The Dutch national sustainability goals are too ambitious for current government plans. Reaching these goals can become even harder than it already is, as the Dutch government faces two challenges that are especially present in the Netherlands. These problems are increasing grid congestion and NIMBY’s contesting the use of land for renewable energy production. Novel, decentralized integrated photovoltaic technologies (PV) can potentially tackle both problems at once. Building-integrated PV (BIPV) and vehicle-integrated PV (VIPV) are examples of this. This research focuses on VIPV. VIPV poses some unique challenges compared to stationary PV systems. Being a dynamic system, shading and module orientation change rapidly over time, whereas for a stationary system these are both optimized for the highest yield results.

This research investigates the feasibility of photovoltaic integration on metro wagon surfaces in the Amsterdam metro network. To this end, an extensive MATLAB model was made. The model uses inputs from sources such as from GVB1, KNMI2 and PDOK3. The model is designed to output the potential yield for metro trains running on the five different metro lines consisting of GVB’s metro network.

The model uses five submodels: a skyline, a metro position, a weather, a temperature and a yield model. The skyline model produced a library of 456 skyline profiles along the aboveground sections of the metro net. The metro position model estimates the positions of the metro trains by using timetables directly from the GVB website. The weather model parses KNMI data and determines the module irradiance using both a BRL and Perez model. The temperature model uses a fluid-dynamic model to model the module temperature and distinguishing between the contributions of the different heat fluxes. The yield model calculates the variable efficiency of the modules and consequently their yield.

The output data is stored in cell arrays. For each of the five metro lines, for each of their two directions, trains were subdivided into three segments and the roof of each segment was subdivided into three sections. Each of these cells contain matrices of 365x5000, the rows representing the days of the year and the columns the amount of timestamps. The three rooftop sections represent the slanted sections on both the port and starboard side of the vehicle (w.r.t. the outbound driving direction) and the flat section in the middle.

Probability distributions of the yield, timestamps and Sky View Factor (SVF) were produced. Besides providing insight in the behavior of the PV system, these also show the amount of instances where the yield and the SVF are 0, representing the amount of minutes where the modules are fully covered by tunnels, viaducts or train station roofs.

The total yield for each of the metro lines varies greatly: 301 MWh for Line 50, 173 MWh for Line 51, 79.8 MWh for Line 52, 91.8 MWh for Line 53 and 108 MWh for Line 54. The differences are explained by the aboveground percentages and the train frequencies on each line. The specific yields
range from ∼590kWh/kWp for the flat roof section on Line 50 and between ∼66 and 82 kWh/kWp for the slanted roof section on the starboard section on Line 52. A heatmap was produced showing the annual yield in kWh/m2 as a function of location for each of the lines, distinguishing between roof sections. The specific yields show that integration is most feasible for Line 50, followed by 51, 54, 53 and 52 respectively. For economic viability, lower specific yields mainly mean a longer payback period, so the choice is up to the GVB if they deem the costs worth the benefits. In conclusion, this model shows promising results for the feasibility of integrating PV modules on the Amsterdam metro network.

With the rapid growth of Agri-photovoltaic (Agri-PV) systems, a spectral analysis that provides high-resolution data becomes necessary. For such a precise application, there is a need for a spectral irradiance sensor. However, for better precision instruments such as spectroradiometers that are available on the market are expensive. So, in light of this information, the thesis aimed to design and simulate a spectral irradiance sensor’s circuit. The goal was to obtain a PCB design for a high-resolution sensor, suitable for Agri-PV and to simulate the designed circuit to confirm the working theory on which the circuit was made. The circuit was designed on ALTIUM designer where the sensing unit and control unit were separated. The sensing unit consisted: photodiodes and amplifier circuits. The control unit consisted of: ADC and a microcontroller (Raspberry Pi) for signal conditioning and analysis. This circuit was then analyzed and validated through PSPICE OrCAD software. The designed PCB of the Sensing unit contains six photodiodes, which increases the resolution of the spectral sensor. These photodiodes could help in collecting data for a narrow wavelength band (say, approx 10nm width) with the help of bandpass filters. As the PCB was designed while keeping in mind the previous casing’s dimensions of the available PVMD sensor (so the new PCB design can fit on it). Hence, this study finds that it is possible to design a cost-effective spectral sensor circuit that is suitable for Agri-PV applications.

Solar photovoltaic (PV) energy has the potential to become a major source of electricity production worldwide. However, the deployment of this renewable energy source comes with several challenges. Besides societal, political, and grid-limiting, several technical limitations decrease the trust in this technology. Within this context arises the Trust-PV project, whose objective is to improve the performance and reliability of photovoltaic systems.

This work contributes to this European project by exploring different power prediction models for several types of PV systems. Considering the broadness of the topic, four parts or blocks are identified. The first part deals with machine learning models to forecast the yield of residential PV systems. The second block focuses on analytical models used during the design phase. The third part is dedicated to systems floating on water. Lastly, a metric to assess the tolerance towards shading of different modules is developed in the fourth block.

