers the extra costs of delivery and account for the bill savings the site might get.
The contributions are a transparent framework that follows Dutch market practices, facil ity scale evidence on feasibility and economics for a medium voltage wastewater treatment plant, and practical guidance for programme design and site operations. The reasoning ex tends to other process driven utilities and it points to a scalable path for distribution system operators to procure dependable congestion relief at a competitive cost.

Nowadays, the rise in energy consumption and the integration of renewable energy resources (RES) have introduced significant challenges to the existing power grid, making it necessary to upgrade the current power system. However, considering the high variability in load and RES and the large number of possible investment options, the power system expansion planning problem results in a large mixed integer linear problem (MILP), or in some cases, a non-linear problem, impractical to solve for real-world scenarios. Time-series aggregation (TSA), capturing representative load and RES patterns, has emerged to reduce the temporal complexity, making the power system expansion planning model much easier to solve while providing similar final results.

Current TSA methods mainly rely on passive clustering, focusing on the statistical proximity between the representative periods and the full-space time-series. However, this approach does not guarantee a satisfactory solution for the final expansion planning problem in general cases, even with predefined extreme periods implemented such as days with maximum load and minimum available RES. The operation of the power system and extreme conditions are highly sensitive to the specific power system configurations, making the standalone TSA method unreliable in practical applications.

To improve the time-series aggregation in terms of the power system operation, firstly, the methods of assessing the performance of the estimated investment decision are explored directly in terms of the objective function. By introducing the full-space operational cost model for the power system with the estimated investment decision, the operational cost error made by representative periods can be obtained, referred to as the operational estimation error. The actual objective difference between the estimated investment decision and the optimal investment decision found by the full-space expansion planning model can also be evaluated, denoted as the optimality gap. It is found that the optimality gap is bounded by the difference in the operational estimation error of representative periods for the power system with the two investment decisions. As the operations of the power system with the estimated and optimal investment decisions are better estimated, the simplified model can provide a closer investment decision.

Subsequently, looking into the operational estimation error, the performance of representative periods in estimating the full-space operational cost is highly unevenly distributed among the full-space time-series. Representative periods fail to accurately estimate a minor portion of the full-space time-series, causing extremely high operational estimation errors that contribute to the majority of the total operational estimation error. This uneven distribution remains as the number of representative periods increases, indicating that standalone TSA methods are not capable of capturing the extreme conditions of the specific power system operation.

Therefore, to improve the time-series aggregation in terms of its ability to better estimate the operation of the power system, bad-performing representative periods and original periods with high operational estimation error can be identified and prioritized, forming a feedback enhancement loop. Case studies show that the feedback enhancement with re-clustering on the bad-performing representative periods improves the optimality gap by more than 50% compared with the standard mean-based clustering method.

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.

The energy transition towards fossil-free systems presents significant challenges in power grid management, particularly in handling distributed and intermittent energy resources. The Netherlands faces substantial grid congestion due to rapid Distributed Energy Resource (DER) integration without adequate flexibility solutions.

While Energy System Optimisation Models (ESOMs) enable robust system planning, they often oversimplify or neglect Demand Response, particularly load-shifting capabilities. This thesis validates modeling frameworks for load-shifting and proposes a novel framework for modeling aggregated load-shifting in large-scale ESOMs. This approach accurately accounts for load-shifting characteristics without requiring extensive process-specific constraints.

Case study results for the Netherlands demonstrate that load-shifting provides significant value in a fossil-free power grid by adding flexibility and reducing overall system costs, particularly in battery energy storage investments. However, controllable flexibility assets remain crucial for peak demand management and facilitating renewable energy integration. Furthermore, power grid configurations incorporating Demand Response showed greater robustness against weather and demand variations, suggesting that networks utilizing implicit flexibility are more economical and risk-averse than those relying solely on explicit flexibility solutions.

Climate change results in governments making policies for more renewable electricity generation. At the same time, electricity prices are rising. These factors result in an increasing number of photovoltaic (PV) systems being installed and connected to the low voltage (LV) grid. More than half of the areas in The Netherlands have capacity issues related to distributed power generation, which can be solved by reinforcing the electricity grid or reducing the combined power being fed into the grid. This thesis aims at investigating the possibilities and effect of reducing PV power put into the grid.

