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The deployment of photovoltaic (PV) systems has increased globally to meet renewable energy targets. Intermittent PV power generated due to cloud-induced variability introduces reliability and grid stability issues at higher penetration levels. Variability in power generation can induce voltage fluctuations within the distribution system and cause adverse effects on power quality. Therefore, it is essential to quantify resource variability to mitigate an intermittent power supply. In this study, we propose a new scheme to classify the sky conditions that are based on two common variability metrices: daily clear-sky index and normalized aggregate ramp rates. The daily clear-sky index estimates the cloudiness in the sky, and ramp rates account for the variability introduced in the system generation due to sudden cloud movements. This classification scheme can identify clear-sky, highly variable, low intermittent, high intermittent and overcast days. By performing a Chi-square test on the training and test sets, we obtain Chi-square statistic values greater than 3 with p-value > 0.05. This indicates that the distribution of the training and test clusters are similar, indicating the robustness of the proposed sky classification scheme. We have demonstrated the applicability of the scheme with diverse datasets to show that the proposed classification scheme can be homogenously applied to any dataset globally despite their temporal resolution. Using various case studies, we demonstrate the potential applications of the scheme for understanding resource allocation, site selection, estimating future intermittency due to climate change, and cloud enhancement effects. The proposed sky classification scheme enhances the precision and reliability of solar energy forecasts, optimizing system performance and maximizing energy production efficiency. This improved accuracy is crucial for variability control and planning, ensuring optimal output from PV plants. [ABSTRACT FROM AUTHOR]

In May 2022, the European Commission adopted a new European Union (EU) Solar Energy Strategy [1] aiming to ensure that solar energy achieves its full potential in helping to meet the European Green Deal's climate and energy targets. A goal of the strategy is to reach nearly 600 GW of installed solar photovoltaics (PV) capacity by 2030. While Europe is a pioneer in the definition of new policy requirements to ensure the circularity and sustainability of PV products, its manufacturing capabilities are limited. The EU mostly imports PV modules from China, which for the last decade has remained the global leader in PV manufacturing across the supply chain. This article aims to provide insight into the solar PV industry and the surrounding policy context, focusing on the manufacturing phase and its climate impact. It provides a comparative overview of the key players in the European and Chinese PV markets with an overview of the whole supply chain (i.e. production of polysilicon, cells, wafers and modules). Having in mind the net‐zero commitments across the globe, and a central role of the solar PV in the energy transition, the demand for PV products is expected to grow exponentially in the next decades. With this in mind, the authors look into environmental impacts from the PV manufacturing. A simplified analysis concludes on the suitability of the PV manufacturing process today and indicates the opportunities for the net‐zero transition in the future. While the focus is on the carbon impacts of the solar PV industry, the authors also identify other relevant aspects (such as circularity), laying the ground for a future research. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive overview of the solar industry's transition towards resilient energy solutions, emphasizing the critical role of data-driven practices in driving this transition through responsible resource management. As continuous technological refinement is essential to optimize solar energy's potential, the smart use of available data plays a significant part in enhancing the accessibility of solar panels. Building upon prior research investigating the influence of Big Data on solar logistics, this paper proposes a hybrid Multi-Criteria Decision-Making (MCDM) methodology based on expert experience, providing practical support in the implementation of data-driven proactive measures within the solar industry. Specifically, this study focuses on measures aimed at effectively implementing two main logistic strategies, which are Route Optimization (RO) and Warehouse Management (WM). A rigorous analysis of criteria and measures considered to be relevant in the literature is first conducted. Criteria will be screened and weighted to eventually act as drivers toward measure assessment and prioritization. A final sensitivity analysis culminates in the formalization of findings and in the formulation of a pragmatic roadmap tailored for solar industry practitioners, designed to increase operational efficiency while integrating key sustainability principles across supply chain endeavors. [ABSTRACT FROM AUTHOR]

