M.R. Vogt
Please Note
24 records found
1
The new setup comprises a server computer, which runs a database that stores the measurements. To measure the modules, the Open-source Photovoltaic Electrical Tool is used. The microcontroller on this device needed to be adjusted to monitor relevant temperatures.
This thesis describes the development of the PostgreSQL database. The database can synchronise weather measurements from an existing database, keep track of modules that were used, and save point and curve measurements of a solar module. It can do this live and retroactively. The supervisor script, developed using Python, is able to schedule the measurements and store the results in a CSV file. A log file is used to store information about errors, and if any errors occur during the operation, the administrator will be notified via email. Lastly, the firmware of the measurement tool was adjusted to monitor the temperature of a power dissipation device and a solar module. If the temperature of the power dissipation device exceeds the configured limit, the output of the solar module will be disabled.
The end result enables users to access the database remotely and collect data about their solar module. The new system has been validated by a set of pass/fail tests conducted on a separate laptop. The final server for the outdoor testing facility, which runs the program, has yet to be deployed for researchers to use. ...
The new setup comprises a server computer, which runs a database that stores the measurements. To measure the modules, the Open-source Photovoltaic Electrical Tool is used. The microcontroller on this device needed to be adjusted to monitor relevant temperatures.
This thesis describes the development of the PostgreSQL database. The database can synchronise weather measurements from an existing database, keep track of modules that were used, and save point and curve measurements of a solar module. It can do this live and retroactively. The supervisor script, developed using Python, is able to schedule the measurements and store the results in a CSV file. A log file is used to store information about errors, and if any errors occur during the operation, the administrator will be notified via email. Lastly, the firmware of the measurement tool was adjusted to monitor the temperature of a power dissipation device and a solar module. If the temperature of the power dissipation device exceeds the configured limit, the output of the solar module will be disabled.
The end result enables users to access the database remotely and collect data about their solar module. The new system has been validated by a set of pass/fail tests conducted on a separate laptop. The final server for the outdoor testing facility, which runs the program, has yet to be deployed for researchers to use.
In the first part of this research a model was developed to determine the surface albedo. The model relieson Sentinel-2 satellite imagery to obtain information on surface reflectance. The albedo is calculatedusing a linear combination of the available spectral bands. To validate this albedo estimation approach,radiative flux data from SURFRAD measurement station in the USA were employed. The results showthat the model produces albedo estimates with a RMSE of 0.032. A bias correction was developed thatdepends on the solar zenith angle at the time the Sentinel-2 image was acquired. The bias correctionwas theoretically derived from the data and the anticipated error introduced by the assumption that thesurface reflection is Lambertian. With this correction applied, the albedo estimation RMSE was reducedto 0.021. The findings highlighted how the Lambertian assumption affects the outcomes and confirmedthat applying a bias correction is appropriate for improving the accuracy of the albedo estimates.
The second model developed in this study quantifies how albedo is altered by the installation of solarfarms. The location of 500 solar farms were sourced from the Solar Asset Mapper dataset developedby Transition Zero. For the albedo change, the difference between the albedo of the PV area andthat of the surrounding area is used. Google Earth Engine was employed to simplify satellite imageprocessing and enhance computational capacity. The mean albedo difference observed was−0.0198.Seasonal variation in the PV region’s albedo was detected, indicating that the solar farm boundariesare incorrectly defined, causing the surrounding area’s seasonality to be introduced into the PV albedo. In addition, the results revealed problems associated with glare.
In the third model, the climatic impact of the albedo changes of solar farms was assessed. To remove low-quality measurements, for example those distorted by glare, the dataset was cleaned before conductingfurther analysis, resulting in a total of 157 farms. Here, the effective albedo of the PV area wascalculated, and both the absolute and effective changes in albedo were applied throughout the remainderof the model. On average an absolute albedo change of−0.0299 and effective albedo change of 0.0496was found for these 157 farms. The radiative forcing caused by the albedo change was calculated withthe use of a radiative kernel dataset. The radiative forcing associated with the solar farm’s avoidedemissions was estimated based on the local electricity carbon intensity. The net radiative forcing wasthen obtained by the sum of these contributions together with the radiative forcing from embodiedemissions. Based on this net radiative forcing, the carbon break-even time was determined for eachfarm. On average, a carbon break-even time of 7.79 yr was found when considering the absolute albedochange, whereas a much shorter average carbon break-even time of 0.41 yr was obtained for the effectivealbedo change. These results indicate that the effective albedo exerts a cooling, rather than warming,effect on the climate, thereby reducing the overall climate impact of solar farms. ...
