Solar Photovoltaic Installations Research Articles

Assessing the adoption level of Solar PV installations, on district scale of urban environment

This study addresses a research gap by implementing a novel methodology that combines GIS and MCDM techniques to evaluate photovoltaic (PV) potential at an urban scale, specifically within Riga, Latvia. Utilizing the AHP and MABAC methods, the research integrates technical, social, and economic criteria to rank urban districts based on their suitability for PV installations. 58 different districts were evaluated according to technical and socioeconomic criteria and were ranked through the MABAC MCDM methodology, from most preferable to less, on a PV adoption level. This comprehensive approach not only identifies the most promising areas for rooftop PV deployment but also emphasizes the importance of considering socioeconomic factors to enhance equity in renewable energy adoption. The findings provide valuable insights for urban planners and policymakers, demonstrating a systematic, data-driven method to promote sustainable energy initiatives in urban environments.

Deep Learning-Driven Thermal Imaging Hotspot Detection in Solar Photovoltaic Arrays Using YOLOv10

The effective management of solar photovoltaic (PV) arrays is vital to maximising energy generation and ensuring long-term performance reliability. The presence of faults, or partial shading, can give rise to hotspots in PV arrays, making their detection critical for timely maintenance and avoiding further impacts on their performance. This paper describes a new approach to the hotspot detection issue in thermal images of solar PV arrays by leveraging deep learning. This study employs the YOLOv10 (You Only Look Once, version 10) model to deliver very high accuracy and speed in identifying and localising hotspots under defined regions of interest (ROIs). The proposed method begins with taking thermal images from multiple solar PV installations while capturing a range of operational conditions and fault types. These images are annotated with hotspot regions to create a robust training dataset. Given YOLO's capability for real-time object detection with high precision and mean average precision (mAP), the YOLOv10 model is then implemented to train on this dataset. Multiple changes were made to the YOLOv10 hyperparameters to optimise them for detecting hotspots in thermal images. The experimental scenario of this paper demonstrates that this approach indeed performs significantly better than standard image processing methods as well as prior deep learning models for detecting both hotspot accuracy as well as speed of processing the images. The YOLOv10 method demonstrated the highest available classification performance with a mAP at intersection over union (IoU) of 0.5 accuracy of 0.91 with inference time suitable for real-time applications. The sample results demonstrated the model's continuous ability to detect hotspots and provide location data under various conditions, such as crossing through different times of day and weather. The results of this study demonstrate that YOLOv10 can be used for improved detection of hotspots in the explicably thermal imagery of solar PV arrays.

Field measurements of wind load effects in a photovoltaic single-axis tracker mounting rail

The demand for solar photovoltaic power installations has resulted in a highly competitive industry. Equipment suppliers are under pressure to reduce design margins for projects to be feasible. Single-axis trackers are widely used to increase yield and reduce the levelised cost of energy compared to fixed-tilt installations. Load coefficients on tracker arrays are usually quantified using model-scale studies conducted in atmospheric boundary layer wind tunnels. These dynamically sensitive structures have been known to suffer wind damage despite the use of modern design methods. Ongoing research into the support structure design is deemed important to ensure long-term reliability. The current study presents full-scale field data acquired over 109 days on an experimental one-in-portrait single-axis tracker facility located in the Western Cape, South Africa. The purpose of these measurements was to quantify wind load effects in two instrumented PV module mounting rails. Frequency domain data showed modal participation of both the torsional and bending modes of the torque tube. The effect of the bending mode was more pronounced for the rail furthest from a pile. Load coefficients calculated using experimental data were generally lower than those presented in ASCE 7-22. Fluctuating stresses at critical locations led to negligible fatigue damage.

Performance Analysis of a 50 MW Solar PV Installation at BUI Power Authority: A Comparative Study between Sunny and Overcast Days

Ghana, being blessed with abundant solar resources, has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority (BPA) exemplifies the nation’s dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system’s performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one year’s worth of monitoring data for the climatic conditions at the facility and AC energy output fed into the grid. These data were used to analyze PV performance on each month’s sunniest and cloudiest days. The goal is to aid in predicting the system’s output over the next 365 days based on the system design and weather forecast and identify opportunities for system optimization to improve grid dependability. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between 16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analyses showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant’s performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant’s performance from deteriorating. Optimizing a solar PV system’s design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days. The study reveals the necessity of hydropower backup during cloudy days, enabling BPA to calculate the required hydropower for a consistent grid supply. Being able to predict the daily output of the system allows BPA to optimize dispatch strategies and determine the most efficient mix of solar and hydropower. It also assists BPA in identifying areas of the solar facility that require optimization to improve grid reliability.

