The urgency of taking actions against climate crisis is unprecedented. The human-produced CO2 is the largest contributor to global warming, which drives the climate change. Many countries around the world have committed to net-zero greenhouse gas emissions in the next coming decades. Power-to-X (PtX) technologies in combination with carbon capture technologies might contribute to a carbon-neutral future, since they can convert electricity and captured carbon to synthetic gases (e.g. hydrogen, methane), chemicals (e.g. propylene, ethylene) and liquids (e.g. methanol). This thesis aims to research the techno-economic potential of a system that is able to produce methanol at industrial scale via a PtX scheme, which is coupled with carbon capture technologies. The system of this thesis consists mainly of PV panels, an alkaline water electrolyzer, a polymer electrolyte membrane (PEM) CO2 electrolyzer and a single-stage Lurgi quasi-isothermal methanol reactor. The feedstocks for this system are water and carbon dioxide (captured from the flue gases of a cement plant). The whole production process of this system has been designed and modelled in such a way, that both electrolyzers can follow the intermittent power supply from the PV panels. The electrolyzer's dynamic operation is controlled by a deterministic control logic, which takes into account the variable energy efficiencies of the electrolyzers and the intermittent power output of the PVs. Moreover, it has been decided that the methanol synthesis is based on the CO2 hydrogenation process, which converts syngas (i.e. a mixture of CO, CO2 and H2) into methanol with the use of the methanol reactor. Hydrogen is produced by the water electrolyzer and the captured CO2 is reduced to CO by the CO2 electrolyzer. As far as the sizing and production results are concerned, the system is able to produce 3.999 kT of methanol per year, consuming 2,147 tons of CO2 per year and requiring an energy input of 30.3 GWh/year. The installed peak PV power is equal to 18 MWp and 99.60% of their energy yield is exploited by the system (the rest is dumped). Due to the implemented control logic, the operating energy efficiency range for the CO2 electrolyzer is 45.07-55.32% and for the H2O electrolyzer is 75.83-81.71%. In terms of economic analysis, the proposed system requires a total capital investment (TCI) of 195.7 M€ and its operational expenditures are equal to almost 2 M€ per year. The performed cash flow analysis showed that the system has an annual gross profit of € 855,535 per year. Despite the yearly profit, it was found that the system's net present value (NPV) is negative and equal to -179.5 M€ at the end of its lifetime (i.e. 20 years). Also, the levelized cost of methanol (LCOM) was found to be equal to 4.44 €/kg, which is almost 10 times higher than the current market price of methanol. Therefore, such an investment would be economically unfeasible, despite its environmental benefit, because it would result in a net loss of capital.

The DC Optimal Power Flow (DC-OPF) problem is a widely-studied topic in the field of power systems. A solution to the problem consists of minimizing the running costs of the power system, through defining the optimal operating state for each entity in the system, while adhering to a set of physical constraints. A lot of research has been conducted on decentralized and distributed solutions to this problem, which, when compared to centralized solutions, offer benefits such as adaptability, reliability and scalability. Nevertheless, most of these solutions have only been evaluated through simulations, while physical applications of these algorithms introduce new challenges, such as noise, delays, and the regulation of physical variables like voltage and current. In this thesis, we focus on a decentralized DC-OPF algorithm based on the Consensus and Innovation method, where system entities utilize their physical measurements and communicate with their neighbouring entities in order to reach consensus on a solution. While previous implementations of the algorithm were tested on simulated environments, this thesis explores and proves the effectiveness of the algorithm implemented for a real DC unipolar microgrid, consisting of power supplies, loads and a Power Circuit Board attached to each, where each device's behavior is governed by its own, exclusive entity. The newly-introduced challenges of a physical environment are accounted for, and any negative effects of them are mitigated as much as possible. Furthermore, the algorithm is successfully modified and extended to handle region DC-OPF, something that has not been attempted before, where a single entity of the algorithm could be responsible for many devices in the network. Finally, it is known that the original algorithm has not been experimented on before on scenarios of islanding and de-islanding, which are of importance in OPF, because a power fault may occur and one area may wish to isolate itself from the faulty area, or perhaps a distributed energy resource should only be connected to the network during certain periods, e.g., during sunlight for solar panels. Hence, this thesis also proves the effectivess of the algorithm on scenarios of islanding and de-islanding, in an innovation site called the Green Village, where technologies in the field of sustainable energy provision are tested and applied in a real-life environment.