Starting with the first block of machine learning techniques for PV power forecasting, Chapter 2 introduces the topic by reviewing a large number of manuscripts. The chapter performs a broad classification of the reviewed literature with the objective to identify trends and gaps in the field. Among the identified trends, one can highlight the high percentage of predictions for the day ahead, the generally low number of systems employed to train the models, and the concentration of systems in mild climates.

The latter points may stem from researchers primarily using the systems available within their institutions. To promote collaboration, Chapter 3 presents a developed website that lists PV power open source databases. The website aims to encourage researchers to train and test their models with different data sources.

One consequence of the concentration of systems in mild climates is that the effect of climate on machine learning models remains underexplored in the literature. Chapter 4 addresses this gap by studying how machine learning models behave for systems located in different climatic zones. The results show that weather homogeneity affects the accuracy of the models. Models developed for systems located in uniform climates - like desert areas - achieve in general higher accuracy than the models developed for systems in highly varying climates - like tropical areas.

Chapter 5 addresses another challenge: creating a single machine learning model able to monitor the performance of a large fleet of residential PV systems. The developed model surpasses in accuracy an analytical reference model but is limited by a fundamental characteristic of machine learning methods: the focus on large errors which resulted in the overlooking of smaller systems. Consequently, Chapter 6 develops a different approach based on the peer-to-peer methodology. In this approach, the power output of similar neighboring systems is compared to identify any malfunctions. The method is tested for the residential fleet of PV systems and proves effective for detecting faults.

Moving on to the second block of analytical power predictions, Chapter 7 presents the PVMD toolbox, a state-of-the-art analytical simulation framework that can predict the power of systems that do not exist yet. The abilities of the toolbox are tested for residential systems in the same chapter and the results show the negative influence that inaccurate input irradiance data has on the predictions.

This importance of accurate irradiance data affects all kinds of PV systems, but especially large-scale ones. Therefore, to monitor them, a proper allocation of irradiance sensors is essential. Hence, in Chapter 8, a software tool is developed to identify the optimal number of irradiance sensors and their position in a PV farm. The tool’s strengths are more prominent in plants located on terrains with significant elevation changes.

The third block focuses on PV systems that are installed on floating platforms rather than on land. Chapter 9 introduces the topic by examining three factors influenced by proximity to water that can impact the production of a floating PV system in a French quarry lake: movement fluctuations, dust accumulation, and module temperature. The results reveal a limited influence of all factors on the production for the period of study therefore facilitating the deployment of floating systems.

The block continues by studying the effect of sea waves for a system located in the North Sea. The simulation results from Chapter 10 reveal that wave fluctuations can have a negative yet limited effect on the DC and AC yield of floating PV systems. These results are further elaborated in Chapter 11, where the model is improved by considering the fluid-structure interaction. This advanced model enables to study the effect of various platform characteristics on the power mismatch losses. The results reveal a trade-off between mechanical stability and mismatch losses.

Finally, the last part deals with the power lost when a PV module is partially shaded. Chapter 12 develops a simulation tool to efficiently calculate the shading tolerability of a PV module given its datasheet. The shading tolerability is a metric that quantifies the resilience towards shading of a PV module, that is how much power is lost when the module is partially shaded. The developed tool is used to create a database of shading tolerability of commercial PV modules, to compare the resilience of different modules towards shading.

The transition to renewable energy sources is crucial for mitigating climate change and ensuring a sustainable energy future. Harnessing the potential of offshore wind and floating solar technologies offers a promising avenue to increase the share of renewables in the global energy mix, reducing reliance on fossil fuels and minimising environmental impact. Offshore floating PV technology in itself and its integration with existing offshore wind farms is a new application, requiring research and development to prove its viability. Within this research field, more comprehensive models are being developed to accurately estimate the power and energy output of floating PV installations, incorporating unique characteristics and conditions specific to these systems. Simulation models for land based PV systems cannot be used as they cause an error due the different environmental conditions. The objective of this thesis is to develop such a model and use it to determine the physical placement of these floating installations in the wind farm to maximise the energy generated.

EE-Farm II is a sophisticated simulation tool designed for evaluating the electrical systems of wind farms, encompassing both AC and DC components which has now been enhanced to integrate solar farm modelling. The model for floating PV was built on the existing EE-Farm II tool in MATLAB. A tilt model from literature is used to analytically determine the effect of sea waves on the tilt of the floating PV and a model to find the effect of static shading of the wind turbine on the floating installation was developed from scratch.

In the model, the effect of degradation has been neglected and it has been assumed that the MPPT of the inverters is ideal. The tilt model shows that on average the annual energy produced would be similar to the case with no tilt effects considered i.e. fixed tilt however, the power variability can be observed on a smaller temporal scale (for e.g. daily) and is dependent mainly on the wind speeds. The energy loss on a floater due to shading in the worst-case scenario for the simulation considered was found to be 10.5% and due to the wind farm being in the North Sea, shading losses are prevalent in the north of the wind turbine.

