Large-scale Solar Photovoltaic Research Articles

Dynamics of large-scale solar PV adoption feedback effects: A technical, economic, and environmental assessment

Large residential solar photovoltaic (PV) penetration has a compound effect on the grid load reductions, PV hosts’ economic savings, and the achievable environmental benefits, which is not fully understood. This study combines process-based energy balance modeling, life cycle assessment, regression analysis, and stochastic demand simulations to assess the technical, economic, and environmental tradeoffs under increased residential solar PV adoption, using Boston, MA as a testbed. It was found that increased PV adoption may lead to a steeper ramp-up in the grid during winter months, but a flattened peak load curve during summer months, emphasizing the need for seasonal time-of-use rates and energy storage. It also reduces electricity wholesale prices, lowering PV hosts' economic benefits by about $15 million under 100 % adoption. The largest buildings present the highest load reduction (top 5.5 %) and environmental benefits (top 16.6 %), but they are the least cost efficient (top 14.5 %), requiring tradeoff balance.

Potentials and opportunities of solar PV and wind energy sources in Saudi Arabia: Land suitability, techno-socio-economic feasibility, and future variability

Solar and wind energy sources hold significant potential to meet the escalating energy demand in Saudi Arabia sustainably. This research aims to assess the feasibility and prospects of deploying solar photovoltaic (PV) and wind energy systems in Saudi Arabia (SA). The study adopts a comprehensive approach, encompassing spatial analysis of land suitability, techno-socio-economic feasibility studies, and prediction of renewable energy (RE) resource variability and trends using machine learning techniques. Geographical Information System (GIS) spatial analysis is employed to identify suitable areas for establishing solar PV and wind farms based on multi-criteria evaluation methods. Techno-socio-economic feasibility analysis of solar PV and wind energy systems, under various configurations and scenarios, is conducted using the System Advisor Model (SAM) tool. Furthermore, a Support Vector Machine (SVM) machine learning algorithm is developed and deployed to forecast Global Horizontal Irradiation (GHI), wind speed, and influential weather parameters at different locations in SA. The projections of these variables derived from global climate models are utilized to analyze prospects and variabilities. The land suitability analysis identified four optimal locations for large-scale solar PV fields in Tabuk, Al Madinah, Makkah, and Riyadh provinces. Additionally, four promising wind farms were found in Al Madinah, Makkah, Riyadh, and Eastern provinces. The analysis also identified one location in Al Jouf province suitable for hybrid systems combining solar PV and wind energy. The techno-economic assessment revealed that wind farms performed best overall, achieving a capacity factor of 42.6 % in Al Madinah province. Although current tariffs render projects economically unviable, solar PV, wind energy, and hybrid solar PV-wind technologies are economically feasible in SA at Power Purchase Agreement (PPA) rates above $32.8/MWh, $26.1/MWh, and $50.6/MWh, respectively. The social development analysis estimated potential job creation from solar PV and wind energy deployment under different scenarios from 2020 to 2060, indicating that more ambitious climate targets could translate to millions of renewable energy jobs. The SVM model predicted solar irradiance with an R-squared value of 0.893. The CMIP6 model was then used to project the GHI for SA in 2049, suggesting an increase of approximately 19 %. Wind speed is also expected to rise roughly 5 % over the same period. The integration of GIS spatial analysis, techno-socio-economic modeling, and machine learning-based forecasting provides comprehensive insights into harnessing solar and wind energy in SA. This study facilitates evidence-based planning and risk assessment, crucial for a sustainable energy transition. The insights and roadmap derived from this research can inform policy frameworks, support the United Nations Sustainable Development Goals (UNSDGs), and attract private investments for RE development in SA. Consequently, this study establishes solar and wind energy as viable and promising solutions for meeting SA's growing energy demands sustainably, while minimizing the associated environmental impact.

Examining Large-Scale Solar (LSS) Photovoltaic (PV) Operating Utilities by using Environmental Impact Screening (EIS)

