Indonesia’s decarbonisation strategy hinges on how quickly the power system can absorb new renewable classes beyond wind and solar, yet the role of marine renewables has rarely been tested atsystem scale across the country’s grid, notably due to cost constraints. This thesis extends the energy system optimization framework by Langer et al. (2024) [1], Calliope-Indonesia, to analyze wave point-absorber and tidal stream resources’ optimal contribution to the national energy system by 2050 under two grid configurations: a Supergrid with inter-island transmission versus today’s fragmented provincial networks.

The methodology integrates new technology definitions, provincial-level resource assessments from ERA5 reanalysis and TPXO tidal data, and hourly generation profiles into the established Calliope model structure. Four research questions examine MRE impacts on storage requirements, transmission expansion priorities, cost competitiveness against established renewables, and optimal system configurations for least-cost decarbonisation. Wave energy uses point-absorber performance matrices calibrated to Indonesian coastal conditions, while tidal analysis applies velocity-power curves for horizontal-axis turbines deployed in high-flow straits.

Results show that transmission architecture controls MRE integration value. Under Supergrid operation, total storage capacity decreases from 135.7 to 125.1 GW with reference MRE costs (−7.8%) and to 120.2 GW under optimistic learning trajectories (−11.4%). Fragmented networks show minimal storage reduction (+0.6 GW), indicating that MRE benefits require coordinated inter-island power flows. Tidal energy displaces storage more efficiently than wave (0.94 versus 0.09 GW per GW installed) due to predictable semidiurnal generation patterns. Grid expansion concentrates in specific high-value corridors rather than uniform network reinforcement: HVDC capacity increases from 97.1 to 137.6 GW, with the Lampung–Banten connection handling disproportionate additional flows.

Cost competitiveness emerges when interconnection enables optimistic learning curves. Under the Supergrid configuration with accelerated cost reduction, tidal energy reaches 66.1 US$/MWh and wave energy 69.5 US$/MWh.This positions both technologies within the competitive renewable band alongside small hydro (67.5 US$/MWh) and geothermal (61.7 US$/MWh). Marine generation reaches 261.4 TWh annually (17.3% of total demand), compared to 122.8 TWh under fragmented operation, showcasing transmission’s role as a primary value driver rather than background infrastructure.

The analysis identifies targeted deployment strategies: wave clusters positioned behind reinforced transmission gateways on high-resource coasts, and tidal installations near demand centres where network access maximizes predictability benefits. However, single-year operational modeling, coarse nearshore resource resolution, and incomplete spatial exclusions limit precision in site-specific assessments. Despite these constraints, the evidence indicates that MRE technologies can contribute meaningfully to Indonesia’s 2050 power system under cost-optimistic assumptions (CAPEX: 986,000 US$(2023)/MW, OPEX: 50,000 US$(2023)/MW) and remain viable even under reference cost scenarios (CAPEX: 1.76 million US$(2023)/MW, OPEX: 88,000 US$(2023)/MW) when supported by strategic interconnection investments and disciplined resource targeting. ... Read more

This study investigates the feasibility of exporting renewable energy from isolated regions using aluminium as a physical energy export medium. Focusing on Iceland, a country with abundant hydropower and geothermal energy but lacking direct grid interconnection, this work evaluates whether aluminium production and maritime shipping can serve as a viable alternative to conventional electricity export via submarine cables. The aluminium effectively “embodies” renewable electricity used during smelting, enabling indirect energy export in the form of a commodity.
To capture the logistical complexity of this shipping-based strategy, an energy system model using the Calliope framework with customized Mixed-Integer Linear Programming (MILP) constraints is developed. These constraints allow the representation of discrete transport events, transit delays, fuel consumption, and shipment scheduling, which are dynamics that traditional energy models typically treat as continuous or overlook entirely. This approach is termed “discrete maritime transport modelling,” referring to the batch-based, non-continuous nature of shipping operations.

Two scenarios are assessed: (1) the discrete shipment of aluminium from Iceland to the Netherlands, and (2) continuous direct electricity transmission via a hypothetical high-voltage submarine cable. The comparative analysis evaluates total energy delivered and total system costs over a fixed operational horizon. Results show that aluminium shipping delivers far more energy (10,625 TWh) at significantly lower cost (33.85 million USD) than electricity transmission (689.76 GWh at 96.72 million USD), despite the latter’s advantage of real-time delivery and grid compatibility. Although these results are context-specific, they suggest that when largescale HVDC infrastructure is cost-prohibitive or geographically constrained, embodied energy export through industrial commodities may offer a competitive alternative.
Sensitivity analyses confirm the model’s robustness under variations in fuel use, system duration, and capacity assumptions. However, the aluminium pathway raises sustainability concerns not yet fully addressed in this study, including fossil fuel reliance in shipping and environmental impacts of metal production. Future work should expand the model to include emissions accounting, lifecycle energy analysis, and more refined operational behaviour of maritime logistics.
Overall, this report contributes a methodological innovation in energy system modelling and offers early evidence that industrial commodities can serve as flexible, scalable, and economically viable vectors for renewable energy export, particularly in geographically isolated, energy-rich regions. ... Read more

