Solar Industry Research Articles (Page 1)

ABSTRACT The UK’s industrial strategy must address four interconnected challenges: transitioning to net-zero, accelerating growth, creating good jobs, and enhancing national security. We argue there are no ‘Swiss Army Knives’ in green industrial strategy that will solve all of these problems at once: each challenge requires its own approach. Drawing on case studies from the UK’s wind and Germany’s solar industries we highlight missed economic opportunities from green technology development without careful and adaptive policymaking. Looking forward, we focus on retrofitting homes and green steel in the UK to explore the trade-offs between decarbonisation, job creation, and economic growth. We chart different pathways the UK could pursue that balance speed of deployment against capturing economic gains domestically. We propose a new decision-making framework to help policymakers navigate these trade-offs. We argue industrial strategy best practice involves adaptive policymaking, conditional public incentives, and collaboration with businesses and communities.

Abstract This study examines mass and heat transfer in a permeable ternary nanofluid flow over a stretching sheet, considering the combined effects of a chemical reaction, Joule heating, an exponentially space-dependent heat source, and an inclined magnetic field. Three types of water-based nanofluids are analysed: mono (Cu), hybrid (Cu + Al 2 O 3 ), and ternary (Cu + Al 2 O 3 + Ag). The governing nonlinear partial differential equations are reduced using similarity transformations and solved numerically via MATLAB’s BVP4c method. The results reveal that ternary nanofluids exhibit superior thermal performance, with significantly higher temperature profiles compared to mono and hybrid nanofluids. The influence of key parameters is also investigated. Increased suction and velocity slip reduce thermal and concentration boundary layers, while higher Biot numbers and heat source intensity enhance temperature profiles. Additionally, Joule heating and magnetic field inclination intensify the heat transfer rate. These findings provide valuable insights for optimizing thermal systems in applications such as solar energy collectors, thermoelectric devices, and chemical processing industries.

This paper explores the evolution of first-generation solar cells by analysing the selection and engineering of materials that led to innovations. It also addresses the potential of using materials other than silicon and issues related to innovative recycling technologies. The paper presents the evolution of the Romanian photovoltaic sector and assesses the life cycle of photovoltaic panels, focusing on the recovery of high-quality raw materials and their reintroduction into the production process to improve the circular economy in this field. As the number of installed panels grows exponentially, so does the need to manage waste efficiently at the end of their life cycle. Photovoltaic panel recycling is slowly but surely becoming a rapidly developing field that is essential for the sustainability of the solar industry. With the growth of production in the Romanian photovoltaic sector, it has been identified that the need for recycled raw materials will increase from 900 prosumers in 2019 to over 100,000 in 2024. In the future, it will be imperative to develop strategies for recovering, recycling and reintroducing materials, which will bring major benefits. This paper’s specific contributions include a bibliometric mapping of EoL-PV research trends, a technology-recycling matrix for modern cell architectures, and a perspective on the Romanian market contextualised within EU policies.

Are critical mineral resources sustainable for China’s solar PV industry? A multi-national ecological niche and grey model approach

Climate-relevant technological innovation in renewable energy sources, such as solar energy, is essential for mitigating climate change and achieving sustainable development. The recent literature highlights substantial patent activity in China’s solar energy industry, which may contribute to the sector’s success in international markets. This study examines the relationship between patent activity and corporate financial performance in China’s solar energy industry from 2012 to 2022 using panel data analysis. The results indicate that patent applications positively impact firms’ corporate performance, showing a time-lag of approximately 6–7 years. Notably, this positive impact is particularly pronounced for firms located in the eastern region and state-owned enterprises. Additionally, we investigate whether CEO duality affects the relationship between patent applications and firms’ corporate performance, seeking to reveal unique development pathways within the industry. These findings are important for understanding how China’s solar energy sector can advance along sustainable development pathways amid the challenges of climate change.

