With the increasing requirements of the semiconductor industry, ASML lithographic machines, essential for chip manufacturing, are becoming more complex. Consequently, the mains distribution system, a crucial part of these machines, is increasingly vulnerable to short circuit (SC) faults due to the growing number of system components. SC assessment of this system in compliance with international standards is vital as they are utilized by companies worldwide.

This thesis provides a standardized assessment method for industrial mains distribution system that complies with international standards. The assessment method includes the determination of available SC current and overall short circuit current rating (SCCR) and is implemented as a Matlab tool. Additionally, this thesis explores ways to increase the SCCR of the system by improving protection and limiting current considering current-limiting alternating current circuit breakers and various direct current circuit breaker (DCCB) technologies, including fault current limiters. Finally, the assessment results are validated through the physical testing results of a branch circuit within the ASML mains distribution system.

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

This thesis creates open-source Python scripts for conducting generation adequacy analysis studies. First, scripts without wind considerations are constructed, and then the scripts have been modified to include wind considerations. Generation and load models have been implemented to obtain the reliability (adequacy) indices Loss of Load Expectation (LOLE) and Expected Energy Not Served (EENS). Two probabilistic methods have been implemented: an analytical method and a non-sequential Monte Carlo Simulation (MCS) method. Both methods use the IEEE load curve for their load model, but different generation models are used. The analytical model uses a recursive algorithm, where generation units are added one by one to obtain a Capacity Outage Probability Table (COPT). The non-sequential MCS method uses state sampling, where a uniform random number is generated for each hourly increment and a state is selected based on the state probabilities of a generator. Wind considerations have been added to the scripts by modelling the power output curve of a wind turbine.

The proposed scripts have been tested on two test systems - the Roy Billinton Test System and the IEEE Reliability Test System. The resulting reliability metrics have been compared with benchmark values in the literature and found to be closely matching. Furthermore, methodological clarity on how to obtain the presented scripts is given. Even though all elements of the implemented methods are present in the literature, not all are clearly explained or combined in one place. This work aims to overcome this limitation.

To keep pace with increasing renewable energy penetration and consequent increase in inverter-based resources in the power grid, it is pertinent for present-day research to address the resulting drop in system inertia levels and its impact on frequency stability. With decreasing levels of inherent rotational inertia present in the system, any sudden disturbance causing an energy imbalance in the grid could lead to more drastic excursions of system frequency than those experienced hitherto. To ensure the resilience of the grid in such scenarios, advanced and competent frequency stability assessment and control methods are required. This thesis presents Neural Ordinary Differential Equations (NODE), a recently introduced family of neural networks, as an effective tool to achieve fast, real time estimates of the expected frequency response trajectory during an energy imbalance event.

Since high-impact frequency instability events are sparse in reality, both real-world grid data and synthetically generated data corresponding to different inertial conditions are used to train predictive NODE models. Firstly, NODE is adapted to frequency prediction applications through relevant data processing steps, and modification of network parameters and algorithmic aspects pertaining to the predictive model definition. Secondly, patterns corresponding to specific sections of the frequency response curve are used to selectively train NODE models. Pattern-specific training methods exhibit better prediction performance when the NODE model encounters frequency behaviour similar to the one it initially trained on. Thirdly, a pre-training approach to cut short on the real-time training time required by NODE models to achieve desired levels of prediction performance is presented. Fast estimates of critical frequency stability parameters like nadir could act as potential triggers for early stability control actions to achieve a more controlled frequency response.

Application of predictive NODE models for different frequency scenarios are presented using three test-cases: normal operating scenario, restoration post-system split scenario and synthetically generated high-impact frequency disturbance scenarios. Model tuning and training methods specific to each test-case are described, and prediction results are evaluated with relevant performance metrics. Finally, a comparison is made between the implementation of NODE among different test-cases and real-world implications of the frequency prediction outcomes from the test-cases are further discussed.

There will soon be a huge influx of EV chargers required in the coming years. Therefore it is important to find out the lifetime of EV chargers like the DC Fast Charger for business cases. These chargers are a combination of multiple power converters to help regulate the grid AC voltage to something that can be used to charge the battery. Multiple Industrial surveys have shown that the most vulnerable parts of power converters are the power semiconductors used inside them. Moreover, research shows that most of the failure in these EV chargers is due to thermal cycling which causes thermo-mechanical fatigue. While there are studies that estimate the lifetime of these semiconductors, it is seldom done in the context of EV charging load profiles. These EV charging load profiles are responsible for long-term power cycling contrary to short-term power cycling which is generally done to estimate the lifetime of the semiconductors. This thesis shall thus choose a popular DC Fast Charger power converter, feed in
a load profile and analyse the effects of thermal degradation of the switches and rectifier diodes present in the circuit. This data will then be used to make an estimate of how long the whole power converter will last thus leading to the lifetime of the DC Fast Charger.

