The increasing penetration of renewable energy sources in modern power systems is essential to achieve sustainable energy transitions; however, their inherent intermittency continues to pose challenges to system reliability and operational stability. Energy storage systems are therefore indispensable, and lithium
ion batteries are emerging as a key technology because of their high efficiency, favorable energy density, and widespread deployment. As lithium-ion batteries are increasingly utilized in electric vehicles and grid integrated applications, accurate modeling of battery degradation has become critical for assessing long term performance and economic feasibility. Vehicle-to-Grid (V2G) operation, which enables bidirectional power exchange between electric vehicles and the grid, offers significant system-level benefits but introduces additional battery cycling and operating stress.

This study presents a two-year simulation framework to investigate lithium-ion battery degradation under driving and combined driving–V2G operations. A comparative analysis is conducted across multiple battery chemistries, including nickel–manganese–cobalt (NMC/Graphite), lithium iron phosphate (LFP/Graphite),lithium titanate oxide (NMC/LTO) and a hybrid configuration combining (NMC/Graphite) and (NMC/LTO) batteries. An empirical degradation model for the (NMC/LTO) battery is developed and validated using experimental data available in the literature. Degradation models for (NMC/Graphite) and (LFP/Graphite)batteries are adopted from existing studies. In addition, a hybrid degradation model combining NMC and LTO chemistries is introduced to investigate the potential advantages of hybrid battery architectures. Battery power profiles derived from the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) driving cycle are used to represent both conventional driving and V2G-enabled operation. Three simulation scenarios are considered: a baseline driving scenario, a V2G operation scenario, and a sensitivity analysis scenario evaluating the influence of increasing the share of (NMC/LTO) in the hybrid configuration.

The results validate the empirical degradation model developed for the (NMC/LTO), and (NMC/Graphite, NMC/LTO),demonstratinggoodagreementwiththeobservedlong-termagingbehavior.A comparative analysis with conventional (NMC/ Graphite) and (LFP/Graphite) chemistries revealed that LTO-based and hybrid configurations exhibit enhanced degradation resistance under normal operating conditions and V2G operation.

The electrification of heavy-duty transport requires the large-scale integration of Megawatt Charging Stations at highway rest areas and fuel stations. This infrastructure imposes significant challenges to medium-voltage (MV) distribution networks due to their high power demand, clustered charging events and limited predictability. These characteristics could lead to voltage deviations, increased line loading, and additional stress on grid assets. In this study, the grid impact of Heavy-Duty Electric Vehicle (HDEV) charging stations of varying sizes is investigated and the effectiveness of BESS-based mitigation strategies under the constant and different network configurations is evaluated. Using time-series power flow simulations in MATLAB/Simulink, the impact of unmitigated HDEV charging is analyses in a radial MV distribution network topology. The results show that the dominant limiting factors of HDEV integration are voltage deviations and increased line loading. Upstream transformer loading does not form the bottleneck in the investigated network. To address these impacts, a number of local Energy Management System strategies are evaluated, including heuristic and optimisation-based control.
The results show that with BESS-based mitigation, serving both the charging station operator and grid operator interests, effectively restores network voltage stability and reduces peak power demand at the charging station. Differences in control strategies have shown distinct charging and discharging patterns of the BESS, which affect line loading and transformer utilisation. Heuristic control provides robust peak shaving by reducing active grid power demand, whereas optimisation-based control showed an improved economic performance and smoother voltage regulation, especially when reactive power is used for voltage support. Simulation with multiple charging stations and decentralised BESS control for voltage regulation resulted in unstable network behaviour, highlighting the necessity of a centrally coordinated voltage regulation mechanism.
Overall, this study provides new insights into the trade-offs in BESS-based mitigation strategies in terms of voltage stability, congestion management and BESS operation for the integration of HDEV charging stations. The findings underscore the importance of coordinated control of decentralised assets to ensure reliable grid integration of these multi-megawatt and highly variable loads.

European and global goals have been set concerning the reduction of carbon emissions over the next decades. In this regard, the residential and mobility sectors constitute two of the main contributors to carbon emissions. For that reason, renewable energy sources such as photovoltaics (PVs), and electric mobility and heating with electric vehicles (EVs) and heat pumps (HPs) will play a major role in the energy transition. However, on the one hand, these low-carbon technologies (LCTs) do not come without various side effects for the future distribution grids. On the other hand, their co-integration with energy storage systems (ESS) into power control systems (PCSs) can not only reduce these side effects but also offer various ancillary services.