An increase in carbon emission which mostly caused by the transportation sector and electric power generation has been a hot topic nowadays in most countries in the world. To tackle this problem, the share of renewable energy use has been increased by up to 14% in the Netherlands. Moreover, the number of electric vehicles (EVs) on the road also reaches a total of 120,000 EVs in 2018. However, the high penetration of renewable energy sources (RESs) such as solar & wind power and the EVs charging in the distribution network could result in a severe problem. One of the solutions to avoid this problem is that switching the uncontrolled charging of EVs into a controlled charging or called as smart charging. Further, an integration between the EVs, RESs and the distribution grid could potentially lead to technical and economic benefits. The focus of this thesis is to develop an optimal power management system (PMS) between the EVs, PV system, and the distribution network. The goal of the power management system is to obtain the minimum operational cost while also considering the technical grid constraints, which subsequently could avoid the grid violation. The proposed power management system will be modeled in a mixed integer non-linear programming (MINLP) optimization problem and executed in General Algebraic Modelling System (GAMS) software. To evaluate the performance of the proposed power management system, a comparison between the with-grid and the no-grid constraints case will be performed through several case studies. This study shows that by implementing the proposed power management system of EVs charging from PV system considering the grid constraints, it could decrease the total operational cost remarkably by 18.16% - 214.08% when compared to the uncontrolled charging scheme. Besides, the grid problem caused by the uncontrolled charging process such as exceeding the allowable voltage deviation and the transformer rated power could be prevented. However, in comparison to the smart charging without considering the grid constraints, the operational cost is increased by 1.43% - 113.20%.

Electric Vehicles (EVs) provide a promising sustainable clean mode of transportation with much fewer emissions compared to the traditional Internal Combustion Engine (ICE) vehicles. However, this can only be considered true if the energy sources used for charging EVs are also from clean energy form. Various Renewable Energy Sources (RES) are available to provide the EVs charging demand. Nevertheless, due to intermittency power generation characteristic of RES, there is a high risk of supply and demand power mismatch. A grid-connected topology system is commonly used as a backup for lack or over power supply from RES generation. Even though, this configuration may lead to new problems of under/over voltage issues and maximum transformer/lines loading capacity due to RES fluctuation power production. The storage system is considered necessary to be installed in the system as a buffer of the power mismatch and also reducing the grid stress. Excellent approximation of storage sizing and placement is mandatory to provide satisfactory influence to the overall system technically and economically. This study proposes optimization strategy for optimal battery sizing and placement with optimal power management system in the EVs charging station using photovoltaics (PV) power in a low voltage distribution network using Mixed Integer Linear Programming (MINLP) algorithm. The lithium-ion battery is considered in this project because of its relatively high power and energy density characteristic along with low self-discharging. The radial low voltage CIGRE benchmark is adopted as a model topology to evaluate the performance of the proposed optimization strategy. Dynamic daily electricity price, residential load, and PV power profile of the Netherlands are taken into account in this project. Furthermore, the research project implements several case studies using DICOPT (Discrete and Continuous OPTimizer) solver in GAMS (General Algebraic Modelling System) software with MATLAB interfacing. As the main result, proper EVs charging strategy with optimal battery sizing and placement with the appropriate power management system is successfully maintaining the system away from the grid violation while at the same time reducing the total cost of the system. Keywords: EV-PV charging, optimal battery sizing and placement, MINLP

Royal IHC is known for building large dredging vessels which collect soil from the seabed. The vessel needs to remain in position, called dynamic positioning, which is done by large thrusters. These are mostly electrically powered thus the total amount of required power can range up to 50 MWs. Royal IHC wants to investigate the possibility of a DC vessel where the synchronous generator’s AC voltage is rectified via a passive rectifier. The question is how stable this is and what influences the voltage stability. The thesis focuses on building a model of a DC powered propulsion system that allows analysis of voltage stability of the vessel. The model is generalized and consists of two parallel diesel generator sets with a passive rectifier. These generators are connected to a common DC bus with constant power loads which represent the loads of the vessel. The model is built in Matlab Simulink using the specialized power system toolbox and is verified with an RScad simulation. A measurement-based analysis tool is developed utilizing prony analysis. This allows analyzing the results of the simulation model and data from the field. Both cases are tested, the tool allows to get insight into the stability of the vessel.