The reliability of power semiconductor devices is an important feature when designing converter, since the semiconductors are prone to failure and a weak link in the system. Power semiconductor devices are susceptible to thermo-mechanical stresses, and should be investigated to make reliable semiconductors. The power losses inside the device resulting in thermal cycling and expanding and contracting the layers of the semiconductors with different rates, are the cause of the thermomechanical stresses. Thermo-mechanical fatigues are the most frequently encountered forms of failure in power devices, e.g. bond wire lift-off, solder cracks, and reconstruction of chip materialization. In this thesis, the end-of-life assessment of the silicon IGBT and silicon-carbide MOSFET are investigated. Specifically the power cycling test can replicate the thermo-mechanical stresses and wear out the device in a couple of weeks [8], [9]. Multple short power cycling tests are executed to find the relationship between the selected parameters and resulting thermal cycle. Furthermore, the thermal response measurements are used to study the thermal behaviour of the semiconductor devices and thermal fatigues. In addition, the thermal model for the test system is arrived, making it possible to create a testbed for the power cycling test for different thermal cycles. Lastly, the end-of-life assessment is executed for 23 days on eight silicon IGBTs.

The small and lightweight Lunar Zebro rover must survive in the harsh lunar environment for several Earth days following its moon landing. The mission of the rover is to map the radiation environment on the moon. The success of the entire mission depends on the Power Electronic System (PES), which supplies power to all subsystems and charges the batteries using solar panels. The current PES of the Lunar Zebro rover does not comply with all mission-specific requirements and does not perform satisfactorily when integrated into the rover. Therefore, the need for a reliable PES that conforms to all requirements arises for the Lunar Zebro rover.

In this research, the design and implementation of an efficient, compact, and redundant PES for the Lunar Zebro rover is developed. First, the optimal Direct Current (DC) bus is designed to obtain a system with the highest efficiency. This is done by modelling the efficiency of the DC/DC converters for different bus voltages and estimating the overall losses in these converters during the deployment of the rover. Moreover, the effect of the bus voltage on the size of the passive components is investigated, and the bus voltage resulting in the most compact system is obtained. It is found that a 12 V bus results in the most efficient and compact system. No additional converter is required that regulates the 12 V output, and the inductance required for each converter is decreased compared to higher bus voltages.

Besides the DC bus design, redundancy methods are compared to obtain the best tradeoff between redundancy and footprint added. The two-phase interleaved converter was found to have only an 8.59% increase in footprint compared to single-phase converters, while failure in a switch, diode, input capacitor, and output capacitor are accounted for in each converter. Finally, the mode of operation that results in the highest efficiency is obtained by designing each converter and modelling the corresponding losses for Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) operation. For both the single-phase and two-phase interleaved converters hold that operating in CCM results in a significant increase in efficiency compared to DCM operation. Moreover, the PES utilising two-phase interleaved converters is more efficient during rover operation than the single-phase counterpart. However, charging is less efficient than for the single-phase counterpart. Simulink and LTspice simulations have been carried out to verify the operation of each converter. Finally, experiments on a functional prototype are carried out to provide experimental validation of the design.

The increasing adoption of renewable energy sources, particularly photovoltaic (PV) systems in residential sectors has raised important energy balancing challenges due to the intermittent nature of energy generation. To address these challenges and prioritize cost savings for residential consumers, this research investigates the integration of battery energy storage systems (BESS) and dynamic pricing strategies through an intelligent energy management system (EMS). Given the stochastic nature of PV generation, market prices, and load profile it is still challenging to achieve optimal control. Therefore Reinforcement Learning (RL)-based EMS is proposed in this research to make real-time optimal control decisions. RL is a machine learning approach where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards. In this study, a deep deterministic policy gradient (DDPG) RL architecture is chosen due to its capability to handle continuous action spaces. In addition, deep learning-based models are employed to forecast uncontrollable load, PV generation and market prices for the integration into the EMS for which Bi-directional LSTM (Long Short Term Memory) was found to be the most accurate for all three uncertain variables. The DDPG algorithm is trained with data from a single household from the Lucerne region, Switzerland for 30 days and tested for a week. The results showed that compared to a deterministic rule-based approach the RL-based EMS increased cost savings for the end consumer by 14.2% but reduced the benefits for the grid operator to alleviate grid congestion quantified in terms of load factor, peak power consumption and ramping. Further work could be undertaken in testing the model on more extensive data and finding the best trade-off between customer and grid operator benefits.