A desire to shift from fossil fuels to non-carbon-emitting energy sources has become an imperative supported by national, state, and local policies. In the U.S., a diverse array of policies at multiple levels of government have helped the solar industry achieve exponential growth. These advancements, however, are mediated by local implementation processes, which might be expected to have a large impact in the context of U.S. federalism. This paper investigates the effects of two countervailing forces – policy incentives and implementation disincentives – on residential solar adoption in New Jersey. The New Jersey case study includes two complementary analyses designed to illuminate policy incentives and implementation disincentives, respectively. The first is an interrupted time-series analysis that evaluates the effects of federal and statewide renewable energy policies on residential photovoltaic (PV) growth. The second is a set of semi-structured in-depth interviews with solar industry experts, providing implementation insights from the solar industry and indirectly portraying residential customers' experiences. Results confirm that market-based instruments at the state-level play a crucial role in increasing the relative financial advantage of these systems and, thus, the attraction of residential solar PV to adopters. In addition, the behaviours of future adopters and the valuation of this technology as an investment in the housing market will impact the spread of residential PV systems in the future. Unfortunately, case study findings also confirm that an absence of standardized solar application procedures and outdated interconnection standards is a significant drag on the adoption rate. This highlights a need for policymakers to place greater emphasis on local implementation pathways in solar policy design. Options to achieve this include more robust ex-ante coordination among state and local levels of government and with industry to standardize implementation processes. While U.S. federalism may at times be constraining, our results suggest that even minimal levels of ex-ante coordination can lead to implementation gains that will have a large impact on solar diffusion outcomes. Federal and state-level financial incentives significantly impact the residential photovoltaic solar adoption rate. The absence of streamlined interconnection application procedures and outdated interconnection standards for the grid limit the residential PV adoption in New Jersey. Our findings underline the importance of paying equal attention to both policy design and to technical standards to underpin implementation processes, especially in the decentralized context of U.S. federalism. [ABSTRACT FROM AUTHOR]

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems. [ABSTRACT FROM AUTHOR]

Research Summary: Research on industry evolution highlights the role of knowledge‐building activities of startups and established firms in shaping knowledge evolution in a nascent industry. Yet, research thus far has overlooked the possibility that differences between startups and established firms might also shape the diffusion of the knowledge that they build. This study abductively explores this conjecture in the context of solar energy by examining the extent to which the photovoltaic cell inventions that firms create spur subsequent inventions by other firms. In contrast with existing literature highlighting differences across firms in the types of inventions they create, this study reveals asymmetries between startups and established firms in the diffusion of the knowledge underlying their inventions, even when they create inventions with similar attributes. Managerial Summary: This study examines differences between startups and established firms in shaping knowledge diffusion in a nascent industry. Using data from photovoltaic cell patents, it shows that startups' inventions spur more subsequent inventions by other firms, even when compared to established firms' inventions with similar attributes. Findings indicate that such asymmetry is not driven by knowledge transfer mechanisms but rather by factors associated with knowledge spillovers. Specifically, findings reveal that university citations of a startup's patents draw other firms' attention to those patents, and that startups are less able to preempt rivals through cumulative inventions and less able to rely on litigiousness to deter rivals from building on their patents. These findings underscore the asymmetric influence startups have in shaping the knowledge underlying a nascent industry. [ABSTRACT FROM AUTHOR]

Solar energy is one of the sources of renewable energy which is clean, affordable, and limitless. Despite Bangladesh having a significant potential for solar energy for its excellent geographic location, relatively low research work on location selection for the establishment of solar plants and little attempt has been taken to expand the solar energy industry. The optimal location for a solar plant to be built is the main objective of this research. Considering several aspects, five potential locations are selected utilizing the geographic data to find the optimal location and the Analytic Hierarchy Process (AHP) which is one of the multi‐models assessment techniques implemented to get the ideal site for solar power plants. While applying this method, some expert survey has been done via Google Form where some of the principal impact factors are set up like questionnaire and obtained the weight for the criteria for the AHP method. The results of the current analysis indicate that Rajshahi is obtained as best location for solar plant establishment followed by Ishwardi, Chattogram, Chuadanga, and Mymensingh. This work will enable to propose an effective, practical, and structured decision‐making framework that could aid policymakers in Bangladesh to select the best locations for solar farms. [ABSTRACT FROM AUTHOR]