Abstract In September 2025, the Government of India made major changes to the Goods and Services Tax (GST). They cut the tax on solar panels and inverters from 12% to 5%. This change also lowered the total project tax from about 13.8% to 8.9%. The goal of these reforms is to reduce project costs, help domestic manufacturing, and speed up India’s shift to clean energy. This research paper looks at the various effects of the GST reforms on solar energy prices, pointing out both the benefits and drawbacks. By reviewing government reports, industry studies, and expert opinions, the study examines how these reforms are likely to make solar energy cheaper and easier to access. It also addresses issues such as supply chain problems, delays in input tax credits, and uncertainties in contracts. The paper provides recommendations to reduce potential issues, making sure that India’s solar energy goals remain strong while supporting economic stability. Key Words: GST Reforms 2025, Solar GST Tariff, Solar Energy, Impact on Solar Industry, Lower GST Rates,

ABSTRACTThe circular economy is an approach to decouple economic growth from environmental impacts. In Europe, its development is driven by policies that guide market dynamics through regulations and by businesses innovating with new circular models. However, top‐down policies and bottom‐up business efforts are often misaligned, and their interplay remains poorly understood. This study addresses this knowledge gap through a case study in the renewable energy sector. We investigate to what extent circular economy policies influence the emergence, diffusion, and scaling of circular business models in the solar industry. We identify four archetypes of business models, explaining how they function, interact, and whether they align with intended policy goals. As a result, we propose six industrial and policy interventions to improve circularity, thus waste management and resource security in the sector. Conceptually, we outline three key circular business model patterns: policy‐gap‐exploiter, policy‐follower, and policy‐pioneering. These insights contribute to bridging the gap between policymaking and business innovation.

In the past decade, Artificial Intelligence (AI) has become a revolutionary power in the solar energy industry, dealing with essential issues related to intermittency, resource forecasting, efficiency of systems, and upkeep. This survey thoroughly examines and synthesizes AI methods—ranging from classical models such as artificial neural networks (ANN) and support vector machines (SVM) to current methods like deep learning, hybrid models, and reinforcement learning—that are employed in solar energy optimization between 2013 and 2023. We analyze the technical merits and demerits of each model along with its practical performance across applications like irradiance forecasting, energy management, and predictive maintenance. Experimental comparisons, case studies, and an advocated theoretical framework are included to substantiate the findings. The review delineates research gaps like unavailability of standard datasets, poor interpretability of models, and difficulty in model deployment in data-scarce domains. It concludes with a forward-looking discussion on trends like edge-AI, federated learning, and explainable AI. This piece of work is set to inform researchers, policymakers, and stakeholders in the industry on how to better utilize AI to facilitate global solar energy uptake.

ABSTRACTWith the rapid development of information technology, innovation no longer takes place within single sectors. Taking China's solar industry as a case, this paper analyzes the network structure of technology convergence between diversified standard industries based on the patent data of 328 cities from 2003 to 2020. From the perspective of both horizontal and vertical dimensions, this paper explores the mechanism of sector convergence on innovation performance and its regional heterogeneity. The results show that the convergence of the manufacturing sector and the production and supply sector of electricity, heating gas, and water is the main component of sector convergence of the solar industry in China. The level of sector convergence shows an overall upward trend and exhibits significant regional differences. The promotion effect of horizontal sector convergence on innovation performance is only reflected in larger cities, higher administrative‐tier cities, and non‐resource‐based cities. Vertical sector convergence exerts significant positive impacts on innovation across cities of different city sizes, administrative tiers, and resource endowments, with stronger effects in smaller cities, lower administrative‐tier cities, and resource‐based cities. This study deepens the measurement knowledge of convergence among diversified standard sectors within a specific industry, reveals the mechanism of dual‐dimensional sector convergence on innovation performance, and offers scientific evidence for developing convergence policies by zone.