This work helped understand the behaviour and make better power variability and energy estimations for offshore floating PV installations and also helped understand how to better place floating PV when integrating it with an offshore wind farm to maximise energy production. This work can help installation companies with their analysis and to make predictions about the energy output and possible variability throughout the year in different temporal scales. Future work should focus on determining the optimal orientation of the panels or strings by improving the model by modelling bypass diodes in the modules, addressing the current limitations due to the simplifications made.

Hydrogen is not only the most common element in the universe, but also a versatile molecule which is an option to decarbonize sectors that are not easily electrified or are energy intensive, such as long-distance transport, chemical industry, and metallurgy among others. Hydrogen gas is typically obtained from fossil fuels, and only a small fraction is produced nowadays from electrolysis, or the process of using water and electricity to produce gaseous hydrogen.
If the electricity for powering the electrolysis process comes from renewable sources, the produced gas will have no associated greenhouse emissions. This is the so-called green hydrogen, which is the base for decarbonization of carbon intensive industries. This work investigates the potential of stand-alone green hydrogen production from solar energy, covering the whole design process, from an allocation and feasibility analysis, to system control. To do so, this thesis is separated in two parts. The first part focuses on the preliminary assessment phase of photovoltaic (PV) systems and the solar resource, while the second covers the integration of PV and electrolysis systems finalizing with a control strategy for these systems.
Chapter 2 presents a methodology for analyzing potential sites for PV deployment, including information on the degradation of the site. This provides the designer with additional information beyond the purely technical and economical layers that are typically considered in this type of study. The more degraded a site is, the more suitable it is for deploying new PV projects, avoiding pristine natural areas. This, combined with mitigation measures can minimize the environmental impact of new PV projects.
An analysis of the efficiency loss of PV systems is discussed in Chapter 3. In particular, the efficiency loss caused exclusively by quick variations in irradiance, as a consequence of passing clouds. These abrupt and quick changes affect not only the solar modules, but components downstream, such as the maximum power point tracker. The implemented algorithm might be sensitive to these changes and move the operating point of the PV module away from its maximum power point, leading to energy loss.
Predicting quick changes of irradiance is a topic covered in Chapter 4. Using sky images and artificial intelligence, it is possible to predict ultra-short-term irradiance. The proposed method is an ensemble of models, each trained on a particular sky condition. Because each model is highly specialized, once the sky condition is determined, the model that performs best on each sky type is employed, leading to lower prediction errors, more precise predictions and lower training data needed. Yet, an accurate prediction is a topic for further research.
The integration of PV with hydrogen systems is introduced in Chapter 5, which presents a literature review on integration methods for PV and electrolyzers as well as the main challenges for operating these systems in a variable manner.
Moving to the design phase, Chapter 6 proposes a sizing procedure, based on Particle Swarm Optimization to minimize the energy that cannot be used by the hydrogen equipment (electrolyzer and compressor), aiming at the maximum energy utilization in the system. Horizontally-placed PV modules provide a good compromise between efficiency, hydrogen production and cost.
Once the system has been designed, Chapter 7 puts together all the topics covered in this dissertation proposing a control strategy for an optimally-sized stand-alone PV electrolyzer systems, without electrical storage. The control is based on prediction of irradaince changes using sky-images. From Chapter 4 it was clear that the prediction using sky images is far from perfect, yet this is needed for control. To solve this problem, the strategy proposed in Chapter 7 relies on information on the uncertainty of the prediction and uses fuzzy logic to account for imperfect predictions. This strategy can effectively smooth power changes without the need of additional storage components.

The increasing deployment of photovoltaic (PV) modules in urban environments, often prone to shading, underscores the critical importance of selecting shading-resistant modules. Unfortunately, a standardized parameter for assessing and comparing PV module performance under shading remains absent. Currently, quantifying a module's ability to withstand shading remains a challenge, as datasheets typically provide vague, qualitative descriptions. This research addresses this issue by presenting the development of a shading tolerance calculator using MATLAB. This calculator yields a numeric parameter known as Shading Tolerability (ST), providing a quantitative measure of a module's ability to cope with shading conditions. By offering a standardized metric for shading tolerance, this tool enables precise characterization and facilitates direct comparisons between different PV modules.

The research's primary objective is to advance the development of a tool that can calculate the ST of any PV module using readily available datasheet parameters. The tool will subsequently be validated through experimental testing and employed to establish a comprehensive database for commercial PV modules, offering guidelines for achieving high ST. Initially, the model transitioned from sectional resolution to cell-level calculations, allowing for a more detailed analysis. The study compares ST results obtained at the cell-level with those at the 12-section level and examines the ensuing impact on ST.

A sensitivity analysis explores the influence of key PV characteristics, including breakdown voltage, nominal operating cell temperature, and bypass diodes, on ST. Notably, the analysis reveals that bypass diodes have a positive effect on ST. Based on these findings, guidelines are formulated to enhance PV module performance under shading conditions and improve the module's ST value.