For Malaysia’s future energy needs, solar photovoltaic (PV) technology and farming is becoming a more costeffective option and technology in achieving Malaysia’s goal of having 31% renewable energy in its power mix by year 2025. Malaysia’s global climate commitment is to reduce its economy-wide carbon intensity (as a percentage of GDP) by 45% by 2030 compared to 2005 levels. Realizing the government’s vision is critical to assisting the country in meeting its Nationally Determined Contributions (NDC) targets (SEDA, 2022). Hence, this paper highlights the evaluation of an existing solar farm located at Seberang Perai Pulau Pinang, Malaysia as a case study to examine the potential environmental impact arising from of large-scale solar (LSS) PV development utilising the Analytic Hierarchy Process (AHP) under the Multi-Criteria Decision Making (MCDM) technique. The approach for this study was conducting in depth interviews with 16 people, including academics, authorities, and experts in the field. Large projects need to be evaluated to make sure that any potential problems are found and resolved early in the planning and design phases. This study considers the effects on the environment and chemicals (EC), as well as occupational safety (OSH) and economic impact (EI). AHP is then used to assess the data and utilized to produce the final component rankings. By showing the outcomes, the bar chart will aid in improved decision-making. The bar chart’s ability to display the results will help in the decision-making. From the results, LSS PV farm were conducive to achieving strong sustainability however in term of environmental degradation, mitigation action must be implemented on site to tackle the arising issues.

The promising future of developing large-scale PV solar farms in China: A three-stage framework for site selection

Large-scale Photovoltaics (PV) play a pivotal role in climate change mitigation due to their cost-effective scaling potential of energy transition. Consequently, selecting locations for large-scale PV power plants has gained worldwide prominence in recent decades. Although massive studies have examined various siting criteria, land consolidation and cost-benefit feasibility issues are not often included due to the lack of a comprehensive and discretionary decision-making system. In this study, we introduced a three-stage framework combining DBSCAN clustering and cost-benefit analysis to identify the most efficient and cost-effective land parcels. The results indicate that while a total area of 425,191 km2 is considered developable for PV installation in China, only 23% of that area (128,588 km2) are consolidated land parcels which are suitable for developing large-scale PV power plants. Moreover, the potential of these consolidated land parcels (8,289,662 gWh/year) can supply about 90% of China's electricity demand. However, when considering the infrastructure investment per kWh, high-potential land parcels doubled the moderate-potential ones. These results emphasize the importance of large-scale PV plant siting as it impacts the efficiency of PV integration and the optimal land use. Hence, this methodology equips decision-makers with a practical and efficient tool for economically developing large-scale solar PV.

Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MWdc) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.

Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant

Malaysia's most plentiful renewable energy (RE) source, solar, only generate power during the day. High penetration of intermittent PV generation, especially from Large-Scale Solar Photovoltaic (LSSPV) plants, may complicate power system planning and operation. Hence, the need to accurately forecast solar generation for sustaining grid stability. This study identified the meteorological variables that significantly impact the power generation of an LSSPV plant using the Pearson Correlation Coefficient (PCC). The LSSPV plant, located in the central area of Malaysia, is used as a case study. To this end, a regression analysis model was developed using Artificial Neural Network (ANN) to predict solar generation with changing meteorological conditions. From August 1, 2020, to July 1, 202,1 and September 1, 2022, to November 1, 2022, a 5-minute dataset is collected. The meteorological variables with the strongest correlation on solar generation were the total global horizontal irradiance and global irradiance on the module plane selected by PCC, followed by PV module temperature, ambient temperature, wind speed, total slope and total horizontal irradiation. The final input to the forecasting model consists of four primary features that indicate a significant correlation with solar PV generation, which are total global horizontal irradiance (W/m²), PV module temperature (°C), ambient temperature (°C) and wind speed (m/s). The simulation findings reveal that the accuracy evaluation metrics are higher, with R² metrics improved by 45% when employing feature selection for prediction. While ANN has been commonly used for solar generation prediction, this study uniquely combines ANN with PCC as a feature selection technique. This method has not been widely explored in the literature and has resulted in significant improvements in the accuracy and robustness of the prediction model. Thus, our study provides a valuable contribution to solar energy prediction and can potentially enhance solar energy utilisation efficiency in various applications.

Implementation of String Inverters for 50 MW Large Scale Solar Power Plant in Malaysia

The growth of large-scale solar photovoltaic (LSSPV) power plant site development in Malaysia has increased the demand for the reliability and potentiality of the system in achieving efficient monitoring, operation, and maintenance for the plants. A configuration of string inverters is applied in a 50 MW large-scale solar photovoltaic installation because of its flexibility and scalable technological advancements. This installation demonstrates that the plant can optimise the power output and monitor the performance of the system up to the string level with a cost-efficient implementation. The output from each photovoltaic (PV) string provides a guarantee of better maximum power point tracking (MPPT) capability. Instead of having shorter DC wires compared to the central inverter, this system offers the potentiality of monitoring the solar PV module at a string level, which also affords scalability for future expansion. The tests conducted verify that the string inverter configuration serves as a reliable outcome for grid code test (GCT) requirements. Furthermore, the string inverters provide a potential approach to achieving maximum power generation in different climate conditions, thus reducing the need for manpower in operation and maintenance activities. The string system offers a feasible implementation of effective configurations and decarbonisation for LSSPV in Malaysia without frequent shutdowns of plant generation due to defects or damage to PV equipment and modules.