With the increasing appearance of DC loads and DC sources in the MVAC distribution grids, the need for DC power systems is getting more and more pronounced. As a result, the MMC has become a promising and innovative technology that can serve as the interface between a DC and AC network. The MMC-based MVDC distribution link is one of the solutions to incorporate DC in power grids. This link consists of two back-to-back configured MMCs connecting two possibly asynchronous AC networks. Generally, an MVDC distribution link is operated at a fixed rated DC voltage set through the MMC. This work explores the possibility of enhancing the DC link voltage beyond the nominal value to improve the power transfer capacity of the distribution link. The enhancement is achieved while preserving the average energy stored in the MMC and maintaining the AC-side harmonic performance.
First, a control strategy was developed that qualified for the static operation of back-to-back configured MMCs. This control strategy was designed using multiple sub-control modules, each regulating one or multiple circuit quantities using a closed-loop PI or PR control strategy. Second, the analytical limitation to the DC link voltage enhancement was determined. This provided the enhancement boundary expression, which revealed the dependence between the enhancement limit and the grid-injected reactive power. Third, the MMC control strategy was adapted to dynamically incorporate the DC link voltage enhancement. This was performed using the open-loop DC link voltage control, which dynamically enhanced the link voltage by using the reactive power input reference. The controller is realised using a finite state machine that provides an event-driven control system with conditional state transitions. Simulations verified the performance of this controller. Finally, the dynamic DC link voltage controller is implemented for a lab-scale MMC and optimally tuned using the MO tuning method. This is to provide experimental verification of the DC link voltage enhancement. Three experiments were performed to: demonstrate the workings of the enhancement concept, verify the analytical enhancement boundary expression, and show the performance of the dynamic DC link voltage controller. The experiments concluded that the open-loop dynamic control successfully achieved the DC link voltage enhancement and limited the grid injection of harmonic elements during system transients.
So it is concluded that the power transfer capacity of an MMC-based MVDC distribution link can be enhanced by applying dynamic DC link voltage control. This while operating with the same submodule stresses and preserving the AC-side harmonic performance. When the DC current of the distribution link is fixed at the rated value, the power transfer capacity of the link increases linearly with the controller enhancement function. For system transients, the dynamic controller uses smart control logic to avoid harmonic element injection. Combined, this concludes that the dynamic DC link voltage control achieves a proper DC link capacity enhancement that meets the requirements for both steady state and dynamic conditions.

The charging of a rapidly increasing number of EVs places significant strain on electrical distribution grids. Controlled (’smart’) charging of EVs offers a solution to this problem by shifting the charging of vehicles to the most optimal moments in time. Smart charging is not a completely new research topic; it has a high Technology Readiness Level and some commercial implementations already exist. There is a large amount of literature available that compares the effect of controlled and uncontrolled EV charging on the distribution grid. However, most of this literature is based on pure software simulations using power flow analysis and mathematical models of EV charging behaviour. Experimentally validating these results would require a large number of EVs and a controllable distribution grid, which is costly and unpractical. Therefore, this thesis presents a compromise; a hardware-in-the-loop (HIL) testbed in which the distribution grid is software-based but the EV and EV Supply Equipment (EVSE) is hardware-based. In this report, the design and implementation of the testbed is discussed, as well as results from HIL simulations that compare the effect of controlled and uncontrolled EV charging in a distribution grid. The low-cost HIL testbed presented in this thesis is based upon an OPAL-RT OP5700 Digital Real-Time Simulator (DRTS) which runs a Newton-Raphson power flow analysis of a Dutch distribution grid. The DRTS also interfaces with all other hardware in the testbed. This includes a power amplifier which generates a three-phase grid at the voltage calculated by the power flow analysis. A commercially available EVSE is connected to this power amplifier and supplies power to an emulated EV which consists of communication hardware and two bidirectional back-to-back DC power supplies. These power supplies act as an AC load. Various communication protocols were implemented to exchange information between the different systems in a manner that closely represents commercial EVs. A smart charging algorithm which determines the optimal charging current setpoints in real-time, based on external factors like local load power consumption, solar irradiance and energy prices, was implemented into the testbed. The testbed has subsequently been used to study the effect of uncontrolled and controlled charging on the simulated distribution grid for a total of eight different scenarios. Of these scenarios, one consists of traditional uncontrolled charging and the other seven are with the smart charging algorithm activated. These include firstly a base case, then two scenarios with an inaccurate solar irradiance and local load forecast, a further two scenarios with non-ideal EV charging behaviour, and lastly two scenarios with respectively centralized and decentralized curtailment implemented. In all scenarios, one EV was implemented as hardware and three others as software, so that a comparison could be made. Even under non-ideal conditions, smart charging is found to reduce costs, grid overloads, and voltage deviations with respect to uncontrolled charging. The implemented centralized curtailment is shown to be more effective than decentralized and further reduces voltage deviations in the grid but at a slightly higher cost than without this curtailment. Lastly, based on observations made in the HIL simulation results, recommendations for future work and improvement of the smart charging algorithm are given.