European policy makers have established a set of 20-20-20 targets for the year 2020 in the energy sector, goals based on security of supply, competitive markets and sustainability. The 3 key objectives include a 20% reduction of greenhouse gases (GHG) and CO2 compared to 1990 levels (and up to 80% by 2050), a 20% improvement in energy efficiency and a 20% share of basis energy consumption from renewable sources. On its own, the building sector is responsible for 1/3 of the world’s energy consumption, of which 60% is due to heating and cooling. In the European Union, this proportion achieves 40% of the total energy consumption, of which heating, cooling and domestic hot water account for around 70%. Fossil fuels represent 75% of the primary energy supply for heating and cooling needs whereas renewable energies account for only 18% followed by nuclear energy with 7%. A brief energy context being stated, this work focused on the assessment and the optimization of a heating, ventilation and air conditioning (HVAC) system, coupled with a building integrated photovoltaic and thermal (BIPV/T) system and a simple BIPV system. The objective was to reduce the overall energy consumption of a very well insulated dwelling aiming to make it a net zero energy building (NZEB). This HVAC system incorporated a dual-tank water-to-water heat pump (WWHP), used for space heating and cooling, using the BIPV/T system as a heat source thanks to preheated air. The dwelling is a so-called Deep Performance Dwelling (DPD) developed by the Solar Decathlon team from Canada: TeamMTL. This DPD was used as case study all along this Master thesis. On the one hand, this study aimed at the modelling of both integrated PV systems which were developed firstly with the MATLAB software and then with the TRNSYS software. One of the objectives was to highlight the impact of the thermal recovery from the BIPV/T system on its PV modules efficiency compared to the BIPV system. Furthermore, the same initial and boundary conditions were used to ensure a truthfulness on-site energy production from both BIPV/T and BIPV systems as well as for the validation of these systems, later integrated to the complete DPD modelling. On the other hand, sensitivity analyses were conducted to investigate the effects of various parameters on the NZEB objective and the on-site energy fraction (OEF) covered by the energy produced by the DPD. These parameters included the efficiency of the PV modules, their tilt angle, the materials used for both floors and walls as well as the size and the control strategy used of both electrical and thermal storage. First, even if the comparison of both MATLAB and TRNSYS modellings led to a similar net increase in the BIPV/T average electrical efficiency of + 0.0014 [-] compare to the BIPV one, the TRNSYS one achieved an 11.0% lower yearly electrical generation due to an intrinsic factor used in TRNSYS called Incidence Angle Modifier (IAM). The impact of this factor affected all the more the thermal yield, namely a reduction by 28.6% for the annual thermal production along with a 32.0% lower thermal efficiency of the BIPV/T system compared to the one modelled in MATLAB. However, given that TRNSYS was required to model the transient behaviours of the DPD, not conceivable with MATLAB due to the time constraint of this Master thesis, it was logically concluded to use the TRNSYS modelling of the integrated PV systems. Then, to have a good representativeness of the performances of the DPD, 4 cities with very different climates were chosen. For each location, the DPD was categorized as a nearly zero energy building (nZEB). Then, the simulation results emphasized that an optimal tilt angle adapted to every location, concrete-based floors as well as high efficiency PV modules allowed for reaching the NZEB objective in 3 cities, the 4th one facing low annual irradiation. Moreover, sensitivity analyses were carried out in order to improve the energy performance of the DPD. Based on the interpretation of the results concerning both on-site energy indices and energy outputs, a better configuration than the one of the as-built DPD was suggested for each city. Indeed, the sensitivity analyses indicated that the bigger the electrical storage, the better the electrical OEF index. Then, a 300-litre hot water tank as well as a 150-litre cold water tank led to a better overall OEF index and a lower energy consumption compared to the initial configuration. These results were obtained by applying the initial control strategy relying on price benefits of the grid based on bidirectional power flows between the batteries and the grid. However, a grid-tied configuration with no more batteries-grid connection and not taking anymore advantage of incentive prices of the grid stood out an even better overall electrical OEF index, making a higher use of the on-site power generation. Finally, the proportion between the on-site PV production and the amount of energy supplied by the grid to meet the demand of the DPD were calculated. Besides, the impact of the solar house in terms of CO2 emissions as well as its advantages for the grid were alluded.

