This thesis delves deeper into the design and implementation of a high-frequency DC to DC converter, focusing on the driving circuitry of a transformer based power conversion system operating at MHz frequencies. Because of more demand for smaller, lighter and more energy efficient designs, high-frequency transformers have become an interesting and promising alternative to the standard lower frequency topologies. The project investigates two different topologies, namely: a self oscillating circuit and a GaN based full-bridge inverter. Each of these accompanied by a rectifier stage.

The final prototype successfully operates at a frequency of at least 1 MHz, generating a sinusoidal waveform at the output of the transformer. The system also meet the requirements for achieving an efficiency of 80%, and design criteria such as load independence, compact size and a minimum input voltage of 50 V DC. However, the output power has not reached the desired level of 50 V at 2 A.

The project stresses the importance of precise timing and dead time control in high-frequency switching. While the self oscillating circuit showed promise in LTSpice simulations, it was not pursued due to its dependency on the load connected. Future work should be done to explore an improved component integration, and more advanced control strategies to reach the full 100 W output capability.

This research aims to tackle the dual challenges of power electronic uncertainties and the intermittency of renewable energy sources by developing a comprehensive reliability model and conducting a probabilistic evaluation of VSC-MTDC-based hybrid AC/DC power systems. With a specific focus on the North Sea region, the study emphasises enhancing the reliability of these systems, which are increasingly utilised for the efficient transmission of offshore wind power. The objective is to optimise key factors such as redundancy, modularity, and maintenance costs, which are crucial for the reliable integration of these systems into existing power grids.

While Modular Multilevel Converters (MMCs) within these systems offer multiple advantages, the proliferation of power electronic components introduces substantial uncertainties, compounding reliability concerns alongside the inherent variability of renewable energy sources. To address these challenges, the proposed composite probabilistic models account for wind speed variability, turbine drivetrain reliability, and the stochastic behaviour of component failures, providing a detailed reliability model.

The findings highlight substantial opportunities for improving system performance through targeted design and maintenance strategies. By investigating the reliability of offshore wind power, MMCs, DC transmission system and the overall AC/DC system, this research provides valuable insights into optimising system performance and ensuring the efficient integration of renewable energy. The research outcomes include a composite (generation and transmission) model, reliability and cost assessment, an optimal cost-reliability strategy for MMC systems, and a constant risk-minimised cost method of substituting conventional generators with offshore wind power contributing to more resilient and cost-effective renewable energy integration.

The outcomes of this study provide crucial insights into enhancing methods for the reliability of hybrid AC/DC systems. The methodologies and results not only align with global sustainability goals but also bolster energy security by laying a strong foundation for future power grid designs that increasingly depend on sustainable energy sources and advanced power electronics.

\textbf{Keywords:} Adequacy, composite power system, multi-state model, offshore wind power, power electronics, reliability evaluation, VSC-MTDC

Voltage source converters (VSCs), particularly modular multilevel converters (MMCs), play a pivotal role in modern power grids due to their application in interconnected systems and renewable energy integration. However, their complex internal dynamics and control systems can cause stability issues under a variety of operating conditions. Impedance-based stability analysis is critical for understanding these interactions and ensuring grid reliability. Traditional small-signal models based on physics and circuit analysis often neglect nonlinearities and practical implementation details, limiting their accuracy. In contrast, black-box impedance measurement techniques necessitate significant computational resources and human effort, particularly when analysing both AC and DC side impedances.

This thesis presents a transfer learning framework for accurate impedance characterization of MMCs. The framework uses a combination of linear time-invariant (LTI) modeling and black-box impedance measurements to estimate impedances based on system-level parameters such as AC and DC side voltages, active power, and reactive power. The methodology involves pre-training artificial neural network (ANN) models on a diverse dataset derived from LTI models, followed by fine-tuning with real-time electromagnetic transient (EMT) simulations. Unlike traditional approaches, which use a single ANN model across the entire frequency range, this study discusses the use of multiple smaller ANN models tailored to specific frequency ranges and operating points, which improves prediction accuracy.

The effectiveness of the proposed transfer learning framework is demonstrated with a practical MMC converter from the CIGRE B4 DC grid test system. The results show that impedance estimation outperforms traditional ANN methods even in scenarios involving confidential converter parameters. This approach allows for precise impedance characterization while reducing computational complexity and data requirements.