The Department of Energy’s Solar Energy Technology Office recently set goals to reduce the cost of recycling photovoltaic (PV) modules to rival that of landfilling by 2030. However, current recycling costs are 3-9x more expensive than landfilling costs, which results in 90% of panels being landfilled. There is significant opportunity posed by the large volumes of crystalline silicon modules set to come offline in the near future, which are estimated to contain $15B worth of recoverable materials over the next 25 years. One key opportunity is that many recyclers struggle to achieve metallurgical grade purity of critical and valuable materials such as silicon, silver, copper, and other trace metals. The full value contained in end-of-life panels is therefore not being recovered and increasing the purity of these materials offers the potential to improve recycling profitability. Here we present a combined method for the upcycling of silicon and the extraction and purification of valuable trace metals by innovative melt purification and deep eutectic separation methods that avoid the use of strong acids. The process was developed on samples received from a variety of scrap and PV-specific recycler partners in the US. Further, we identified high value, specialty material applications that can utilize upcycled silicon at higher than metallurgical grade purities but that do not require the high purities associated with the solar or semiconductor industries (e.g. > 9N). The process presented here is benchmarked against state-of-the-art techniques for silicon purification and recovery of trace metals to determine economic viability, environmental impact, and ease of implementation for recyclers. The aforementioned upcycling and extraction process will not only benefit recyclers and further a circular economy for the PV industry, but will enable onshoring of domestic supply chains for energy and semiconductor relevant critical materials.

The research thoroughly examined the worldwide movement towards renewable energy and found notable differences in adoption rates and technical improvements across countries. It also highlighted the possibility of a dramatic change in energy paradigms. Using information from the renewable energy map scenario, research suggests that 2050 renewable energy sources may account for as much as two-thirds of the world's primary energy supply, a significant increase above the reference scenario's modest prediction of 24%. Germany and Denmark, in particular, members of the European Union, stand out as leaders in this shift because of their excellent renewable mix and integration of wind energy. Fast progress is being made in Asia, where nations like China and India show yearly growth rates in the solar and wind industries exceeding 30%. The Americas—well represented by the US, Canada, and Brazil—showcase a variety of renewable integration, with each country's contributions differing. Meanwhile, Middle Eastern nations are gradually broadening their energy portfolios, and although Africa shows promise, the shift is hindered by infrastructure issues. The report highlights the clear worldwide trend towards renewable energy sources. Still, it also draws attention to the persistent inequalities shaped by a wide range of geopolitical, technical, and economic factors. The study findings clarify the present situation and future direction of renewable energy adoption. Still, they also emphasize how crucial it is to implement specific regulations, make targeted investments, and form partnerships to hasten this worldwide change.

Long-term stock price analysis is essential for understanding market dynamics and supporting investment decisions. Using data from the National Stock Exchange, the study focuses on Solar Industries India Limited's closing prices between January 1, 2000, and December 31, 2024. The primary objectives are to use time series analysis to model and predict closing prices, identify underlying trends, and investigate relationships with trading activity. An ARIMA model was used with Minitab software to account for data noise, trends, and seasonal fluctuations. Statistical criteria guided the model selection process, providing optimal fit and reliability. These charts helped determine trends, patterns, and seasonal elements. A total of 729 monthly observations were examined, and the best-fitting model was chosen using the Akaike Information Criterion (AICc). The Ljung-Box test verified that the ARIMA (0, 2, 1) model was the best model, as it had the lowest AICc and strong residual diagnostics (p > 0.05 for most lags). The forecasts showed anticipated pricing ranges with increasing uncertainty over time. Further statistical analysis was conducted to investigate the relationships between trading activity and stock price. While insights into the relationship between trade volume and price movement provide useful perspectives for market analysis, a strong forecasting model can help stakeholders make well-informed decisions. Overall, the integration of exploratory research with time series modeling provides a comprehensive framework for analyzing stock price behavior and forecasting future trends.

This study proposes the application of the methodological framework for functional analysis of technological innovation systems in the context of the implementation of a regional smart specialization strategy. The case focuses on the solar energy industry in the Arequipa region - Peru. For this purpose, interviews, surveys and workshops were conducted with more than 70 stakeholders. The results allowed for the formulation of specific initiatives based on the identification of certain blocking mechanisms. The proposed approach can contribute to the development of innovation policies with regional scope, especially in territories with low scientific-technological infrastructure.

Abstract This study explores the critical factors influencing customer satisfaction and service quality in the solar energy industry, focusing on Alishan Solar Pvt Ltd. Through a mixed-methods approach involving quantitative surveys and qualitative interviews, the research identifies key determinants, including product quality, service delivery, and customer support. The study utilizes the SERVQUAL model to assess service quality dimensions and proposes actionable recommendations for enhancing customer experiences and fostering long-term loyalty.