Solar photovoltaic (PV) has seen the most rapid growth among the renewable energy sources in the last decade. The market share of bifacial PV modules is expected to rise up to 70% in 2033. Such technology enables different farm configurations; however, it introduces complexities during the modelling phase, particularly concerning the estimation of incident irradiance on the rear side of the modules. The aim of this thesis is to investigate the potential of E/W vertical bifacial PV farms, in terms of market revenues, with respect to N/S tilted counterparts. A bifacial PV model is developed to estimate the power generated by a large-scale farm. This is based on the view factor concept and a 2D assumption is adopted to enable fast simulations. Such model takes into account the non-uniformity of the incident irradiance as well as the spectral impact. A multi-dimensional matrix approach is implemented to minimize the computational time while performing the calculations for individual cells and wavelength values. The model is validated in collaboration with the company KIPP&ZONEN, focusing on the broadband rear irradiance and its non-uniformity. Overall, the model shows sufficient agreement with the measured data, namely a mean bias deviation of −1.29W /m2 (−2.22%) and a RMSE of 12.65W /m2 (21.69%) are obtained. The validation highlights larger errors in case of higher tilt values, especially during the clear days and at the edge of the modules. Specifically, the error is proportional to the amount of unshaded ground seen by a cell, in alignment with the limitations of the view factor concept. The profitability of vertical modules is studied in relation with various variables, including design parameters, market conditions, curtailment strategies and hybrid vertical/tilted configurations. A global scale is achieved by extending the simulation to 102 locations worldwide. To calculate the revenues of the PV farms, electricity price curves are modelled considering lower noon prices, hence different market conditions are identified by the minimum price and the ratio between morning/evening and noon prices. Whether vertical or tilted configuration is favourable in terms of market revenues is not dependent on the curtailment strategy unless the maximum power is limited to 70% of the nominal value. Combining vertical and tilted modules within a PV farm is found to be advantageous only in case heavy curtailment is applied. Among the design parameters, the row-to-row distance has a higher impact on the market revenues with respect to the modules’ elevation. Specifically, larger distances are favourable for both configurations even though such benefit is more evident for vertical modules whereas optimal height values can be identified. A minimum ratio between morning/evening and noon prices is calculated, which represents the lower limit for the higher profitability of E/W vertical modules with respect to the N/S tilted case. Such value is dependent on the specific location and the design parameters. The locations characterized by a low diffuse fraction are recommended to implement vertical PV farms.

With the rapid increase in renewable energy demand, various kinds of renewable technologies have been realized. Of particular interest is that they require a certain amount of space, and they may conflict with other land utilization purposes such as food production, protected nature areas, and life-sustaining services, thus the land use pressure is multiplying. For this reason, spatial management is required to analyze the optimal locations for such implementation.

A site suitability analysis is one of the applications that could be used to address this issue. Typically, it was done by mainly examining two constraints: technical and economical criteria, and excluding natural locations from the analysis. The challenge is that without the consideration of an environmental aspect, rich nature areas that are not included in the protection zones, cannot be identified. Therefore, this research aims to conduct the site suitability analysis for ground-based solar energy technology in the Netherlands and advance a suitability model by incorporating the environmental criterion in the assessment.

The study was designed into four phases. Beginning with Phase 1, a compatibility index was developed based on the concept of area degradation. This technique evaluates the compatibility level of an area in terms of an environmental constraint by quantifying the existing land degradation. Subsequently, it was combined with other factors from technical and economical criteria, constructing the suitability index in Phase 2. The Analytic Hierarchy Process (AHP) is a method that was adopted in this combination process. At the end of this phase, five suitability maps were generated from the shift in focus among technical, economical, and environmental criteria. Later in Phase 3, an additional suitability map was developed by analyzing the locations of existing solar projects in the Netherlands. Finally, an example of applying the suitability results was demonstrated in Phase 4 through a case study that set an energy target of 35 TWh as a minimum requirement for solar energy development.

As a result, the preferable locations were specified by the suitability model for this energy realization. They are mostly distributed in the western part of the country (Zeeland, Zuid-Holland, and Noord-Holland provinces) around the major urban and industrial sectors. The proportion of land features in these areas is comprised of 0.4% for border of infrastructure, 17.9% for natural areas, 19.3% for urban areas, and 62.4% for agricultural areas.

