This study presents a comprehensive Life Cycle Assessment (LCA) comparing HyET Solar's Powerfoil modules to conventional Passivated Emitter Rear Contact (PERC) technology. Through both current and future scenario analyses, utilizing tools like Integrated Assessment Models (IAMs) and Shared Socioeconomic Pathways (SSPs), the research examines key impact categories, including Global Warming Potential (GWP), Terrestrial Acidification, Land Use, and Material Resources.
Current scenario findings reveal that PERC modules exhibit significantly higher environmental impacts across most categories. The GWP of PERC modules is notably high at 50.1 grams CO2-equivalent per kWh, primarily driven by energy-intensive processes in inverter and silicon wafer production. Powerfoil modules, especially the triple-junction architecture, demonstrate reduced impacts with emissions as low as 23.8 grams CO2-equivalent per kWh.
Future scenario findings project even greater environmental benefits for Powerfoil modules by 2050. Efficiency improvements and material optimizations, including the replacement of existing top encapsulant and reducing the carrier foil thickness, could bring down the emissions to 6.2 grams CO2-equivalent per kWh, which is almost 70% lower than PERC modules.
The study also identifies critical hotspots in the Powerfoil modules’ lifecycle, providing a quantitative analysis of environmental impacts after replacing these hotspots in future scenarios. Furthermore, the Global Sensitivity Analysis highlights that module efficiency and solar irradiation are critical factors influencing environmental outcomes. These findings highlight the potential of Powerfoil modules as a more sustainable alternative in photovoltaic technology, with substantial implications for reducing the environmental footprint of energy systems.

The agricultural sector holds the potential to integrate a resilient and renewable energy system due to high penetration of renewable energy sources. This study addresses the obstacles towards this transition. The first obstacle is the Dutch electricity grid. As of 2024, the greatest bottleneck in the Dutch energy transition is the lagging grid capacity. Instead of relying on grid power, matching local generation and demand of energy provides an elegant alternative. The second obstacle is that on-farm activity is powered mainly by diesel, since the majority of the demand is from tractor activities. To these two ends, this report investigates two primary aims: first, to provide an overview of the hourly and seasonal energy demand for dairy and open-field farms; second, to evaluate the feasibility of decarbonizing on-farm energy systems given the current grid capacity. The first aim was achieved by identifying the main energy-consuming activities in both dairy and open-field farming, revealing that dairy farming has consistent day profiles throughout the year, whereas open-field farming shows significant variability depending on crop needs and practices. The activities constituting most of the electricity demand on dairy farms have been found to be 1) milking, 2) milk cooling, 3) water pumping and 4) lighting. Feeding and grassland management (fertilizing and mowing) have been identified as the main diesel-consuming activities. For open-field farming, electricity is consumed largely for crop storage in the form of cooling or ventilation, or a combination. This contribution is small compared to the diesel consumption for field operations (mainly for tillage).
The second aim involved accurate modeling of various photovoltaic (PV) system architectures with TNO’s BIGEYE software and linear programming (LP) optimisation using Open Energy Modeling Framework (OEMOF) to assess their impact on the feasibility of decarbonization. The findings suggest that while the choice of PV systems can dampen the mismatch peaks by several percentages, the type of farm and its specific practices play the most significant role. For open-field farms, minimal crop storage requirements and sowing/harvesting periods, such as broad beans and wheat, that align with PV yield profiles are more suitable for achieving self-sufficiency. Short-term forms of energy storage, shifting loads and clipping maximum PV power are measures that directly enhance self-sufficiency and decarbonization without additional grid reinforcement. The highest self-sufficiency rates reach to 82%. In the discussion, the potential contribution of long-term storage needed to decarbonize the farm types with high mismatch levels is discussed. The report concludes with an outlook for further studies to enhance the reliability of energy demand data and suggests actionable and effective alternatives to grid reinforcement for the successful electrification and decarbonization of the agricultural sector. The most straightforward and pressing step in further research is broadening the scope to matching mismatch profiles with other prosuming systems, such as other farmers, towns and businesses, forming resilient energy communities

Silicon carbide (SiC) and silicon oxide (SiOx) have garnered increased attention to reduce the parasitic absorption at the p layer when compared to p-aSi.An outstanding question is how the integration of p-SiC and p-SiOx impacts the metastability of the solr cells. Previous work in the PVMD group at TU Delft has studied the stability of the p-nc-SiOx in detail revealing from experimental results that stable efficiency remains elusive.
This thesis investigates the impact of p-a-SiC on the light-induced degradation (LID) of p-i-n solar cells, aiming to compare its degradation performance with that of p-nc-SiOx. Prior to comparison, p-a-SiC fabrication via plasma-enhanced chemical vapor deposition (PECVD) is extensively explored, as it serves as a widely used light absorber in thin-film silicon solar cells. Various parameters such as power pressure and gas flow rate of the source gas for silicon carbide formation are meticulously studied to achieve optimal P-a-SiC performance. Once the optimal fabrication conditions are identified, light absorption experiments are conducted and characterized using JV and EQE measurements to assess LID performance.