While there is evidence of substantial improvement in efficiency and cost reduction from the integration of Robotics, Artificial Intelligence, and Drones (RAID) in solar installations; it is observed that there is limited oversight by international standards such as the International Electrotechnical Commission (IEC) in terms of the hazards and untapped potentials. This is partly because it is an emerging application and generally burdened with social acceptability issues. Thus, the safety regulations applied are adaptations of device-specific regulations as deemed fit by individual companies. Also, due to the fast-paced technological development of these platforms, there is huge potential for applications that are not currently supported by the device-specific regulations. This creates a multi-faceted demand for the establishment of standardized, industry-wide polices and guidelines on the use of RAID platforms for Solar PV integrations. This work aims to address critical safety concerns by conducting a comprehensive high-level system examination applicable to the monitoring and maintenance of Solar PV systems. Standard safety assurance models and approaches are examined to provide a safe autonomy perspective for Solar PVs. It is considered that, as RAID applications continue to evolve and become more prevalent in the Solar PV industry, standardized protocols or policies would be established to ensure safe and reliable operations. [ABSTRACT FROM AUTHOR]

Solar energy has been the most emphasized issue in recent years, as it is sustainable and causes zero emissions. In the solar cell industry, new manufacturing protocols have led to the development of materials with enhanced properties. Over the past decades, perovskite solar cells (PSC) have obtained a power conversion efficiency (PCE) to be 25% due to the development of synthesis techniques, electrode materials, etc. There is an important relationship between the thickness of the transport layers (hole and electron) in the case of improving the yield of perovskite solar cells. We have investigated the influence of the acid-assisted and acetylacetone-assisted (AA) methods on TiO2 films and thus the effect on the PCE of perovskite solar cells. Perovskite (CH3NH3PbI3) layer and different compact TiO2 (c-TiO2) layers have been coated by the spin coating method, and the overall experimental section is made in the nitrogen medium at room temperature. Through an acid-assisted method, the cracked c-TiO2 film was formed. The planar solar cell structure of ITO/AA-TiO2/CH3NH3PbI3/P3HT/Ag resulted in 0.03% of PCE. However, the perovskite solar cells with a mesoporous solar cell structure of ITO/ AA-TiO2/m-TiO2 /CH3NH3PbI3/P3HT/Ag resulted in 0.1% of PCE. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a phenomenon that connects all physical objects to one another and makes it possible for them to interact and collaborate via the web and other technologies. This phenomenon is also known as the internet of things. It is necessary for the devices to be self-sufficient, easily locateable, and unobtrusive to the human eye. A streamlined mathematical model that contains a variety of distinct laws serves as a metaphor for our whole body of information. This photovoltaic model represents our knowledge in its totality. This all-encompassing comprehension is built on top of a compilation of statutes, which serves as the framework. Depending on the goal of the user, it is possible that the rules will need to be altered in order to either maximize the benefits or minimize the amount of money spent on electricity. This will depend on how the rules are changed. [ABSTRACT FROM AUTHOR]

This paper reviews the impact dust accumulation for long-term on the performance of photovoltaic (PV) modules. It examines accumulation impact on the PV efficiency, their solar energy production, and their lifetime. The paper also discusses the various strategies for preventing dust accumulation, such as waterproof coatings, hydrophobic coatings, and anti-static coatings. Finally, the paper provides a comprehensive review in dust control and highlights potential future research directions. This review provides an in-depth analysis of PV behavior and its effect by dust accumulation and provides useful information for researchers and practitioners in the solar industry. [ABSTRACT FROM AUTHOR]

Under the background of global energy transformation and structural upgrading, the development of solar photovoltaic industry in various countries has been paid attention to, and solar photovoltaic products occupy an important position in the international trade of renewable energy. The signing of the RCEP agreement can create favorable external conditions for the trade and industrial cooperation of solar photovoltaic products, which has attracted global attention. Therefore, based on the background of RCEP, it is of practical significance to analyze the development and export competitiveness of solar energy products in the agreement countries. This paper uses TOPSIS to establish a comprehensive evaluation index system for the international competitiveness of solar photovoltaic products to study the international competitiveness of solar photovoltaic products in China, Japan, and Korea under the context of RCEP. The study shows that (1) China's international competitiveness in solar photovoltaic products is strong and continues to improve, while Japan is declining and Korea is growing slowly. (2) There are significant differences in the components that lead to international competitiveness among different countries. Based on the above findings, this paper proposes corresponding policy recommendations for the three countries. [ABSTRACT FROM AUTHOR]