This thesis develops a mathematical optimization model to optimize charging schedules and energy management for electrified aircraft at Rotterdam The Hague Airport (RTHA), addressing research gaps in adapting airport infrastructure for electric aviation. Based on a real-life flight schedule from 2019, the model determines a Battery Energy Storage System (BESS) size while minimizing operational costs, such as grid electricity, photovoltaic (PV) use, BESS degradation, and flight delay/cancellation penalties, while trying to maintain the schedule as closely as possible. Five electric aircraft types, ranging from a 2-seater flight school aircraft, to a 90-seater commercial aviation model, were considered with a Constant Power - Constant Voltage (CPCV) charging profile, alongside a detailed mission energy analysis. Seasonal simulation for January, April, July, and October 2019 showed delays averaging from 2 minutes in July while peaking at 45 in January, alongside three flight cancellations due to high energy demands. Optimized BESS sizes range from 7 MWh to 12 MWh. Optimization of the model showed a reduction of up to €500,000 weekly when compared to a baseline case. Sensitivity analysis showed that increasing the grid import limits from 3.5 MW to 5 MW gave better grid reliability, with less delays and cancellations, while a decrease to 2 MW showed increases in delay times and cancellations. When the export limit was reduced from 7.5 MW to 5 MW, the delays increased due to constrained energy offloading, while increasing it to 10 MW decreased the delays and cancellations by one. A 1 MWh BESS increase reduced cancellations by one and total delay time by up to 5 hours. Adjusting the turnaround times by ± 15 minutes demonstrated the model’s resilience to stricter turnaround times, but a 4-hour delay increase was present with extensions. The findings of the thesis show the critical role that BESS capacity and grid limits play in ensuring operational efficiency for the electric aviation infrastructures of the future, but also the cost and delay reduction by optimizing charging schedules.

The electrification of the heavy-duty freight sector requires a robust charging infrastructure that balances operational needs with grid and cost constraints. This thesis develops an integrated modeling framework to simulate charging demand and optimize the placement and configuration of high power charging stations for HDEVs. The first phase involves modeling charging demand by simulating energy depletion across real world truck trips. Using detailed vehicle specifications and regulatory driving limits, State of Charge (SoC) calculations identify when and where trucks are likely to require charging. These simulated charging events reflect realistic operational behavior across Dutch and cross border freight routes. The second phase applies a Mixed-Integer Linear Programming (MILP) model to determine optimal station locations and configurations. The optimization selects candi- date stations, either generated trough regulatory rules (AFIR) or traffic clustering, and minimizes system wide costs while accounting for wait times, charger availability, and grid connection limits. Events are assigned to specific stations and time slots to ensure feasible, cost effective infrastructure deployment. Two station placements strategies are evaluated for the year 2025 and 2030: (1) AFIR based regulatory , and (2) demand driven locations based on clustering from simulated charging events. The simulated electrification rates are 0.75% for 2025 and 7.5% for 2030. The optimization model provides the number of chargers, where to place them, and how charging events are assigned. From these results, demand curves at the station are created. The findings show that demand based placement can reduce wait times and overall installation cost, while better handling growing charging needs compared to AFIR compliant layouts. Note that the AFIR layout station locations are fixed to the regulatory minimum requirements, and only the number of chargers is adjusted to meet future demand. This thesis presents a practical method for planning charging networks using real truck data. It shows how simulated charging demand can help design better infrastructure, taking into account operational limits and policy goals to support the shift to electric freight transport in Europe.

Decarbonisation and efforts to reduce living expenses are driving interest in multicarrier energy systems (MCES) that integrate electricity, heat and e-mobility. These integrated networks require sophisticated energy management strategies to address uncertainties in energy supply, demand, and weather conditions.

This thesis seeks to develop a reinforcement learning (RL)-based energy management system (EMS) for a multicarrier residential building at TU Delft's Green Village. The case study household integrates photovoltaic and solar thermal systems, an electric vehicle (EV), lithium-ion battery, heat pump, and thermal storage. Current management uses white-box model predictive control (MPC) which, while effective, demands significant computational resources. In addition, its implementation requires expertise in optimal control and physics-based modelling.