This report proposes a design of a 1-megawatt data center that utilizes hydrogen as the primary fuel for power and cooling. Currently, data centers rely on utility grids for their operation. The growing global efforts to achieve sustainability introduce intermittent renewable energy sources, such as solar and wind, into the power grid, which goes against the reliability principles of the data center industry. As computer technologies further permeate our everyday lives, their energy consumption and environmental impact become greater.

The feasibility study aims to investigate the potential of using hydrogen as a primary energy carrier in data center applications in terms of reliability, efficiency, electrical and hydrogen topology, infrastructure layout, and control strategies. A comprehensive review of commercially available and emerging hydrogen technologies related to storage, transport, electricity generation, and cooling was conducted. Based on this literature review, two designs for a hydrogen data center were developed. A modular approach to the design was proposed, where each module is self-contained and responsible for providing the servers with 1 megawatt of electrical power and cooling.

The first low-temperature design utilizes a hydrogen pipeline with pressure swing adsorption purification supplying hydrogen gas to a cogeneration system. This system combines a low-temperature Proton-Exchange Membrane fuel cell and a direct-fired hydrogen single-stage absorption refrigeration cycle into a single unit to deliver stable power and cooling to the servers. The findings indicate that the overall energy efficiency of this design configuration is high, and the technologies are mature enough to be used today. However, the study identified various technological limitations of purification systems that must be addressed with further research and development.
The second high-temperature design eliminates the need for a purification system, relying instead on high-temperature Proton-Exchange Membrane fuel cells coupled to a double-effect absorption refrigeration system. Unfortunately, this design is not feasible in 2024 because the high-temperature fuel cells are in the early stages of development.

A cost analysis was conducted for both designs in the current (2024) and projected (2030) scenarios. The results indicate that at the current level of hydrogen technology development and manufacturing, the operational costs of the low-temperature configuration were comparable to the traditional data centers. However, if hydrogen technology continues to develop according to the projected trends, the proposed designs can achieve up to 50% cost savings compared to the grid-powered data centers. This research showcases the potential for hydrogen as an energy carrier in data center applications, encouraging further research and exploration of hydrogen technologies.

This thesis investigates the formation of hotspots in single junction amorphous silicon thin film solar modules through experimental methods. Employing electroluminescence (EL) imaging and infrared (IR) imaging, the study aims at identifying and classifying defects that can be used to predict hotspot formation and assesses the endurance of different sized modules.
To predict hotspot formation on small 30 by 30 cm monolithically interconnected modules, the research first categorises shunts into four different classes based on their severity and localization (Mode A, Mode AB, Mode B and Mode C). Mode A defects are severe and cover a large area, Mode B defects are severe and localized, Mode AB defects are severe and rather localized and Mode C defects are weak and localized. It was found that only shunts that are severe and localized lead to the formation of hotspots (Mode B and rather localized Mode AB). Since no hotspots formed at locations without predictors EL based shunt classification has proven to be an effective predictor with high predictive accuracy for hotspots.
Industrial modules with a dimension of 190cm by 30 cm exhibit similar defect behavior, however the interaction between multiple hotspots within one cell leads to some exceptions. When multiple defects are located within the same cell, the most severe and localized defect forms a strong hotspot, while the other defects either form weaker hotspots or no hotspots at all. Due to a higher current level also Mode C defects can lead to hotspot formation when they are the most severe defect within a cell. Furthermore locations that show defect cluster or current crowding also experience strong hotspot formation. Lastly, shunts that were found to be originating from scribe defects are prone to form strong hotspots.
EL based shunt classification has therefore proven to be a reliable predictor for hotspot formation on modules of different sizes. Furthermore this procedure did not induce any performance losses on the modules. However the EL imaging procedure itself is quite time intensive and therefore not suitable as a quality test that could be implemented at the end of a production line.
Using IR imaging in reverse bias, hotspots can reliably be identified on small and large modules. Due to higher current levels, hotspots can easier be identified on large modules. However high reverse currents can lead to significant damage and loss of performance of the modules. IR imaging in reverse has proven to be a faster alternative for hotspot detection, suitable for production line integration.
The findings contribute to improved defect identification and hotspot prediction techniques, enhancing the reliability of solar module manufacturing and maintenance processes. Future work should focus on refining defect classification and exploring the behaviour of hotspot formation and impacts on reliability in field applications.