A longitudinal analysis of small-scale solar energy generation in the United States is used to demonstrate how transition studies can explain nonlinearity in multidecade changes of consumption-production systems. Nonlinearity involves uneven development of sustainability innovations with episodes of rapid growth but also periods of slow growth, stalling, or even collapse. Factors that affect the increasing feasibility and attractiveness of small-scale solar include technological improvements, declining costs, and changes in global energy markets. However, a more complete explanation of nonlinearity highlights the importance of a type of systems analysis that also includes strategic action and broader societal and policy changes. Specifically, efforts by the utilities constrained the growth of small-scale solar by weakening policy support because of the perceived threat, but the solar industry and advocates responded with countervailing action in a changing context. As the transition developed, strategic action (including goals, targets, tactics, and coalition partners) changed and became more conflictual. However, by the beginning of the 2020 decade, the development of microgrids, digital technologies, storage, and virtual power plants in combination with net-zero energy policies provided indications of potential for a reconfiguration of the relationship that could be less polarized and conflicted. [ABSTRACT FROM AUTHOR]

If oil sands are to be eliminated from the energy market to protect the global environment, human health and long-term economic welfare, a significant number of workers will be displaced in the transition to renewable energy technologies. This study outlines a cost-effective and convenient path for oil and gas workers in Alberta to be retrained in the burgeoning solar photovoltaic (PV) industry. Many oil and gas workers would be able to transfer fields with no additional training required. This study examines retraining options for the remainder of workers using the most closely matching skill equivalent PV job to minimize retraining time. The costs for retraining all oil sands workers are quantified and aggregated. The results show the total costs for retaining all oil sands workers in Alberta for the PV industry ranges between CAD$91.5 m and CAD$276.2 m. Thus, only 2–6% of federal, provincial, and territorial oil and gas subsidies for a single year would need to be reallocated to provide oil and gas workers with a new career of approximately equivalent pay. The results of this study clearly show that a rapid transition to sustainable energy production is feasible as costs of retraining oil and gas workers are far from prohibitive. [ABSTRACT FROM AUTHOR]

The most effective approach to subsidizing power producers in order to bolster the penetration of renewable energy is continuously debated. To address this issue, this paper undertakes an in‐depth investigation into a solar farm investment problem, aiming at capturing the common interests and frictions among the government, the solar system developer, and the utility. In this paper, both static and dynamic game models are proposed with the consideration of the uncertainty of solar power generation. Before investing in a solar farm project, a potential solar farm investor takes into account of subsidy rate regulation for utilities enacted by the government, the panel price announced by the solar developer. In this study, we identified two thresholds of solar panel cost, which provide valuable insights for government policymakers when formulating an appropriate subsidy rate for the solar energy industry. Besides, a optimal policy for each stakeholder has been exposed. The finding from the dynamic model discloses the profound influence of various behaviors on the long‐term profitability of each stakeholder. Finally, based on the real‐world solar power data in Orlando, a case study is presented to expose a more tangible prospect for industrial application. [ABSTRACT FROM AUTHOR]

This paper presents the effectiveness of the ensemble empirical mode decomposition-long short-term memory (EEMD-LSTM) model for short term load prediction. The prediction performance of the proposed model is compared to that of three other models (LR, ANN, LSTM). The contribution of this research lay in developing a novel approach that combined the EEMD-LSTM model to enhance the capability of industrial load forecasting. This was a field where there had been limited proposals for improvement, as these hybrid models had primarily been developed for other industries such as solar power, wind power, CO2 emissions, and had not been widely applied in industrial load forecasting before. First, the raw data was preprocessed using the IQR method, serving as the input for all four models. Second, the processed data was then used to train the four models. The performance of each model was evaluated using regression-based metrics such as mean absolute error (MAE) and mean squared error (MSE) to assess their respective output. The effectiveness of the EEMD-LSTM model was evaluated using Seojin industrial load data in Vietnam, and the results showed that it outperformed other models in terms of RMSE, n-RMSE, and MAPE errors for both 1-step and 24-step forecasting. This highlighted the model's capability to capture intricate and nonlinear patterns in electricity load data. The study underscored the significance of selecting a suitable model for electricity load forecasting and concluded that the EEMD-LSTM model was a dependable and precise approach for predicting future electricity assets. The model's robust performance and accurate forecasts showcased its potential in assisting decision-making processes in the energy sector. [ABSTRACT FROM AUTHOR]