The transition to a low carbon energy system is focusing attention on the synergies between large scale offshore wind and green hydrogen production. These synergies can have system-wide benefits for the integration of wind farms to the power system, whilst improving their economic performance. Offshore wind investments face multiple uncertainties: commodity prices fluctuate and impact turbine cost, vessel rates required to install the turbines are dynamic and the electricity price fluctuates daily. Additionally, to realize wind-hydrogen synergies (hybrid powerplants) the electrolyzer is an additional source of cost uncertainty. On the contrary, the revenue uncertainty of the wind farm is expected to be mitigated through the use of Hydrogen Purchase Agreements. To take informed investment decisions regarding wind-hydrogen synergies, Vattenfall, a leading European utility, is interested in analyzing the trade-off between increased cost uncertainty and reduced revenue uncertainty.

This thesis investigates how integrating a 400 MW onshore electrolyzer with a 2 GW bottom-fixed wind farm affects the project’s economic uncertainty. To answer the question, a three-step methodology is applied. Firstly, the economics of the offshore wind farm and hybrid powerplant are modelled without the presence of uncertainty, using Vattenfall’s techno-economic model. This model considers investment and operation costs, along with the revenues of the wind farm from electricity sales on the power market. For the revenues of the hybrid powerplant, an optimization algorithm deciding when it is optimal to produce hydrogen or electricity is employed. Secondly, the uncertainties in key commodities (steel, aluminum, copper and shipping fuel oil), vessel day-rates, electricity prices and electrolyzer costs, are defined. Thirdly, the uncertainties are sampled through a Monte Carlo simulation to create 10,000 different realizations of the project, covering the entire range of possible outcomes. By combining the techno-economic model, the dispatch algorithm and the Monte Carlo approach, the uncertainties of the offshore farm and hybrid powerplant can be quantified, and their impact on the project economics can be evaluated.

The methodology allows to compare the effect of hydrogen integration in the business case uncertainty of an offshore wind farm. On a deterministic comparison, the two projects perform similarly. For the considered wind farm, the inclusion of the electrolyzer adds an additional e94 M
in cost uncertainty, while it reduces revenue uncertainty by e18 M. These results hold true for the considered offshore farm, given projected conditions and modelling assumptions. For this case, the offshore wind farm is marginally more certain in terms of economic returns. Given the two projects produce similar investment returns, accepting the increased uncertainty of the hybrid powerplant is non-economical.

The results of the thesis do not favor an investment in hybrid project in terms of uncertainties. However, this result is true for the project investigated and as conditions change and hydrogen technology matures, the analysis can shift in favor of hydrogen. Specifically, hydrogen is a large
scale infrastructure with the first full scale projects currently under development. With more project-experience gained, the uncertainty in costs can be reduced, favoring the hybrid projects. With increased support for large scale hydrogen production, the proposed framework for continuous analysis can be leveraged to keep pace with the external macroeconomic changes. The methodology can be extended and applied to other hybrid solutions, such as hybrid projects with batteries. ... Read more

Achieving Europe’s carbon neutrality by 2050 demands the expansion of all of the renewable energy sources, and especially onshore wind. Constraints such as aging wind turbines reaching their operational lifetime, limited land availability, and mounting social and environmental pressures constrain the escalation of new sites. The strategy of wind repowering—replacing turbines reaching their end-of-life with larger, higher-performance models on existing footprints— promises to increase the installed capacity, leverage the existing grid and site infrastructure, and reduce generation costs.

This thesis delivers the first continental-scale, quantitative evaluation of the onshore wind repowering strategy in Europe by collecting a European wind-park database, overlaying site classifications from the Global Wind Atlas and EuroDEM elevation topography, and applying a lifecycle capacity model to project decommissioning and repowering trajectories through 2050. Complementary modules estimate repowered rotor dimensions, select candidate turbines, and compute energy yields using ERA5 reanalysis, while a cost model integrates decommissioning expenses, projected CAPEX/OPEX, and financial metrics (LCOE, NPV, IRR). A spatial-footprint submodel then quantifies land requirements under competing repowering and greenfield-replacement scenarios.

Multiple repowering approaches were applied with fluctuating spatial constraints, resulting in an additional 60–82 GW of nameplate capacity by 2050—equivalent to a 33–45 % increase over a straight decommissioning-and-replacement baseline—and can generate up to 425.34 TWh annually, covering approximately 14.8 % of Europe’s 2022 electricity demand versus 11.9 % or 342.87 TWh under the baseline. From a financial standpoint, assuming a wholesale electricity price of €80/MWh and a moderate (14 %) learning‐rate scenario, repowering marginally lowers the mean LCOE from €68.0/MWh (replacement) to €67.4/MWh; over 46.6 % of sites achieve a higher NPV under repowering; By reusing foundations, roads, and grid connections, repowering cuts additional land requirements by 37–41 % relative to a straight replacement baseline. These results demonstrate that under moderate technological progress, repowering can cost-effectively expand Europe’s wind energy production, maximize site efficiency, and minimize environmental footprint.