This thesis investigates the optimisation of fixed-tilt bifacial agricultural photovoltaic (agri-PV) orchard systems using a multi-objective approach, focusing on maximising both crop Photosynthetically Active Radiation (PAR) and PV radiation. Due to the complexity of modelling agri-PV systems, advanced light simulation tools like Radiance, a backward ray-tracing software, were integrated into the study. Although effective, this software introduces significant computational intensity and stochastic behavior.

The primary aims of this thesis were to develop an optimisation algorithm for such irradiation models and to optimise the system by maintaining separate objectives for crops and PV modules. This separation eliminates inter-connectivity between the conflicting metrics during the optimisation process. Additionally, a single-objective optimisation was conducted by summing the two conflicting objectives to assess the impact of this separation.

The research concluded that Bayesian optimisation with Gaussian Processes is most suitable due to its efficiency and ability to handle noise. Applied to an apple orchard case study in northern Italy, the algorithm demonstrated significant advantages over classical Radiance-based methods, achieving high accuracy in a fraction of the time. Results indicated that multi-objective optimisation offers more robust and informative solutions compared to single-objective optimisation.
In conclusion, this thesis enhances the understanding and optimisation of agri-PV systems, enabling quicker, more accurate analyses and providing practical solutions for farmers and other users.

The motivation of this study stems from the reality that 2.2 million households in South Africa remain unelectrified, with the majority residing in informal settlements—communities that are remnants of the apartheid era, designed to enforce racially segregated living. While existing off-grid solutions provide basic electricity access to these households, they fail to meet the growing demand for higher levels of electricity access that local residents desire and can afford. This project aims to address this gap between the limited supply capacity of current off-grid solutions and the enhanced electricity services that communities are eager to receive.

Through site visits and consultations with local developers, specific design considerations and unique challenges were identified. These efforts revealed the limitations of existing open-source design tools, which do not comprehensively address the entire system’s design requirements for the proposed solution of a PV-powered DC microgrid. Consequently, a new framework was developed, integrating open-source tools and custom Python scripts based on established methodologies.

The comprehensive design framework was applied to the informal settlements of Oudtshoorn and Daveyton, demonstrating its ability to generate feasible, scalable, and adaptable microgrid designs. Overall, this project provides a solid foundation for advancing electrification efforts in underserved communities.

Global average temperatures are setting new records compared to the previous decade, primarily due to the accumulation of greenhouse gases such as CO₂. The growth potential in emerging economies worsens the current global situation. These CO₂ emissions are needed to be curtained and efforts are underway. The emissions target set by the International Maritime Organization seems far-fetched given the forecast for the growth of the shipping sector. Although electrification has proven to reduce emissions, the energy efficiency metrics (EEDI, EEXI) are incapable of providing the quantitative benefits that can be realized by switching to a bipolar DC (BiDC) grid when compared with the AC grid.
This thesis aims to evaluate the feasibility of BiDC grids over the complete operating profile of the ship by comparing ten arrangements, five for AC and five for DC, with the Key Performance Indicators (KPIs). The arrangements are two diesel generators (AC1 & DC1), two diesel generators and battery (AC2 & DC2), two diesel generators, battery, and shore power (AC3 & DC3), one diesel generator and battery (AC4 & DC4), one diesel generator, battery, and shore power (AC5 & DC5). The comparison is made for key performance indicators like CO₂ emissions, fuel consumption, electrical power requirement, propulsion power cable losses, capital costs, operating costs, propulsion system weight, and load carrying capacity of the ship. In this study, a ferry is chosen to compare the two grid architectures.
To establish the required background knowledge for the reader of this thesis, the literature on the current emission regulations, the different propulsion systems, and the differences in AC and DC grids are presented. The ship’s propulsion and auxiliary power demand are not freely available and are in the possession of the ship operator. Therefore, first, a preliminary methodology was developed for estimating the ship’s mechanical power requirements. Second, the ship’s electrical power requirements are estimated from the understanding of the literature. The cable power losses are also calculated for the AC and BiDC grid topology. Third, the diesel generator model was developed from the data points of a marine diesel generator for the AC and BiDC grid operation, i.e., the fixed speed and variable speed operation. Finally, an optimization problem was formulated for all the ten arrangements for minimising the CO₂ emissions over the complete operational profile of the ship.
Overall, it was observed that the minimum emissions achieved by BiDC grids, when compared with AC grids for all the arrangements, the emissions from the former were lower. Furthermore, these results depend on the operating profile of the ship, the shore infrastructure, the diesel generator and battery configuration and their technical parameters. However, these findings demonstrate the possible potential of BiDC grids in emission reduction from the shipping sector.