The developed RL-based EMS provides a computationally efficient alternative, leveraging data-driven methods to learn system dynamics. The RL approach was benchmarked against the existing Expert, a day-ahead MPC planner. Performance was evaluated in terms of operational safety, grid energy exchange costs, and satisfaction of EV state of charge (SoC) demands.

The RL agent achieved performance comparable to that of the Expert in managing the MCES, while improving accessibility for developers lacking control theory expertise. The RL agent displayed consistent and near-optimal performance, resulting in only a 4% increase in grid exchange costs while improving both EV charging compliance and safety constraint adherence.

A literature review of RL applications for residential EMS is presented alongside an investigation of advanced policy update algorithms, deep neural network architectures, temporal feature engineering, and reward shaping strategies to analyse their impact on EMS performance.

The implemented RL-based control solution has been shown to manage electrical and thermal subsystems while maintaining safety and minimising costs. The computational efficiency and reduced modelling requirements of an RL agent highlight its suitability for small-scale MCES applications, such as residential or office buildings, where MPC may not be practical.

The goal of this thesis is to analyze the limitations encountered in real-time smart charging during DPMand to integrate them into a smart charging algorithm that schedules EVs. A base case scenario willbe set up to reflect the present dynamics of DPM in a three-phase grid. This scenario will serve as a benchmark for evaluating the smart charging algorithm. The smart charging algorithm itself will employ mixed-integer linear programming (MILP) within a Receding Horizon Optimization (RHO) framework aimed at minimizing charging costs while maintaining a high State of Charge (SOC) for the EVs. To integrate the practical limitations, the grid constraints of a three-phase system will be analyzed and incorporated into the optimization.
Furthermore, a laboratory setup will be constructed to validate the charging behaviors that need to be incorporated into smart charging algorithms and to explore new EV charging behaviors in an experiment. The charging phenomena identified as significant will be integrated into the smart charging algorithm, with a lookup table, to assess their influence on the overall system performance.
The results of this investigation provide valuable insights regarding the charging behaviors and the impact of incorporating these behaviors into a smart charging algorithm. A degradation of the charging efficiency was noted at low charging currents, with the significance of the degradation varying between EV types. Additionally, a voltage discrepancy of the pulse width modulation (PWM) signal of the EVSE was observed, causing an offset between the setpoint of the charging current and the real charging current transmitted by the EVSE.
When these results were incorporated into a smart charging algorithm with a lookup table, clear improvements were seen in the total charged capacity compared to a smart charging algorithm that did not include the lookup table. At a grid capacity of 125 A, the smart charging algorithm with the lookup table provided an increase in charging capacity of 5.23% and an increase of 1.40% for a grid capacity of 175 A. Furthermore, it was observed that the smart charging algorithm with the lookup table could increase the total charged capacity for the low grid capacity of 125 A by 3.97% compared to the charging algorithm that performed immediate charging and DPM.
Both smart charging algorithms, with and without lookup table, show the same reduction in cost compared to the base case algorithm that performs DPM and immediate charging. A decrease in the average charging cost to 4.88% at a grid capacity of 125 A was noted. When the grid capacity is increased, the charging cost could be further decreased to 12.23%, as the increased capacity allows for greater flexibility in scheduling EVs during periods of lowest prices, optimizing the utilization of available power.

This thesis investigates the optimal sizing and siting of energy storage systems (ESS) within a distribution grid, focusing on the provision of ancillary services and the intelligent power control of flexible loads, including electric vehicles (EVs), heat pumps (HPs), and renewable energy sources (RES). The study aims to investigate the use of ESS in combination with EVs to participate in the automatic frequency restoration reserves (aFRR) market. The effect of different seasons and the sensitivity of electricity market prices to the optimal sizing are analysed.

The first level involves day-ahead scheduling of flexible loads (EVs, HPs) and ESS allocation. This stage utilises mixed-integer programming (MIP) to achieve optimal ESS sizing and siting based on day-ahead electricity prices, predicted data on EV arrivals and departures, and building occupancy. Additionally, the aFRR prices are known a day ahead, and the most lucrative time intervals for revenue maximisation are selected. The objective function minimises the total cost, which consists of penalties related to customer satisfaction, grid import/export cost, revenues from aFRR provision, and BESS CAPEX and O\&M cost. Two different MIP formulations were used to solve the optimisation in Gurobi using Python programming language.