In the first part of this research a model was developed to determine the surface albedo. The model relieson Sentinel-2 satellite imagery to obtain information on surface reflectance. The albedo is calculatedusing a linear combination of the available spectral bands. To validate this albedo estimation approach,radiative flux data from SURFRAD measurement station in the USA were employed. The results showthat the model produces albedo estimates with a RMSE of 0.032. A bias correction was developed thatdepends on the solar zenith angle at the time the Sentinel-2 image was acquired. The bias correctionwas theoretically derived from the data and the anticipated error introduced by the assumption that thesurface reflection is Lambertian. With this correction applied, the albedo estimation RMSE was reducedto 0.021. The findings highlighted how the Lambertian assumption affects the outcomes and confirmedthat applying a bias correction is appropriate for improving the accuracy of the albedo estimates.
The second model developed in this study quantifies how albedo is altered by the installation of solarfarms. The location of 500 solar farms were sourced from the Solar Asset Mapper dataset developedby Transition Zero. For the albedo change, the difference between the albedo of the PV area andthat of the surrounding area is used. Google Earth Engine was employed to simplify satellite imageprocessing and enhance computational capacity. The mean albedo difference observed was−0.0198.Seasonal variation in the PV region’s albedo was detected, indicating that the solar farm boundariesare incorrectly defined, causing the surrounding area’s seasonality to be introduced into the PV albedo. In addition, the results revealed problems associated with glare.
In the third model, the climatic impact of the albedo changes of solar farms was assessed. To remove low-quality measurements, for example those distorted by glare, the dataset was cleaned before conductingfurther analysis, resulting in a total of 157 farms. Here, the effective albedo of the PV area wascalculated, and both the absolute and effective changes in albedo were applied throughout the remainderof the model. On average an absolute albedo change of−0.0299 and effective albedo change of 0.0496was found for these 157 farms. The radiative forcing caused by the albedo change was calculated withthe use of a radiative kernel dataset. The radiative forcing associated with the solar farm’s avoidedemissions was estimated based on the local electricity carbon intensity. The net radiative forcing wasthen obtained by the sum of these contributions together with the radiative forcing from embodiedemissions. Based on this net radiative forcing, the carbon break-even time was determined for eachfarm. On average, a carbon break-even time of 7.79 yr was found when considering the absolute albedochange, whereas a much shorter average carbon break-even time of 0.41 yr was obtained for the effectivealbedo change. These results indicate that the effective albedo exerts a cooling, rather than warming,effect on the climate, thereby reducing the overall climate impact of solar farms.
Although PS cells have already surpassed the record efficiency of c-Si ones at the laboratory scale, their performance under real outdoor conditions remains uncertain. Moreover, perovskite devices face notable stability challenges, raising questions about their long-termviability compared to modules purely based on c-Si PV technology. This thesis investigates the outdoor potential of Smart c-Si PV modules as well as PS PVmodules by modeling their performance, optimizing designs under various conditions, and identifying acceptable performance degradation rates.... ...
Although PS cells have already surpassed the record efficiency of c-Si ones at the laboratory scale, their performance under real outdoor conditions remains uncertain. Moreover, perovskite devices face notable stability challenges, raising questions about their long-termviability compared to modules purely based on c-Si PV technology. This thesis investigates the outdoor potential of Smart c-Si PV modules as well as PS PVmodules by modeling their performance, optimizing designs under various conditions, and identifying acceptable performance degradation rates....