Access the open-source model on GitHub - https://github.com/AngelosChatz/Evaluating_Wind_Repowering ... Read more

The transition to de-fossilized energy systems plays a central role in achieving climate neutrality in Europe, and the transport sector is pivotal in this transformation. Nowadays, passenger vehicles represent an important share of the final energy consumption and the greenhouse gas emissions. As internal combustion engine vehicles (ICEVs) are gradually phased out and replaced by battery electric vehicles (BEVs), and hydrogen fuel cell electric vehicles (FCEVs), understanding the evolving energy demand profile of passenger transport becomes more and more critical. This thesis addresses this challenge by quantifying the way in which the differences in powertrain technologies, in the vehicle types, and in weather conditions influence the energy consumption and the total energy demand across European regions.

The core of this research is the enhancement of the Vehicle Consumption Assessment Model (VCAM), a simulation platform capable of evaluating the energy consumption of different powertrains under various environmental and operational conditions. The model was extended to include a detailed representation of FCEVs and used under dynamic weather profiles and region-specific fleet compositions. These advancements allowed the simulation of real-world driving scenarios by using both historical and projected climate data, as well as the assessment of policy pathways and fleet evolution trends through 2050.

The methodological framework followed a multi-layered approach. First, a powertrain comparison for BEVS, FCEVs, and ICEVs has been made to evaluate them across different vehicle segments, considering performance under different driving cycles. Second, a temperature sensitivity analysis has been made for forty years of temperature data for Greece, Germany, and Finland, which were used to quantify the impact of cold and hot conditions on energy consumption and range for each powertrain technology. Lastly, a scenario-based analysis has been performed to scrutinize the effects of different IEA policy pathways (STEPS, SDS, NZE) and IPCC climate scenarios (RCP 2.6, 4.5, 8.5), as well as the influence of the growing SUV market share, on the passenger vehicle energy demand in 2050.

The results show that the energy demand is highly sensitive to the selection of powertrain technology, with BEVs offering the highest efficiency, as well as the highest sensitivity to ambient temperature. The FCEVs perform more consistently across extreme temperatures, but consume more energy than BEVs. ICEVs are the least efficient vehicles, but they present a moderate sensitivity to temperature because of their capability to use the engine's waste heat to cover the thermal loads. Vehicle size can significantly alter consumption, especially for electrified vehicles in extreme climates.

Regarding the regional effects, the projections for the passenger vehicle energy demand differ substantially. Germany's demand remains the highest due to population and mobility volume, while Finland shows the greatest sensitivity to climate conditions. Greece, where the most moderate climate conditions exist, presents the lowest variability. Across all technologies, BEVs offer the highest efficiency but also the greatest vulnerability to temperature extremes, with the energy consumption rising to more than 40% in cold conditions. FCEVs, which are less efficient overall, keep a more stable performance across temperature variations. The scenario analysis made shows that the ambitious decarbonization strategies (STEPS, SDS, NZE) could reduce total passenger vehicle energy demand by more than 60% relative to 2019 levels. However, this reduction is sensitive to fleet composition. To be more specific, for example, an annual SUV market growth of 2 percent could increase energy demand by up to 19% in Germany compared to a no-growth baseline. Similarly, consumption in the coldest years can exceed the warmest by 8 - 15 % for FCEVs and BEVs, depending on the region.


In conclusion, this thesis provides a detailed and geographically differentiated understanding of the passenger vehicle energy demand during the energy transition. It underlines the need to plan, while considering climate conditions, segments, and technologies to ensure that the electrification of the transport sector aligns with the broader goals. ... Read more

The increasing integration of offshore wind energy into volatile electricity markets necessitates more frequent curtailment as a response to negative pricing conditions. Although curtailment can mitigate short-term financial losses, its long-term impacts on offshore wind turbine (OWT) degradation remain largely unaddressed. This thesis investigates the hidden costs associated with curtailment, focusing specifically on the degradation of critical turbine components, including the gearbox, blades, and support structures. By developing and comparing optimisation models, this research evaluates the effectiveness of coordinated curtailment strategies that integrate asset health considerations with market-driven objectives.

Three distinct optimisation frameworks are proposed: a baseline reflecting current market-driven curtailment practices, a centralised strategy optimising asset health and financial performance collectively, and a decentralised approach applying the Alternating Direction Method of Multipliers (ADMM) to address fragmented stakeholder interests. Results indicate significant benefits from centralised coordination, showing a notable reduction in investment costs and a substantial increase in overall profit, as well as improved return on investment. However, contractual complexities and data-sharing constraints can hinder centralised implementation, highlighting the potential advantages of a decentralised strategy.

The findings underscore the value of internalising asset health into curtailment decisions, demonstrating clear financial and operational improvements. Looking ahead, the value of centralised coordination is expected to grow as electricity markets experience increasing negative price events and greater volatility, driven by a higher share of variable renewable energy sources. Consequently, internalising asset health in curtailment strategies will become increasingly essential for future energy systems.