The advanced development of large-scale solar power plants (LSSPs) has made it necessary to improve accurate forecasting models for the output of solar energy. Solar energy is still hampered by the lack of predictability in its output, which remains a major hurdle in the solar industry. This paper focuses on triple deep learning (DL) techniques such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolutional Neural Network- Long-Short Term Memory CNN-LSTM to address this problem. These techniques are utilized in solar energy variables (SEVs) such as power generation (MWh), soiling loss (%), and performance ratio (PR %) to determine the optimal forecast model. The novelty of this research is that it is the first time that important solar system parameters such as PR and soiling loss have been studied to predict a feasible forecast model using a different DL scheme. The SEVs real-time dataset is procured from the largest solar plant in Pakistan, titled "Quaid-e-Azam Solar Park" (QASP). The main significance of the study is that ANN, RNN, and CNN-LSTM-based models were developed in the DL process through feature generation, data scaling, training, and testing steps to predict the optimal model. The prediction values were compared with the solar plant's actual values over the last 7 years, and then a comparison was made to predict the future forecast trend over the next 20 years. The aim and goal is to develop three models to investigate the accurate results of time-series forecasting on the SEVs dataset, as well as to evaluate the performance measure errors to determine the appropriate model. Based on the forecasting/prediction graphic and error results, it was demonstrated that the CNN-LSTM hybrid model is a more capable forecasting model for the output of power generation and PR values and a more accurate predictor of the future trend over the ANN and RNN models. However, the ANN model is slightly better performed in predicting of soling loss value than CNN-SLTM and RNN. Thus, the CNN-LSTM hybrid model is an optimal model for SEVs, which can guarantee a variety of LSSPs of similar nature in the following time-series forecasting investigations which shows key findings and its importance for the solar industrial forecast issue. [ABSTRACT FROM AUTHOR]

India's demand-supply imbalance electricity market results from the country's rapid population growth and extensive industrialization. Due to increased costs, many residential and commercial customers have difficulty paying their electric bills. Households with lower incomes are confronted with the most severe energy poverty in the entire country. A sustainable and alternative form of energy is required to solve these issues. Solar energy is a sustainable energy choice for India; however, the solar industry faces many issues. One of them is managing end-of-life cycle photovoltaic (PV) waste, as massive deployment of solar energy capacity has resulted in generating large amounts of PV waste, ultimately affecting environmental and human health. Therefore, this research employs "Porter's Five Forces Model" to analyze the factors that significantly impact the competitiveness of India's solar power industry. The inputs for this model consist of semi-structured interviews conducted with experts in the solar power industry on various solar energy-related issues and a critical analysis of the national policy framework using the relevant literature and official statistics. The impact of five significant stakeholders of the solar power industry on solar power generation in India is evaluated: buyers, suppliers, competitors, substitutes, and potential competitors. Research findings indicate the Indian solar power industry's current status, challenges, competition environment, and future estimates. This study will help the government and stakeholders to understand the intrinsic and extrinsic factors affecting the competitiveness of the Indian solar power sector and suggest policy recommendations to formulate procurement strategies that promote sustainable development within the solar industry. [ABSTRACT FROM AUTHOR]

The article describes the world's experience in developing the solar industry. It discusses the mechanisms of state support for developing renewable energy sources in the cases of five countries that are the most successful in this area—China, the United States, Japan, India, and Germany. Furthermore, it contains a brief review of state policy in producing electricity by renewable energy facilities in Kazakhstan. This paper uses statistical information from the International Renewable Energy Agency (IRENA), the International Energy Agency (IEA), British Petroleum (BP), and the Renewable Energy Network (REN21), and peer-reviewed sources. The research methodology includes analytical research and evaluation methods to examine the current state of solar energy policy, its motivators and incentives, as well as the prospects for its development in Kazakhstan and in the world. Research shows that solar energy has a huge development potential worldwide and is sure to take its place in gross electricity production. This paper focuses on the selected economic policies of the top five countries and Kazakhstan, in what may be considered a specific research limitation. Future research suggestions for the expansion of Renewable Energy (RE) in Kazakhstan could include analysing the impact of introducing dedicated policies and incentives for solar systems and exploring the benefits and challenges of implementing large RE zones with government–business collaboration. [ABSTRACT FROM AUTHOR]