Abstract Radio frequency capacitively coupled plasmas (RF CCPs) are widely used in the solar cell industry. With the increase of the chamber size and driving frequency, for the purpose of higher production efficiency, radial uniformity problems become more rigorous and deserve more attention and in-depth studies. A two-dimensional (2D) fluid model coupled with an electromagnetic transmission line model is used to study a capacitively coupled discharge operated in a mixture of silane/hydrogen and is driven by a single RF source at 27.12 MHz, which is typical for microcrystalline silicon deposition processes. The 2D geometric structure of the chamber includes electrodes with the radius of 140 cm and thickness of 1 cm, while the gap between the electrodes is 1.6 cm. An increase in pressure from 1 to 4 Torr is found to lead to a reduction in the uniformity of the radial potential amplitude distribution on the powered electrode, indicating a shortening of the electromagnetic wave wavelength. At higher pressures, the electron density exhibits a pronounced peak near the radial discharge center, resulting in non-uniform radial density distributions of ions, radicals, as well as film thickness. The simulated trend in film thickness distribution changes is in agreement with the experiments, which both show a deterioration in uniformity and the deposition rate does not increase at higher pressures. The density distributions of electrons and positive ions are primarily related to their generation rates as well as axial transport. However, radial transport phenomena are more important for negative ions, especially near the electrode edges at which a negative ion density peak is observed, although the local negative ion generation rate is very low. Detailed analyses regarding the electron and ion transport, as well as the reactions of radicals are presented to understand the internal physical mechanisms in this large area RF CCP.

In recent years, the remarkable development of the solar power industry has been undeniable. This led to the necessity of monitoring and maintenance of solar power plants due to many issues and failures that affect the efficiency and lifespan of the system. One of the most common fault cases of solar systems is the hot spot in photovoltaic panels created by the mismatch of cells in the panel due to incompatible environmental conditions or physical damage causing the heat up in lower power produced cells. The hot spot is detected through thermal image capture by a thermal camera and analyzed through image processing techniques with manual adjustment and testing. With recent significant achievements and growth of machine learning methods in image processing and technical diagnosis, it is greatly possible to develop a machine learning model to perform hot spot detection for higher efficiency and automation. In this study, our research group proposes an application of RetinaNet to develop a model capable of detecting hot spots in photovoltaic panels through processing thermal images. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Silicon is a tetrahedral substance where the central atom is connected with four faces by an angle of [Formula: see text]C. This substance shows many industrial applications, including chips and solar cells industries. The silicon usually exists in three different phases (i.e., solid, liquid and vapor) depending on the operating thermodynamic conditions. Apart from the major phases mentioned above, it shows the existence of low-density liquid (LDL), high-density liquid (HDL), low-density amorphous (LDA) and high-density amorphous (HDA) phases by distinguishing them based on their density. The substance may be referred to by the corresponding physical state (i.e., HDL, LDL, HDA and LDA) for particular targeted or desired applications. Based on the existence of the various physical states of silicon, it is important to understand and investigate its thermodynamic behavior, specifically under a supercooled region. Furthermore, the tetrahedral substance (i.e., silicon) represents the density and heat capacity anomalies in the supercooled state/region, which usually refers to the region close to liquid–liquid phase transition temperature ([Formula: see text]). Hence, by considering all the above aspects, in this study, we have focused on understanding and investigating the thermodynamic changes and their dependency on phase change phenomena under the supercooled region (i.e., close to [Formula: see text]). Furthermore, from the comparison perspective, we have performed this study by employing three different potential models (i.e., the Stillinger–Weber (SW) Potential Model, the Environment-Dependent Inter-atomic Potential (EDIP) Model and the Tersoff Potential Model). All the predicted results of this study are consistent in showing the independence of the thermodynamic changes while transforming from one phase to another (i.e., HDL to LDL) with respect to the corresponding force field model and liquid–liquid phase transition temperature ([Formula: see text]). All the corresponding results reported here are significant which shows the interesting observations on SW-silicon, EDIP-silicon and Tersoff-silicon under the supercooled region, which may further be applicable to define the best quality of silicon at [Formula: see text] for many desired industrial applications.

