Solar Photovoltaic Installations Research Articles

Predictive Modeling of Solar PV Panel Operating Temperature over Water Bodies: Comparative Performance Analysis with Ground-Mounted Installations

Solar panel efficiency is significantly influenced by its operating temperature. Recent advancements in emerging renewable energy alternatives have enabled photovoltaic (PV) module installation over water bodies, leveraging their increased efficiency and associated benefits. This paper examines the operational performance of solar panels placed over water bodies, comparing them to ground-mounted solar PV installations. Regression models for panel temperature are developed based on experimental setups at BITS Pilani, India. Developed regression models, including linear, quadratic, and exponential, are utilized to predict the operating temperature of solar PV installations above water bodies. These models incorporated parameters such as ambient temperature, solar insolation, wind velocity, water temperature, and humidity. Among these, the one-degree regression models with three parameters outperformed the models with four or five parameters with a prediction error of 5.5 °C. Notably, the study found that the annual energy output estimates from the best model had an error margin of less than 0.2% compared to recorded data. Research indicates that solar PV panels over water bodies produce approximately 2.59% more annual energy output than ground-mounted systems. The newly developed regression models provide a predictive tool for estimating the operating temperature of solar PV installations above water bodies, using only three meteorological parameters: ambient temperature, solar insolation, and wind velocity, for accurate temperature prediction.

Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach

Solar energy shines as a beacon for sustainable development, with rooftop solar photovoltaic (PV) installations playing a crucial role. This study proposes a novel framework to precisely assess citywide existing solar power generation and analyze future potential under various rooftop utilization scenarios (10–50 %). To illustrate the methodology, the existing solar PV yield and future potential of Islamabad are explored as a case study. Employing open-source satellite data and deep learning (DL), this study quantified the electricity generation from current solar infrastructure and projected future possibilities. DL models like Mask R–CNN and Deeplab-v3 are trained on extensive custom datasets for solar panels and rooftops extraction. The study delineated 19,000 solar arrays, covering an area of 0.8 sqkm, and extracted 75.08 sqkm of suitable rooftops for future installations. The analysis revealed, current solar infrastructure generates 141.42 GWh electricity, satisfying 6.34 % of Islamabad's annual energy demand. Utilizing 50 % rooftop area could generate 6578 GWh annually, meeting 294 % of the city's electricity needs. This research contributes to the strategic planning for solar energy infrastructure, demonstrating the substantial role rooftop solar can play in meeting urban energy needs. This can inform policy decisions and guide investment opportunities for expanding clean energy in the city.

Short-term solar photovoltaic power forecasting using ensemble forecasting strategy for renewable resources based power systems

Environmentally-friendly renewable energy sources have been developed and commercialized to mitigate impact of climate change on the environment. Solar photovoltaic (PV) systems have gained much attention as a power generation source for various uses, including the primary utility grid power supply. There has been a significant increase in both on-grid and off-grid solar PV installations. Because of the highly unpredictable nature of solar power generation, it is crucial to forecast solar power accurately for renewable resources-based power systems. In this research, a swarm-based ensemble forecasting strategy has been proposed to predict solar PV power by combining three strategies, i.e., particle swarm optimization-based gated recurrent unit (PSO-GRU), PSO-based long short-term memory (PSO-LSTM), and PSO-based bidirectional long short-term memory (PSO-BiLSTM). Bayesian model averaging (BMA) combines the output of the proposed strategy by aggregating the output of each swarm-based approach. The performance of the suggested approach is evaluated and verified using historical data of solar PV power which is acquired from Griffith University, Australia. Python 3.11 is used to validate the performance of the proposed ensemble strategy and compared it with several competing strategies. The proposed ensemble strategy outperforms other comparative strategies in terms of RMSE, NRMSE, and MAE.

Assessing soil pollution concerns in proximity to Fence-type solar photovoltaic system installations

This study conducted in the Kyungpook National University Eco-friendly Agriculture Research Centre between 2022 and 2023 investigates the environmental implications of fence-type solar photovoltaic (PV) systems in diverse agricultural settings. Despite the increasing adoption of solar energy for climate change mitigation, there is a noticeable gap in research regarding the potential environmental impact of these specific PV systems. Focusing on heavy metal concentrations, including Cadmium (Cd), Copper (Cu), Arsenic (As), Mercury (Hg), Lead (Pb), Hexavalent Chromium (Cr+6), Zinc (Zn), and Nickel (Ni), across distinct fields, the study reveals significant fluctuations. Notably, the Rice Field experienced a substantial increase in Cd levels from 0.47 mg/kg in 2022 to 1.55 mg/kg in 2023, while Cu and Pb concentrations decreased to acceptable levels in 2023. The findings underscore the dynamic nature of heavy metal concentrations, emphasizing the importance of continuous soil quality monitoring to prevent contamination. This research provides valuable insights into the impact of fence-type solar PV system installations on agricultural soil quality, emphasizing the urgent need to secure these ecosystems through vigilant monitoring and environmental management practices.

Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data

Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learning approaches for predicting electricity production from solar photovoltaic installations at a regional level in Italy, not using data on individual installations. Addressing the challenge of diverse data availability between pinpoint meteorological inputs and aggregated power data for entire regions, we propose leveraging meteorological data from the centroid of each Italian province within each region. Particular attention is given to the selection of the best input features, which leads to augmenting the input with 1-hour-lagged meteorological data and previous-hour power data. Several ML approaches were compared and examined, optimizing the hyperparameters through five-fold cross-validation. The hourly predictions encompass a time horizon ranging from 1 to 24 h. Among tested methods, Kernel Ridge Regression and Random Forest Regression emerge as the most effective models for our specific application. We also performed experiments to assess how frequently the models should be retrained and how frequently the hyperparameters should be optimized in order to comprise between accuracy and computational costs. Our results indicate that once trained, the model can provide accurate predictions for extended periods without frequent retraining, highlighting its long-term reliability.

Innovative decision-making for solar photovoltaic system installation in residential energy hubs: A probabilistic dynamic planning approach

This paper introduces an innovative probabilistic dynamic planning model tailored to optimize photovoltaic (PV) system installation within Residential Energy Hubs (REHs). The REH amalgamates diverse components, including PV systems, Combined Heat and Power (CHP) units, and gas boilers, to holistically fulfill residential thermal and electrical energy demands. The model's primary objective is to achieve an optimal equilibrium between minimizing initial investment costs associated with PV systems and managing ongoing energy expenses within the REH framework, encompassing potential PV energy losses over the planning horizon. To augment the flexibility and benefits of PV systems for residential consumers, the model integrates several pioneering features. Particularly notable is the introduction of a heating energy storage system, which complements the REH's thermal requirements and adeptly reacts to fluctuations in gas and electricity prices, thereby maximizing PV system benefits and ensuring homeowners' cost efficiency. Moreover, the model addresses the inherent uncertainty tied to PV generation by employing a two-point estimation method, offering a more realistic depiction of system performance and potential outcomes. To resolve the two-objective problem, an initial conversion into a single-objective problem via a weighted summation method precedes the application of an optimization algorithm inspired by symbiotic organisms. The paper demonstrates the practical application and efficacy of the proposed model through extensive simulations spanning a 10-year planning horizon. The results underscore the model's effectiveness and versatility in guiding residential consumers to make informed and economically viable decisions regarding PV system installation within the broader spectrum of their energy requirements. This research represents a significant stride towards empowering homeowners with invaluable tools for embracing sustainable energy solutions.

Serviceability analysis and feasibility study of ballasted rooftop PV system on existing concrete roof slab buildings

Solar Photovoltaic (PV) installation on existing structures became popular for the purpose of reducing carbon emission and improving energy efficiency of buildings. Ballasted PV systems which can impose an additional dead load are preferred by building owners as direct roof penetration method can pose a potential threat to the insulation, water proofing as well as the warranty of roof membranes. Ballasted PV systems have been questioned for structural capability, economic feasibility as well as sustainability stand points. This study focuses on determining an the optimal ballast number to resist uplift wind pressure. It additionally considers concrete roof slab serviceability with respect to long-term deflection, and provides a feasibility study of PV installation. Based on the results of the study, the ballasted PV system does not yield a significant influence on structural serviceability of building. The economic feasibility analysis considers the current level of incentives. Economically a ballasted PV system can be an attractive investment with approximate payback period of 7.6 years. Without incentives, the payback period is 24 years.

