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.

The Helix approach is a dynamic control method that mitigates wind turbine wake effects by pitching the turbine blades individually. In current literature the method is mainly researched with a high-fidelity actuator line model (ALM). Because of the computational constraints imposed by this model, the effect of the Helix has not been researched for turbine arrays longer than 3. To investigate the effects of the Helix on a farm size scale, a lower fidelity model capturing the Helix is developed in this research. This model is based on a uniform actuator disk model and is called the H-ADM. The H-ADM is validated in laminar conditions at a fine resolution by comparing wake recovery to the ALM. The H-ADM captures both the additional wake mixing and wake displacement caused by the Helix. Furthermore, the H-ADM correctly predicts differences between the counterclockwise (CCW) and clockwise (CW) Helix. Due to modelling assumptions, the H-ADM can run simulations at a coarser resolution and with a smaller time-step than the ALM. Computational gains are achieved with the H-ADM as a result. This research shows that similar simulations are executed over 640 times faster for the H-ADM compared to ALM. The model is used to gain a deeper understanding of the mechanisms causing differences between the CCW and CW Helix. It turns out these differences fade away if no rotational force is exerted by the wind turbine. Finally, the computational gains of the H-ADM are leveraged by applying the Helix on the first row of a large scale wind farm with 48 turbines. For the considered farm the CCW Helix seems to outperform the CW Helix in terms of total power gains.

This thesis evaluates AI-based weather forecasting models—specifically GraphCast and Pangu-Weather—against traditional numerical weather prediction models like the ECMWF High-Resolution Model (HRES) and the Aspire Meso and LES models, focusing on the Netherlands due to its dense observational network and wind energy infrastructure. The study examines operational considerations, including computational costs and hardware requirements, highlighting that while AI models offer significant computational efficiency during inference, they require substantial resources for training and have limitations in local adaptability and variable inclusion (e.g., Pangu-Weather lacks precipitation data). Performance analysis demonstrates that GraphCast consistently outperforms HRES, Pangu-Weather, and Aspire Meso across various meteorological variables and lead times. Despite the advantages, the models exhibit baseline errors due to shared data sources like ERA5, leading to correlated errors that limit the effectiveness of ensemble forecasting. The research underscores the potential of AI models to enhance forecast accuracy and reduce imbalance costs in wind energy production but also emphasizes challenges related to the black-box nature of AI models, data bottlenecks, and limitations imposed by neural scaling laws, where larger models do not necessarily yield better performance. Recommendations for future work include incorporating more observational data, enhancing model modularity and adaptability, improving temporal resolution, expanding variable ranges, emphasizing validation against observational data, and exploring advanced ensemble techniques. The findings suggest that while AI-based models like GraphCast and Pangu-Weather hold significant promise for advancing weather forecasting, addressing limitations in data quality, model architecture, and operational flexibility is crucial for realizing their full potential in operational settings.

In this thesis, both a 10 MW Alkaline Electrolyser (AE) and a 10 MW Proton-Exchange Membrane (PEM) electrolyser system are evaluated through a comprehensive techno-economic feasibility study, spanning a 25-year operational lifespan. The systems are designed to serve a dual function, producing green hydrogen at 350 bar for utilisation at a hydrogen refuelling station for heavy-duty trucks, as well as recovering waste heat via a tie-in on the cooling system of the electrolyser to directly supply a medium-temperature district heating network at 70 °C. The latter is achieved by connecting the tie-in to a heat exchanger, resulting in a cost-effective heat recovery without the implementation of an expensive heat pump. Both electrolysis systems are operated at 80 °C and powered by offshore wind power, delivered to the electrolyser system through a virtual PPA.

ERA5 data on the wind speed was employed, which was converted into power data via the wind farm power curve. The wind farm power curve was produced by coupling wind farm power production data to the ERA5 wind speed. This method proved to be effective in simulating the power production of a wind farm, as it included the wind farm wake effects and the global-blockage effect.