Technological advances, cost reduction, depletion of fossil fuels, environmental concerns, and growing energy demand are expanding photovoltaic solar energy (PV) in more latitudes and locations. A simple and effective procedure to assess the PV potential of a particular region is to analyse its climatic conditions. In general, climatic studies use the K¨oppen-Geiger (KG) climate classification as a reference. However, KG is solely based on temperature and precipitation, resulting in an unsatisfactory scheme for analyses in the PV field, since the most important variable, solar irradiation, is not considered. Thus, in 2019 Ascencio-V´asquez et al. developed a new worldwide classification based on temperature, precipitation, and solar irradiation: the K¨oppen-Geiger-Photovoltaic (KGPV) climate classification. Even though KGPV is a good improvement, it just consists of a simplified version of the KG groups subdivided into four levels of irradiation: low, medium, high, and very high. Hence, the climate parameters are not considered in a combined manner in the sorting process.

In this project, a new worldwide climate classification directly applicable to PV has been developed. Machine Learning proved to be a convenient tool to achieve this objective. First, supervised learning served to identify and assess the climate variables more correlated to the specific energy yield. More specifically, a Linear Regression model was implemented. Subsequently, these variables were used to create the classification by applying k-means, a clustering algorithm. The classification was optimised following a comprehensive qualitative analysis, resulting in a scheme based on seven climate variables and 20 clusters. By contrast, KGPV considers five variables. Even though it contemplates 24 groups at first, half of them are neglected based on a land-surface ratio and population density criterion, resulting in a classification based on 12 clusters. Hence, the methodology proposed in this work enables identifying new relevant regions. Moreover, “Machine Learning driven PV-climate classification” presents a satisfactory correlation with the specific energy yield, except for very low values, where the correlation is minor.

Lastly, the relationship between climate and degradation rate was explored. The complexity and non-linear behaviour of degradation demand an alternative approach. Random Forests was proposed, but it showed poor performance. It is necessary to be able to predict non-linearities and, at the same time, keep a logical mathematical relation between the supervised and clustering algorithms. In this regard, Multivariate Adaptive Regression Spline (MARS) might be a promising option.

The impedance-based approach is promising to analyse the harmonic emission and stability of a converter-based system, e.g., an EV fast charging station. When extracting the accurate impedance model of an EV charger, the knowledge of the charger’s circuit and controller parameters is indispensable. However, EV chargers’ manufacturers do not disclose the design parameters of chargers since they are confidential designs. Therefore, developing an approach to estimate the charger’s design parameters is practically beneficial for establishing the charger’s impedance model and thereby analysing the harmonic and stability of the charger-grid system.

Recent works on physics-informed machine learning provide a promising data-efficient approach to estimating unknown parameters of a system with a known physical model. However, to the author’s knowledge, such a technique has not been explored extensively in the power electronics domain. Thus, exploring whether PINN is also suitable for the parameter estimation of power electronic converters and how to implement this technique is worthwhile. In this thesis, to begin with, a Physics Informed Neural Net model is developed as a proof of concept to estimate the parameters of the boost converter. And subsequently applied to the AFE converter of the Fast-Charging Station to explore the scalability and generalisation of the model developed.

Implementing the physics model within the neural net is still an open problem; hence the physics parameters are estimated similarly to the weights and biases during the training process using the existing optimiser. This approach requires diving deeper into the development of the neural net, and hence instead of eager execution, TensorFlow’s computational graph method is used to build the neural net and the physics model. This allows complete flexibility to build the network from scratch with added complexity.

In the PINN model developed for the boost converter, the mean estimation error was 0.89%, and the parameters were estimated in 25.76 seconds. Because of the complexity of the physics model, optimizing the PINN model was challenging for the AFE converter. With a mean estimation error of 30.25%, the PINN model for the AFE converter took 361 seconds to execute. For both models, less than 150 data points collected over 25 switching cycles peak-to-peak values are used. The proposed PINN model developed in this thesis thus proves to be highly data-efficient with quick convergence. Despite the success of the developed PINN model, several drawbacks of PINN-based parameter estimation are also noticed and summarised.