Heliostat Surface Prediction via Physics-Aware Deep Learning
A Feasibility Study
Performance and Reliability of Liquid Encapsulated PV Modules
Manufacturing, Accelerated Ageing and Proposing Improvements for Liquid Encapsulated PV Modules
ules. To achieve this, suitable liquids are selected. Subsequently, several one-cell mini-modules are hand-manufactured, which are filled with air, the selected liquids, and laminated with EVA. The results are obtained by subjecting the modules to 30 cycles of humidity freeze testing and by measuring their electrical characteristics under standard testing conditions. Initial performance measurements show that all four tested liquids, including water (3.7%), polydimethylsiloxane (PDMS) (6.2%), mono propylene glycol (MPG) (5.1%), and glycerol (5.1%), offer substantial efficiency improvements over air-filled modules, with PDMS even slightly outperforming EVA (5.5%). A major point of failure is the PIB edge seal, especially at the liquid injection points, indicating a need for improved manufacturing techniques. The module failures also allowed for disassembly trials, which show that liquid-filled modules can be completely disassembled with ease, allowing for full material recovery. This highlights the reusability potential of liquid-filled designs due to the absence of more permanent encapsulant layers like EVA. The humidity freeze accelerated ageing, subjects the modules to extremely low and high temperatures of -40 °C and 85 °C, whilst also subjecting them to 85% relative humidity. Intermediate visual and electroluminescence inspections revealed mechanical failure in air-filled modules due to edge seal flattening and cell breakage. Whilst after the full 30 humidity freeze cycles the relative degradation in module efficiency in both PDMS and glycerol encapsulated modules (both 5.2%) are comparable to that of an air-filled module (5.5%) but worse than that of EVA (3.9%), whilst the module encapsulated with MPG shows the lowest degradation (2.8%). These results highlight the potential of MPG as a stable encapsulant and underscore the importance of redesigning the liquid injection method for reliability of the polyisobutene edge seal
The humidity freeze accelerated ageing subjects the modules to extremely low and high temperatures of -40 °C and 85 °C, whilst also subjecting them to 85% relative humidity. Intermediate visual and electroluminescence inspections revealed mechanical failure in air-filled modules due to edge seal flattening and cell breakage. Whilst the full 30 humidity freeze cycles show that relative degradation in module efficiency in PDMS and glycerol encapsulated modules (both 5.2%) are comparable to those of an air-filled module (5.5%) but worse than that of EVA (3.9%), whilst the module encapsulated with MPG shows the lowest degradation (2.8%). These results highlight the potential of MPG as a stable encapsulant and underscore the importance of redesigning the liquid injection method for reliability of the polyisobutene edge seal. ...
ules. To achieve this, suitable liquids are selected. Subsequently, several one-cell mini-modules are hand-manufactured, which are filled with air, the selected liquids, and laminated with EVA. The results are obtained by subjecting the modules to 30 cycles of humidity freeze testing and by measuring their electrical characteristics under standard testing conditions. Initial performance measurements show that all four tested liquids, including water (3.7%), polydimethylsiloxane (PDMS) (6.2%), mono propylene glycol (MPG) (5.1%), and glycerol (5.1%), offer substantial efficiency improvements over air-filled modules, with PDMS even slightly outperforming EVA (5.5%). A major point of failure is the PIB edge seal, especially at the liquid injection points, indicating a need for improved manufacturing techniques. The module failures also allowed for disassembly trials, which show that liquid-filled modules can be completely disassembled with ease, allowing for full material recovery. This highlights the reusability potential of liquid-filled designs due to the absence of more permanent encapsulant layers like EVA. The humidity freeze accelerated ageing, subjects the modules to extremely low and high temperatures of -40 °C and 85 °C, whilst also subjecting them to 85% relative humidity. Intermediate visual and electroluminescence inspections revealed mechanical failure in air-filled modules due to edge seal flattening and cell breakage. Whilst after the full 30 humidity freeze cycles the relative degradation in module efficiency in both PDMS and glycerol encapsulated modules (both 5.2%) are comparable to that of an air-filled module (5.5%) but worse than that of EVA (3.9%), whilst the module encapsulated with MPG shows the lowest degradation (2.8%). These results highlight the potential of MPG as a stable encapsulant and underscore the importance of redesigning the liquid injection method for reliability of the polyisobutene edge seal
The humidity freeze accelerated ageing subjects the modules to extremely low and high temperatures of -40 °C and 85 °C, whilst also subjecting them to 85% relative humidity. Intermediate visual and electroluminescence inspections revealed mechanical failure in air-filled modules due to edge seal flattening and cell breakage. Whilst the full 30 humidity freeze cycles show that relative degradation in module efficiency in PDMS and glycerol encapsulated modules (both 5.2%) are comparable to those of an air-filled module (5.5%) but worse than that of EVA (3.9%), whilst the module encapsulated with MPG shows the lowest degradation (2.8%). These results highlight the potential of MPG as a stable encapsulant and underscore the importance of redesigning the liquid injection method for reliability of the polyisobutene edge seal.