The surge in global interest in installing solar panels in recent years has been remarkable, in 2022, 250.000 MW of new capacity, primarily silicon (Si) based cell technology was installed (Chowdhury et al. 2020). Only in the past few years, there has been a substantial amount of solar panels, installed in the only 2000´s reaching end-of-life (EoL), with the big wave of EoL panels approaching. In 2050, there will be approximately 7 million tonnes of EoL panels. This implies the potential recovery of 7.000 to 70.000 tonnes of silver (Ag) and 210.000 to 350.000 tonnes of Si (Weckend et al. 2016). Without a comprehensive strategy for recycling them, we face the prospect of accumulating a mountain of EoL solar panels in the coming decades. Moreover, during the slicing of purified silicon ingots into wafers, the process of cutting multi-crystalline silicon ingots generates up to 40% Si kerf slurry waste. The rapid expansion of the global photovoltaic (PV) industry has resulted in a significant increase in diamond-wire saw kerf slurry waste. To reduce the costs of producing crystalline Si and mitigate environmental pollution caused by EoL solar panels and Si kerf slurry, recycling these materials has become imperative. In this work, the recycling of Si from waste solar wafers and Si kerf slurry in the solar industry are investigated using both pyrometallurgical and hydrometallurgical methods.

Photovoltaics (PV) is the fastest growing portion of the energy sector and the lowest-cost form of renewable energy today. PV development will continue to accelerate as the world strives to meet climate goals and reduce dependence on fossil fuels. The need for open, sun-abundant landscapes puts the solar (PV) industry in competition for land with agriculture and threatens intact native rangeland, stimulating valid concerns about negative impacts to ecological processes and function, such as soil stability, nutrient cycling, water cycling, and biodiversity. The co-location of PV energy generation and agriculture, commonly referred to as agrivoltaics, and specifically livestock grazing, offers a win-win scenario. While sheep grazing is being broadly employed, there is increasing interest in cattle grazing on PV projects. Our research seeks to understand if we can co-optimize cattle grazing and PV energy generation by designing and managing Solar Savannas, where ecological processes, capture of solar energy, and animal health and agricultural performance are equally prioritized. This is not only a question of dual-use but of multi-value. For agrivoltaics to be a meaningful sustainability solution, we hypothesize that dual-use must provide multiple values, including ecosystem service provision, agricultural production, and PV energy generation, bolstering community relationships and social and economic viability. We are engaged in ongoing research to ensure and measure this multi-value system, building ecological, agricultural, and financial models. What we learn from the modeling of this multi-value approach to eco-agri-energy systems is intended to eliminate tradeoffs of either-or systems, reduce barriers to dual-use systems, and finally, promote holistic solutions for the integration of multi-value systems. The purpose of this paper is to present a novel foundational framework, the Solar Savanna, and establish a theoretical basis for research and agrivoltaics practices.

Downward shortwave radiation (DSR) to the Earth’s surface is an essential renewable energy component. Accurate knowledge of solar radiation, i.e., solar energy resource assessment, is a prior requirement for the development of the solar energy industry. In the framework of solar resource assessment, site adaptation refers to leveraging short-term, high-quality ground-based observations as unbiased references to correct long-term, site-specific gridded model datasets, which has been playing an important role in this research area. This study evaluates 12 probabilistic site adaptation (PSA) methods for the correction of the hourly DSR data from multiple gridded DSR products in the Western Sichuan Plateau (WSP). Surface pyranometer observations are used as the reference to adapt predictions from two satellite products and two reanalysis products, collectively. Systematic quantification reveals inherent errors with root mean square errors (RMSEs) > 200 W/m2 across all datasets. Through a comparative evaluation of three methodological categories (benchmarking, parametric/non-parametric, and quantile combination approaches), it is demonstrated that quantile-based ensemble methods achieve superior performance. The median ensemble (MED) method delivers optimal error reduction (RMSE: 163.97 W/m2, nRMSE: 34.43%). The resulting optimal dataset, with a temporal resolution of 1 h and a spatial resolution of 0.05° × 0.05°, identifies the WSP as a region of exceptional energy potential, characterized by substantial annual total solar radiation (1593.10 kWh/m2/yr) and a stable temporal distribution (negative correlation between the total solar radiation and the coefficient of variation). This methodological framework provides actionable insights for solar resource optimization in complex terrains.