In today’s society, the need for alternate high-speed data transmission solutions has increased due to the radio frequency (RF) spectrum’s growing congestion. Visible Light Communication (VLC)-based LiFi has emerged as a possible alternative to current wireless technologies like WiFi. The difficulty, though, is that the photodiode performance as a receiver for VLC under outdoor conditions rapidly degrades. To solve this problem, the photodiode receiver was replaced by a PV cell receiver with the primary advantage of being optimized for outdoor conditions.
While earlier research has concentrated on light and modulation optimization to
improve the performance of VLC systems, our research adopts a novel strategy by
analyzing the effect of different light wavelengths on seven different PV cell technologies in hopes to design an optical filter and realize noise-free VLC for PV cells. We specifically are interested in identifying the LED wavelength that has the greatest bandwidth in order to increase the possible data transmission speeds in PV-VLC systems. In order to do this, a thorough characterization of several colored LEDs with different wavelengths was carried out across seven PV technologies, including PERC, AL-BSF (5INCH), AL-BSF (6INCH), SHJ, IBC, Busbar-free Al BSF, and TOPCon. Each LED wavelength was tested under three different intensities of light (100, 300, and 500 W/m2).
In terms of PV technology, TOPCon demonstrated superior performance at low bias voltages, while Busbar-Free Al BSF(EEPV) outperformed the other PV technologies at higher bias voltages, especially at the maximum power point. Furthermore, the analysis of light intensity revealed that the bandwidth does not only depend on capacitance but also on the internal resistance of the PV laminate. For the c-Si solar laminates tested the considerably larger resistance at lower light intensities in the bias voltage interval from 150mV to 450mV resulted in lower bandwidths at lower light intensities. The measurement results under different LED colors, concluded that when operating near maximum power point, the variation in bandwidth between different colored LEDs could significantly affect data rates, particularly when considering the higher SNR results at lower bias voltages that contribute to achieving faster data rates in the PV-VLC system.

The transition from a centralized to a decentralized energy infrastructure is one of the most discussed features of the future energy system. With the fast growth of renewable energy technologies, which can be integrated in the built environment and in contexts like small production centers, the development of distributed energy generation and storage systems closer to consumers is expected to play a significant role in driving the change. Within this context, the role of Energy Communities is emerging and is at the center of numerous studies. The Green Village in Delft is developing the 24/7 Energy Lab project, focusing on providing reliable, affordable and clean energy to a small-scale energy community by means of a system composed of solar panels for energy generation, batteries for electrical energy storage, and an hydrogen storage system consisting of electrolyzers, fuel cells and hydrogen tanks for seasonal energy storage.

Previous research has highlighted how an off-grid configuration would result in inconveniently high costs for the community's users, if compared to the average cost of energy in The Netherlands. The aim of this thesis is to study the system in a grid-connected configuration, and in particular to find the optimal sizes of the components in order to achieve the best trade off between three conflicting objectives : minimizing total costs, maximizing self- sufficiency and maximizing reliability. After modeling the system's components and their mutual interactions, the optimization was carried out on MATLAB using a variant of the NSGA-II algorithm, which provides a Pareto Set of equally optimal solutions for the problem. The solutions were then ranked with a Technique for Order Preference based on Similarity to the Ideal Solution (TOPSIS), to assist the decision-making process.

The simulations have determined that an installed capacity of 85.41 kWp (composed of 234 panels of 365 Wp each) results in the most effective choice for the solar energy generation, irrespective of the external conditions imposed. The optimal storage capacity, however, results significantly more influenced by factors such as grid imports limitations and price uncertainties. Under the conditions of limited imports from the grid, an optimal capacity of 75 kWh in the form of batteries was found. In general, the study confirms that the adoption of an hydrogen storage system is far from being convenient on a small scale residential level, regardless of the pricing conditions. The research has also posed an accent on the incremented costs incurred to reach full reliability of the system with low values of dependence from the grid, due to the high costs of the necessary storage equipment. Additionally, despite the best solutions found represent the optimal compromises balancing the conflicting objectives, reasonable solutions in terms of costs faced by the Community's users are usually not among the first choices of the ranking algorithm, mainly because they necessitate of at least 50% of the load to be supplied through grid imports.

Global warming represents the most significant threat to humankind, making the need for renewable energy more crucial than ever. However, in densely populated areas near the coast, electricity production faces competition from various sectors such as agriculture, housing, and tourism. To address this challenge, one viable solution is to explore offshore electricity production.

Building upon this context, this research delves into investigating the wave-induced effect on power mismatch losses along a PV string in offshore floating photovoltaic (OFPV) systems. OFPV offers a promising solution for generating electricity in unused marine areas, complementing offshore wind energy. Although OFPV holds great potential, our understanding of its complexities remains limited, particularly regarding the impact of wave-induced power mismatch losses. To bridge this knowledge gap, a comprehensive approach is taken. A floating structure is modeled using the Bernoulli-Euler beam theory, while the fluid domain is analyzed using potential flow/linear wave theory. Structural behavior is examined in the frequency domain through the application of a FEM with the package Gridap in Julia. The wave amplitude spectra are determined using the Jonswap sea spectrum, with consideration given to four distinct sea states based on the Douglas sea scale: slight, moderate, rough and very rough. The optoelectrical modeling is conducted in pvlib in Python.

The results reveal that monthly energy losses due to power mismatch are negligible during summer months for all sea states studied. However, in winter months, monthly energy losses exceed 1%, with daily losses reaching up to 6%. Additionally, the orientation of the PV string is identified as a crucial parameter for minimizing losses. Finally, the findings indicate that using either a thick structure with a stiff and dense or a thin structure with a flexible and lightweight material can help reduce energy losses caused by power mismatch.

