Photovoltaic Production Research Articles

Photovoltaic power one-day and multistep-hourly AI predictions using node-by-node evolved binomial tree structures to form L-transformed PDE modules

Abstract Photovoltaic (PV) power is generated by two common types of solar components that are primarily affected by fluctuations and development in cloud structures as a result of uncertain and chaotic processes. Local PV forecasting is unavoidable in supply and load planning necessary in integration of smart systems into electrical grids. Intra- or day-ahead modelling of weather patterns based on Artificial Intelligence (AI) allows one to refine available 24 h. cloudiness forecast or predict PV production at a particular plant location during the day. AI usually gets an adequate prediction quality in shorter-level horizons, using the historical meteo- and PV record series as compared to Numerical Weather Prediction (NWP) systems. NWP models are produced every 6 h to simulate grid motion of local cloudiness, which is additionally delayed and usually scaled in a rough less operational applicability. Differential Neural Network (DNN) is based on a newly developed neurocomputing strategy that allows the representation of complex weather patterns analogous to NWP. DNN parses the n-variable linear Partial Differential Equation (PDE), which describes the ground-level patterns, into sub-PDE modules of a determined order at each node. Their derivatives are substituted by the Laplace transforms and solved using adapted inverse operations of Operation Calculus (OC). DNN fuses OC mathematics with neural computing in evolution 2-input node structures to form sum modules of selected PDEs added step-by-step to the expanded composite model. The AI multi- 1…9-h and one-stage 24-h models were evolved using spatio-temporal data in the preidentified daily learning sequences according to the applied input–output data delay to predict the Clear Sky Index (CSI). The prediction results of both statistical schemes were evaluated to assess the performance of the AI models. Intraday models obtain slightly better prediction accuracy in average errors compared to those applied in the second-day-ahead evening approach. However, the first-morning strategy application includes a much more complicated and time-consuming procedure, so in conclusion, the second one-mode series processing could be recommended. Model statistics are generally more successful than NWP data processing due to limitations, delays, and unavailability of free radiation or cloudiness forecasts in the systems. An all-day model enables the prediction of complete PVP periods in sequenced computing with acceptable operational quality in the late afternoon, which is inevitable in day-ahead management and load scheduling in smart grids based on PV energy.

A novel approach for identification of PV cell model parameters with Grey Wolf Election-Based Optimization algorithm

In the past few years, photovoltaic production has significantly increased worldwide and has become a necessary element for achieving global agreements to minimize carbon dioxide emissions. Therefore, a precise and reliable simulation of PV panels is of increasing interest to researchers. However, this task is predicated on determining the PV model parameters; thus, it continues to be a highly challenging task. To overcome this challenge, we propose a swarm-based human optimization algorithm, i.e. the Grey Wolf Election-Based Optimization algorithm. This new approach combines inventive human behavior in the election process with the powerful search ability of grey wolves. The root-mean-square deviations predicted by the Grey Wolf Election-Based Optimization algorithm for a RTC France cell are 7.7386E-04, 7.5644E-04, and 7.4965E-04 for single, double, and triple-diode models, respectively. A total of thirteen of the most cited and newest meta-heuristic algorithms were employed as competitors to adequately assess the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm outperformed the compared algorithms and provided more efficient solutions.

Agrivoltaics development progresses: From the perspective of photovoltaic impact on crops, soil ecology and climate.

Agrivoltaics, the simultaneous use of land for both agriculture and photovoltaic (PV) energy production, has gained significant attention as a sustainable land-use strategy. This review investigates the progress of agrivoltaics from the perspective of its impacts on crops, soil ecology, and climate. First, the impacts of agrivoltaic systems on solar radiation were assessed, including soil humidity and, temperature dynamics, while considering the potential for changes in soil microbial communities. Second, how PV panels influence crop growth, yield, and quality through the modification of light distribution, temperature regulation, and soil humidity were explored. Lastly, the challenges and propose recommendations for optimizing the integration of agriculture and PV systems were addressed. This review aims to highlight the benefits and trade-offs of agrivoltaics, offering a roadmap for future research and implementation strategies to enhance the synergy between renewable energy production and sustainable agriculture.