The second level involves real-time control, where ESS operation is re-optimized using real-time data. This stage adjusts for forecast errors through rolling horizon optimisation. Within this level, the scheduled aFRR reserves are deployed.

The findings reveal that the optimal integration of ESS, considering additional revenue from aFRR provision, is achieved by using a centralised ESS closer to the substation. In contrast, optimal ESS integration for energy arbitrage alone involves sharing capacity between nodes but requires a lower CAPEX cost of BESS to be economical. The results indicate that optimal BESS allocation can differ based on specific conditions, such as imbalance price distribution, grid limits, peak load and EV flexibility. To maximise revenue and efficiency, the study suggests placing the BESS at nodes with the lowest resistance. Winter and summer results are similar in the optimal placement and sizing of BESS, although the operation of BESS and EVs is different. It was observed that during the winter, V2G utilisation was higher. The main difference for the seasons is in the case of only energy arbitrage. The cost savings from ancillary service provision of either BESS, EVs or both show greater potential for summer. Both of the proposed optimisations can find a solution, although the non-convex MIQCP formulation did not guarantee a global optimum for all cases. However, the iterative method proved to be more robust but had a longer simulation time.

This master thesis focuses on developing a Physics-Informed Neural Network (PINN) to predict the degradation of lithium-ion batteries, aiming for a balance between computational efficiency, data requirements, and prediction accuracy. A thorough comparison between physical models and data-driven models was conducted, assessing their theoretical foundations, computational demands, and performance in predicting battery behavior. This evaluation led to the selection of the most suitable model for integration into the PINN framework, demonstrating the significant potential of PINNs as performance models for lithium-ion batteries.
The implementation revealed that while integrating physical laws into the neural network reduces data requirements and enhances interpretability, achieving high accuracy in degradation predictions remains challenging. The study highlights the strengths and limitations of PINNs for battery degradation prediction, showing they can effectively bridge the gap between purely data-driven approaches and traditional physical models. However, further advancements are necessary to refine these models and enhance their predictive capabilities. This thesis emphasizes the need for ongoing research and development to fully utilize PINNs in battery lifecycle management, pointing to their potential for more efficient and accurate degradation predictions.

This study explores the integration of diverse flexible assets, including electric vehicles (EVs), stationary battery energy storage (BESS), heat-pumps and photovoltaics, into a localised smart grid to enhance aFRR ancillary service provision and lower overall grid costs. The performance of the grid was evaluated by simulating and analysing various loads, including residential and commercial building heating, EV charging profiles, as well as photovoltaic generation and battery energy storage. This analysis covered six cases across different seasons and years, with each case assessed over a oneday period. Within the grid, three different nodes are analysed; a Residential, Commercial and Mixed node, all with different power consumption and behaviour profiles differentiating between the seasons. The findings demonstrate the potential of EVs to deliver a significant amount of aFRR ancillary power, while still satisfying minimum state of charge requirements set by vehicle owners. Utilising both BESS and EVs in aFRR results in substantial grid cost savings and, in some years, potential profits. However, participation in
aFRR services results in a higher average cost for imported energy (without taking into account aFRR revenue). This rise is attributed to maintaining sufficient capacity for aFRR provision, where the revenue per unit of energy is generally higher. The combination of vehicle-to-grid and BESS-to-grid without aFRR provision showed the lowest cost of imported energy, since here only day-ahead prices are used to optimise load scheduling. In addition, operating a localised grid with regards to imbalance prices can result in 80-160% portfolio savings for balancing responsible parties (BRPs), due to the concept of passive balancing, where the combination of EV and BESS showed to be most promising. The research contributes to the field by providing an insight and potential framework to leverage flexible assets in both commercial and residential smart grid environments, showcasing a significant step toward sustainable energy management, taking into account the perspective of multiple market parties.