The penetration of the renewable energy sources in the global market has been constantly growing over the last years. This trend is expected to accelerate even more in the future due to the improving technologies, the economies of scale, the competitive supply chains and the improving developer experience. The main challenge for a faster and wider implementation of these resources is their intermittent character and the limitations that this implies. A concept that successfully deals with this variable energy output is the Hybrid Power Plant (HPP). HPPs combine at least two different sources of energy with the goal of delivering stable power with reduced fluctuations throughout the year. In addition, HPPs present various other synergies such as in operational costs or in the developing processes, that could lead to a reduced Levelized Cost of Energy (LCOE). The goal of this thesis is to assess the potential synergy of a combined wind and solar park for the case of the Energy Storage Lake (ESL) of the project Delta21. The Delta21 project has a twofold character: firstly, the usage of the ESL as a large battery integrated with green energy production and secondly the protection of the inland against floods due to high sea level or superfluous river discharge. The main challenge for this specific case is the large water level fluctuations within the lake in a daily basis due to the operation of the lake. The feasibility of a hybrid wind and solar park can be examined by various different perspectives and disciplines. Among these relevant components, the most frequently studied ones are the optimization of the energy resources, the use of common electrical infrastructure and the effect of the intermittent wind turbine shadows on the solar panels. Due to the special conditions, with the large water level fluctuations met at the lake, two more components become critical. These are the type of foundation of the wind turbines and the mooring configuration for the floating solar units. This project is evaluating a new potential synergy that regards the use of the wind turbine towers as anchor- ing points for the floating solar units. Therefore, the research is focused on the estimation of the forces acting on a floating solar unit. These forces are a combination of wind loads acting on the solar panels and the freeboard of the floaters, and wave forces acting on the submerged part of the floaters. In particular, the technology of the floating solar boat as introduced by GroenLeven and the small-scale wind turbines as designed by Dutch Wind are adopted for this project. Analytical formulations are used for the calculation of the wind loads. As for the wave forces, the linear potential theory is used and the calculations of the hydrodynamic coefficients and wave exciting forces are performed through the Boundary Element Method (BEM) software NEMOH. After the computation of first order wave forces and the corresponding responses of the floaters, the far-field approach is used for the estimation of the Quadratic Transfer Function (QTF) of the drift force. The methodology developed in this project and the accuracy of the model are validated with the use of reference data that regard the interaction of connected floating units placed in a close proximity and the corresponding second order wave forces as predicted by the far-field approach.

In the Netherlands, like in several other countries, there is a need to increase the contribution of Solar Energy to the energy generation technologies portfolio. To fully achieve integration and to use the entire potential of photovoltaic energy, certain levels of public knowledge and acceptance of the technology must be reached. In this context the role of scientific websites and information sources on Solar Energy gains importance significantly.

In this paper the development of a new improved version of the Dutch PV Portal (DPVP), a website on photovoltaic energy on Netherlands territory developed by the Photovoltaic Materials and Devices (PVMD) research group of TU Delft, is developed. After a prelaminar website quality assessment to establish the preferred areas of improvement and overall quality status, a set of improvement objectives were defined. These included: i) Integration of real-time real life performance data of a case study in the DPVP, ii) creation of a shading function using LiDAR data, iii) Improvements of the mathematical model, iiii) Creation of a real-time alarm for performance sensitive parameters.
These features are developed in the form of a case study monitoring platform which is integrated in the website. To do this a set of alarm systems as swell as visual monitoring tools were developed.

Furthermore, with the use of the newly integrated data and existing weather databases, an accuracy analysis of the DPVP model is performed. Thanks to this, accuracy and limitation of the website’s mathematical model is assessed, and areas for future improvements are identified.

Future energy systems are expected to rely increasingly more on distributed energy resources (DER). Prosumers that own energy resources such as photovoltaic (PV) generation and energy storage systems, will play a crucial role in the realization of DER’s in future power systems. Limited grid connections due to local electricity congestion make it more difficult for prospective industrially-sized prosumers to find suitable locations for their business operation. Energy resources such as batteries and PV, can be used to fulfil the desired electricity demands while adhering to the congestion related limits. By combining multiple energy resources and controlling them together the operational cost is drastically reduced. This thesis develops a stochastic optimization model that is able to find the optimal dispatch strategy for energy resources while adhering to constraints set by the DSO in a congested area. Also, a deterministic model is developed to be used as a comparison to the stochastic model. A deterministic model is simple and fast but it does not accurately represent the stochastic nature of PV generation, electricity consumption, and market prices. The stochastic model uses a set of different input scenarios for each time step and finds the optimal strategy considering all scenarios. In this thesis two models that consider stochasticity are developed: a stochastic and a robust model. For the robust model, the imposed grid constraints from the Distribution System Operator (DSO) have to be respected at each time step for each scenario and are classified as hard constraints. The model is able to find a optimal strategy for a week in each season when considering 11 scenarios. All predetermined constraints are respected for each simulation and while this makes the strategy reliable it also makes it conservative. For the second stochastic model, the DSO grid supply constraint is designed to be flexible and allow for occasional overshoot. In this case, flexibility is obtained by constraining the statistical distribution of grid power rather than the value at each time step and scenario. The calculated control strategy results in a simulated revenue increase between 3.67% and 4.78% depending on the season. For each season the objective cost reduces relatively more than the occurrence of grid overshoot. The term grid overshoot is used to indicate a situation where the DSO imposed grid limit is exceeded for a time step. The overshoot remains within the predetermined value threshold of 4% for all simulations of the stochastic optimization model.