Machine Learning Prediction of Photovoltaic Hydrogen Production Capacity Using Long Short-Term Memory Model

The yield of photovoltaic hydrogen production systems is influenced by a number of factors, including weather conditions, the cleanliness of photovoltaic modules, and operational efficiency. Temporal variations in weather conditions have been shown to significantly impact the output of photovoltaic systems, thereby influencing hydrogen production. To address the inaccuracies in hydrogen production capacity predictions due to weather-related temporal variations in different regions, this study develops a method for predicting photovoltaic hydrogen production capacity using the long short-term memory (LSTM) neural network model. The proposed method integrates meteorological parameters, including temperature, wind speed, precipitation, and humidity into a neural network model to estimate the daily solar radiation intensity. This approach is then integrated with a photovoltaic hydrogen production prediction model to estimate the region’s hydrogen production capacity. To validate the accuracy and feasibility of this method, meteorological data from Lanzhou, China, from 2013 to 2022 were used to train the model and test its performance. The results show that the predicted hydrogen production agrees well with the actual values, with a low mean absolute percentage error (MAPE) and a high coefficient of determination (R2). The predicted hydrogen production in winter has a MAPE of 0.55% and an R2 of 0.985, while the predicted hydrogen production in summer has a slightly higher MAPE of 0.61% and a lower R2 of 0.968, due to higher irradiance levels and weather fluctuations. The present model captures long-term dependencies in the time series data, significantly improving prediction accuracy compared to conventional methods. This approach offers a cost-effective and practical solution for predicting photovoltaic hydrogen production, demonstrating significant potential for the optimization of the operation of photovoltaic hydrogen production systems in diverse environments.

Tips and Tricks for a Good Encapsulation for Perovskite‐Based Solar Cells

ABSTRACTEncapsulation is a critical topic to ensure the successful implementation of perovskite photovoltaics. Recently, vacuum lamination has been shown as a promising approach that combines compatibility with current industrial processes in conventional photovoltaic (PV) manufacturing and suitability to achieve good results with perovskites. Here, we explore some of the attractive encapsulation materials in terms of their ability to prevent moisture ingress, withstand elevated temperatures, and have suitable mechanical properties to avoid thermomechanical issues. We utilized the previously suggested concept of the “perovskite test,” an optical test with simple sample fabrication, for evaluating encapsulation quality and validated the findings with the full solar cell stack. Unsurprisingly, encapsulants without an edge sealant showed insufficient protection from moisture. Ionomer in combination with butyl edge seal showed the best barrier properties; however, this stack led to rapid delamination of the cell layers in thermal cycling tests. Configuration with only edge sealant does not have such an issue in principle (no mechanical stress applied), but an absence of the polymer in the stack is unfavorable in terms of optical design and sometimes showed perovskite degradation that we assign to trapped moisture in the butyl itself. Polyolefin with butyl edge sealant is not free of degradation but showed the most promising compromise by passing the damp heat test and showing fewer issues in the thermal cycling experiments. In general, our material study and optimization presented in this manuscript show that a holistic approach is needed when choosing an optimal encapsulation scheme for perovskite devices.

Digital transformation and pathways for promoting global value position: an empirical study in Chinese manufacturing industries

Abstract Digital transformation offers new avenues for the photovoltaic manufacturing industry and his paper, grounded in value chain theory, collaborative management theory, and value chain cost control theory, examines digital transformation through three key dimensions: process optimization, platform ecosystem development, and data-driven strategies. The results show that digital transformation has effectively streamlined R&D, procurement, production, and sales costs. Notably, the R&D expenditure as a percentage of operating income decreased from around 4.2% to 3.1%. Furthermore, after digital transformation, significant improvements were seen in supplier delivery qualification rates, market share, and customer satisfaction.

Evaluation of Energy Potential in a Landfill Through the Integration of a Biogas–Solar Photovoltaic System