... Read more

This thesis investigates the commercialization of next-generation nuclear fission technologies, focusing on the barriers, strategies, and timelines necessary for their deployment. With the growing need for clean and reliable energy, nuclear fission offers a low-carbon solution, but its commercialization is challenged by regulatory, financial, and technological hurdles. The research uses a dual-method approach, combining desk research and expert interviews. Through desk research, commercialization timelines of 10 key companies developing next-generation nuclear reactors were mapped. These companies are at different stages, ranging from ideation to prototyping, with varying timelines influenced by strategic partnerships, regulatory approvals, and technological progress.

The expert interviews reveal significant barriers to commercialization, including lengthy regulatory approval processes, high upfront costs, and public skepticism surrounding nuclear technology. Financial constraints, especially the difficulty in attracting investment due to long payback periods, were noted as major impediments. Experts also highlighted the need for technological innovation, particularly in materials testing and reactor designs, to meet stringent safety standards.

In response to these challenges, the thesis identifies strategies such as early engagement with regulatory bodies, diversification of funding sources, and open communication to address public concerns. Technological advancements, such as modular reactor designs and improved safety features, are critical to overcoming these hurdles. By combining the insights from commercialization timelines and expert interviews, this study offers recommendations to accelerate the commercialization process, emphasizing the need for policy support, innovative financial models, and continuous technological development. Nuclear fission remains a promising solution to the global energy crisis, but coordinated efforts are required to unlock its full potential. ... Read more

The transportation sector continues decarbonizing with the increasing number of Electric Vehicles (EVs) replacing gasoline and diesel cars every year. However, the integration of vast amounts of EVs introduces complexities in energy distribution and grid stability. Charge Point Operators (CPOs), positioned at the intersection of EVs and the grid, play a critical role in managing these complexities. They ensure that the charging infrastructure meets the needs of both EV users and the grid, highlighting the importance of smart charging strategies.

In this thesis, a smart charging approach is proposed from the point of view of a CPO. The proposed approach aims to optimize the charging schedules for EVs parked at a commercial building's parking lot. The objective of the optimization problem is to minimize the Power Setpoint Tracking (PST) error, which indicates the error between the contracted energy in the day-ahead market by the CPO and the aggregated consumption of charging stations the next day. This optimization involves complex sequential decision-making, where the uncertain nature of EV arrivals and departures demands a fast and adaptive solution. Thus, this thesis proposes a Markov Decision Process (MDP) formulation and solves it using the Deep Deterministic Policy Gradient (DDPG) algorithm to minimize the PST error by scheduling the charging of EVs. DDPG is chosen for its ability to efficiently handle complex problems with continuous state and action spaces, making it ideal, considering the uncertainties inherent to the arrival of EVs and the charging process. Additionally, DDPG's application in a commercial building's parking lot, where EV arrival and departure patterns are usually consistent, further solidifies DDPG as a strong alternative.

Evaluating the proposed DDPG approach with alternative benchmarks, such as the uncontrolled "charge as fast as possible" (CAFAP) and the optimal solution obtained through a Mixed Integer Non-Linear Programming (MINLP) formulation, signifies DDPG's superior performance in several metrics. Specifically, it outperforms the CAFAP algorithm by achieving a reduction in PST error by an average of 34% for a parking lot with 10 chargers over 12 hours of charging for a day. This highlights DDPG's efficacy in optimizing EV charging schedules over the CAFAP algorithm. Moreover, DDPG's model benefits from the ability to be trained offline with historical data and deployed online once trained. This approach allows for rapid, dynamic rescheduling of charging in real-world operations, offering speed advantages over the theoretically optimal solution, which requires prior knowledge of arrival and departure times and State of Charge (SoC) of EVs. All experiments validating these findings were conducted within the EV2Gym, a Gym environment specifically designed to simulate the EV charging scenarios.

Lastly, this thesis contributes to the field by demonstrating how RL, through the use of DDPG, can optimize PST for EV charging in a commercial building's parking lot. By offering a detailed comparison with other algorithms and showcasing the scalability and adaptability of DDPG, the research provides valuable insights for CPOs and stakeholders in the energy sector. ... Read more

Routine maintenance is an essential requirement for the optimal functioning and longevity of any technical system that has been constructed. The issue occurs when the maintenance planning for such a structure becomes necessary. The adverse weather conditions prevalent in the Netherlands contribute to the heightened danger associated with the duty of a maintenance worker. Additionally, it is vital to comprehend the optimal time frame for minimising revenue losses when allocating time towards maintenance activities rather than operational tasks.

The objective of this project is to create a methodology for enhancing and improving the management of asset maintenance planning. This process is carried out by developing two statistical models. The primary objective of this study is to examine the feasibility of utilising API data from wind forecast sites to provide accurate production forecasts for individual wind turbines up to a 7-10 day period in advance. Furthermore, this study aims to forecast the electricity prices for the upcoming week by analysing historical data and recent day-ahead pricing. The outputs generated by these models are subsequently aggregated to yield a single outcome in terms of revenue.