Due to the much needed energy transition as agreed under the Paris climate agreement, it is expected that large amounts of variable renewable energy supply will be connected to the Dutch Transmission Network. Future social, economic and technological developments will determine both the amount of electrification as the central or decentral nature of the future supply of electricity. By investigating different reports with scenarios for 2030 and 2050, key uncertainties for the Extra High Voltage (EHV) network can be identified. As of now, no open source model of the Dutch EHV network is available for research. In this thesis a synthetic model of the Dutch EHV network is created from only publicly available data in DIgSILENT PowerFactory. The created synthetic model can be used as a tool to investigate future steady-state power flows considering the sensitivities of the topology, component parameters and different operational scenarios. Besides this, a method is created for generation of future scenarios in Python. Using this model and method, simulations were done to gain key insights into the Steady-State security of the future Dutch EHV Transmission Network. For the case study, it is investigated what the effect of future additions of variable renewable energy sources (VRES) and selected locations of 10 GW installed capacity of electrolysers would be on the Dutch EHV transmission network for 2030.

Rising global temperatures have shifted mankind’s methods of energy production towards a more sustainable manner. Solar energy technologies have become one of the world’s leading methods of producing clean, sustainable energy. However, this technology has been facing some adversities in the field of thermal management. The majority of solar modules in the commercial market are monofacial modules, for which many thermal management solutions exist. For bifacial solar modules, this field is still being researched. This, together with the fact that bifacial modules are projected to reach a 40% market share by the year 2028, prompts the need for thermal management solutions for this technology. The proposed solution is an integrated heat sink (IHS) built in a bifacial solar module. This was thoroughly researched using the multiphysics, FEM-based software COMSOL. The geometry was drawn using the COMSOL environment and a hybrid mesh was created to accommodate the module. Initial issues and computation errors led to decreasing of model dimensions. A 1 cm by 1 cm glass bifacial module was created and debugged successfully. This model was then simulated using two COMSOL studies: ’Heat Transfer in Solids’ and ’Surface-to-Surface Radiation’. All of these simulations were performed using four illumination combinations (front illumination/back illumination [W/m2]): 1000/50; 1000/100; 1000/200 and 1000/300. After successful simulations of the 1 cm by 1 cm model, the model was scaled up to a 4 by 4 (67.31 cm by 67.31 cm) glass bifacial module. The results for both the 1 cm by 1 cm module and the 4 by 4 module were promising. In the case of 1000 W/m2 front illumination and 300 W/m2 back illumination, the 1 cm by 1 cm solar cell showed a temperature drop of 0.92 degrees Celsius and a relative efficiency gain of 0.359%. The solar cells in the 4 by 4 module showed a temperature drop of 1.33 degrees Celsius and a relative efficiency gain of 0.519%. Next, temperature simulations were performed with varying IHS thickness, from 1 mm to 10 mm. Taking into consideration that with increase thickness the temperature did not reduce significantly and that the module weight was being increased, a 1 mm IHS was found to be the most beneficial. Yield calculations were performed on a 2 by 2 (34.29 cm by 34.29 cm) bifacial module, both with and without the utilization of the IHS. It was found that with the utilization of the IHS, the financial gain was 0.05 euros/m2/year. Lastly, lifetime prediction analyses were performed to receive insight on the effect of the IHS on the lifetime of a bifacial module. It was found that the lifetime gain for a bifacial module with the IHS is about 26 days, corresponding to a relative lifetime gain of 0.68%. It should be noted however, that the lifetime of a module without the IHS under the simulation conditions was 10.27 years, far below commercially reported lifetimes. This is due to a high maximum operational temperature and a large difference between the minimum and maximum operational temperature.
The proposed integrated heat sink showcased positive effects in temperature management and efficiency gain in bifacial solar modules. The use of a hybrid mesh proved beneficial for the computation time and different scenarios can now be simulated. However, yield calculations only showed a very small financial gain and lifetime prediction analyses showed a very small lifetime gain. Further improvements include a design that can increase the heat transfer of the IHS, such as varying the thickness of the bottom glass layer or utilizing a transparent backsheet. The possibility of connecting the IHS to the frame of the module to further increase the heat transfer to the environment should be investigated. Also, different material options for the IHS and the addition of heat dissipation fins should also be researched.