Despite these mitigation strategies, the rapid growth in PV deployment poses significant challenges for aluminum supply, as global aluminum production is projected to be only 176 Mt by 2050, suggesting substantial supply pressures. Moreover, aluminum production is both energy- and carbon-intensive, contributing significantly to global greenhouse gas emissions. The cumulative emissions associated with aluminum use in PV systems are projected to reach 3534 Mt CO2eq from 2020 to 2050, highlighting the urgent need for decarbonization in aluminum production. The study emphasizes the critical importance of developing a closed-loop aluminum recycling system for PV components to form a circular economy, which could reduce primary aluminum demand and associated emissions. By adopting a multi-faceted approach, including improvements in technology, materials, and recycling processes, the PV industry can mitigate its environmental impact and support the global transition towards sustainable energy. ...
Despite these mitigation strategies, the rapid growth in PV deployment poses significant challenges for aluminum supply, as global aluminum production is projected to be only 176 Mt by 2050, suggesting substantial supply pressures. Moreover, aluminum production is both energy- and carbon-intensive, contributing significantly to global greenhouse gas emissions. The cumulative emissions associated with aluminum use in PV systems are projected to reach 3534 Mt CO2eq from 2020 to 2050, highlighting the urgent need for decarbonization in aluminum production. The study emphasizes the critical importance of developing a closed-loop aluminum recycling system for PV components to form a circular economy, which could reduce primary aluminum demand and associated emissions. By adopting a multi-faceted approach, including improvements in technology, materials, and recycling processes, the PV industry can mitigate its environmental impact and support the global transition towards sustainable energy.
Investigating moisture ingress in PV modules
Alternative simulation methods for improved accuracy
non-Fickian diffusion, a dual-transport method is used; the study finds the approach to deliver more accurate results for EVA, but not for PET. To simulate material degradation, an adapted version of the diffusion coefficient equation is proposed, incorporating a degradation constant based on the materials’ properties. The findings are then used to analyse the behaviour of other PV materials and behaviour in different climates. The simulations find very slow moisture ingress for ionomer under non-Fickian diffusion and a strong deviation from Fickian diffusion. In EVA/PET simulations, non-Fickian behaviour is found to deviate more from Fickian behaviour in warmer climates. Degradation constants are found for the other PV materials. The approach shows promising results for the TPO/PET and ionomer/PET simulations, showing degradation in proportion with their material properties. However, simulations that include EVA appear to strongly limit moisture diffusion, indicating a revision of the EVA degradation constant should be made. TPO/PET degradation simulations in show minimal degradation over 20 years in different climates, but more material degradation in colder climates is found. ...
non-Fickian diffusion, a dual-transport method is used; the study finds the approach to deliver more accurate results for EVA, but not for PET. To simulate material degradation, an adapted version of the diffusion coefficient equation is proposed, incorporating a degradation constant based on the materials’ properties. The findings are then used to analyse the behaviour of other PV materials and behaviour in different climates. The simulations find very slow moisture ingress for ionomer under non-Fickian diffusion and a strong deviation from Fickian diffusion. In EVA/PET simulations, non-Fickian behaviour is found to deviate more from Fickian behaviour in warmer climates. Degradation constants are found for the other PV materials. The approach shows promising results for the TPO/PET and ionomer/PET simulations, showing degradation in proportion with their material properties. However, simulations that include EVA appear to strongly limit moisture diffusion, indicating a revision of the EVA degradation constant should be made. TPO/PET degradation simulations in show minimal degradation over 20 years in different climates, but more material degradation in colder climates is found.
Managing the twilight
A comprehensive forecast and analysis of PV End of Life at a Global, Regional and National scale
...