In existing standards for photovoltaic (PV) plants, little guidance is provided in the spatial allocation of pyranometers. No universal and easily scalable algorithms exist that help designers of monitoring infrastructure in solar parks choose the most representative positions for irradiance sensors.

To fill this gap, a software tool is created for determining the most representative sensor locations requiring only the PV plant's main characteristics, layout, and future data usage as input. If necessary, advice on the number of sensors can be given.

The sky view factor (SVF) was determined for all relevant planes in the PV plant. This was done by adding all SVF contributions of N_a altitude bands and NA azimuth slices. The horizon obstruction at each location was determined using 30m spatial resolution digital surface model (DSM) data from Sentinel Hub imagery service. Two SVF modelling variables were optimised by varying them in SVF calculations at 3800+ existing PV plant locations in Europe. N_A,optimal=1080 and r_max,optimal=2000m were found.

The Perez model stood out from a literature review of sky diffuse model comparison studies. Moreover, six decomposition models were compared using data from twelve European Baseline Surface Radiation Network (BSRN) weather stations. A seasonal bias was found and compensated in an attempt to improve the already best-performing BRL model. The average normalised root mean squared error (nRMSE) improved from 31.8% to 30.5%. The average absolute relative mean bias error (rMBE) improved from 11.2% to 10.6%.

The software tool determines SVF maps for different orientations and tilts. Ground albedo time series is extracted from the NASAPOWER database, and historical global horizontal irradiance is imported from PV-GIS. Plane-of-array irradiance maps are constructed through transposition modelling. Subsequently, error maps are created, from which the location with minimal measurement deviations can be extracted.

The software tool was tested for a case study in Eisleben (Germany) and Kolindros (Greece). The relative prevented measurement deviation (rPMD) was up to 1.2% in the Kolindros case, with an average of 0.8% compared to 0.3% in the Eisleben case. Instantaneous measurement deviations up to seven times the rPMD were seen. Furthermore, a simplified allocation algorithm only based on the SVF maps was found, only valid under the current assumptions.

The purpose of this thesis research was to perform a photovoltaic (PV) performance assessment and comparison between a small-scale land-based PV system and an off-shore off-grid floating PV system. Also from this research design guidelines for off-grid off-shore floating PV systems were developed. The research experiment was carried out in The Netherlands, with one system floating in the North Sea and one system standing on-land. To be able to have a fair comparison two identical 4.32 𝑘𝑊 PV arrays were built simultaneously, consisting of two parallel strings, each with six 360 𝑊𝑝 bifacial PV modules connected in series. Each of the two systems was equipped with a monitoring set up consisting of temperature sensors, pyranometers, reference cells, power meters and an inclinometer for the off-shore system. Additional measurements concerning wind, wave and water temperatures off-shore were taken from the Royal Netherlands Meteorological Institute (KNMI) weather station Hollandse Kust Zuid Alpha (HKZA), which was situated in the same region of the North Sea. The two PV experimental set-ups were compared between 15th November 2022 and 1st January 2023. On the DC side being measured, the on-land PV system recorded 96.3 𝑘𝑊ℎ while the off-shore PV system recorded 93.4 𝑘𝑊ℎ, which was subject to lower irradiance levels. The performance ratio (PR) found off-shore during the period of research was 0.92 while on-land this value was 0.934 between the two PV strings, however these values should be re-validated due to a potential malfunction of the Hall effect sensors. The PR off-shore was higher than expected from literature for a similar system being simulated. With respect to module temperatures recorded off-shore, a low thermal variation was found when compared to on-land, with the average module temperature off-shore being 11.3 𝑜𝐶 higher than on-land during this period of research. Ambient temperatures as well as irradiance on the Plane of Array (POA) were found to have a larger influence on the module temperatures for the on-land system than for the off-shore system. Module temperatures throughout the PV array off-shore were found to be the equal. Additionally, four OFPV linear regression models derived from the empirical measurements were presented and compared with existing empirical FPV models found in literature, which were found to be subject to be site and design specific. Additional environmental effects on the module temperature concerning wind, wave and water temperatures were presented, highlighting the dominant effect of the water with respect to module temperatures. The off-shore floating PV system withstood waves of up to 3.4 meters while the system maximum tilts were found to be to be 13.33 𝑜 and 17.36 𝑜 for the X and Y axis of the PV floater respectively. No permanent soiling due to bio-fouling or salt deposition was detected after one month of measurements but dynamic soiling, in the form of bio-fouling, snow and water splashing were identified, with the latter being the most predominant. The work done through this thesis lays the foundation for further research and comparisons of off-shore floating PV systems. It is recommended to perform this comparison for a period of a complete year to yield annual conclusions.