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.

This thesis deals with the Life Cycle Assessment (LCA) of the manufacture of an EXASUN X-Glass photovoltaic (PV) module to understand its true environmental impact. The methodology and general guidelines for conducting an LCA on a Photovoltaic (PV) system are developed, within the scope of this thesis. The LCA follows a standard framework, proposed by International Organization for Standards (ISO) and International Energy Association (IEA). LCA is a technique used to understand the total environmental impact of all the processes involved in the manufacture of a particular product. It takes into account all production process stages, to check the highest impact involved in production. The results from this LCA study are obtained using the SimaPro software. These results quantify the production impact and aid in understanding where and how to improve the carbon footprint of associated production processes. The environmental impact categories from the ReCiPe 2016 assessment methods were used in this study. All the impact categories such as global warming potential, ozone depletion, photochemical ozone formation, etc. were taken into consideration. This includes all the mid point and end point categories offered by ReCiPe method. Additionally, the cumulative energy demand method was used to calculate the total renewable and non-renewable energies mix, used in the production process. Subsequently, using this information, impact categories such as energy payback time, net energy ratio and GHG emissions rate were obtained. This study is based on the data obtained from Ecoinvent LCA database, accessed through the SimaPro software. This data was then changed according to the inventory update published by IEA PVPS. PV cell data was obtained from literature. However, the remaining materials used in the X-Glass module’s manufacture were noted and their associated data was used to develop an appropriate LCA model. The model incroporated factors including transport, quantity, etc. The functional unit (FU) in this study is 1 m2 of X-Glass production. For better analysis, all the impacts caused by manufacturing 1 X-Glass module were used to compare the results. Results clearly show that the contribution of PV cells production to the environmental impact is high when mono-Si is used by the PV module. The energy requirements and geographical influence of these energy mixes that are involved in the PV cell manufacture are clearly explained. The cells for a typical X-Glass manufacture were modeled with a Chinese energy mix as the cells were produced in China. The energy payback time (EPBT) of X-Glass module installed in the Netherlands are 1.3 years with 73 g CO2/kWh GHG emission rate. The CO2 offset period was found be 3.9 years. These modules were then compared to a similar model developed for an European energy mix. The differences in results, due to the change in energy mix show that he energy payback time for manufacturing the same X-Glass module was found to be 1.3 years, with GHG emission rate of 32 g CO2 eq. The CO2 offset period for these was 1.7 years. The obtained results were cross-validated based on literature, outlining various LCA studies conducted on similar PV technologies. Thus, based on the results, it can be concluded that the environmental performance of X-Glass module was found to improve by 40% when cells from Europe are used as the EPBT of EU based model was found to be 1.3 years with an emission rate of 32 g CO2 eq.

By 2050, the global demand for food and energy is expected to grow by 70% and 50%, respectively, as a result of the increase of the world’s population. To keep up with the growing world population, significant changes have to take place in the agricultural sector regarding land and energy use. In this work, two developments in the agricultural sector are combined: the integration of photovoltaic (PV) modules in a horticultural system and the use of light emitting diodes (LEDs) as supplemental lighting source for crops. Four greenhouse systems having different configurations of these two technologies and a plant factory are designed. The performance of these LED-based agrivoltaic systems are analysed for the crops lettuce and tomato and for three different climates: Sweden, the Netherlands and the United Arab Emirates. This study shows that the integration of a PV system and LED technology in a greenhouse system improves the crop production per unit of energy compared to conventional greenhouse systems. Furthermore, it has been found that the production of crop per piece of land can be significantly increased by the use of a plant factory. In extreme climates, the plant factory is the optimal system for the cultivation of crops.