The results for the PERC panel produced in 2022 are: climate change 1.09E-02kcCO2/kWh, ozone depletion1.09E-08 kg CFC11/kWh, ionising radiation 2.50E-05 kBq U-235/kWh, photochemical ozone formation 4.12E-05 kg NMVOC/kWh, particulate matter 2.69 disease/kWh, non-cancer human health 7.37E-11 CTUh/kWh, cancer human health 2.35E-11 CTUh/kWh, acidification 4.40E-05molH+/kWh, freshwater eutrophication 5.39E-07 kg P/kWh, marine eutrophication 5.12E-06 kg N/kWh, terrestrial eutrophication 5.50E-05 mol N/kWh, ecotoxicity 1.06E-02 CTUe/kWh, land use 1.24E-03 pt/kWh, water use 5.25E-05 m3/kWh, resource use fossil 1.59E-01 MJ/kWh, resource euse mineral & metals 1.29E-06. In terms of climate change a PV panel has lower emissions than wind power and the Europe electricity mix, but higher emissions than nuclear power and hydro power. PV has lower particulate matter emissions than nuclear, wind power, and the Europe electricity mix, and higher than hydro power. For noncancer human health, PV is lower than nuclear and wind power, but higher than hydro power and Europe electricity mix. PV power has a lower amount of acidification than wind power, nuclear power and the Europe electricity mix, but higher than hydro power. For ecotoxicity PV has a lower value than wind power and nuclear power, but higher than hydro power and the Europe electricity mix.
...
The results for the PERC panel produced in 2022 are: climate change 1.09E-02kcCO2/kWh, ozone depletion1.09E-08 kg CFC11/kWh, ionising radiation 2.50E-05 kBq U-235/kWh, photochemical ozone formation 4.12E-05 kg NMVOC/kWh, particulate matter 2.69 disease/kWh, non-cancer human health 7.37E-11 CTUh/kWh, cancer human health 2.35E-11 CTUh/kWh, acidification 4.40E-05molH+/kWh, freshwater eutrophication 5.39E-07 kg P/kWh, marine eutrophication 5.12E-06 kg N/kWh, terrestrial eutrophication 5.50E-05 mol N/kWh, ecotoxicity 1.06E-02 CTUe/kWh, land use 1.24E-03 pt/kWh, water use 5.25E-05 m3/kWh, resource use fossil 1.59E-01 MJ/kWh, resource euse mineral & metals 1.29E-06. In terms of climate change a PV panel has lower emissions than wind power and the Europe electricity mix, but higher emissions than nuclear power and hydro power. PV has lower particulate matter emissions than nuclear, wind power, and the Europe electricity mix, and higher than hydro power. For noncancer human health, PV is lower than nuclear and wind power, but higher than hydro power and Europe electricity mix. PV power has a lower amount of acidification than wind power, nuclear power and the Europe electricity mix, but higher than hydro power. For ecotoxicity PV has a lower value than wind power and nuclear power, but higher than hydro power and the Europe electricity mix.
Modelling moisture ingress and impact on PV module degradation
Development of a FEM model to predict the moisture ingress and module degradation under different conditions
These results are used to find a relation between ambient conditions and the results delivered by the COMSOL model. A simplified relationship is found that holds for the different climates and encapsulants. It is found that the effective relative humidity in the environment is the key parameter in determining the amount of water that will be in the module once it reaches equilibrium. The time that it takes for a module to reach its moisture equilibrium content is determined by the temperature. The presence of these simplified relations can help in estimating the moisture ingress behaviour of a model without the need of carrying out a full FEM simulation. However, the dynamics of the system when using different backsheets does not follow the same simplified relations.\\
The degradation caused by water in the module is also studied. An analytical model is used to predict the degradation observed during damp heat tests. Due to the properties of the analytical model a different approach has to be followed for real life conditions. The degradation model is used to compare the expected degradation under different conditions. This shows that the expected degradation is larger in hot and humid climates while it is minimized in colder climates. The general degradation trend observed for the different climates is: Tropical > Arid > Temperate > Continental > Polar. ...
These results are used to find a relation between ambient conditions and the results delivered by the COMSOL model. A simplified relationship is found that holds for the different climates and encapsulants. It is found that the effective relative humidity in the environment is the key parameter in determining the amount of water that will be in the module once it reaches equilibrium. The time that it takes for a module to reach its moisture equilibrium content is determined by the temperature. The presence of these simplified relations can help in estimating the moisture ingress behaviour of a model without the need of carrying out a full FEM simulation. However, the dynamics of the system when using different backsheets does not follow the same simplified relations.\\
The degradation caused by water in the module is also studied. An analytical model is used to predict the degradation observed during damp heat tests. Due to the properties of the analytical model a different approach has to be followed for real life conditions. The degradation model is used to compare the expected degradation under different conditions. This shows that the expected degradation is larger in hot and humid climates while it is minimized in colder climates. The general degradation trend observed for the different climates is: Tropical > Arid > Temperate > Continental > Polar.
