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.

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

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.