Sensitivity analysis of distributed photovoltaic system capacity estimation based on artificial neural network

Residential solar photovoltaic (PV) system installations are expected to continue increasing due to their growing cost competitiveness and supportive government policies. However, excessive installations of unknown behind-the-meter solar panels present a challenge for accurate load prediction and reliable operations of power networks. To address such growing concerns of distribution network operators (DNOs), this research proposes a novel model for distributed PV system capacity estimations. Innovative extracted features from 24-hour substation net load curves were fed into a deep neural network to estimate the PV capacity linked to the substation feeder. A comprehensive study into the sensitivity of the model’s accuracy to specific temporal scales of data collection, number of households served by a substation, and proportion of PV-equipped properties was conducted. This study revealed that a model developed to be used exclusively in summer achieved a 18.1% decrease in estimation root mean squared error (RMSE) compared to an all-year model, whilst using only a third of the training data amount. Similarly, compared to an all-year model, RMSE decreased by 26.9% when only data from Mondays to Thursdays were used to train and test the model. Also, for the all-year model, the most accurate estimations occur when 20% to 80% of households have PV systems installed and estimation percentage error tend to remain constant at around 10% when more than 20% of households have PV systems installed. A machine learning-ready dataset of substations with known PV capacity and experiment results are both useful to inform DNOs on the potential of the proposed method in reducing grid operation costs.

COMPARATIVE ANALYSIS OF CLOT-ADJUSTED AHI-8/9 AND FY-4A SOLAR RADIATION DATA FOR SOLAR PV POWER POTENTIAL ASSESSMENT IN THE PHILIPPINES

Abstract. The theoretical solar photovoltaic (PV) power (PPV) potential in the Philippines has been estimated to range from 1.87 to 3.19 kWh/kWp daily and 681.80 to 1,162.58 kWh/kWp yearly using the shortwave radiation (SWR) product derived from the Advanced Himawari Imager-8 and 9 (AHI-8/9) satellite data. However, solar radiation measurements, especially for satellite-derived data, must be accurate when used to estimate the maximum solar energy available for solar PV power production. The objective of this study is to evaluate the satellite-derived measurements of AHI-8/9 SWR and Fengyun 4A surface solar irradiance (SSI) data products in the estimation of theoretical PPV output in the Philippines. Moreover, selected actual solar PV installation sites located across the Philippines, such as in the cities of Baguio, Cebu, and Tagum, are included in the analysis PV power production data. Both SWR and SSI products were adjusted using the cloud optical thickness (CLOT) product for cloud effects and consequently, obtain the maximum solar PV power potential. In this study, SWR and SSI products were analyzed to determine which factors may have contributed to the differences in the estimated values. The in-situ PV power production data was compared to the two satellite-based solar radiation data to assess the degree of correlation between the two. Diurnal trends for representative areas in Luzon, Visayas and Mindanao show that for all sites, the SWR data agrees with the actual PV production data better than the SSI data. Results show that the range of CLOT-adjusted solar irradiance values are higher when using SSI than SWR. It was also observed that CLOT reduces the solar radiation by an average of 19.36%, 16.08% and 10.54% during the cool dry season (December to January), rainy season (June to November) and hot dry season (March to May), respectively. In future studies, the adjusted solar radiation (R') data may be used instead of the raw AHI8 SWR or FY-4A SSI to estimate the theoretical maximum solar PV power potential (PPV). Geomorphological and meteorological factors can also be considered in the estimation of effective PPV.

FACTOR ANALYSIS AND COST CALCULATION OF SOLAR ENERGY IMPLEMENTATION IN AVIATION MAINTENANCE, REPAIR & OVERHAUL (MRO)

Background: The global imperative to address climate change has heightened awareness of sustainable practices across industries. Emissions from the aviation industry contribute significantly to global warming, with Maintenance, Repair, and Overhaul (MRO) activities contributing to aviation emissions. Hence, efforts are needed to reduce emissions by transitioning from fossil energy to renewable energy. Methods: Used pairwise comparisons among seven factors was conducted by collecting data using structured interview from 15 expert respondents, comprising staff, managers, senior managers, and directors from five major Aviation MRO facilities in Indonesia equipped with hangar facilities. The respondents were selected based on their expertise and experience in the Aviation MRO industry. Following the structured interview, life cycle cost calculations were performed to compare the use of electricity from the national grid (PLN), leased Solar PV, and purchased Solar PV. Results: Findings revealed positive acceptance across Technical Awareness, Economy, Environment, and Policy aspects in the first structured interview section. Then provided insights into respondents' priorities, with Cost, Lifecycle, Environmental Impact, Government Support, Capacity, Quality, and Payment Scheme ranked in order of importance in selecting electricity energy sources. Additionally, life cycle cost calculations demonstrate significant cost efficiency in both investment and leasing schemes for solar photovoltaic installation, highlighting the industry's potential contribution to emissions reduction. Conclusions: Integrating renewable solar energy into hangar operations presents an opportunity for the MRO industry to enhance business efficiency while advancing environmental sustainability. Government support and incentivization are essential to encourage broader adoption of renewable energy initiatives within the Aviation MRO sector.