This report presents an analysis of the emergence of residential batteries from a consumer’s perspective and their effect on the load peak of the distribution grid. First, the future of residential batteries was evaluated in social and technical context by mapping the present situation, its trends, and its possible drivers. Three different battery control strategies were analysed: maximising self-consumption, market price arbitrage, and load peak reduction. The corresponding battery profile was determined with a rule-based approach. Second, load summation was used to simulate battery development scenarios within the distribution network, after which the effect of residential batteries was determined on a selected substation with a load peak analysis. From consumer perspective, residential batteries are driven by the phase-out of the net-metering scheme, increasing wholesale daily price spread, a decline of investment costs, or renewed tariff structures. The results showed that residential batteries become economically attractive when net-metering is abolished and the daily electricity price spread increases. The load profile results of the selected substation show a reduction of both size and number of overload hours for all control methods in case of medium and low emergence of residential batteries. In case the batteries are used to reduce individual load peaks, this reduction is largest and is smallest when batteries are used for market price arbitrage. Battery usage for self-consumption and peak reduction mainly operate during summer months when PV generation is higher. Therefore, demand load peaks in winter are not relieved as much. Although it was found that grid reinforcement is still not alleviated for the selected substation, the maximum and minimum load peak are unified leading to a reduction of required reinforcement capacity. It is found that retail price regulations strongly influence which control method will be dominant. Incentives to increase self-consumption or reduce load peaks will relieve pressure on the grid. However, critical grid congestion moments are not explicitly relieved. Therefore, further research is required to identify methods to utilise the batteries for DSO control during critical peak moments or winter seasons with low self-consumption usage.

Given the merits of photovoltaic technology (PV), rooftop PV systems are becoming more prevalent in residential and commercial areas. As a result of the growing number of photovoltaic systems connected to low voltage (LV) distribution grids, a significant portion of power flow from photovoltaic arrays to the LV grid, As the PV penetration increases, this effect becomes more severe. This reversal of power flow (from the load side to the grid side) results in a voltage rise problem along the distribution feeder. Such an issue is more pronounced near the point of common coupling (PCC). Overvoltages are not desired as they can damage consumers’ electronic devices, burn the insulation and can trip protective equipment, causing sudden power outages. The principal objective of this study is to achieve 100% PV penetration in a low voltage distribution network while maintaining voltages within the grid constraints by employing numerous voltage control techniques. To conduct PV penetration studies, the European low-voltage distribution benchmark network developed by CIGRE is selected. PV systems of various configurations are designed and integrated into the CIGRE LV network. The results of the PV penetration analysis on the CIGRE network suggest that with the increase in the distance between the source and the load, the penetration level decreases drastically. The worst-case network model in which the penetration level is reduced to 65% is selected for employing voltage control strategies. Two main strategies have been used to improve PV penetration. In the first category of methods, the reactive power is controlled by the PV system so that voltages at the PCCs are within the grid voltage limits. Four different reactive power control methods are employed. The second category of methods are based on active power control, in which the voltages are controlled by batteries/electrolyzers by absorbing excess power generated by the PV system, avoiding reverse power flow into the grid. The results obtained reveal that all the techniques employed are successful at increasing PV penetration to 100% without violating grid voltage limits. However, it is concluded that, batteries/electrolyzer systems provide better value in terms of effectiveness in solving the voltage rise issue. They also offers lower losses, and multiple-use cases.

Overhead power lines play a key role in the transmission and distribution of electrical power. Traditionally, the operating limits of these overhead lines are fixed and determined through static line rating. These limits are designed under conservative conditions such as high loads and extreme weather scenarios. However, the global energy demand is expected to increase substantially in a few decades. Furthermore, there will be higher levels of penetration of intermittent renewable generation in the power system due to energy transition in the future. In this thesis, a Dynamic Line Rating (DLR) method is developed to enable the determination of the varying operating limits of the overhead lines. It is realized by calculating the maximum allowable current for the line considering the real-time monitored electrical and weather quantities while meeting the requirements for the design and security criteria of the line. This is a more economical and feasible solution to improve the existing power system compared to constructing additional infrastructure. This thesis aims to develop a real-time dynamic line monitoring system by utilizing Phasor Measurement Unit (PMU) data for a 50kV distribution grid. The Real-Time Digital Simulator(RTDS) is used with an interface to MATLAB to realize the DLR model at computational speeds equivalent to real-time operations. A parametric sensitivity analysis is conducted to understand the influence of each parameter in the DLR model and its working. Case studies are conducted by using the DLR model on the 50kVdistribution grid, which showed an increase of 20% on the line ampacity. Lastly, a comparative analysis of the DLR model and the P341 MiCOM DLR relay results are performed by applying RTDS Hardware-in-the-loop (HIL) testing. The results of the DLR model are found to be in accordance with those obtained by the relay which also validates the model. Finally, recommendations and future work are proposed such as DLR implementation for cables using PMU data.