This thesis aims to construct a systematic framework for integrating Electric Vehicles (EVs) into Low Voltage (LV) distribution grids. The ultimate goal is to develop a multi-functional, flexible and reliable smart charging (SC) algorithm enabling EV mass deployment in LV distribution grids. The framework for achieving the main research objective is segmented into several key parts:

- Conducting a thorough study on the EV mass deployment in distribution grids through grid load flow analysis.
- Performing a comparative investigation of representative heuristic EV charging tactics to establish a foundation for a smart charging algorithm.
- Developing a Power Transfer Distribution Factors (PTDF) based grid congestion prevention mechanism from the Distribution System Operator (DSO) perspective in anticipation of widespread EV connections.
- Designing, refining and validating a flexible, efficient and reliable hierarchical mixed integer programming (MIP) EV smart charging algorithm.
a. The developed algorithm is equipped with a passive stochasticity processing function and considers practical constraints in protocols such as IEC/ISO 15118 and IEC 61851-1. It is verified and assessed in a Power Hardware-In-the-Loop (PHIL) testbed.
b. Based on the experimental results, the algorithm's effectiveness is further enhanced in: charging current command levelling for a steadier charging process, upgrading grid balancing services, and acquiring a higher level of proximity to optimality. The stochasticity managing function is also upgraded for ad hoc admittance of (future) erratic charging events and self-correction of charging parameters.
c. The advanced EV smart charging algorithm is then assessed by comparing with uncontrolled and one heuristic charging method presented in part 2 above.

The emergence of electric mobility in urban public transportation, with a particular focus on electric buses, presents a promising solution to address emissions and environmental concerns. However, a significant challenge lies in ensuring continuous bus operation without the need for frequent charging. In-motion charging (IMC), often referred to as dynamic charging, is a concept engineered to overcome this challenge.

A comprehensive study was undertaken to know the potential and practicality of intercity IMC buses. A bus model was developed to assess the power traction capabilities of these vehicles. This model served as the foundation for exploring four distinct charging scenarios, each characterized by varying charging powers and strategies. An investigation into the battery load profiles observed in intercity IMC buses across the different charging scenarios was done. This was essential for understanding the intricacies of power demand, especially in scenarios where the bus relies heavily on catenary charging. Notably, the introduction of in-motion charging in the first scenario (IMC only) underscored the critical role of catenary charging power in meeting operational demands. Subsequent integration of a stationary charging system at Arnhem Central showcased the potential to reduce catenary charging power, offering prospects for enhancing battery health. IMC with opportunity charging and overnight charging were also explored.

The second part of the thesis delved into the comparative effect of various charging scenarios on the aging of commonly employed battery chemistries in IMC vehicles. Using comprehensive battery models, the aging dynamics under diverse conditions were studied. It became evident that each scenario held distinct implications for battery aging.

Lastly, the study addressed the pivotal question of cost-effective battery selection for intercity IMC buses. An exploration of four distinct scenarios, in conjunction with different battery chemistries, yielded valuable insights into their respective performances. Lithium-titanate (LTO) batteries consistently emerged as the preferred choice. Their extended lifespan, reduced replacement frequency, and overall cost-effectiveness positioned them as the frontrunners in the context of intercity IMC bus systems. This consensus held across most scenarios, underscoring the practicality of LTO batteries.

In conclusion, the transportation sector's substantial impact on greenhouse gas emissions and urban air quality necessitates innovative and sustainable solutions. Intercity IMC buses, along with optimal battery selection and charging strategies, represent a promising avenue for sustainable urban transportation. Among battery chemistries, LTO batteries was proven to be the most cost-effective choice for powering intercity IMC bus operations.

Aviation is a growing industry responsible for over 2% of the energy-related CO2 emissions in 2021. To achieve the 'Net Zero Emission by 2050 scenario', the aviation industry is turning towards modern propulsion technologies that reduce carbon and NOx emissions. Research is taking place on many prospective aircraft designs, such as hybrid/turbo-electric powertrains, fuel cell/liquid hydrogen-powered aircraft and fully electric aircraft. Although fully electric aircraft offer the cleanest possible air travel, these aircraft are considered the solution for short-haul flights due to the range extension issue caused by the deficient specific energy of batteries compared to currently used aviation fuel. The electric aircraft concept designers rely on the potential development in battery technology while proposing their designs, and sporadic state-of-the-art battery designs are present concerning electric aviation.