Real time performance assessment of utility grid interfaced solar photovoltaic plant

Continuous monitoring of large-scale solar photovoltaic (PV) installations is necessary to check the deterioration and monitor the performance of the PV plant. Fault diagnosis is crucial to ensure the PV plant operates safely and reliably. This paper presents a diagnosis methodology based on current-voltage (I-V) and PV characteristics to monitor and assess the behavior of solar PV. In this paper, I-V curve characterization using an I-V curve tracer is used to check the deterioration and diagnosis of the PV panels. The real-time performance of the 50.4 kWp rooftop solar grid interfaced PV plant is investigated and analyzed using I-V and PV curve tracers in real-time conditions. The overall performance of solar PV is assessed on a real-time test system in different scenarios such as variable climatic conditions, partial shading conditions, aging of solar panels, short circuit conditions, and dust decomposition. Furthermore, the performance assessment of solar PV is evaluated using performance indicators such as open circuit voltage index, short circuit current index, fill factor, and performance ratio.

Demarcation of suitable site for solar photovoltaic power plant installation in Bangladesh using geospatial techniques

The major goal of this research is to adopt analytical hierarchy process (AHP) based geospatial technique to select suitable zone for the solar photovoltaic (PV) power plants. Nine thematic layers altogether—slope, global horizontal irradiation (GHI), relative humidity, direct normal irradiation (DNI), elevation, distance from major roads, distance from protected areas, rainfall, and land use/land cover (LULC)—are combined through overlay analysis in ArcGIS to create the final map of suitability for the placement of solar photovoltaic (PV) power plants in Bangladesh. This map has been classified into five categories namely, restricted zone, less suitable zone, moderate suitable zone, good suitable zone, and excellent suitable zone. These categories are covered by 7.28%, 16.61%, 28.51%, 27.77%, 21.83% land of total area in Bangladesh respectively. The findings of this research have been presented that ‘the excellent suitable’ and ‘good suitable’ areas for the construction of solar power plants are in the western and northwestern part (Rajshahi, Pabna, Sirajganj, Natore, Naogaon, Chapainawabganj, Bogura, Faridpur, Jessore, Jehenaidha, Magura, Kushtia, Choudanga, Meherpur) of the study area which contain a high value of global horizontal irradiation, direct normal irradiation, elevation and low value of slope, rainfall, temperature, relative humidity. Besides the restricted and less suitable zone for installing solar photovoltaic (PV) power plants indicates a high value of rainfall, slope, temperature, relative humidity and low value of global horizontal irradiation, direct normal irradiation, and elevation. Bangladesh's currently operational solar plants were taken into consideration for this study's validation purposes. The proposed framework may potentially be used in different locales on a national and worldwide scale. This study offers a consistent GIS process for the accurate, inexpensive implementation of a solar energy plan to achieve environmentally friendly goals.

Promoting Sustainable Development Goals by Optimizing City-Level Solar Photovoltaic Deployment in China.

Solar photovoltaic (PV) installations, which enable carbon neutrality, are expected to surge in the coming decades. This growth will support sustainable development goals (SDGs) via reductions in power-generation-related environmental emissions and water consumption while generating new jobs. However, where and to what extent PVs should be utilized to support SDGs must be thoroughly addressed. Here, we use multiple PV deployment scenarios to compare the benefits of PVs and related SDGs progress in 366 prefectural-level cities in China. We developed an assessment framework that integrates a PV allocation model, an electricity system optimization model, and a benefit assessment approach. We identify vast differences in PV distribution and electricity transmission and elucidate trade-offs and synergies among the SDGs under various PV implementation scenarios. The water conservation-oriented scenario yields substantial carbon reduction, air pollutant mitigation, and water saving cobenefits, leading to the greatest SDGs improvements. Prioritizing employment creation enhances job-relevant SDGs but inhibits environmental resource benefits. SDGs in less developed cities present greater progress across all scenarios. This study highlights the need to consider spatial heterogeneity and the potential trade-offs between different SDGs and regions when designing energy transition strategies.