The performance of the AE system was simulated through a semi-empirical model for both the polarization and Faraday efficiency curve, while the performance of the PEM electrolyser system was simulated by an empirical approach for the polarization curve and a semi-empirical model for the Faraday efficiency curve. A degradation efficiency method is proposed, which employs a constant degradation factor to describe the decreasing performance over the lifetime of the stack. The degradation efficiency effectively illustrated the heat-producing degradation in electrolyser cells.
The techno-economic aspect of the research involved a detailed analysis of the Levelised Costs of Hydrogen and Heat (LCoH2 and LCoHeat). The LCoH2 of green hydrogen from the AE system was 6.08 euro/kg, while the LCoHeat of the recovered waste heat was 1.57 euro/MWh. For the PEM electrolyser system, the LCoH2 was determined to be 5.59 euro/kg, while the associated LCoHeat for the recovered waste heat was 1.55 euro/MWh. The profits of selling the recovered waste can be utilised to decrease the LCoH2. When a recovered waste heat-selling price of 50 euro/MWh was assumed, the LCoH2 of the AE and PEM electrolyser system decreased by 0.64 euro/kg and 0.44 euro/kg, respectively.

The sensitivity analysis on the LCoH2 indicated that the PPA price was the most influential factor on the LCoH2, followed by the Capital Expenditures (CAPEX) of the electrolyser system, and the start-of-life stack efficiency. When assessing the LCoHeat, the sensitivity analysis revealed that the most impacting parameters on the LCoHeat were the capacity of the installed electrolysis plant, the discount rate and the CAPEX of the heat exchanger.

We aim to make solar power forecasts using regression for grid optimization. We forecast from 1 to 6 hours ahead. The forecasts prove to be accurate with a skill score of 20% compared to persistence for a lead time of one hour.

Sequential decision-making under uncertainty is an important branch of artificial intelligence research with a plethora of real-life applications. In this thesis, we generalize two fundamental properties of the decision-making process. First, we show that the theory on planning methods for finite spaces can be extended to infinite but countable spaces. Second, we propose a unified model of reinforcement learning algorithms that employ the principle of optimism in the face of uncertainty. This model is used to explain why these methods are efficient. We use the developed theory to design novel algorithms. Depending on the user's needs, these algorithms can either automate the decision-making process completely, or provide advice in decision-support systems.

We start with presenting the basic concepts from the theory of decision-making and discuss the two approaches to it: planning and reinforcement learning. We look at a few typical sequential decision-making problems of increasing difficulty. In particular, we present a game that involves grid navigation and the problems of warehouse management and wind farm operation. Next, we survey the state-of-the-art methods for solving such problems.

Based on this analysis, we identify the following research opportunities. In planning, models with non-stationary and countably-infinite data remain relatively untreated because they are equivalent to infinitely-dimensional optimization problems, which are notoriously difficult to solve even approximately. In reinforcement learning, optimistic approaches lead to computational efficiency, yet the theory of optimism remains undeveloped. Moreover, while reinforcement learning shines at playing games, such as chess, shōgi, Go, and StarCraft II, its practical applications remain few.

Next, we overview a mathematical framework of sequential decision-making under uncertainty known as the Markov decision process. We explain how the goal of the decision-maker can be expressed as an optimization problem and present two approaches to achieving this goal. The first—more common—approach assigns so-called values to different actions. The other approach uses so-called occupancies that tell how often the agent should choose the actions instead of evaluating how good these actions are. In fact, the two approaches are known to be dual to each other. While this duality is well studied in the finite case, the infinite case is less explored. To address this knowledge gap, we present a new dual formulation for countable problems, both finite and infinite.

Afterwards, we use the dual formulation to design a new planning algorithm for infinite-horizon problems with non-stationary data. These problems are essentially infinite-dimensional optimization problems and as such are impossible to solve exactly using the standard approaches. We show that they can be solved by changing what is defined as optimal behavior: instead of seeking universally optimal policies, we consider initial-decision-optimal ones. Instead of planning all of the actions beforehand, these policies can be used to plan given the currently observed data. When the next decision is required, the process can be repeated in the same manner, leading to an optimal decision-making strategy. Our approach uses the occupancy-value duality to rule out suboptimal actions based on so-called truncations: finite-time approximations of the infinite-horizon decision-making problem.

We extend the truncation approach to a more general setting of decision-making problems with countably-infinite state spaces. Instead of time-based truncations, we consider state-based ones. This allows us to limit the amount of data required to make the decisions and to design an algorithm for a class of problems that are otherwise unsolvable to optimality. This approach belongs to a family of methods called policy iteration: starting from an initial policy, it constructs a series of improvements in the decisions while ruling out choices that are provably suboptimal.