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.

A System Dynamics Model for Rooftop Solar PV System Development in Indonesia

Despite of Indonesia’s vast solar energy potential according to the Indonesian Ministry of Energy and Mineral Resources, Indonesia’s total installed capacity of rooftop solar photovoltaic (PV) is very far from Indonesia’s target of 3.6 GW in 2025. Indonesia once applied net metering scheme for rooftop solar PV policy and was expected to be able to boost rooftop solar PV growth. A system dynamics approach is used in the research to develop an assessment model to evaluate the policies’ impact on residential rooftop solar PV system growth. A Causal Loop Diagram was established then transformed into Stock and Flow Diagram (SFD) using software Vensim PLE 10.1.3, which was used to simulate several policies’ scenarios related to residential rooftop solar PV adoption and CO2 emissions reduction. Ten scenarios were simulated in this study involving three groups of intervention: initial net metering tariff, reduction on initial solar PV cost, upper limit of ROI, and combination of initial net metering tariff and initial solar PV cost reduction. The simulations revealed that combination of increasing net metering tariff to 80% & initial cost reduction 30% has the highest potential solar PV installations, the highest CO2 emissions reduction, and the lowest accumulation cost of net metering in 2030. This study can be used as reference by the policy makers in Indonesia to formulate the optimum policy to boost rooftop solar PV growth as the simulations shows that residential rooftop solar PV with the right intervention can meet the government’s target of rooftop solar PV in 2030.

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.

Research and design an electric car charging station using solar energy integrating an IoT technology monitoring system

As technology advances, the cost of renewable energy equipment decreases, which has resulted in a massive increase in solar photovoltaic installations. In this study, we designed and calculated a solar power system for a charging station for 12 electric cars that integrates an energy protection system using IOT technology. The analysis results of the independent EV-PV charging station model and the grid-connected model with storage have shown the efficiency and profitability of each model. The solar energy system utilizes an IoT-based monitoring system to enhance the capacity and quality of electricity for electric vehicle charging stations. This is achieved through a sensor network that monitors factors affecting the efficiency of solar panels, such as dust, solar radiation intensity, weather conditions, and the tilt angle of the solar panel arrays. Smart Monitoring displays the daily usage of renewable energy, helps the users to analyze energy usage, will help us in its maintenance, fault detection, and will give us a record of all the data and electricity issues.

Stormwater runoff calculator for evaluation of low impact development practices at ground-mounted solar photovoltaic farms

Estimating runoff at ground-mounted solar photovoltaic (PV) installations is challenging because of the disconnected nature of impervious solar panels and the pervious ground surface underneath and between panel rows. There is a need for improved tools to estimate how low impact development practices at these solar installations affect stormwater runoff. The objective of this study was to develop an innovative spreadsheet-based runoff calculator that rapidly estimates stormwater runoff from ground-mounted solar PV sites. The calculator is built on a 2-D hydrologic model (Hydrus-2D/3D) calibrated and validated using experimental data from five commercial solar farms in Colorado, Georgia, Minnesota, New York, and Oregon. The Hydrus-2D/3D hydrologic model was then used to generate nomographs for stormwater runoff that were incorporated into an easy-to-use Excel-based solar farm runoff calculator. This calculator allows for rapid estimation of NRCS stormwater runoff curve number (CN) values at solar farms by considering several complex factors unique to PV installations including: soil and topographic characteristics, surface cover, disconnected impervious surface factors associated with various solar panel designs, and climatic factors. The solar farm runoff calculator quickly estimates runoff CN for pre- and post-construction scenarios, and can estimate actual depth of runoff based on a user-specified 24-h design storm depth. Factors that have the most significant impact on stormwater runoff include design storm return frequency, soil texture, soil bulk density, and soil depth. Ground surface cover has a moderate impact on stormwater runoff, and factors that have a lesser impact on stormwater runoff include slope and array size, spacing and orientation on the landscape. The runoff calculator allows for accurate estimates of runoff generated by disconnected impervious surfaces and low impact development practices at solar farms as affected by a wide range of site-specific conditions.

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.