SPATIAL INVENTORY OF SOLAR PHOTOVOLTAIC (PV) INSTALLATIONS USING REMOTE SENSING AND MACHINE LEARNING: CASE OF PAMPANGA PROVINCE, PHILIPPINES

Abstract. In recent years, the cost of photovoltaic (PV) technologies significantly declined, boosting the expansion of solar PV installations in the country. This rapid development in solar PV utilization necessitates an effective detection method capable of delineating both utility-scale and distributed PV installations to generate a complete inventory of solar PV installations for status monitoring and implementation of appropriate programs of stakeholders and decision-makers. This study aims to detect and delineate solar PV installations in Pampanga, Philippines using different band combinations of Sentinel-2, Sentinel-1, and indices (NDVI, NDWI, and PVSI) through machine learning with the aid of open-source geographic information system and remote sensing software. Moreover, an alternative approach to identifying solar PVs using the combination of pixel-based classification (PBC) and object-based classification (OBC) was introduced. Training and validation data were acquired from the satellite images. The accuracy of each approach in PV detection was then compared using three classifiers: Support Vector Machine (SVM), Random Forest, and Naive Bayes. Results showed that SVM has the best performance for PBC while Random Forest demonstrated the highest accuracy for OBC. A post-processing procedure was also implemented using a set of spectral rules to further refine the results of image classification. The delineation accuracies of the post-processed data over the ground-truth data showed that the methodology is effective in delineating utility-scale installations for both Sentinel-2 and Planetscope. However, the detection of distributed PV systems showed limitations particularly when dealing with small solar PV installations (less than 45 pixels) due to Sentinel-2’s coarse spatial resolution.

Numerical Simulation and Mathematical Analysis of Meta Heuristic MPPT System for Solar Photovoltaic Applications Under Non-Linear Operational Conditions

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Detection and analysis of deteriorated areas in solar PV modules using unsupervised sensing algorithms and 3D augmented reality

Solar Photovoltaic (PV) systems are increasingly vital for enhancing energy security worldwide. However, their efficiency and power output can be significantly reduced by hotspots and snail trails, predominantly caused by cracks in PV modules. This article introduces a novel methodology for the automatic segmentation and analysis of such anomalies, utilizing unsupervised sensing algorithms coupled with 3D Augmented Reality (AR) for enhanced visualization. The methodology outperforms existing segmentation techniques, including Weka and the Meta Segment Anything Model (SAM), as demonstrated through computer simulations. These simulations were conducted using the Cali-Thermal Solar Panels and Solar Panel Infrared Image Datasets, with evaluation metrics such as the Jaccard Index, Dice Coefficient, Precision, and Recall, achieving scores of 0.76, 0.82, 0.90, 0.99, and 0.76, respectively. By integrating drone technology, the proposed approach aims to revolutionize PV maintenance by facilitating real-time, automated solar panel detection. This advancement promises substantial cost reductions, heightened energy production, and improved performance of solar PV installations. Furthermore, the innovative integration of unsupervised sensing algorithms with 3D AR visualization opens new avenues for future research and development in the field of solar PV maintenance.

Solar PV power plant revamping: Technical and economic analysis of different alternatives for a Spanish case

The operation of solar photovoltaic installations for many years may lead to problems such as reduced energy efficiency and aging of components. Under this framework, replacing certain components in the facility to extend the service life of the power plant is viewed, in some cases, as a feasible strategy. However, despite its enormous strategic interest for the solar PV industry, we find that revamping PV installations has been the focus of very few scientific publications, and even less so when it comes to analyzing real facilities. Thus, this paper performs a techno-economic analysis of three revamping strategies at a real PV power plant located in the south-east of Spain. The strategies consist of substituting, in each case, the PV modules, the inverter, and both components. The simulated results show that the highest production value by installed power is obtained when both the modules and the inverter are replaced. In the same vein, the economic analysis reveals this scenario as the most favorable in terms of profitability. Based on the results obtained, an interesting future line of research would be focused on the extension of the analysis at country level to study the joint impact of numerous revamping actions.

Investigating Barriers to Low-Carbon Policy Implementation among Mining Companies in Ghana