Grid reinforcement can not happen instantly and requires a considerable investment while currently not enough skilled labourers are available. Together with this labour shortage and time delay, a large cost is associated with this action. Another factor is the eventual over generation from distributed energy resources (DER’s) reducing power grid stability. All these considerations provide the basis for research into the possibilities of LV PV curtailment. There is a significant amount of research on the effects of curtailment, however this research mainly focuses on the LV effects or looks at not technically feasible curtailment options.

The current overloaded grid can be solved by different ways of curtailing LV PV alongside medium voltage (MV) and high voltage (HV) curtailment. Different options result in more curtailed energy correlated with lower grid loading or less curtailed energy but more residual network loading. The current German-implemented method sets a power limit of 70% of the installed power for small PV systems and requires a gradual curtailment possibility for larger systems. This approach has proven to provide a good trade-off between loading reduction and generation losses for now. Other curtailment may be more effective in the future. Therefore the research question of this thesis is How will LV connected PV curtailment affect the loading of the MV and HV network?.

In order to determine the impact of different curtailment methods a simulation based approach was chosen. Further on in this thesis the generation of power consumption and generation data will be discussed, as well as the implemented and explored curtailment methods. The selected networks are based on the amount of LV PV present together with the network capacity relative to the total amount of installed PV generation.

At the moment MV to LV transformers are the main bottleneck, which can only be solved by installing larger transformers or curtailing LV connections. When analysing all other assets an optimisation approach reduces grid loading the most, however this is far from necessary. In order to keep MV cable loading and HV to MV transformer loading within limits any form of curtailment is functional, even curtailment methods which do not impact LV connections.

The main takeaways from this thesis are that curtailment will become necessary in the coming years. Combined with the fact that LV curtailment has to be implemented within 10 years in order to avoid overloading the MV to LV transformers.

All in all a system wide approach is preferred, as this reduces loading with minimal energy curtailment. This can be achieved with an optimisation approach or propagating rules associated to the maximally loaded asset. Considerations, which should be explored further, are the financial impact, social impact of curtailment and complexity of the system. As these impact customers both directly and indirectly.

In light of the energy transition to a fossil-free energy system, Europe is experiencing a colossal shift toward renewable energy generation. To facilitate the rapidly growing demand for clean energy, new technologies, and resources are being investigated. Airborne wind energy (AWE) and floating wind turbines have the potential to unlock untapped wind resource potential and contribute to the balancing of the system in unique ways. So far, the techno-economic potential of both technologies has only been investigated at small scale, while the most significant benefits will likely play out on a system scale. Demonstrating the economic feasibility and additional benefits of emerging technologies in an energy system context is vital to accelerate political traction and funding.
This research aimed to find the main system-level trade-offs involved with integrating AWE and floating wind turbines in a highly-renewable future energy system. To do so, a modelling workflow was developed that consists of future costs and performance estimation, wind resource assessment and integration into a high-resolution large-scale energy system cost-optimization model, based on the Calliope modelling framework. The investigated region contains 10 countries in the North Sea region. The wind resource and system balancing are hourly-resolved. Key findings include:
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Onshore AWE significantly outperforms onshore wind turbines due to higher wind resource availability.
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The main limiting factor in large-scale onshore AWE deployment is the spatial energy density.
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Offshore AWE shows highly identical performance compared to offshore wind alternatives.
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Deployment of offshore AWE is mainly cost driven.
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Floating wind turbines demonstrate great potential because of the high capacity factors that can be achieved in high wind resource areas where conventional offshore wind is not technically feasible.
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Offshore wind potential in general strongly depends on available onshore technical potential.
The outcomes show significant potential for both emerging technologies that could be realized in the near future. This study provides first exploratory findings that lay the foundation for future studies in the context of this research topic. Multiple directions for follow-up research have been identified to quantify this potential in more detail.

Achieving the goals of the Paris Agreement requires a significant transformation of current energy systems. The energy sector has hundreds of technologies and millions of actors working together to balance the system. Researchers are using computer-based models to understand the techno-economic impacts on the energy system due to changes in one or more energy system components. Modelling to generate alternative (MGA) is an energy system optimization method which generates hundreds of equally possible near-optimal energy system configurations. From the point of view of an analyst, it becomes difficult to provide in-depth analysis for future action due to the sheer amount of data generated by the MGA optimization method. In other words, while analyzing the results of the MGA-based model, one of its strengths becomes its weakness.