Changing Environmental Tides of Amsterdam’s Future PV Systems
A multi-scenario projection for the environmental performance of residential PV systems in Amsterdam
downcycling, meaning that not even 35% is completely recovered. The remaining materials and sometimes the entire module is dumped in a landfill at e1 per module. Recycling these panels can cost between €15 to €30 per panel and post recycling a minimum value of €6.6 and a maximum of €21 can be derived from the recovered materials. However, these materials cannot be directly utilized to manufacture PV panels without further processing. The thesis estimates the quantity of materials in PV systems, such as silver, copper, silicon, glass, and aluminum. This estimation includes the weight of each material within PV modules, as well as the monetary value associated with these materials. In the year 2030, about €86 billion and €58 billion worth of silicon and silver, respectively, are contained in the installed PV panels. If the prevalent EoL processes are followed, these materials will be unaccounted for at the end of their lifetime. All processes must be economically viable and operate within well-established financial boundaries. In this study, the concept of the Levelized Cost of Electricity (LCOE) is utilized as a standardized metric for comparing a new PV module versus a refurbished module and setting up boundary conditions. To emulate the market, two scenarios are considered. On the one hand, the first scenario considers the entire system cost, including the second-hand PV module, Balance of Plant (BoP), and soft costs. In this scenario, a minimum second lifetime of 23 years ensures a positive cash flow for the manufacturers/suppliers. On the other hand, the second scenario considers the placement of a second-hand module into an existing system (eliminating the need for additional BoP and soft costs) and shows that no minimum second life of the panel is needed to ensure a cash inflow for the manufacturers/suppliers. The effect of subsidies and policies on LCOE are also analyzed utilizing discount rates. In general, the higher the discount rate, the higher the resultant LCOE. Finally, a market structure that utilizes the concept of a Product Service System (PSS) and aims to facilitate the utilization of second-life PV modules along with a proposal for the positioning of a Product Service System Provider (PSSP) is presented. Integration of the PSSP into the existing market structure is proposed in a stage-wise manner, utilizing the distribution system operator (DSO) for effective implementation. To achieve this integration, two strategies are recommended, one based on the size and capacity of the installed systems and the other based on geographical boundaries. Additionally, a brief overview of PV subscribe, which is a business model that stimulates the second-life market, is provided. ...
downcycling, meaning that not even 35% is completely recovered. The remaining materials and sometimes the entire module is dumped in a landfill at e1 per module. Recycling these panels can cost between €15 to €30 per panel and post recycling a minimum value of €6.6 and a maximum of €21 can be derived from the recovered materials. However, these materials cannot be directly utilized to manufacture PV panels without further processing. The thesis estimates the quantity of materials in PV systems, such as silver, copper, silicon, glass, and aluminum. This estimation includes the weight of each material within PV modules, as well as the monetary value associated with these materials. In the year 2030, about €86 billion and €58 billion worth of silicon and silver, respectively, are contained in the installed PV panels. If the prevalent EoL processes are followed, these materials will be unaccounted for at the end of their lifetime. All processes must be economically viable and operate within well-established financial boundaries. In this study, the concept of the Levelized Cost of Electricity (LCOE) is utilized as a standardized metric for comparing a new PV module versus a refurbished module and setting up boundary conditions. To emulate the market, two scenarios are considered. On the one hand, the first scenario considers the entire system cost, including the second-hand PV module, Balance of Plant (BoP), and soft costs. In this scenario, a minimum second lifetime of 23 years ensures a positive cash flow for the manufacturers/suppliers. On the other hand, the second scenario considers the placement of a second-hand module into an existing system (eliminating the need for additional BoP and soft costs) and shows that no minimum second life of the panel is needed to ensure a cash inflow for the manufacturers/suppliers. The effect of subsidies and policies on LCOE are also analyzed utilizing discount rates. In general, the higher the discount rate, the higher the resultant LCOE. Finally, a market structure that utilizes the concept of a Product Service System (PSS) and aims to facilitate the utilization of second-life PV modules along with a proposal for the positioning of a Product Service System Provider (PSSP) is presented. Integration of the PSSP into the existing market structure is proposed in a stage-wise manner, utilizing the distribution system operator (DSO) for effective implementation. To achieve this integration, two strategies are recommended, one based on the size and capacity of the installed systems and the other based on geographical boundaries. Additionally, a brief overview of PV subscribe, which is a business model that stimulates the second-life market, is provided.