The TUDelft spectrally resolved albedometer is a measurement device whose design began in 2019 and aims to accurately reconstruct spectral irradiance and spectral albedo using reconstruction techniques which enable this device to be produced at a lower cost than its competitors.
The measurement of spectral albedo using an albedometer device is a crucial component in predicting energy yield for bifacial PV panels, expected to become the dominant photovoltaic technology by market share in 2030 [13]. Building on the work of previous thesis projects at TUDelft, this thesis focuses on the following three topics of improvement:

Accuracy improvement in spectral reconstruction: The albedometer device is recalibrated according to the recently recalibrated EKO device, succeeding in reducing the error uncertainty in the first and last wavelength ranges of the spectral irradiance reconstruction. Whilst the average errors lie outside of acceptable uncertainty bounds, the systematic errors in the first and last wavelength bands are identified and proposed solutions involve adjusting the PSO algorithm and using machine learning to improve the prediction of atmospheric absorption parameters such as total precipitable water.
Spectral albedo reconstruction using Machine Learning: Machine learning techniques are employed to reconstruct down-facing spectral irradiance, achieving errors below ±5% for various sky classes and demonstrating the method’s potential for spectral albedo reconstruction in future work.
Improving device usability: The Albedometer App centralizes and automates data processing code, simplifying spectral irradiance and albedo reconstruction processes and greatly enhancing user experience.

This research is key for the development for the albedometer’s accuracy, functionality and usability. By integrating the model for spectral albedo reconstruction this thesis advances the overall development of the albedometer device, bring it one step closer to realising its full potential as a high accuracy, low cost measurement device, making it a valuable tool for spectral albedo reconstructions and precise energy yield predictions in the bifacial PV sector.

Changes in social, cultural, and economic paradigms have resulted in a collective shift from rural to urban areas over the last few decades. Cities are expanding to accommodate the ever-increasing population influx in urban areas. New buildings, roads, airports, and harbors are only a small part of urban development. It also includes the use of new agricultural lands and changes to water resources surrounding urban areas. These new developments invariably introduce complex geometry onto surfaces as well as a plethora of new materials into a field, which leads to the change in local albedo. Albedo indicates how well a surface reflects solar energy, and it is defined as the ratio of total reflected irradiation from a surface to total incident irradiation on the surface. Albedo has a wide range of applications such as estimating the total heat content of an area and assessing the global warming potential. In this project, we used our Geometric Spectral Albedo model developed in the PVMD research group and open source LiDAR data (AHN1 to AHN4 collected over roughly 25 years) to investigate how urban development affects the local albedo.

The energy transition is one of the major challenges of the 21st century, impacting the way energy is generated, conserved and consumed. Energy generation becomes more and more decentralised, intermittency and fluctuations suddenly are becoming topics of interest within day-to-day life and energy system operators are facing many new obstacles never encountered before. In this context, the anticipation for hydrogen as a resource for energy conservation and -management is big. This research focuses on the optimisation of the hydrogen pathway for investment and operational models.

The hydrogen pathway as aforementioned is divided over three technologies: hydrogen generation with means of water electrolysis, also known as 'green hydrogen', storage in compression vessels and reconversion of hydrogen into electricity in the form of a fuel cell technology (also known as Power-to-Gas).

The research focuses on identifying technical parameters and operational policies of the water electrolysis systems that can be translated into optimisation constraints, assessing the level of detail required to create an accurate optimisation model. A generic model is developed that can be scaled for further research, making different case studies and sizing possible. The research compares the performance of the models in terms of accuracy to the computational burden. The comparison is done for the level of detail and complexity added to the model.

After a literature review of technical parameters and operational policies regarding the technologies, two models were created in a mathematical framework. The two models proposed were Linear Programming (LP) and a Mixed-Integer Programming (MIP) Model. On the LP model 6 different sensitivity analysis has been performed, to be precise on Capital Expenditures (CAPEX), efficiency, lifetime, ramping rates, interest rates and finally different time horizons. The outcome of these analyses is that the technology mix can best be used in a combined manner, whereby each component of the mix contributes towards minimising the objective value: the Total Annualised Cost.

Lastly the two models are compared with different types of configurations, each with a different set of constraints. The constraints to be modelled were: minimum uptime and downtime, start-up costs, degradation due to cycling and finally the part-load operation.

The growing global energy demand and correlated rise in carbon emissions are increasing the need of renewable energy sources. This spread requires land to be occupied, competing with other activities such as agriculture and residency. This project can help the spread of photovoltaic (PV) technologies in an environment still little explored: the water. Offshore floating photovoltaic (OFPV) gains increasing attention in research due to a substantial reduction in land occupancy and a lower operating temperature. Therefore, this study aims to evaluate the power output of a OFPV system located in the North Sea considering the effect of the waves on tilt and azimuth of PV modules.

First, the JONSWAP spectrum theory was employed to simulate the sea surface. Then, the interaction between the waves and the floater was modelled to obtain the orientation of the PV modules. The system energy yield was simulated through the PVMD Toolbox, a physics-based tool developed by the PhotoVoltaic Materials Devices Group (PVMD Group) at Delft University of Technology. Finally, experimental analysis was conducted on a commercial string inverter emulating the DC power output from the OFPV modelled plant.