After that, we turn to reinforcement learning. For a long time, the only provably efficient reinforcement-learning methods were model-based ones; recently, a family of model-free optimistic methods emerged, each of them accompanied by an analysis of how sample-efficient the method is. We, too, study optimistic reinforcement learning, but in contrast to the existing research, we seek to understand not how efficient it is, but why it is efficient. Our analysis results in a formula that explains the three factors that cause regret—the efficiency loss—in optimistic reinforcement learning: the problem size, the measure of exploration, and the estimation error caused by the mismatch between the realized transitions and their true distribution. It can be applied to all of the existing algorithms as well as new ones. We design one such new algorithm and show how our theoretical framework can facilitate the proof of its efficiency.

Finally, we consider a high-impact real-world sequential decision-making problem known as active wake control. Wind turbines can negatively impact each other with their wakes. These wake-induced losses can be reduced by changing the turbine orientations. Unfortunately, the optimal control strategy is non-trivial. To address this, existing approaches use simplified wake models in combination with numerical optimization methods; instead we propose to use model-free reinforcement learning. As a first step towards this goal, we present a wind farm simulator that is suitable for reinforcement learning and better reflects the realities of wind farm operation than other existing tools. Using this simulator, we show that previous research used a suboptimal action representation in this problem; we identify two alternatives, both of which improve the learning efficiency. Additionally, we demonstrate that reinforcement learning is robust to errors in the observations, providing further evidence that it is a fitting approach to active wake control.

Our contributions advance the state of the art in the theory of sequential decision-making under uncertainty and its applications. These advances hint at unexplored connections between countably-infinite planning and optimistic learning, which may lead to even more efficient algorithms for sequential decision-making under uncertainty in the future.

With increasing renewable energy and cross-sectoral electrification price volatility is increasing. Flexibility through demand-side management and electric storage has the potential for reducing price volatility. In this thesis, using duality theory the flexibility required for constraining price to a maximum limit is quantified.
Coordination Strategies for Reducing Price Volatility in Local Electricity Markets investigate three case studies varying with respect to type and degree of flexible resource aggregation for constraining price. Insights generated are relevant for regulators, aggregators, energy communities, and scholars focusing on the engineering and economics of local energy systems.

The energy transition requires significant investments in new power generation, storage, and transmission technology to achieve emission reduction targets while ensuring a stable supply of electricity. However, the increasingly complex, uncertain nature of the power system creates challenges for investors and requires advanced research and modeling. An exploratory analysis is preformed to determine if and how energy system optimization modeling can be used to explore the long-term economic feasibility of investments in the power sector in light of the energy transition. A modeling framework is developed utilizing energy system optimization modeling paired with the uncertainty analysis, modeling to generate alternatives, to explore and provide insights into the profitability of investments in the range of possible near-optimal power system configurations that meet government emission reduction targets. We apply the modeling framework in a case study of the Dutch power system, focusing on investments in the 2030 time frame. The case study is used as a proof of concept of the developed methodological. The case study finds that the developed modeling framework can provide insight into the range of near-optimal power system configurations that meet emission reduction targets and, through an economic analysis, can shed light on the economic feasibility of different technologies. In the particular case of the Netherlands, the case study finds a wide range of possible future near-optimal energy systems exist to meet emission reduction targets, but a lack of profitability of investments indicate policy mechanisms or alternative market arrangements are needed to ensure economic incentives exist for the necessary investments. Further research should include a parametric uncertainty analysis to provide a complete and comprehensive overview, including all types of uncertainty in the system and to identify the affects different types of uncertainty have on the profitability of investments.

The goal of this thesis is to identify under which boundary conditions
there is a business opportunity for an innovative electric thermal energy storage, with regards to the future electricity and heat markets. To meet this target, a novel optimization model has been developed as the main tool for the analysis of the storage and its profitability under different situations.