The predictive model for electricity prices utilises data sourced from ENTSOE to generate an aggregate of electricity prices spanning the previous 7-8 years. This aggregate is subsequently adjusted by incorporating the electricity prices observed within the most recent three-week period. The model exhibits high accuracy in predicting day-ahead pricing during weekdays, but its performance is not consistently replicated on weekends.

The wind turbine output forecast model utilises operational archival data and high-resolution 10-day forecasts obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF). A correlation has been established between the archival data and the historical turbine data provided by Green Trust Consultancy for the designated wind farm. The aforementioned correlation is subsequently employed to establish a connection between the output of a turbine and the real-time forecast data. The accuracy of the power generation forecast decreases from the initial day to the tenth day of the projection, resulting in inconsistent outcomes.

The combined outputs of these two models yield a solitary outcome that aids in predicting the potential revenue loss for the selected turbine during the period of maintenance-induced idleness. The limitations inherent in both models contribute to the generation of imprecise outcomes inside the revenue model pertaining to wind turbines. The model accurately predicts outcomes in two out of the four tested scenarios.
... Read more

The establishment of a well-developed charging infrastructure is imperative for the broader adoption of Electric Vehicle (EV) and necessitates the formulation of an effective charging infrastructure policy. To navigate the intricacies involved in the policymaking process, the incorporation of EV charging models can be advantageous. Existing research indicates that models have a significant impact on facilitating policymaking in the broader energy sector. Nevertheless, it remains unclear whether computer-based models exert a similar influence on the EV charging policies. Previous studies lack comprehensive insights into the practical application of models in EV charging policy processes and the resultant policy modifications due to the unique attributes of both EV charging models and policies. Furthermore, there exists a lack of systematic understanding regarding the utilization of charging infrastructure models. Given these gaps in knowledge, this research aims to investigate the following question: How does modeling evidence influence EV charging infrastructure policymaking in the UK and the Netherlands?
This study finds that EV charging models have exerted substantial influence across various stages of local policy cycles, significantly shaping decision-making processes. Such impact has pre- dominantly concentrated on the practical and operational aspects of the models, primarily concerning the optimal number and spatial distribution of charging points. However, there remains a noticeable lack of attention to strategic considerations pertaining to broader energy transition and green transport initiatives. This oversight is particularly evident in the insufficient exploration of how EV charging in- frastructure can be effectively integrated into a more extensive and long-term blueprint. This research highlights the need for a strategic-level approach to comprehend the interplay between EV charging networks and the larger energy transition agenda, encompassing themes such as renewable energy integration, smart grid compatibility and urban planning synergies. Consequently, policymakers and modelers should expand their planning of charging infrastructure to encompass the broader landscape and envision how EV charging models can harmonize with sustainable urban development, ensuring a cohesive and effective implementation within the overarching framework of environmental conservation and sustainable mobility. ... Read more

Currently, there are optimization models that are able of modeling building renovation, but their scope is limited only to the heating system. There are also models that consider interactions between different energy subsystems, but they lack the ability to simulate building renovation. Having those two things combined would be beneficial as the impact of renovation on the power system might be significant.
The goal of this thesis is to assess the impact of building renovation on the energy system. It was done with the use of Euro-calliope model, which is capable of minimizing the cost of the whole energy system. In the current state, Euro-calliope model does not offer the chance to renovate the building stock. Therefore, the aim of this work is to introduce the building renovation option subject to the software objective function and reshape, when necessary, the heating sector.
The main outcome is that the heating sector significantly affects the distribution of power generation sources as most of heat is supplied via heat pumps. The penetration of renovation increases the fraction of energy generated by photovoltaics in the energy mix. However, when it comes to absolute values, in all scenarios wind farms are dominating.
The cost-optimal renovation always results in a higher renovation level than the currently present renovation levels. However, those levels are usually lower than the currently imposed local renovation standards. Moreover, the renovation has a positive impact on decreasing the variability of the system costs for scenarios with low renewable supply.
... Read more

The report focuses on the analysis of a cost-optimal hydrogen network in Europe within the context of an integrated energy system. It addresses the need for understanding the required capacity and spatial distribution of hydrogen infrastructure to meet the growing demand for clean energy carriers. Previous studies often overlook the integration of a hydrogen network and lack optimized designs considering a sector-coupled energy system.

To overcome these limitations, this report models hydrogen pipelines using the Calliope energy system modeling software with the objective of determining the necessary capacity and distribution of a hydrogen grid in Europe under a fully renewable scenario. Modeling was conducted with a spatial resolution of 35 nodes and a temporal resolution of 2 hours over a full year, using the 2018 weather data. The software uses a linear optimization method to determine the optimal configuration of the hydrogen network.

This study formulates several allocation scenarios for electrolysis capacity to examine the capacity and spatial distribution of an optimal hydrogen network under different conditions. It is found that an energy system with hydrogen hubs requires the development of extensive new pipeline infrastructure, while a system with a more balanced distribution of electrolysis across Europe requires less pipeline capacity and relies mostly on repurposed infrastructure. The estimated capacity of the network ranges from 135 to 244 TWkm. This is 40% to 70% lower than what is estimated in the European Hydrogen Backbone (EHB) vision.