The concept of reconfigurable battery packs involves using power switches to modify the arrangement of connected battery cells based on specific requirements. This innovative technique can potentially significantly reduce the weight of battery packs. The primary objective of this thesis was to conduct a comprehensive analysis and comparison between fixed configuration and reconfigurable battery packs in the context of electric aviation. It was imperative first to design these battery packs to facilitate this comparison. Given the limited availability of open data on electric aircraft designs, the power profile was estimated using available reference aircraft specifications and reasonable assumptions. The literature review on power systems in aircraft revealed a significant correlation between system-level voltage and the weight of power cables. This discovery led to estimating an optimal system-level voltage, a critical constraint in battery sizing. For the fixed configuration battery pack, sizing was conducted using both a high-specific energy cell and a high-specific power cell. The design of a reconfigurable battery pack involved strategically leveraging both cell types. This innovative approach created a reconfigurable battery pack capable of dynamically connecting and disconnecting an internal high-specific energy battery pack called the 'primary battery pack' and a high-specific power battery pack known as the 'secondary battery pack' through power switches, allowing them to complement each other during high-power demand phases of flight, such as take-off and climbing.

Software simulations were conducted for the validation of this technique. These simulations revealed that the reconfigurable battery pack experienced higher C-rates than the fixed configuration battery pack. Given that higher C-rates can impact battery health by inducing capacity loss over multiple cycles, a preliminary ageing analysis was performed to quantitatively assess the adverse effects of higher C-rates on the reconfigurable battery pack.

The results quantified that around 400 kg of potential weight savings is possible by employing reconfigurable battery packs over fixed configuration battery packs at only 0.4% more capacity loss over 500 charging-discharging cycles. The weight savings can be translated into three different scenarios. Firstly, payload weight capacity can be enhanced. Secondly, flying with lesser weight will offset the power profile, saving energy. Lastly, an additional number of cells equivalent to the mass saved can realise the range extension of the electric aircraft.

Batteries play a crucial role in powering contemporary devices and systems, ranging from smartphones and electric vehicles to renewable energy storage. With the increasing demand for more efficient and reliable battery technologies, the need for accurate monitoring and assessment of battery condition and performance has also grown. Online estimators, that continuously analyse battery conditions in real-time, have emerged as valuable tools to meet these goals. This thesis focuses on constructing model-based online state estimators for real-time estimation of a battery's State of Health (SOH).

These estimators, necessitate a state space model for a lithium-ion cell. In the initial part of the thesis, a physics-based reduced-order model (ROM) of a lithium-ion cell is developed. This model accounts for major aging mechanisms such as Solid Electrolyte Interphase (SEI) layer formation, Loss of Active Material (LAM), and Lithium Plating (LIP). To incorporate temperature effects on cell parameters, a simplified lumped thermal model is integrated into the battery model. The model is subsequently transformed into a state space model using the Discrete Realization Algorithm (DRA) process.

Building upon the noisy outputs from the ROM models, a set of five estimators is formulated: State of Charge (SOC), Voltage, SEI loss, LAM loss, and LIP loss estimators. These estimators are constructed based on Kalman filters and collectively contribute to real-time prediction of a battery's SOH. Given their reliance on the model, the ROM model's output is employed as a reference to gauge the precision of the estimators.

To address the real life scenarios and to check the robustness of the estimators, a series of sub-questions were analysed:
Sub question1: How to estimate the SOH of a cell in real-time using adaptive control techniques ?
Sub question2: How fast can the estimator react to changing initial states of the cell?
Sub question3: Can the estimators be adapted to concurrently estimate both the states and the time varying cell parameters in case of an old cell? How fast can this be
achieved in real-time ?

Overall, this research contributes to the development of model-based online estimators, which are poised to have a significant impact on enhancing battery performance, prolonging lifespan, and facilitating the transition towards a more sustainable energy future.

There has been significant research into the use of multilevel inverter topologies using Wide Band Gap (WBG) semiconductors in Electric Vehicle (EV) powertrains. This combination promises an increased power density and efficiency which should boost the viability and adoption of EVs. However, a reliable power supply is required for the inverter switches, which counters some of the challenges presented by these topologies. These challenges include the issue of high side power supply and Electromagnetic Interference (EMI) mitigation. To begin with, certain requirements for a high side power supply are drawn up and various architectures are compared based on their merits and demerits. Based on this comparison, the LLC resonant converter is chosen as the most suitable topology due to its high efficiency, power density and potential for EMI mitigation. The operating principles of this topology are examined and used to optimally design a one output LLC converter. These principles are then further extrapolated to the design of a LLC converter with six isolated outputs which are required for a multilevel converter. Both these designs are then prototyped and tested to verify their performance. It is found that the multi output converter performs at par or better than the commercially available solutions, while fulfilling the design requirements.