The Distribution System Operator (DSO) Alliander has the ambition to explore possibilities of improving its predictive demand modelling applications. This research aims to contribute to one of these, the Advanced Net DEcision Support (ANDES) model, which provides detailed predictions for long-term capacity planning. This thesis pursues two main objectives: Firstly, the creation of a probabilistic power demand model which reflects the volatile nature of real customer demand in an adequate manner. Secondly, the development and evaluation of methods to estimate certain model output quantities of interest in a computationally efficient manner. To this end, variance reduction techniques have been investigated. Self-tuning Importance Sampling (IS) methods giving different weight to time steps and/or customer load profiles have been developed. In order to make the optimisation stage superfluous, additionally an approach to find a generalised asset distribution has been investigated. The essential finding from evaluating all considered methods was that their performance in terms of efficiency and accuracy depends mainly on two variables – the order of magnitude of the estimated quantity and the number of customers connected to an asset in question. For small assets and an estimated overload probability of the order 1e-5 or smaller, all profile IS methods and especially the generalised bin probability IS showed the strongest performance with average speed-ups of 5-30 times with respect to the reference method of sampling full annual traces. For assets with more than 80 customers and small overload probabilities, the profile IS methods were found to frequently produce estimates of a significantly too small order of magnitude. Conventional Monte Carlo (MC) sampling and time step IS, in turn, produced reliable estimates regardless of the number of customers. For assets of all sizes with an estimated overload probability of the order 1e-4 and larger, conventional MC sampling showed the best performance with speed-ups above 5 times. Overall, conventional MC sampling performed robustly in all circumstances, while IS demonstrated its potential to significantly increase the estimation efficiency of rare event probabilities in certain cases. To determine which magnitudes demand maxima and minima can potentially reach, Extreme Value Theory (EVT) has been applied. The computationally more efficient methods and extreme value inference were considered not compatible, sampling full annual traces appears to be required for a reliable estimation of maximum and minimum demand return levels. Based on the findings of this thesis, a flexible algorithm could be investigated in future research which employs IS for rare event probabilities and conventional MC sampling otherwise. For an integrated evaluation of all risk metrics, the algorithm could initially sample 200 entire annual traces to be used for extreme value inference.

The growing demand and improvements in manufacturing capabilities, supported by government subsidies, has allowed the increase in the installed capacity of utility scale solar parks. Due to the remoteness in their location, the costs associated with dispatching personnel for maintenance is extremely high. A major contribution towards unscheduled downtime of these plants is due to the inverter faults. Currently, reactive and preventive maintenance are the most prevailing methods to identify and fix inverter faults. The presumption that the components will not under-performor fail until the scheduled visit, leads to a significant loss of production and revenue. To deal with the disadvantages of current maintenance methods, the solar industry is very keen on understanding the possibility of early detection of inverter faults by implementation of predictive maintenance. This research assessed the applicability of Machine Learning (ML) towards early signal detection of inverter faults in order to generate predictive maintenance alerts. The data for building the ML algorithms was acquired from a Shell owned 26.6 MWp utility scale solar park located in Moerdijk, The Netherlands. The early signal detection algorithmdeveloped, was based on the comparison between the actual and the predicted active power. The model built to predict the active power was based on two supervised learning methods;Elastic Net and Gradient BoostingMachine (GBM) with quantile regression. These models were capable of predicting the active power with a Mean Absolute Error (MAE) of 0.98kW & Root Mean Square Error (RMSE) of 1.8kW using Global Plane of Array irradiance (GPOA) and module temperaturemeasurements available from theMoerdijk data. The early signal detection relied on differentiating between prediction error and actual error. A window was created to encompass the maximum extent of prediction errors to avoid any false positive signals. This window for elastic net was found to be ¡¾ on the lower side and 2¾ on the upper side. Although when elastic net method was tested on 337 inverters- by looking at their residual variation 1-week prior to registered fault- it was found that the predictions suffered a periodic structural error. This was due to the erroneous predictions at times with extreme irradiance values. To mitigate, this the GBM with quantiles of 0.01 and 0.99 of GPOA was built to create a range of predictions giving rise to a wider range for normal operation. The results from both the algorithms indicated no early signals for inverter fault detection. This was partly due to data quality issues with fault tags in the Supervisory Control and Data Acquisition (SCADA) monitoring system; only 7 actual fault cases were identified. Additionally, the economic feasibility of implementing predictive maintenance was found to potentially reduce the current Operational Expenses (OPeX) by up to 10%. Despite the issues with data quality, an approach of using ML towards early fault detection for inverters in utility scale solar parks has been realised through this research.