In the scenario with hydrogen hubs, the study identifies four hydrogen corridors that align with the vision presented in the EHB report, with Britain, Ireland, Denmark, and Portugal as key hydrogen producers. Furthermore, the analysis highlights the significant role of salt caverns as the predominant storage technology for hydrogen, despite uncertainties surrounding their capacity estimates. The optimal storage capacity in salt caverns ranges from 42 to 178 TWh when accounting for cost and weather uncertainty.

The analysis initially considered a self-sufficient energy system, but the sensitivity to international imports was also considered. To analyze this aspect, hydrogen imports from four North African countries were included. The findings revealed that international imports play a relevant role in shaping the optimal configuration of the hydrogen network. Imports increase the required investment in infrastructure but also reduce hydrogen storage capacity and its associated uncertainty. Changes in the network configuration result in a 6% reduction in total system costs due to decreased renewable energy capacity and reliance on external hydrogen supply.

Overall, this study emphasizes the need for accurate electrolysis allocation estimation, alignment with international import planning, and efficient utilization of storage technologies in the development of hydrogen infrastructures. The findings contribute to informed decision-making and the creation of sustainable hydrogen networks that integrate effectively with renewable energy systems. ... Read more

The ever-rising greenhouse gas emissions move the European Union to transition towards a future decarbonized energy system. To achieve this, the future energy system will mainly comprise variable renewable energy generation sources. Energy production from these sources is susceptible to weather fluctuations. Uncertainty of future weather scenarios translates into energy system models that help decision-makers to design the future renewable energy system. Energy system models often use a single weather year to simulate weather behaviour. Thus, many energy system models fail to take weather fluctuations between various years into account. Identifying energy system designs that are robust to weather fluctuations, i.e. systems that are able to suffice demand regardless of the weather circumstances, is therefore key.

The research described in this thesis has aimed to develop and test a method to give insight to decision-makers into the composition of robust and efficient energy systems, given weather uncertainty. To achieve this, the SPORES methodology has been used and extended to identify energy system configurations that are both robust and efficient. For this, a decision option space for decision-makers has been created that has been diversified based on renewable energy generation and storage technologies. To test the developed method, the North Sea region has been used as a case, as this is the region thought by policy to have great potential to house renewable generation sources in Europe.

The method developed in this research systematically covers the decision option space over three weather scenarios (worst, typical and best). From these decision spaces, configurations that meet demand with installed capacities that exist across the whole weather options space have been selected as robust. Clustering was used to identify types of energy system configurations, having commonalities in the installed generation capacities. Energy efficiency has been identified as key for measuring energy system performance. This research, therefore, takes curtailment and energy system yield into account to quantify efficiency. Using a Pareto analysis, both robust and efficiency-wise high-performing energy system configurations were identified as most promising for decision-makers.

The no-regret decisions, visualized by the SPORE-core, are minimum capacities required across the whole decision space. Results showed that robust energy systems are typically comprised of balanced configurations, meaning that solar PV and wind power both have the largest capacity of energy generation sources. The balanced configurations also contain high transmission capacities and typically no storage capacities indicating energy is distributed rather than stored. The robust and efficient configurations need additional capacity investments on top of the no-regret decisions. Especially solar PV needs a large increase in capacity when robustness and efficiency are required. Combined heat and power from biofuels and electrolysis capacity are also key to robust and efficient configurations. Additional results showed that the majority of robust and efficient configurations utilised more offshore than onshore wind capacity.

The findings of this research are based on a case of the North Sea energy system with a high level of aggregation and are thus of limited use for precise designs of the North Sea energy system. The method created in this study can be adapted to contain more detail and offers space for researchers to include their own performance indicators. However, this research already used significant computational efforts, so adding more resolution and detail will mean the computational process can be restricting. Future research should focus on using the developed method to select promising and robust energy system configurations with higher levels of detail and conduct further weather scenario analyses on the selected configuration. ... Read more

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. ... Read more

The integration of variable renewable sources and electrification is straining electricity grids worldwide, resulting in congestion events. This research aims to understand the consequences of unmet electricity demand caused by demand congestion, to support informed decision making. Using a mixed methods approach, the study focuses on the medium voltage distribution network in Amsterdam, examining the implications of congestion for different customer segments. The research finds that demand congestion leads to a lack of universal access to electricity, as it becomes spatiotemporal-specific and dependent on the type of connection required. The study identifies industry, commercial business, public services, and urban development as key segments with varying responses to congestion. The societal impacts of congestion hinder sustainable development across environmental, social, and economic dimensions. CO2 emissions, housing delays, and employment losses are highlighted as significant impacts in Amsterdam. To mitigate these impacts, it is recommended to foster collaboration among stakeholders within the multi-actor network and make segment-specific as well as area-targeted investments. The research establishes a foundation for further studies on demand congestion and offers insights for effective congestion management in urban areas. ... Read more

The escalating impacts of climate change have placed energy-related issues at the forefront of global political agendas, highlighting the critical role of energy policy in shaping sustainable futures. Energy policymaking is inherently complex, as it must reconcile the interests of multiple stakeholders while managing a highly interconnected and technically intricate energy system. Energy system modeling has emerged as a crucial tool for supporting policymakers in navigating this complexity, giving rise to a model-policy interface that mediates the translation of quantitative insights into actionable policy decisions. However, the occurrence of energy crises, which are historically frequent and impactful, raises important questions about how such crises influence the functioning of this interface and the role that energy system modeling plays during periods of heightened urgency.