Anomaly detection using K-Means and long-short term memory for predictive maintenance of large-scale solar (LSS) photovoltaic plant

Detecting and monitoring faults in solar photovoltaic (PV) systems is crucial to ensure optimal efficiency and prevent safety and fire hazards. However, the conventional operation and maintenance (O&M) of solar PV systems do not utilize machine learning for fault detection and classification. This poses challenges for plant operators, especially those managing large-scale solar (LSS) PV plants, who typically rely on manual approaches to screen large amounts of electrical data and inspect numerous string panels. Consequently, the cost of O&M is high. This study aims to use advanced machine-learning techniques to detect anomalies in a Large-Scale Photovoltaic (LSSPV) plant. The study collects data from the plant which located in the central of Peninsular Malaysia and employs K-Means for clustering and Long-Short Term Memory (LSTM) for anomaly detection in the predicted electrical current of string modules. The model is developed using Jupyter Notebook from the Python Package Index. To validate the accuracy of the proposed model, the study compares LSTM with Artificial Neural Network (ANN) using relative error as the evaluation metric. The results indicate that the LSTM method identifies anomalies in the predicted output current of the string modules more accurately and with lower relative error than the conventional ANN technique. The proposed model could help plant operators perform predictive maintenance of LSSPV plants at a minimal cost and time.

Performances Analysis of Three Grid-Tied Large-Scale Solar PV Plants in Varied Climatic Conditions: A Case Study in Algeria

Currently, for the determination of the suitable and optimal PV power plant according to the climate conditions of the concerned region, researchers focus on the estimation of certain performance factors, which are reported to be the key parameters for the analysis of the performances of grid-connected photovoltaic (PV) power systems. In this context, this paper focuses on on-site real-time analysis of the performance of three solar photovoltaic plants: Sidi-bel-Abbés (12 MWp), Laghouat (60 MWp), and Ghardaïa (1.1 MWp). These plants are located in different regions experiencing diverse climatic conditions in Algeria. The analysis was carried out by the standardized norms of IEC 61724, using monitoring data collected over one year. The photovoltaic power plants were evaluated in terms of performance factors, such as the reference yield (Yr), final yield (Yf), performance ratio (PR), and capacity factor (CF). On the other side, based on real data collected at the concerned sites, two linear functions depending on solar irradiance and the PV module temperature for each site are proposed for the evaluation of the generated alternative power output (PAC) for the three PV plants. The obtained results based on the study presented in this paper can help designers of PV power plants of different technologies and different climate conditions to precisely decide the convenient technology that allows the best production of the electrical energy for grid-tied PV systems. Furthermore, this study can contribute in giving a clear vision of the implementation of upcoming large-scale solar PV power plants in Algeria within the studied area and other areas.

An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting

The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm.

Spatial patterns and environmental benefits of photovoltaic poverty alleviation programs in China

The photovoltaic poverty alleviation (PVPA) project in China integrates clean energy production into poverty reduction, gaining significant academic interest. However, a systematic assessment of the spatial distribution and emission reduction performance of existing PVPA power plants at the site level remains deficient. Based on comprehensive data collection and spatial visualization of all PVPA plants, we revealed spatial distribution patterns and estimates potential environmental benefits for each plant. During 2013–2019, the construction of approximately 180-thansand PVPA plants in China had resulted in an overall installed capacity of 21.77 GW. The spatial layout showed a decreasing trend from east to west, with distinct clusters at the border areas of different provinces. The cumulative emission reduction benefits amounted to 636 million tons, with large-scale solar photovoltaics and the North China Grid contributing significantly. Emission reduction efficiency per unit of installed capacity in the north was higher than the south. This study provides an initial site-level assessment of the PVPA program in China and emphasizes the need for integrating economic feasibility and environmental benefits, along with developing complementary policies and exploring innovative “photovoltaic plus” channels tailored to local contexts in the future.

Analytic Hierarchy Process (AHP) for Environmental Screening of Solar Photovoltaic Utilities in Pulau Pinang, Malaysia.