Wind Power Producers (WPP) incur a negative result through mandatory participation on the balancing market. This study analyzes the performance of multiple improved offering strategies for a WPP offering both on the day-ahead market and the balancing market. It does so on revenue, as well as its impact on the grid and electricity market as a whole. It tests two different stochastic optimization models, one under the price-taker assumption, one under the price-maker assumption and one analytic model, which is a closed-form reformulation of the latter. It also introduces a framework for modeling the stochastic processes, which form the input to the stochastic optimization models. This framework contains an innovation in the form of an extended Gaussian Copula model to directly generate the entire scenario tree, which is tested against a conventionally reduced symmetric tree. It also introduces a framework for evaluating each step of the stochastic process modeling, with the aim of enabling structured improvement. The study shows that when taking the influence a WPP has on the imbalance price into account in the analysis of revenues, proper dependency modeling between the processes as well as endogenizing the impact a strategy has on the imbalance price in the stochastic optimization model are strict requirements for positive real-world performance. Furthermore, it shows that successful strategic offering may prove valuable for the grid and market as a whole.

Offshore wind energy is considered as a powerful form of renewable energy generation. It plays an important role in accelerating the world’s transition towards sustainable energy sources and reducing carbon emissions by fossil fuels. This study focuses on the advancement of service planning, related to this renewable energy source, by researching high resolution metocean modelling. The research aims to assess and advance the modelling performance of typical metocean parameters by using atmospheric large-eddy simulations coupled to a spectral wave model. The GPU-Resident Atmospheric Simulation Platform (GRASP) coupled to Simulating WAves Nearshore (SWAN) was used to simulate the atmospheric- and oceanic conditions in the Gemini wind farm, located in Dutch waters. Large-scale boundary- and initial conditions were provided by the fifth generation of ECMWF’s ReAnalysis (ERA5). Relevant metocean parameters were modelled using two different coupling configurations. The one-way coupled simulation concerns the forcing of SWAN by GRASP friction velocities, for an accurate representation of the one-way momentum exchange to the ocean surface. The two-way coupled simulation concerns the momentum exchange of the friction velocity and roughness length. To accurately represent the sea surface roughness, the parameterization of Taylor and Yelland (2001) was used in this study. Both coupled configurations were used to simulate the first two months of 2017, which were subsequently validated using the available observations.
This study revealed that both coupled simulations caused a reduced value for the roughness length in wind- and wave wake conditions. Furthermore, a spatially averaged reduction in the sea state is observed due to the wake effect, where the magnitude of this wave deficit follows the line of a typical wind turbine thrust curve. The effect is however small compared to a realistic significant wave height. Besides, the two-way coupled simulations provided higher average roughness lengths in comparison to the one-way coupled simulations. This resulted in higher friction velocities and drag coefficients for the two-way coupled simulations, which subsequently reduced the time- and slab averaged wind profiles.
Moreover, the modelling performance of SWAN improves when it is forced by GRASP friction velocities instead of ERA5 wind fields. In addition, the established two-way coupled simulation is proven to be an enhancement for the spectral wave model in comparison to the one-way coupled configuration. The performance of the atmospheric large-eddy simulation could also benefit from the two-way coupled configuration. However, it is sensitive to the implemented roughness length parameterization.

To limit global warming to 2 degrees Celsius in 2100, the European power sector needs to transform. Renewable energy capacity is required to reach this ambition, together with transmission and storage capacity to maintain grid stability. Carbon capture and storage could potentially decrease the required renewable energy capacity, while simultaneously increasing grid flexibility. This research focuses on finding a cost-optimal composition of technologies, while abiding by the goals set in the Paris climate treaty. To investigate this objective, two optimisation models are compared: a linear programmed optimisation model with a high temporal and low technical resolution, and a unit commitment model with a low temporal but high technical resolution. Fixed costs and installed capacities are determined by the linear model. Subsequently, both models calculate the optimal generation mix. Three scenarios for 2050 were evaluated: a 'business as usual' reference scenario with maximum emission comparable to 2014 levels, a scenario with a maximum emission consistent with a 96% reduction in 2050 compared to 2014 and a scenario with that same target and the possibility to implement carbon capture and storage. The results show that the goals set by the Paris agreement increase system costs of the European power sector, but the increase is moderated by deployment of carbon capture and storage. Compared to the linear model, unit commitment increases variable costs, but this increase is modest, serving as confirmation that a linear modelling approach can be sufficient for answering a wide range of questions. To increase accuracy, one might integrate both models and provide a complete answer that integrates long time-spans and a wide variety of technical constraints.