This research investigates the influence of crisis situations on the use and impact of energy system modeling in policymaking. The study employs a mixed qualitative approach, combining a comprehensive literature review with semi-structured interviews of experts involved in the model-policy interface, including energy modelers, policymakers, and industry professionals. The interviews were analyzed using qualitative content analysis, which generated three key thematic insights regarding stakeholder roles, crisis urgency, and the impact of uncertainty on modeling’s influence.

The first theme, stakeholder reflections on roles and challenges, highlights how modelers and policymakers perceive their positions within the interface. Modelers see themselves as providing supportive, analytical input, while policymakers emphasize the value of interaction with modeling experts to inform decision-making. Nonetheless, challenges persist due to the inherent complexity of models, time constraints in policymaking, and gaps in technical expertise among stakeholders, which can hinder effective communication and utilization of model results.

The second theme, urgency modulates the role of modeling in crises, differentiates between low-urgency and high-urgency crises. In low-urgency situations, the model-policy interface experiences increased collaboration, alignment, and deliberation, allowing for more considered and evidence-based policy decisions. Conversely, high-urgency crises shift decision-making to the political domain, constraining administrative freedom and reducing the opportunity for detailed modeling input. As a result, the ability to leverage quantitative insights diminishes, and rapid policy responses often take precedence over model-informed strategies.

The third theme, uncertainty, addresses the nuanced impact of crisis-induced ambiguity on modeling. While increased uncertainty can elevate the demand for model-based insights, several factors complicate this relationship: models may not be suitable for crisis-specific problems, inherent uncertainties may already dominate, and the time required for detailed modeling may limit its relevance in urgent scenarios.

The findings align partially with existing literature on the model-policy interface, particularly regarding challenges in knowledge translation and the time-intensive nature of modeling. They extend the literature by emphasizing the role of crisis perception—specifically urgency and uncertainty—in shaping the effectiveness and influence of energy system models. The study highlights the need for frameworks that can systematically assess the impact of crises on model utilization and offers a foundation for future research to explore strategies that enhance the resilience and responsiveness of the model-policy interface under crisis conditions. ... Read more

The European energy system faces one of its greatest challenges: transitioning from a system dominated by fossil-based energy sources to a completely climate neutral system in 2050. Energy system models provide useful tools that can help to navigate this complex task of designing a future energy system by modelling future systems and assessing the impact of future design choices.

Current literature adopts either a step-wise optimisation towards a final configuration of an energy system or deploys a modelling to generate alternatives (MGA) approach to generate a diverse set of system configurations. While the first approach provides temporal insights, it may biased and miss less trivial solutions, the second approach offers robustness but lacks insights into timing of technol- ogy deployments and neglects existing infrastructure. This highlights a gap in combining the strengths of both approaches to understand the dynamic between short-term decisions and long-term flexibility, while being robust under changing conditions.

The purpose of this study is to develop a method that combines the strengths of both modelling approaches. A method is developed and applied to two case studies to uncover how policy targets influence the maneuvering space towards a climate neutral European energy system. Many European nation states have set ambitious targets to increase renewable generation capacity while also phasing out fossil-based power generation. It is important to analyse impact of such targets on the rest of the energy system in the short- and the long-term. Aggressive phase-out or growth of deployed infrastructure might work in the short term but could restrict flexibility of options further into the future.

This study uses spatially explicit practically optimal results (SPORES) for 2030 and 2050 provided by the sector-coupled Euro-calliope model, which is an adaptation of the MGA approach. Energy system characteristics were found by analysis of the distributions of primary energy sources and power sector technology deployments. Trade-offs were uncovered by computing Pearson correlation of technology deployments on national and European scale. A k-means clustering algorithm was applied to condense the set of hundreds of SPORES to manageable amount of scenarios that is more accessible for poli- cymakers. The scenarios reveal trends and trade-offs between the two different time-frames. Finally, two case-studies were presented that use a filtering of the SPORES to reveal the impact of the 2030 PV capacity target in Germany and the 2030 offshore wind capacity target in the Netherlands on the maneuvering space of their respective future energy systems. Key finding of this study include:
• Phase-out of fossil-based energy sources by 2050 is enabled by a doubling of renewable elec- tricity generation by 2030 and a more than ten-fold increase of renewable electricity generation in 2050.
• Solar (PV) and wind turbines are must-have technologies by 2050, however trade-offs exist in the the proportional balance between them, and the timing and location of deployment.
• Early phase-out of coal-fired power generation can reduce flexibility in the system as it is often accompanied by high deployment of gas turbines and PV that could create lock-in risk.
• Germany’s ambitions to deploy 215 GW of PV by 2030 requires increased coal-fired generation capacity to remain in the cost-efficient design space of 2030. Thus the PV target introduces lock-in risk of coal-fired power generation, which requires a complete phase out by 2050.
• The Dutch offshore wind target of 21 GW by 2030 introduces a potential conflict between deploy- ment of offshore wind and growth of PV and onshore wind that is required for 2050.