Microgrids are a promising tool that can help transition the electricity grid towards a smart grid. They can provide significant benefits to the power grid in the form of increased reliability and flexibility. The microgrid controller sets the electricity consumption and generation of all controllable devices within the microgrid and controls how much electricity is exchanged with the main electricity grid. Model Predictive Control (MPC) has been proposed as a method for developing such microgrid controllers. MPC can deal well with the many different constraints that are imposed on the control of the microgrid. Because controlling a microgrid involves switching devices on and off, microgrids are often modelled as a hybrid system. A downside of MPC for hybrid systems is that, a mixed integer optimization problem must be solved at every control step. Solving mixed integer programs is computationally complex, especially for large scale problems. The difficulty of the optimization problem limits the response time of the microgrid controller. Parametric MPC has been suggested as a way to reduce the computational complexity of the controller and to create an efficient single level MPC controller. In parametric MPC the control input is parametrized according to a set of parameters and a control law. Instead of determining the inputs directly by solving an optimization problem, the optimization problem determines the optimal parameters of the control law. This method can reduce the number of optimization variables significantly and increases the scalability of the controller. To ensure the parametric controller performs well a good parametric control law should be chosen. In this research a new way to determine the parametric control law is proposed. The control law is represented as a combination of expressions. These expressions are represented using a set of expression trees. Using expression trees a wide array of different non-linear functions can be represented. During an offline optimization step, optimal control inputs are determined using a regular MPC controller on a set of scenarios. Expression trees that are able to parametrize the control inputs well are then learned from these control inputs using a genetic algorithm. By using learning methods to determine the control law, the design of the parametric controller can be automated. Using this method there is no need for the control system engineer to determine a parametric control law through trial and error testing. This can speed up the design process and could allow parametric MPC to be used for systems for which input parametrizations are difficult to find. The effectiveness of the proposed approach is illustrated through a case study in which a microgrid is simulated with 2 controllable generators, renewable generation, an uncontrollable load and local energy storage. The performance of the parametric MPC controller determined in the offline optimization is compared with a handcrafted parametric MPC controller and a regular MPC controller. The estimated economic cost of operating the simulated microgrid using the different controllers is compared. Furthermore the computational complexity of the different controllers is analysed. The results show that the offline trained parametric controller achieves similar performance in both operating cost and computational complexity as the handcrafted parametric controller. This shows that the proposed offline optimization algorithm can be used to determine an effective control law for a parametric MPC controller. This will make it easier to design parametric MPC controllers for different systems in the future.

Climate change is one of the biggest challenges of this time. One of the necessary changes to combat this challenge is a shift from conventional power resources, such as coal and natural gas, to large scale deployment of renewable resources, such as wind and solar power. However, renewable energy resources are volatile because they depend on the weather, which poses a problem because supply and demand should always be exactly matched. Along with a shift in energy production, also appliances that use non renewable energy are becoming electric, e.g., electric heating and cooling in buildings and electric vehicles. This causes an increase in electricity demand and is a burden on the main grid. To reduce the demand on the grid, it is important that a part of the energy is produced locally by distributed renewable resources, such as residential solar panels. To maintain a stable operation of the grid, the smart grid paradigm has to be adopted. Advancements in information and communication technologies provide the means for an efficient management of the grid using various services, such as distributed generation and storage. Because of the conflicting nature of the agents in an electricity network, game theory is a widely used framework to tackle this problem. In a realistic model of a power grid coupling constraints must be taken into account, because the agents have shared resources. Therefore, the economic dispatch problem is modeled as a noncooperative game with coupling constraints, called a generalized game. Three distributed algorithms are derived to reach a generalized Nash equilibrium. It is shown through simulations that these algorithms have the potential to ensure a safe and efficient operation of the grid, while minimizing the cost of power usage. To reduce the cost even further, a method is presented to make more optimal use of the storage units.