With emphasis on finding storage solutions for renewable based power plants, hydrogen has emerged as one of the prominent options. Hydrogen required in fertilizer, oil and gas industries etc., is produced using fossil fuels which emits carbon dioxide 10 times the produced hydrogen. It is important to produce the hydrogen from green energy sources for this industrial use or for the energy storage use. Solar energy harvested from Photo-Voltaic (PV) technology can be used to produce hydrogen in an alkaline electrolyzer. Directly coupling the PV and electrolyzer systems will have least components contributing to inefficiencies, complexities etc. In this study, a tool was made using MATLAB-Simulink, to optimize the PV-Electrolyzer directly coupled system. In literature many authors have done the same but none of them have included variation in space irradiance over PV farm, variation in PV module parameters due to manufacturing defects or defects arising over the period of use. These variations affect the IV curve of PV modules and in turn affect the performance of the whole system. It was observed that after optimizing, the coupling efficiency in the range of 90-95% could be achieved in directly coupled systems with shorter string lengths of PV. This was even after including the variation up to 20% in parameters like irradiance and PV module parameters. If the best configuration is not available in the market, then the set of next best configurations is available as output of simulation. Even with variation of 10% in input parameters, the maximum difference between the global maximum and other local maximums in the results is 3%. This 3% compromise results in energy loss of 870 kWh and 148 euros loss per year for a 50 kWp system. Which is 2.96 Euros/kWp loss, at 1 GW scale it may result into a loss of 2.96 million Euros, if we ignore the variations. In comparison to the DC-AC-DC configuration that comprises of inverter, rectifier and transformer, the directly coupled system performed better. The efficiency was almost 5-10% more for directly coupled system in comparison to DC-AC-DC system. The weighted efficiency for both the configurations were calculated, where the weights were based on the occurrences and the energy contribution of an irradiance bracket. The weighted efficiency for directly coupled system was 95.7% and for DC-AC-DC was 90.63% for Amsterdam. Even after considering all the seasonal, weather parameter and module parameter variations, directly coupled PV-Electrolyzer systems with shorter string length and bigger electrolyzer cells gave the best efficiency amongst all, which was above 90%.

Within the coming years it is expected that PV installations could be established on every possible surface and terrain, within the urban environment. Due to the complex morphology of buildings in urban environments,systems will likely be more susceptible to partial shading compared to other conventional PV systems. In particular bifacial PV modules are highly susceptible to shading since they rely on irradiance on both the front and the back surface. To better estimate the irradiance received by such systems, particularly in urban environments, much research is conducted to develop fast and accurate simulation tools. The research described in this report investigated the different simulation frameworks that have been developed for modeling bi-facial PV systems, and develop a new simulation framework capable of simulating irradiance within urban environments. Available models capable of simulating rear irradiance differ in input variables considered,simulation time required and accuracy of predictions. Empirical models, for example, can result in inaccurate predictions since not all variables affecting rear irradiance are considered. Only the 3D view factor, back-ward and forward ray tracing methods fulfill the requirements to simulate the backside irradiance/irradiation when the aim is to perform simulations in more complex urban environments. Out of the existing simulation models tested, the backward ray tracing model performed within the Radiance software proved to be the fastest simulation tool for modeling yearly irradiation or a single irradiance measurement incident under free horizon conditions. Time of use (TOU) simulations, however, results in longer simulation time for backward ray tracing performed in Radiance, since a ray tracing simulation at each time instance is required. To calculate the irradiance a receiving surface receives in an urban environment a method is worked out based on view factors and ray casting. Within the software Rhinoceros a CAD design of the surrounding environment is created, while the plug-in Grasshopper is used for the ray casting and mathematical calculations. Through a series of sanity checks, it was determined that the developed methods are reliable for calculating irradiance/irradiation when the aim is to perform simulations in more complex urban environments. It was also identified how different sky models can result in large differences in irradiance simulated. Sky models such as Isotropic, Hay and Davis or simplified Perez underestimates the irradiance, when receiving surfaces are tilted in comparison with the Perez luminance distributed sky model. The model was validated using monitoring station measurements and compared with simulations performed with other ray tracing models. DHI, DNI measurements obtained from the Solys 2 are used for replicating the irradiance measured at the dual-axis and single-axis tracker. Since their orientation was fixed throughout the measurement period they are referred to as POA 1 and POA 2 respectively. With POA 1 and 2 having a 90 and 30 degree tilt respectfully and an azimuth of 67 and 180 degree respectfully. Making POA 1 the front/back side of a typical (bifacial) east-west configuration and POA 2 the front of a tilted (bifacial) module. Two measurement days are considered, a fully overcast day and a clear sunny day. For the overcast day, an relative RMSE value of 23.09% and 11.7% are achieved for POA 1 and 2. For both cases the irradiation was mostly underestimated, considering the negative MBE and positive MAE. On the clear sunny day, relative RMSE values of 20.35% and 5.26% are achieved for POA 1 and 2. The model again mostly underestimates on the sunny day. A simple DHI correction with the factor 1 divided over the SVF at the Solys 2 is performed in order to investigate the impact of potentially corrupted DHI measurements on the irradiance simulations. With relative RMSE value of 19.96% and 5.2% recorded for POA 1 and 2 on the overcast day. While on the sunny day relative RMSE values of 19.38% and 3.77% are achieved for POA 1 and 2 respectively. Only slightly changing the model predictions. When compared to ray tracing models the model performs slightly better than the forward ray tracing model. A possible reason why the forward ray tracing was underestimated was proposed to be due too the small aim area used. However increasing the aim area would require a larger number of rays to maintain the same accuracy which results in longer simulations.Possible improvements to the model could be, adding reflected irradiance term on reflecting surfaces and the Perez luminance distribution sky model.