Efforts for carbon emission reduction have been identified as a major climate change mitigation target in the Paris Agreement. The 26th Conference of Parties to the United Nations Framework Convention on Climate Change highlighted the worldwide effort for cleaner and greener mining, urging the mineral industry to embrace greener operational emissions reduction technologies and strategies. At the Paris Conference, Ghana pledged to reduce CO2 emissions by 15% and increase its renewable energy share to 10% of the national energy mix. Ghana’s efforts to implement a low-carbon policy were, however, hampered for several reasons. Ghana is Africa’s leading and the world’s seventh largest gold producer. Mining has contributed significantly to foreign currency acquisition and government revenue. The gold mining sector contributes approximately 95% of Ghana’s total mineral revenue. Considering these situations, the fundamental question is how Ghana’s mining industry can contribute to CO2 emission reduction efforts. This paper investigates barriers to low-carbon emission policy implementation among mining companies in Ghana. We adopted 5-point Likert scale statement questions. The questionnaire survey was conducted among Ghana’s four large-scale gold mining companies. As the sample number of available companies is limited, we used a descriptive analysis to assess the responses. The results show that the companies expressed widely different ways to respond to the issues of cost, a government policy for carbon emission reduction, and the acquisition of low-carbon technologies. Those companies that are relatively less prepared for this endeavor emphasized the uncertainties of securing sufficient energy in case of using low-carbon technologies. They also expressed a need for more trained workers to handle low-carbon technologies. We found that a major challenge among companies was the expected cost involved in implementing a low-carbon emission policy. There was a communication gap with the relevant government ministry about low-carbon mining options where the government should be able to facilitate the involvement of low-carbon technology providers, such as solar Photovoltaics installation.

Solar governance for the transborder agglomeration of the Greater Geneva based on the solar cadaster development

As cities are major energy consumers challenges arise in densely populated areas that limit solar resources, hindering the deployment of urban solar power plants. To address this, the paper underscores the importance of digital tools for modeling solar energy accessibility within the urban fabric, focusing on rooftops, building facades, and other relevant locations. In this context, the development of the solar cadaster in Greater Geneva is presented in the paper within the framework of the European INTERREG G2 Solar project (2019–2022). The solar cadaster relies on a rich database from the Geneva Land Information System (SITG), offering comprehensive data on Digital Surface Models, building and roof cadasters, land cover, and more. Notably, this data, including the solar cadaster outputs, is accessible to the public as open data, fostering its extensive use in academia for teaching and research. It serves as a living laboratory for urban solar studies, with its open data approach contributing to widespread adoption. The project, a collaborative effort involving academia, energy utilities, and public institutions on both sides of the border, aims to unite stakeholders and foster a dialogue on solar energy in the region, in line with its commitment to addressing climate change, striving for carbon neutrality by 2050. The paper articulates the steps, methods, and outcomes of the project, offering insights into the challenges of implementing a shared online application tool in a cross-border territory. The propositions are based on feedbacks from stakeholders and lessons learned from case studies. The paper also delves into the shift in the business model for solar photovoltaic installations, emphasizing the need to model self-consumption in buildings. It concludes by highlighting the solar cadaster as a unifying means, fostering cohesion among diverse stakeholders in the region’s energy sector and propelling them toward common governance around solar energy.

Material intensity and carbon footprint of crystalline silicon module assembly over time

The growing solar photovoltaic (PV) installations have raised concerns about the life cycle carbon impact of PV manufacturing. While silicon PV modules share a similar framed glass-backsheet structure, the material consumption varies depending on module design, manufacturer, and manufacturing year, leading to varying carbon emissions. However, current life cycle assessment (LCA) studies and public inventory databases of silicon PVs lack an assessment of the variability in commercialized solar module designs and their potential impact on module reliability and carbon footprint. The present study aims to address this research gap by providing a temporal analysis of aluminum and glass intensity in crystalline silicon modules produced from 2006 to 2021. The material inventory data is obtained from specification sheets of 167 crystalline silicon modules produced by 31 manufacturers. Subsequently, we use the collected material inventory to estimate the carbon footprint of manufacturing silicon modules in multiple countries over the past decade. The results reveal a 30% reduction in the aluminum intensity used for frames from 2010 to 2021, while the solar glass thickness remains relatively constant. Additionally, the comparison among manufacturer tiers indicates that more reliable modules tend to use more materials for module production. Moreover, the comparative life cycle assessment of modules manufactured in various countries demonstrates a significant impact of local electricity mixes on the carbon footprint of module manufacturing. Modules manufactured in China exhibit the highest carbon emissions, followed by Malaysia (4–9% lower than China), South Korea (15–16% lower than China), the US (17–18% lower than China), Thailand (19–21% lower than China), Turkey (18–21% lower than China), and Vietnam (25–30% lower than China). Overall, the present study highlights the importance of up-to-date data on material inventory and local electricity mixes in evaluating the environmental impacts associated with PV manufacturing. Lastly, we advocate for integrating the variability of module designs and manufacturing locations within low-carbon solar module criteria and guidelines, recognizing the importance of adaptability in achieving sustainable solar manufacturing.