Large-scale solar photovoltaic (LSSPV) farming activities have increased the global popularity of solar energy installations as one of the highest absolute generation growth rates of all renewable technologies. With higher gas and coal prices and rising carbon prices, have resulted in higher electricity prices, making distributed PV as an increasingly appealing alternative for consumers driving investment. However, due to a lack of suitable sites and environmental conditions, building large-scale installations is becoming increasingly challenging. There is a debate among many stakeholders and experts about the actual environmental benefits and consequences altered by this farming, which could have negative effects on both natural and anthropogenic settings. Hence, this paper focuses on the Environmental Impact Screening (EIS) by employing the Analytic Hierarchy Process (AHP) as part of the Multi-Criteria Decision Making (MCDM) in examining the environmental impact arising from a LSSPV project in Pulau Pinang, Malaysia. The methodology for this study was conducted by evaluating results from in-depth interviews with relevant stakeholders, including expert input from academicians, solar consultants, relevant local authorities, etc. This investigation proposed a multicriteria approach to conveying the main environmental aspects of the LSSPV project and assessing the magnitude of impact and significance generated by it. Large projects necessitate an evaluation to ensure that potential problems are identified and addressed early in the planning and design phases. This study had considered the environmental and chemical (EC) impacts, as well as occupational safety (OSH) and economic impact (EI). The data is then analyzed with AHP to determine the final component rankings. The final model provides a structured approach that incorporates detailed criteria that reflect the direct and indirect impacts arising from this LSSPV project, and it is intended to provide assessment magnitude and estimate scenarios based on different stakeholder perspectives in directing for a better decision. According to the study’s findings, sufficient environmental mitigation steps have been implemented prior to project completion to mitigate and gauge all potential environmental degradation.

The geography of sustainability transition and materiality: grid-tied solar photovoltaic technology in Sri Lanka

ABSTRACT The geography of sustainability transition (GeoST) literature views solar photovoltaic (PV) as an off-the-shelf, footloose, absolute technology for bringing about a technological shift in locations with high solar irradiation. Herein, I argue against viewing solar technology in these ways, highlighting that consideration should also be given to the configuration of solar PV that suits the contextual conditions. To this end, I offer empirical evidence for the need to approach solar PV diffusion through a relational perspective. Accordingly, different solar PV technology formats may become necessary for its successful implementation in diverse contexts. Supportive empirical evidence comes from Sri Lanka’s large-scale grid-tied solar PV implementation. I conclude the paper with an analytical consideration of the influence of material factors as being as important within GeoST as that of intangible factors, and that technological shift should be pursued within a location’s contextual relational materiality.

Stability model integration for large scale solar photovoltaic system using Western electricity coordinating council model

<span lang="EN-US">Due to the increased demand for renewable energy, the interest in the large-scale solar photovoltaic (LSSPV) power plant has recently grown dramatically. However, when a large amount of electricity is produced from the LSSPV power plant to the grid interconnection, the system commonly experiences instability and thus disrupt the grid system in disturbance issues such as bus fault, line-to-line fault, three-phase fault, and tripping. This sudden disturbance occurrence is tended to interrupt the stability of the system from providing balanced electrical production within the electrical grid. A dynamics response from the simulation is used to study the stability and the behavior of the photovoltaic (PV) plant into the grid interconnection by developing 118 bus system. The observation of critical clearing time (CCT) duration shows that the result from the simulation where the duration takes less than t=15 s for the system to get back to its pre-fault condition in three-phase fault and tripping in a dynamic simulation to shows that the system reaches its stability been observed through the simulation result by using from user-specific models to generic models like those advocated by the Western electricity coordinating council (WECC) in power system simulator for engineering (PSSE) software.</span>

Power Efficiency Analysis based on Lightning Effect on Large-Scale Solar Photovoltaic System

Currently, a large-scale solar photovoltaic (LSSPV) has become one of the fastest developments of electrical generation power for Malaysian Renewable Energy. However, lightning strikes are common in Malaysia due to its geographical position and tropical climate, especially in the open space of large-scale site power plants. This paper discussed the scientific basis and key assumptions that should be included in the layout of a lightning protection scheme in a large-scale solar photovoltaic installation. The framework proposal aims to ensure compliance with standards and guidelines, mitigating the risk of significantly induced currents in the LSSPV power plant. A 2.5 MW solar PV farm system was modelled using MATLAB software for assessing a solar PV farm’s electrical performance under various lightning current waveforms and lightning magnitude currents. These lightning strikes will impact photovoltaic modules, thus deteriorating the system’s ratio generation. The effects of lightning current on LSSPV were accessed based on simulation work in MATLAB Simulink. The induced current protection scheme covered rating selection and collecting data on lightning that may hit the modules or arrays. This data was then analysed according to the standard guidelines to ascertain the most effective protection measures for the 50MW PV farm within the LSSPV system. Minimising the maintenance and replacement cost of equipment is possible. The performance of a 2.5MW solar PV power plant with a grid connection was evaluated. A maximum and minimum power output of 2.47MW and 522.50kW, respectively, were observed. However, when the lightning impulsive current is included through the system, the power output decrease to 407.33kW due to the high magnitude induced.