The waves generally cause lower irradiance hitting PV modules. However, fluctuations do not always have a negative influence. For example, the research found that with a calm sea, a system under the effect of waves produces 1% more than an offshore stationary 0° tilt plant. Nevertheless, the variable PV orientation scenario shows substantial losses for higher sea agitation states compared to the 0° tilt situation. Over a year, an OFPV under waves effect loses 0.84% and 17.97% of the production compared to stationary 0° and optimal installation tilt, respectively. From the laboratory activity, it appeared that with the same irradiance, the oscillations have a negative impact on the efficiency, especially that of the maximum power point tracking (MPPT) block, which reaches -3.2% with rough sea.

In the end, we can conclude that the power output losses are not as dramatic as expected and that the development of OFPV technology will probably depend on future costs for offshore installations and the competition for inland areas.

Photovoltaic technology has become one of the leading renewable alternatives for energy production. One main factor in the diminished performance of a photovoltaic module is partial shading, due to the resulting mismatch conditions in the module. However, it is difficult to understand how a specific PV module reacts to shading in comparison with others, as currently the ability of a module to withstand shading is usually expressed in vague qualitative terms on its datasheet. In this work, the development of a shading tolerability calculator in MATLAB was completed. This tool can relatively easily and quickly calculate the shading tolerability of a module as a numeric parameter (ST), which can then be used for characterization and comparison purposes.

First a MATLAB based model to simulate the IV characteristics of a PV module under different conditions (including partial shading) was developed. The model was developed at a cell level, and was translated to a module level by taking the series connection of cells into account, and modeling the impacts of reverse bias and bypass diodes operation. Validation with experimental data showed errors at Pmpp remained below 4.5%.

Next, the shading scenarios to be considered were defined and developed. The objective was to determine Pmpp of a given PV module under all possible shading scenarios, using the IV simulation model developed. The possible shading scenarios were based on a PV module split into 12 equal sections, and considering two irradiance levels: 100 W/m2 for shaded sections, and 1000 W/m2 for unshaded. To improve the speed of the model, which was an important aim within this project, the existence of equivalent scenarios based on the symmetry of the module was utilised. The Pmpp value was only simulated once for every unique scenario, greatly reducing the required number of simulations and simulation time.

Based on the above, the development of a calculator for the shading tolerability parameter of a PV module was accomplished. The ST values for more than 40 PV modules were calculated, giving ST% values ranging between 22% and 29%. Correlations between different module parameters were explored to see their impact on ST. One main result seen was the impact of bypass diodes on ST, specifically the considerable positive effect of a higher number of bypass diodes. Another was the positive correlation between temperature coefficient of open circuit voltage and ST.

Finally, a case study for the calculation of ST for a half-cell butterfly module was implemented. This involved modeling parallel connections in PV modules, as well as updating the IV simulation model to include this new type of PV module configuration. The ST values for two half-cell butterfly modules were calculated, giving ST% values of around 42%. This was significantly higher than those calculated for the conventional modules, highlighting the improved shading tolerance of half-cell butterfly modules. The adaptability of this model to be able to calculate the shading tolerability of any type of configuration of PV module was also demonstrated through this case study, paving the way for future research.

High operating temperatures harm photovoltaic (PV) modules. The increase in temperature of the cells leads to lower open circuit voltage and higher short circuit current ultimately leading to a lower power output due to a negative thermal coefficient. Hence, lowering the temperature of commercial crystalline silicon modules during their operation becomes desirable, as it increases the system's energy yield and prolongs the module's lifespan. Throughout the years several attempts have been made to decrease the temperature of PV modules, characterized my two main groups of cooling techniques, active and passive cooling techniques. Active cooling techniques require additional power to cool the panels whereas passive cooling techniques rely on natural convection and require no additional power input. In this thesis, we experimentally investigate the cooling potential of a novel passive cooling method that integrates an internal heat sink into PV modules. The basic idea of the technology is to create a thermal circuit beneath the solar cell that allows for the extraction of the heat directly from the solar cell to the outside environment. The experiments started on a single solar cell, to find the best configuration. We adapted the optimized design onto a 2x2 PV and a 3x3 PV module. Finally, the internal heat sinks were connected to a frame. We observed a temperature reduction of around 4-5°C on the module with the internal heat sink compared to a Standard module under the same environmental conditions.

In this thesis, a new photovoltaic fault detection and classification method is proposed. It combines the generation of a synthetic photovoltaic training database and the use of a machine learning model to detect and classify faults in small-scale residential PV systems. The database was generated in Matlab, and the machine learning modeling was done with the scikit-learn library for Python. From the modeled PV systems, solely power yield is used as an indicator, combined with system age and meteorological conditions. Using these features, four types of machine learning models are used to detect malfunctioning PV systems and classify short-circuit faults and open-circuit faults. This thesis also shows the benefit of a synthetic PV training base as opposed to alternative methods, with increasing performance due to control of database balance.

The result of this thesis is a method that can be used to construct a model for detection and classification of photovoltaic faults, specific to a single residential PV system. Malfunctioning systems can be detected with an accuracy of 80.4% using a random forest algorithm. For fault type classification, an F1-score of 0.759 was achieved, also using a random forest.