Recent studies have shown that energy distribution systems based on hybrid (or parallel)point-to-point AC and DC links are interesting solutions for capacity enhancement andon-line improvement of energy efficiency under specific operating conditions, particularlyfor high power, medium to high voltage levels and long-enough distances, e.g.>5km.Additionally, due to the power controlability provided by the power electronic circuitswithin the DC links the parallel distribution system can be re-configured through GISdisconnectors operating with improved switching performance. In this work this featureis explored and strategies for the system re-configurations are proposed. These aim toprovide a nearly zero current switching for the GIS in order to prevent its deteriorationand to extend the number of maneuvers for the typical GIS lifetime.
In fact, the proposed re-configuration strategies rely on the DC bus voltage and powercontrols of a Back-to-Back (B2B) power electronic system constructed with Voltage SourceConverters (VSC). Herein, three-wire three-phase two-level VSCs with thrid-order AC har-monic filters (or LCL filter) are adopted in the B2B system. Moreover, Grid-side CurrentControls (GCC) for the inner/fast control loops of the front- and back-end circuits are im-plemented in order to enhance the system performance against grid disturbances. Herein,a notch filter-based GCC scheme with a harmonic rejection control is proposed. This isable to deliver attenuation to the LCL filter resonances while suppressing the harmonicsin the grid-side currents originated when the AC voltages are distorted. Interestingly,the parallel AC and DC links provides a low impedance path for the zero-sequence com-ponents which can be created by the power electronics, thus the implementation of aZero-sequence Circulating Current (ZSCC) controller becomes necessary for the properoperation of the system. Hence, ZSCC controllers for the VSCs are used, while bothfront- and back-end circuits are modulated with constant switching frequency using theconventional sinusoidal PWM strategy.
All in all, results are obtained in both, computational simulations carried out in MAT-LAB/SIMULINK and laboratory experiments performed in a5kVA VSC or a B2B circuit,are used to verify the study and to prove the superior performance of the investigatedand proposed technical concepts. Herein, more specifically for the re-configuration studyof the parallel AC and DC links, the standalone mode control of the receiving-end VSCin the full DC link configuration is investigated and the smooth transition between grid-connected and standalone modes are realized in both simulation and experimental tests.

Low powered electric vehicles and electricity generation with the help of renewable energy technology are two kind of technological advancements which are taking place to meet growing global energy demand while reducing greenhouse gas emissions together at the same time. Achieving these objectives indicate shift from energy demand followed by fossil fuels to renewables. Renewable energy technology and plug-in vehicle electrification adoption have substantial problems associated with it, however there are ways to exploit their potential in order to develop sustainability in the field of electricity and transportation.

This thesis aims at designing the Roadside Photovoltaic (PV) system that act as a major supply of electrical power to charge Electric Bikes (E-bikes). The expansion and rapid growth in E-bikes is tremendous in the Netherlands, therefore the charging of E-bikes is chosen as an application to build the Roadside PV system for campus located at Mekelweg, Delft. The E-bike dynamic consumption is modelled to calculate the driving efficiency of an E-bike, and thus this model is used to compute the state of charge of incoming E-bikes in order to determine the daily consumption of charging station. Furthermore, for maximizing the energy yield of the PV system, the optimal orientation of module is analysed. The mounting and placement of these optimally oriented modules beside the road is decided such that the overall effect of shading is minimal. Moreover, as it is a Roadside PV, the system is compared with the solar road technology to see the difference of how the system behaves when placed optimally beside the roads on poles instead horizontally on roads. Finally, performance of entire grid-tied Roadside PV system with storage is determined according to chosen application.