The transportation sector is moving towards a green future with a rapidly growing EV market and consequently, the demand for EV chargers is rising too. With the increased interest in 800V batteries and the current presence of 400V and 600V batteries, the voltage range for EV chargers is wide (150V-1000V). In this thesis, a wide-output EV charger based on two DAB converters with series-parallel output is proposed. A novel method of reconfiguration is introduced to allow live transitioning between series and parallel mode. The circuit of a 25kW converter is optimized to achieve optimal efficiency during a charging cycle. A comparison between advanced DAB modulation strategies is made and Triple Phase Shift (TPS) modulation in combination with Minimum Current Stress Optimization (MCSO) improves the efficiency of the DAB at low voltage and low power operation. The converter operates at >97.6% efficiency in the full range of 150V-1000V with a peak efficiency of 98.8%. A 25kW converter is modelled and simulated in PLECS, and a 200W hardware prototype is built to verify the new method for live transitioning.

As more and more solar panels are installed on households, a problem arises when the power from the solar panels fluctuating throughout the day due to clouds passing over the solar panels. This in turn causes voltage flickers in the household, which can be visible/irritable to the human eye and damage appliances in the household. These voltage flickers can exceed limits set by the IEC standard 61000-3-7 or the visible flicker threshold on the Low Voltage (LV) network set by Qualtech [1]. This problem can be solved by making use of an Energy Storage System (ESS) which delivers active power in order to reduce and avoid the voltage fluctuations above the visible flicker threshold. In this report, a novel control algorithm is designed and implemented in two different ways to control the active power dispatch of the ESS. The main goal for the control algorithm is to reduce or avoid any visible voltage flickers from occurring and minimizing energy usage asmuch as possible in order to open the possibility for a Supercapacitor Energy Storage System (SESS) application in the future. For this, a power to energy ratio of higher than 70 is needed to achieve this. The primary control algorithm makes use of a moving average with a weight distribution that is optimised for smoothness and accuracy. Furthermore, extra layers of control was added to the control algorithm tominimize energy usage. The first implementation of the control algorithm, called power control, makes use of the Photovoltaic (PV) system output power to dispatch the appropriate amount of power from the ESS. The second implementation, called voltage control, makes use of the measured voltage at Point of Common Coupling (PCC) to dispatch the ESS power. Simulation results using pre-existing PV system data showed that the power control implementation was not able to fully eradicate all of the measured visible and annoying voltage flickers. The voltage control implementation was able to do so within the window of operation. Furthermore, simulation results showed that the energy usage from the control algorithm with a power control implementation uses 87% percent less energy than a control algorithm using conventional moving average. The energy usage from the control algorithm with a voltage control implementation uses 95% less energy than the conventional moving average control algorithm. Furthermore, the power to energy ratio of the control algorithm with a power control implementation was around 98.9 and with a voltage control implementation the power to energy ratio was around 190.2. This shows a clear implementation of a SESS in the future. Two different experimental setups were built and commissioned. The first setup having the Battery Energy Storage System (BESS) connected via an inverter on the same Alternating Current (AC) bus with the PV system. The second setup has the BESS connected on the same Direct Current (DC) bus with the PV system which is then connected to an inverter. Experiment results showed that the DC connected ESS experiment setup operating in power control mode had the best performance in terms of avoiding any visible voltage flickers from occurring. While the AC experimental setup and mode of operation (voltage and power control mode) did not operate properly due to the slow inverter response time. In terms of energy usage, both experimental setups had very low ESS energy usage. Although the results showed that power to energy ratio from both experimental setups using both power and voltage control did not exceed the set goal of 70 due to the limitations of both experimental setups. Finally, a conclusion based on the results is given with future work and as well as recommendations for the continuation on this research topic.

Electricity grids are increasingly congested as the world moves toward a sustainable, electrified future. Expanding and upgrading these infrastructures is costly and challenging on a technical and administrative level and even redundant when considering that some sub-parts of these grids, such as electric transportation networks, are massively underutilized. This thesis investigates, in four parts, the potential of one of these high-power infrastructures, the trolleybus grid, to become a sustainable, multi-functional, and multi-stakeholder backbone to urban power grids by integrating renewables, storage, and EV chargers, all while remaining ready for their next generation of sophisticated, high-power transport fleets such as In-Motion-Charging buses...