In this project, a new worldwide climate classification directly applicable to PV has been developed. Machine Learning proved to be a convenient tool to achieve this objective. First, supervised learning served to identify and assess the climate variables more correlated to the specific energy yield. More specifically, a Linear Regression model was implemented. Subsequently, these variables were used to create the classification by applying k-means, a clustering algorithm. The classification was optimised following a comprehensive qualitative analysis, resulting in a scheme based on seven climate variables and 20 clusters. By contrast, KGPV considers five variables. Even though it contemplates 24 groups at first, half of them are neglected based on a land-surface ratio and population density criterion, resulting in a classification based on 12 clusters. Hence, the methodology proposed in this work enables identifying new relevant regions. Moreover, “Machine Learning driven PV-climate classification” presents a satisfactory correlation with the specific energy yield, except for very low values, where the correlation is minor.
Lastly, the relationship between climate and degradation rate was explored. The complexity and non-linear behaviour of degradation demand an alternative approach. Random Forests was proposed, but it showed poor performance. It is necessary to be able to predict non-linearities and, at the same time, keep a logical mathematical relation between the supervised and clustering algorithms. In this regard, Multivariate Adaptive Regression Spline (MARS) might be a promising option. ...
In this project, a new worldwide climate classification directly applicable to PV has been developed. Machine Learning proved to be a convenient tool to achieve this objective. First, supervised learning served to identify and assess the climate variables more correlated to the specific energy yield. More specifically, a Linear Regression model was implemented. Subsequently, these variables were used to create the classification by applying k-means, a clustering algorithm. The classification was optimised following a comprehensive qualitative analysis, resulting in a scheme based on seven climate variables and 20 clusters. By contrast, KGPV considers five variables. Even though it contemplates 24 groups at first, half of them are neglected based on a land-surface ratio and population density criterion, resulting in a classification based on 12 clusters. Hence, the methodology proposed in this work enables identifying new relevant regions. Moreover, “Machine Learning driven PV-climate classification” presents a satisfactory correlation with the specific energy yield, except for very low values, where the correlation is minor.
Lastly, the relationship between climate and degradation rate was explored. The complexity and non-linear behaviour of degradation demand an alternative approach. Random Forests was proposed, but it showed poor performance. It is necessary to be able to predict non-linearities and, at the same time, keep a logical mathematical relation between the supervised and clustering algorithms. In this regard, Multivariate Adaptive Regression Spline (MARS) might be a promising option.
The impacts are quantified in three categories: global warming potential, eco-cost of resource scarcity and total eco-cost. The findings indicate that, because of rapid technological advancements, the recycling and replacement of 10-year old decommissioned modules generally yield greater environmental benefits than local reuse: the net benefit in terms of global warming is greater after only 5 years. In addition, the calculations show that reusing decommissioned modules in a new PV system is only the preferred strategy from a global warming perspective if the modules are less than 5 years old, if that system is intended to have a (financial) lifetime of 10 years or longer.
However, reuse in a selected European Union member state can provide greater benefits in the global warming potential and total eco-cost impact categories than recycling and replacement. The advantage of export is driven by higher annual irradiation as well as a higher emissions intensity of the electricity mix.
These results contrast the conventional belief that reuse is always environmentally preferable to recycling. Based on this research it can be argued that in most cases of premature decommissioning, there is no strong environmental incentive to reuse the modules, provided that new PV modules are widely available or that the materials go directly to the production of new modules. The annual efficiency increase of PV technology was identified as a key parameter for this outcome. ...
The impacts are quantified in three categories: global warming potential, eco-cost of resource scarcity and total eco-cost. The findings indicate that, because of rapid technological advancements, the recycling and replacement of 10-year old decommissioned modules generally yield greater environmental benefits than local reuse: the net benefit in terms of global warming is greater after only 5 years. In addition, the calculations show that reusing decommissioned modules in a new PV system is only the preferred strategy from a global warming perspective if the modules are less than 5 years old, if that system is intended to have a (financial) lifetime of 10 years or longer.