Rise in distributed energy resources has prompted a shift in the way electricity market operators function. Traditionally, centralized optimization techniques are used by such operators to plan for economic dispatches of power tominimize the overall operational costs and increase system social welfare. Due to the dispersed nature of renewable energy sources as scattered nodes in a system, it can get difficult to accommodate them in a centralized optimization problem. Also, sharing of complete information for such nodes can create privacy issues. This motivates research in the field of distributed optimization techniques. This thesis aims to develop a distributed optimization algorithm for a DC distribution system which would take into account network congestion and line losses and ultimately provide a more precise optimal solution. Based on past research for distributed optimization approaches for AC systems, the Consensus and Innovations approach was used to model an algorithm and provide nodal optimization for a DC system with minimal data exchange. The developed model was implemented on various DC network topologies like meshed grids, single line networks, T Shaped networks, etc. and a converged output of system variables was accomplished. The results were also compared with a centralized optimization approach to check for deviations. The decision variables for the developed approach were found to be well within the deviation range of 2 percent. The algorithm managed to provide distributed optimization within a DC system while minimizing power generator operational costs and cost associated with network losses.

Technical developments in generation and demand of energy will motivate significant change in the electric power grid, both on the transmission and the distribution level. A major innovation would be the successful transformation of the current passive power grid towards an active and ICT-based smart grid. Among the technical efforts that will help to pursue this goal, the renewed interest in DC (direct current) distribution and transmission applications is playing an important role. In particular, the interest in the DC universal distribution networks is renewed since most of the renewable energy generation technologies (e.g. PV modules, fuel cells) and loads (e.g. LED lighting, electric vehicles) are DC-native. Their direct connection would allow to skip conversion steps, thus providing higher efficiency. The focus of this thesis lies on the steady-state power flow analysis, a numerical study used in electrical engineering to assess the flows of power in the network. The aim of the thesis is to review the state of the art in computational methods for AC and DC power flow analysis and to determine a suitable method to develop a power flow tool for the DC framework. The literature study revealed that most algorithms aim to solve the non-linear power flow problem without taking into account characteristics typical of future DC networks, such as highly meshed topologies and constant power converters. An innovative power flow method has there- fore been developed in order to include different node behaviours, such as constant voltage, constant current, constant impedance, constant power and I-V droop control. A case study based on the IEEE European Low-Voltage Test Feeder is analysed to provide an example of the application of the power flow tool. The thesis shows that it is possible to linearize the system equations considering the constant power node either as a current source or as a parallel of current source and impedance. Both methods allow very fast convergence for complex meshed networks, and can therefore be adopted for diverse studies such as market analysis and N-1 redundancy analysis, among others.

Despite of the technological advancements on renewable energies and integration of those into the grid, there are still around 800 million people who lack access to the electricity. In order to tackle this problem, solar home systems are being deployed especially in rural areas to create units for decentralized energy generation, based on DC microgrid premises. Integration of solar home systems, especially in developing countries, has great positive effects on communities' economic and social conditions. In line with transition to cleaner energy resources and utilization of these, this thesis has been carried at DC Opportunities RD to facilitate the rural electrification project. Within this project, the thesis focuses on implementation of a general adaptable firmware based on USB Type-C power delivery protocol on several components of modular solar home system. Firstly, the report analyses the existing solar home systems with their characteristics on power delivery and energy management. The limitations on the existing projects are studied and the main motivation on deploying the firmware is pointed out. The main objective of the thesis is hence is formulated as achieving and implementing the USB Type-C power delivery protocol with data acquisition layer on prototypes of charging station, power banks and charging hub, that is tailored to rural applications. The firmware design topology is followed to take into account the modularity, scalability and applicable to energy management. The USB Type-C power delivery firmware is developed by studying and adapting two open source packages. Several adaptation layers are developed and the firmware is implemented using micro controllers, from STM32, that are capable of running operating system with no faults, deploying several layers of tasks and state machines. Power delivery library functions are tackled with layers of software architectures and data monitoring and transmission layer is deployed, in order to validate the system as well as the hardware. Implemented firmware is developed on the prototypes and tests are run with the current editions of the prototype. The energy management layer and formulated test case will be run and tested on March 2020, in the field test in Ethiopia. This field test will provide results and data, thanks to the developed firmware and acquisition system, in order to achieve a step closer to the ultimate goal of the project, which is implementing low voltage DC microgrid in rural areas.