These findings contribute to research by offering a methodology that improves understanding of the dynamics within the European energy transition. This study has, for the first time, placed MGA solutions in the context of the multi-decade transition. By analysing the change between the current system, the design space spanned by the 2030 and 2050 SPORES, new insights about the time dependent trade- offs within the energy system and the limitations of the SPORES method have emerged. ... Read more

To mitigate the effects of climate change, CO2-emmissions will have to be reduced. This can be done by substituting the use of fossil, CO2-heavy by the use of renewable, CO2-neutral energy carriers. Hydrogen is such a renewable energy carrier that can be produced in a CO2-neutral way. The potential of hydrogen is recognized by the European Commission as specific renewable hydrogen goals are stated in their Fit for 55 package, which is a plan to reduce greenhouse gas emissions within the European Union with 55% by 2030. These goals relate to both the production and use of renewable hydrogen within the European Union.  For hydrogen to be qualified as renewable, the European Commission has proposed a set of requirements that its production has to comply with. One of them is temporal correlation, requiring the amount of energy used by a hydrogen production plant to match the amount of energy generated by one or more connected renewable energy plants. Renewable energy plants generate a variable output of energy as they rely on variable energy sources such as wind. This causes the production of renewable hydrogen to be variable as well. The fact that this production of hydrogen is variable can be problematic as some industrial processes require or perform better with a constant input of hydrogen. Energy storage systems, such as lithium-ion batteries, can be integrated in hydrogen plants to stabilize the hydrogen production from a renewable energy source. This works through locally storing part of the energy generated during peaks to be used at times when there is a deficit of energy. The purpose of this study is to see how different energy storage systems influence the performance of wind-hydrogen plants. A wind-hydrogen plant is a hydrogen plant producing hydrogen with an electrolyzer powered with energy from a wind park. Based on the outcomes of the study, wind-hydrogen plants can be designed incorporating the most adequate energy storage system for its purpose. The outcomes of the study can also be used by policy-makers to see how the temporal correlation requirement of the European Commission influences the feasibility of the hydrogen goals in the Fit for 55 package. The performance of wind-hydrogen plants with different energy storage methods is evaluated using a simulation model. After performing experiments for wind-hydrogen plants with different types of energy storage systems, their influence on the performance of the wind-hydrogen plant can be analyzed.  Results show that the hydrogen production from a variable energy source is lower and less stable than that from a continuous source. These effects can be mitigated with the use of energy storage systems. However, the introduction of these systems makes wind-hydrogen plants less efficient and drive up the costs of hydrogen production. This illustrates a trade-off as energy storage systems do increase and stabilize hydrogen production, but at a lower efficiency and higher cost.  Comparing the performance of individual energy storage systems shows that the use of lithiumion batteries to store energy results in the highest and most stable hydrogen. At the same time, the use of lithium-ion batteries is by far the most expensive option. The use of a hydrogen energy storage system results in the most efficient and cheapest hydrogen production. This shows that another trade-off is at play when selecting an energy storage system to integrate in a wind-hydrogen plant.

Based on the results, the effects of the temporal correlation requirement on the feasibility of the Fit for 55 renewable hydrogen goals can be analyzed. The requirement makes the hydrogen goals in the Fit for 55 package less feasible as it causes a decrease in renewable hydrogen production. It also makes it less fit for use by the industry as the hydrogen supply will become less stable. Energy storage systems can be used to mitigate these negative effects, however, these cause a decrease in hydrogen plant efficiency and therefore an increase in loss of renewable energy. Also, it drives up the production cost, making renewable hydrogen less attractive to use by industry and increasing barriers for new renewable hydrogen production to enter the market. The recommendation is for the European Commission to investigate an alternative requirement than the temporal correlation to ensure the renewable production of hydrogen. ... Read more

Amidst the discourse regarding the decentralization of urban energy systems, energy community has emerged as a solution for optimizing the electricity demand and distributed generation. Community energy projects also facilitate collaboration amongst local prosumers. An energy community is a collective of residential electricity consumers (or prosumers) and non-energy small and mediumsized enterprises (SMEs) formulating a social network involved in decentralized energy production. This study is focused on exploring demand response opportunities in community energy projects located in the Netherlands to reduce their dependence on the grid. Existing studies on community energy projects are primarily focused on residential members, and have little to no inclusion of nonresidential community members. However, recent studies regarding demand response in the energy community highlight the benefits of having a mixed configuration of residential and non-residential members. Introducing non-residential community members such as SMEs, offices, and schools with a complementary demand profile can help the community in attaining self-sufficiency through demand response. Formulating energy communities with a mixed configuration (i.e. including residential and non-residential community members) optimizes local electricity generation and consumption thus avoiding congestion in the distribution network.... ... Read more