Risk Assessment and Mitigation Strategy of Large-Scale Solar Photovoltaic Systems in Pakistan

Risk Assessment and Mitigation Strategy of Large-Scale Solar Photovoltaic Systems in Pakistan

Spatial modelling the location choice of large-scale solar photovoltaic power plants: Application of interpretable machine learning techniques and the national inventory

The optimum site selection of solar photovoltaics power plant across a given geographic space is usually assessed by using the geographic information system based multi-criteria decision making methods with various restriction criteria, while such evaluation results vary with criteria weights and are difficult to be validated in real life practices. To address this issue, this paper uses a national inventory dataset of large-scale solar photovoltaics installations (the land coverage area ≥ 1 hm2) to investigate the spatial location choices of solar power plants with the aids of interpretable machine learning techniques. A total of 21 geospatial conditioning factors of solar energy development are considered. The location choices of solar photovoltaics installation are then modeled with the multi-Layer perceptron, random forest, extreme gradient boosting models for each land cover type (e.g. cropland, forest, grassland, and barren). The SHapley additive explanation and variable importance measure methods are adopted to identify key criteria and their influences on the solar photovoltaics installation location selection. Results indicate that the random forest model presented the better performance among three machine learning models. The relative importance of conditioning factors revealed that the vegetation index and distance to power grid were always the most important predictors of solar photovoltaics installation location. Furthermore, topographical factors and transportation convenience may have a moderate impact on the spatial distribution of solar photovoltaics power stations. Unexpectedly, most of resources endowment and socio-economic factors play a negligible role in determining the optimal siting of solar power farms. Simulated solar photovoltaics installations probability maps illustrated that the most suitable regions account for 4.6 % of China’s total land area. The evidence-based method proposed in this research can not only help identify suitable solar photovoltaics farm locations in terms of various decision-making criterion, but also provide a robust planning tool for sustainable development of solar energy sources.

LVRT and Reactive Power/Voltage Support of Utility-Scale PV Power Plants during Disturbance Conditions

This paper proposes a control technique for a large-scale grid-connected photovoltaic (PV) plant that maintains the connection of an inverter to the grid voltage under different types of faults, while injecting a reactive power to accommodate the required grid connection. This control strategy is suggested to improve the low-voltage ride-through (LVRT) capability of grid-connected PV power generation plants. A 20 MW solar PV power plant is modeled and simulated using Matlab/Simulink. The power plant is composed of 10 parallel groups of arrays with a power rating of 2 MWp. The solar PV arrays are connected to a medium-voltage side-rated 22 KV to the utility grid. A dynamic analysis of the grid-connected large-scale solar PV power plant is introduced. This analysis is accomplished in order to determine the impact of three-phase short-circuits at the point of common-coupling (PCC), where the solar PV power station is connected to ensure a practical voltage level by injecting active and reactive power. The reactive power support allows for faster restoration of voltage values at the PCC. When subjected to transient disturbances, the stability of the studied system relies on both the type of the disturbance and the initial operating situation. The disturbance may be either small, resulting from electrical load changes, or large, such as from a transmission line short-circuit (fault) and significant generator loss.

Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.

Solar Energy: Applications, Trends Analysis, Bibliometric Analysis and Research Contribution to Sustainable Development Goals (SDGs)

Over the past decade, energy demand has witnessed a drastic increase, mainly due to huge development in the industry sector and growing populations. This has led to the global utilization of renewable energy resources and technologies to meet this high demand, as fossil fuels are bound to end and are causing harm to the environment. Solar PV (photovoltaic) systems are a renewable energy technology that allows the utilization of solar energy directly from the sun to meet electricity demands. Solar PV has the potential to create a reliable, clean and stable energy systems for the future. This paper discusses the different types and generations of solar PV technologies available, as well as several important applications of solar PV systems, which are “Large-Scale Solar PV”, “Residential Solar PV”, “Green Hydrogen”, “Water Desalination” and “Transportation”. This paper also provides research on the number of solar papers and their applications that relate to the Sustainable Development Goals (SDGs) in the years between 2011 and 2021. A total of 126,513 papers were analyzed. The results show that 72% of these papers are within SDG 7: Affordable and Clean Energy. This shows that there is a lack of research in solar energy regarding the SDGs, especially SDG 1: No Poverty, SDG 4: Quality Education, SDG 5: Gender Equality, SDG 9: Industry, Innovation and Infrastructure, SDG 10: Reduced Inequality and SDG 16: Peace, Justice and Strong Institutions. More research is needed in these fields to create a sustainable world with solar PV technologies.