This thesis aims to design a clustering-based analysis method and Python package (MGA analysis package) based on the designed method to analyze the results of the MGA based optimization model using machine learning algorithms. The package is developed for energy analysts and users of the MGA optimization method to simplify the analysis of results. MGA analysis package clusters the solution space and identifies representative solutions for each cluster. Along with that, it provides a regional equality index to assess the distribution of energy infrastructure. An interactive dashboard is designed to use the MGA analysis package with three to four user inputs and provides information graphically and in text format. The MGA analysis package is hosted on the git-hub (https://edu.nl/gmarp) for applications and further development.

Although there is a common understanding that the use of variable renewable energy sources (VRES) is needed in our collective attempt to decarbonise society, the type of technology that we should deploy, and where is not so clear. Stakeholders from real-world projects use the outcomes from optimisation models to aid their decision-making process. One of such outcomes that decision-makers use is the price information of energy carriers in the required temporal and spatial resolution. In the current state, price information is embedded in the form of shadow prices within linear optimisation problems. As a result, price information is relatively easy to extract for conventional, power-sector focussed energy system models. However, when these energy system models are multi-carrier and sector-coupled in which energy carriers undergo several conversion stages, the extraction of price information becomes less trivial. Furthermore, even when price information is extracted in the form of shadow prices, they might not represent real-world price information. This master thesis research aims to develop a generally applicable price information generation method to extend the use of shadow-price based methods in conventional models to more complex multi-carrier fully sector-coupled models. The price information generation method is developed in Python which is tested within the Calliope modelling framework for a multi-carrier fully sector-coupled energy system. It does this by extracting the shadow prices from a linear optimisation problem of the North Sea Euro Calliope model which is adapted from the Euro-Calliope model. The shadow prices are then compared against current real-world prices for the energy carrier electricity. Results show that the shadow prices do not represent real-world prices accurately, however the use of shadow prices can be extended to understand trade-offs between different configurations of fully sector-coupled energy system models. A use case for the shadow prices has been conducted to analyse the price stability of Dutch electricity prices for different hydrogen shares within the energy system for different weather scenarios. Initial results show that the price stability of electricity in the Netherlands could be improved by increasing the share of hydrogen in the energy system. The increase of the hydrogen share within an energy system does not significantly affect the payback time of the energy system and the levelized cost of energy (LCOE) for electricity technologies. This research project shows that shadow prices could be used to understand trade-offs in different configurations of fully sector-coupled energy systems and aid the decision-making for the type and location of technologies to fulfil energy demands in the future. Recommended future research include an improvement of the North Sea Calliope model using a bottom-up approach and the analysis of other sectors within the fully sector-coupled energy system such as hydrogen and heat.

The swift reduction of human’s carbon footprint is essential to prevent irreversible damage to the climate and to meet climate policy targets. Designing flexible and reliable future energy systems is a big contributor to meeting these goals. While energy system models have improved in the last few decades, they remain vulnerable against parametric and structural uncertainty due to the varying characteristics of parameters and the hardship of modelling all constraints and drivers accurately. This thesis proposes a method that addresses both uncertainty types in energy system modelling by applying SPORES cost optimisation and Monte Carlo scenario modelling simultaneously.

The main case study uses 27 input scenarios with varying outcomes for grid electricity price, solar yield and energy consumption to provide insight in a 100 household neighbourhood energy system with heating, cooling, electricity and hydrogen as energy carriers. With 1377 (near-)optimal solutions, a novel approach in analysis and post processing is used to provide 52 useful configuration options that each have their strengths and weaknesses to different political, economical, social and technical drivers. These configurations are tested for cost, security of supply, CO2 emissions and grid dependency. Those results are visualised through ridge plots and statistical tables to provide a clear overview between each configuration’s trade-offs. An example is included to show how those results can be used for improving energy system design in practice.

This thesis shows that two methods can successfully be combined into one universal one, while providing valuable design insights for energy systems under uncertainty. Furthermore, this method can be applied to a wide variety of energy systems, as long as its possible components, their technical aspects and their allowed interactions are known beforehand. As many future energy system aspects are uncertain, it should be seen as a vital tool to help speed up the decarbonization.