This thesis focuses on analysing, troubleshooting and testing a USB-C to AC Inverter with USB-C power delivery, as well as implementing control- and protection systems for the flyback converter and implementing the H-Bridge switching control. The starting point of this project is a design that has already been manufactured as a PCB by DC Opportunities. A description of the existing design will be given, complemented with the implementation of the control and protection systems. Furthermore, an overview of the testing process will be portrayed and the results will be presented and interpreted. The USB-C input will use the power delivery protocol and a Flyback converter to get to 400V DC, followed by a full-bridge inverter for a 230Vrms AC output. The inverter is part of a rural electrification project in which the goal is to provide electricity to people in rural, unelectrified areas. Initially, electricity is to be provided in the form of a charging station at a central kiosk or shop where appliances such as phones can be charged. Gradually, people can climb up the electrification ladder by purchasing power banks that can be charged at the charging station to provide the user with electricity in their home. Eventually, people will move further up the electrification ladder all the way to local generation via solar panels with battery storage at home. When enough people have local generation, a DC micro-grid can be constructed. The USB-C to AC inverter is there to give people the possibility to use AC powered appliances while the number of available DC powered appliances is limited. This gives people more options, while still keeping the advantages of a DC power system

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.

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.