Battery electric vehicles have gained a substantial market share in the last few years. The call for good charging infrastructure is urgent with the rapid shift from carbon-based cars toward battery electric vehicles. However, the present electrical distribution networks are not designed for large additional loads. Many cities have traction networks such as trams, metros, or trolleybuses for public transport purposes. This thesis explores the potential for charging electric vehicles directly from these traction networks. A case study uses public charging behavior to simulate EV charging on the trolleygrid network of Arnhem. Moreover, six smart grid methods are evaluated to increase a traction network's electric vehicle charging potential. Various trolleygrid parameters such as the trolleybus intensity, section length, and the charger's location on the trolleygrid play a role in the charging potential. An individual area can fully charge up to 111 electric vehicles daily. This study shows that increasing the substation capacity and introducing smart charging are the two smart grid methods that increase the potential up to 201 EVs/day. Adding an extra overhead line has a minor effect on the charging potential (+12 EVs/day). Increasing the substation nominal voltage has the same impact as the last mentioned method but is cheaper to implement. At the connection point of two isolated sections, the best smart grid methods are introducing a bilateral connection and using a multi-port converter. Charging battery electric vehicles from traction networks could be a suitable alternative to increase the charging possibilities in cities.

Trolleybuses are electric buses than run on electric power from overhead electric power lines (catenary) like trams do. Although is a service that dates all the way back to 1882, it has managed to regain interest in the recent years due to the constant electrification of various aspects of the world. This comes as a response towards environmental challenges as, even today, the majority of the energy necessary to cover our need comes from fossil fuels, making the transition to renewable energy sources more vital. One promising form of renewable energy source is solar energy and its use with photovoltaic (PV) systems. An interesting implementation of such systems is in catenary grids like trolleybus grids. Nevertheless, their integration has a low potential as the gaps in the trolleybus schedule and the intermittent behavior of the PV electric power leave a lot of excess PV electric energy unused. Possible solutions to this problem could be either the storage of this excess energy in on-board energy storage systems (OESS) or stationary energy storage systems (SESS). Besides that, in preparation for the future, more attention is given every day to the multifunctionality of trolleybus grids. This is related to parameters such as voltage drops, that have important role in rendering the trolleybus grid capable of being expanded with components other than trolleybuses, such as electric vehicle chargers.

The objective of this thesis work is to find out which energy storage systems (on-board or stationary) are most favorable for a PV-powered multifunctional trolleybus grid for increasing the PV system utilization and improving its multifunctionality. Parameters related to the multifunctionality of trolleybus grids can be the yearly electric energy consumption and the voltage drops.

For the conduction of this work is used an existing, verified trolleybus grid model of Arnhem in MATLAB that has realistic trolleybus electric power profiles as an input. By using the backward-forward sweep method are determined precise values such as the total electric power needed, voltage drops, ohmic losses and more. Also, is used as an existing, verified PV system model that provides the PV electric power as an output based on measured data. Then is developed a model for the on-board energy storage system which with the help of a constraint checking algorithm, that simulates the energy storage technologies, it provides new, adjusted trolleybus electric power profiles that are used as input to the trolleybus grid model. Finally, for the stationary energy storage system, the main trolleybus grid model is expanded with storage capabilities based on voltage control and a similar constraint checking algorithm for the storage technologies.

The results show that the impact of stationary energy storage systems on the PV system utilization and on parameters of the trolleybus grid in general is heavily correlated to the control strategy used. The technology, the PV system size, the trolleybus grid section, and the targeted outcome (improve the PV system utilization or reduce the voltage drops) have key role to the selection of the control strategy. The comparison of stationary energy storage systems to other energy storage systems cannot be straightforward. Besides that, on-board energy storage systems can perform better on parameters regarding the multifunctionality of the trolleybus grid such as the total yearly electric energy consumption and the mitigation of the severity of the voltage drops. On the other hand, stationary energy storage systems can perform well on a wide range of parameters according to their control strategy. For the one used in this work, they perform better on parameters regarding the PV system utilization, managing to have a positive impact.