However, reuse in a selected European Union member state can provide greater benefits in the global warming potential and total eco-cost impact categories than recycling and replacement. The advantage of export is driven by higher annual irradiation as well as a higher emissions intensity of the electricity mix.
These results contrast the conventional belief that reuse is always environmentally preferable to recycling. Based on this research it can be argued that in most cases of premature decommissioning, there is no strong environmental incentive to reuse the modules, provided that new PV modules are widely available or that the materials go directly to the production of new modules. The annual efficiency increase of PV technology was identified as a key parameter for this outcome.
This thesis mainly focuses on the cradle-to-gate stages of LCA, more specifically in raw material extraction and manufacturing of one perovskite PV module. This work selects a perovskite solar module with a mesoporous TiO2 scaffold as the studied module, then defines the goal and scope, including the definition of the research goal, functional unit and system boundaries of this LCA study. Followed by the selection of Ecoinvent V3.8 and Idemat 2023 databases, a new life cycle inventory (LCI) is created both in material and energy aspects. Based on the existing literature inventory, some changes and improvements are made to specialise the life cycle inventory data. Due to the limitation of existing databases, the missing materials and data are collected, self-calculated or replaced to complete the LCI. After calculating and integrating the LCI data by mass allocation, three impact categories are chosen to conduct the life cycle impact assessment (LCIA), which are separately climate change, human health and resource use (fossil). Next, the thesis compares the LCIA results in three different perovskite PV modules, one is the studied perovskite PV module with a silver cathode, one is the same studied module but with a gold cathode, and the other is the literature’s perovskite PV module (with a gold cathode). This thesis compares the environmental impact results in material, energy consumption and total three perspectives, simultaneously analysing the different LCIA performances between the metal gold and silver. Finally this work exerts the
contribution analysis on three life cycle impact categories, explains the LCIA results of three different perovskite solar modules and proposes further research advice.
The LCIA results illustrate that compared to the literature’s module, the studied perovskite PV module with silver cathode has the lowest life cycle environmental impacts in all three impact categories. More specifically, 50% in climate change, 12% in human health and 33% in resource use (fossil) compared to the literature. Furthermore, the metal gold has the highest contribution in all three categories, FTO and energy contribute the second and third both in climate change and resource use (fossil), and silver takes the second occupation in human toxicity. ...
This thesis mainly focuses on the cradle-to-gate stages of LCA, more specifically in raw material extraction and manufacturing of one perovskite PV module. This work selects a perovskite solar module with a mesoporous TiO2 scaffold as the studied module, then defines the goal and scope, including the definition of the research goal, functional unit and system boundaries of this LCA study. Followed by the selection of Ecoinvent V3.8 and Idemat 2023 databases, a new life cycle inventory (LCI) is created both in material and energy aspects. Based on the existing literature inventory, some changes and improvements are made to specialise the life cycle inventory data. Due to the limitation of existing databases, the missing materials and data are collected, self-calculated or replaced to complete the LCI. After calculating and integrating the LCI data by mass allocation, three impact categories are chosen to conduct the life cycle impact assessment (LCIA), which are separately climate change, human health and resource use (fossil). Next, the thesis compares the LCIA results in three different perovskite PV modules, one is the studied perovskite PV module with a silver cathode, one is the same studied module but with a gold cathode, and the other is the literature’s perovskite PV module (with a gold cathode). This thesis compares the environmental impact results in material, energy consumption and total three perspectives, simultaneously analysing the different LCIA performances between the metal gold and silver. Finally this work exerts the
contribution analysis on three life cycle impact categories, explains the LCIA results of three different perovskite solar modules and proposes further research advice.
The LCIA results illustrate that compared to the literature’s module, the studied perovskite PV module with silver cathode has the lowest life cycle environmental impacts in all three impact categories. More specifically, 50% in climate change, 12% in human health and 33% in resource use (fossil) compared to the literature. Furthermore, the metal gold has the highest contribution in all three categories, FTO and energy contribute the second and third both in climate change and resource use (fossil), and silver takes the second occupation in human toxicity.