• There is a big difference in solar PV production when simulated with different software such as PVsyst and COMSOL. • Changing the height of a bifacial solar installation does not make a big change in the production. • There is a large variation in solar production when simulating days with bad weather conditions with two different softwares. • With the same simulation method between softwares, PVSyst gives us a higher value for solar production in almost all cases. The solar energy industry is in constant evolution and the use of bifacials photovoltaic (PV) modules is one of the most recent innovations the industry adoptions. And even though bifacial technology has been present for as long as the monofacial technologies, bifacial solar modules have started to attract a lot of market attention; in fact, recent studies point out that the increasing interest for bifacial PV has aroused so much to predict a market share of 70% in 2030. The key feature that makes this possible is its design, which allows the module to capture sunlight from both sides of the panel increasing solar generation capacity by up to 25%, however this depends on factors such as: the irradiance factors, the module efficiency, the ground albedo, the tilt angle, the elevated height to the ground of the structure, the inter-row spacing and the array shading. Modeling and simulation of photovoltaic (PV) modules play an important role for technology development and evaluation of new designs and their interaction with the previously mentioned key factors. The project intends to analyze and comparing two irradiance numerical models. This comparison is made by simulating an identical bifacial PV solar installation (set-up) but using two different software with the capacity to carry out this type of analysis. Firstly, the study using PVsyst software, known for its widespread application in solar energy, and the other software was COMSOL Multiphysics, a Finite Element Method (FEM) based multiphysics simulation software to study and analyze optical and thermal performance of PV modules. For this comparative study, fixed irradiance conditions (during the year) were established, such as the equinoxes and solstices, to have different irradiance conditions and study their effect on the modeled bifacial modules. Finally, parametric studies of the following factors (design parameters) were carried out: ground albedo, tilt angle, inter-row spacing and height to the ground. In order to study the changes that these cause in the behavior of our system and also as a complement to this study and considering the great importance of the albedo for a bifacial installation, a field work has been carried out to measure it experimentally and validate the simulations. [ABSTRACT FROM AUTHOR]

Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault). [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) is a novel and eco-friendly power source. India's vast solar resources present tremendous solar energy use prospects. The solar PV growth in India has spanned over fifty years, with a significant increase during the past decade. To meet the requirements of the rapidly expanding PV power market in India, it is essential to define, scrutinize, and comprehend the industry's development path and features. This study comprehensively analyzes the current state of solar resources, the future growth prospects of the solar PV sector, and the major factors that influence the industry's smooth growth. The study relates to the following five major factors: technological R&D, industrial planning, rules and regulations, power pricing guidelines, and projects enticement programs. To explore these elements, a multifaceted approach consisting of an inclusive literature research, statistical data inquiry, legislation review, and regulatory and policy analysis is undertaken. Analyzing the usual occurrences, the development process, and the features of the five elements permits the growth of development route models. The findings of this study provide information regarding the current development and future prospects of PV power sector in India. In today's rapidly changing social, economic, and technological landscapes, policymakers have the opportunity to gain better understanding from the ever-evolving practices and new advances in the business paradigm. [ABSTRACT FROM AUTHOR]

Vietnam has enormous potential for solar power thanks to its favorable geographical location. In recent years, the Vietnamese government has strived to develop renewable energy. The objective of this paper is to measure the operational efficiency of solar photovoltaic (PV) power plants using data envelopment analysis (DEA) with the epsilon-based measure (EBM) model. The utilization of the EBM model is due to its advantage to integrate radial and non-radial measurements to get a more exact estimate of relative efficiency. Using the merits of the EBM model, a case study of 18 decision-making units (DMUs) in Vietnam is presented. In the proposed model, three input variables are selected, which are capital cost, installed capacity, solar irradiation, while energy production and consumer density are considered as output variables. Following that, the technique for order of preference by similarity to ideal solution (TOPSIS) method is used to assess the validity and applicability of results. The results illustrate that two methods reach common rankings, in which the priority rankings of the best performing DMUs are very similar. This shows that the applied models are robust in nature. This paper offers significant materials that serve as practical and timelier solutions for decision-makers in the operating and management strategies of the solar energy industry, also a critical guideline in many related decision-making problems for any other industries around the world. [ABSTRACT FROM AUTHOR]

Renewable energy sources are gaining popularity because they are less expensive and more efficient than traditional energy sources. Using data from 1991 to 2018, we examined the asymmetric impact of solar energy consumption on CO2 emissions in the top-ten solar energyconsumer economies (China, the United States, Japan, Germany, India, Italy, Australia, Vietnam, South Korea, and Spain). Earlier research has used a panel data technique, which has produced consistent conclusions on the solar power-CO2 emissions association, despite the fact that some economies have no evidence of such a linkage. The current study, on the other hand, employs a unique methodology known as "quantile-on-quantile," which can evaluate time-series dependence in each economy separately to give world yet country-related information for the association among the variables. The findings investigate how quantiles of solar energy consumption quantiles influence CO2 emissions quantiles asymmetrically by giving an appropriate structure to apprehend the whole dependency pattern. The results suggest that, except in Spain and India, solar energy consumption minimizes carbon dioxide emissions at various quantiles. However, the strength of nonlinear association in solar energy-CO2 emissions nexus varies from country to country that needs individual attention and caution for governments in developing the policies related to the solar industry and the sustainable environment. [ABSTRACT FROM AUTHOR]

• A new method for recycling DWSSP was proposed. • Direct current, as an auxiliary means, was used to enhance impurity separation process. • Recycled silicon had a purity of near 5.5 N. As the unavoidable by-products in the production of solar cells, the recycling and reuse of diamond-wire sawing silicon powder (DWSSP) have always been a key issue in the solar energy industry. However, until now, there is no effective method to achieve effective recovery of high-purity silicon. In this work, a new method was proposed to achieve the recycling of DWSSP. Direct current, as an external means, was introduced into the recycling process of DWSSP, and its influence mechanism on impurity removal process was also discussed. By optimizing the local temperature gradient at the front of the Solid-Liquid interface, and the impurity diffusion behavior at the front of the interface was improved. After purification, the impurity content in the silicon ingot is reduced significantly. Among them, Al, as the main metal impurity element was reduced to less than 5ppmw (from 13580ppmw), and Ni (80.78ppmw), Fe (57.60ppmw), Ti (5.79ppmw), etc. decreased to less than 1ppmw. This work improved the purification efficiency of DWSSP, and put forward an optimized recycling method, which is expected to realize the direct reuse of DWSSP in the photovoltaic industry. [ABSTRACT FROM AUTHOR]

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• A robust transient thermal model for PTC collectors is proposed. • A heuristic optimisation approach is applied to the design of PTC thermal fields. • Single collector optimal dimensions and overall network arrangement are defined. • PTC's versatility is evidenced by investigating its applications across a range of low to high temperatures. This study presents the development of a transient model for designing and optimising parabolic trough solar thermal plants for industrial processes. The model is validated against published experimental data and other models from the literature. A heuristic optimization methodology based on Particle Swarm Optimization (PSO) to maximise the Present Value of the Life Cycle Energy Savings (PVLCES) is employed. This methodology is applied to three case studies. The results highlight the versatility of PTC (parabolic trough collector) technology in handling a wide range of temperatures. Additionally, the study reveals the optimal PTC aperture width dimensions that maximise the capture of solar thermal energy while increasing the solar fraction. Specifically: For low-temperature processes, the optimum aperture width is 5.0 m; in medium-temperature processes, the optimum aperture width increases to 5.5 m; for high-temperature applications, the optimal value is 5.8 m. Finally, the optimised networks achieve the following heat load-to-area ratios: Low-temperature applications: 0.57 kW/m²; medium-temperature applications: 0.58 kW/m²; high-temperature applications: 0.6 kW/m². [Display omitted] [ABSTRACT FROM AUTHOR]

Demand on the energy sector has increased significantly due to the incredible evolution of the industry and urbanization. Photovoltaic (PV) technology is rapidly evolving to meet the demands of people in the United Arab Emirates (UAE) by generating more electricity. The UAE has demonstrated that it has the world's highest rates of sun exposure, indicating a significant efficiency in solar energy development. This might be a way to cut down on fossil fuel consumption and greenhouse gas emissions even further. In this review, we discuss five major aspects of solar energy utilization and projects within the framework of the UAE starting with (i) recent advances in solar scenario and development trends, (ii) electricity production, consumption, and tariffs, (iii) focus on various key aspects of photovoltaic solar installation projects inside the Dubai, Abu Dhabi emirates and other smaller projects in the UAE, (iv) different solar projects outside the UAE, and (v) the solar energy industry, including trading, assembly, manufacturing, installation, and other activities in the UAE. The review finishes with a summary of problems, current developments, and future outlooks. [ABSTRACT FROM AUTHOR]