The integration of biogas and photovoltaic solar energy systems in sanitary landfills represents a promising strategy for sustainable energy generation and efficient urban waste management. This study evaluates the potential for biogas and photovoltaic energy production in two cells of the Municipal Landfill of Chihuahua, Mexico. Using the LandGEM and MMB models (Landfill Gas Emission Model and the Mexican Biogas Model), biogas generation was estimated by considering the composition of the landfill gas and the characteristics of the cover in each cell, revealing notable differences due to their operational periods and waste deposition. Photovoltaic simulations, conducted with the HelioScope software 2020, evaluated spatial configurations and solar radiation data. The generation potential for 2025 was simulated using predictive models, yielding results between 25.48 and 26.08 MW for the biogas–photovoltaic system, depending on the orientation of the panels and the optimization of the coverage. The novelty of this work lies in the combined evaluation of biogas and photovoltaic potential within a single landfill site, integrating advanced modeling tools to optimize system design. By demonstrating the feasibility and benefits of this hybrid system, the study contributes to clean energy solutions, environmental mitigation, and improved waste management strategies. Our findings emphasize the importance of site-specific management practices and predictive modeling to enhance renewable energy production and reduce greenhouse gas emissions, supporting sustainable urban development initiatives.

Identifying weather patterns responsible for renewable energy droughts over India

Abstract. Energy systems across the globe are evolving to meet climate mitigation targets. This requires rapid reductions in fossil fuel consumption and significant uptake of renewable generation. Renewable energy sources are weather-dependent, causing production to vary at timescales from minutes to decades ahead. A consequence of this variability is that there will be periods of low renewable energy production, here termed renewable energy droughts. This energy security challenge needs to be addressed to ensure grid stability. India is chosen as a study area as it is a region that has both a large proportion of renewable generation and good subseasonal predictability. In this study, we use synthetic wind and solar photovoltaic production timeseries, previously derived for the Indian energy grid using ERA5 reanalysis from 1979–2022, to identify historical renewable energy droughts. These are defined as periods where wind and solar potential is in the lowest 2.5 % compared to climatology. These events commonly occur from November–February, with the longest historical event being 9 d long. We identify the weather regimes that cause the largest renewable energy droughts over India and investigate potential sources of predictability. Existing large-scale daily weather types and impact-based patterns are used to investigate the different weather patterns causing renewable energy droughts. Renewable energy droughts are caused by low seasonal wind speeds in combination with weather patterns bringing high cloud cover. These are mainly weak northeast monsoon and western disturbances. Sources of potential subseasonal predictability are considered for the largest renewable energy droughts, including the Madden Julian Oscillation and Boreal Summer Intraseasonal Oscillation. Although both have a stronger relationship with high energy potential days, links between phases of these two oscillations and renewable energy drought days are identified. These could help to provide early warnings for challenging security of supply conditions in the future.

Photovoltaic Farm Production Forecasting: Modified Metaheuristic Optimized Long Short-Term Memory Based Networks Approach

Photovoltaic Farm Production Forecasting: Modified Metaheuristic Optimized Long Short-Term Memory Based Networks Approach

Quantifying the accelerated diffusion and cost savings of global solar photovoltaic supply chains.

Solar photovoltaic (PV) is critical for achieving the 2030 global target of tripling renewable power capacity. Over the past two decades, the global supply chain has significantly reduced the cost of solar PV products enabling widespread adoption. However, many countries are now implementing decoupling measures to enhance supply chain security and boost local economies. Here we quantify the impact of decoupling measures on solar PV deployment and module costs in China, the EU, the US, and Japan, using a methodology that combines the learning curve with the Bass diffusion model. Our results indicate that decoupling measures could lead to a significant increase in PV module costs and a marked slowdown in global deployment due to the disruption of free flows of capital, technology, talent, and information. The global supply chain remains essential for addressing the climate crisis. Countries should balance growth, security, and climate objectives in their green industrial policies.

Review of Issues and Opportunities for Glass Supply for Photovoltaics Production at MultiTerawatt (TW) Scale

Review of Issues and Opportunities for Glass Supply for Photovoltaics Production at MultiTerawatt (TW) Scale

DESIGN AND OPTIMIZATION OF SOLAR PARKING CANOPY AS A PART OF ENERGY EFFICIENT URBAN PLANNING

This paper proposes the optimal use of available space on canopies of parking spaces in urban areas for the installation of low and medium power photovoltaic systems and electricity production. Simulations show that a PV1 system offers a return on investment of 187% with a payback period of 5.6 years, while a PV2 system achieves an ROI of 91.9% with an 8.4-year payback. These findings emphasize the cost-effectiveness and environmental benefits of solar parking canopies in urban planning. The obtained electricity could be used to power nearby residential and business premises or electric car chargers. Also, there is a possibility of direct injection of produced electricity into the distribution network. Two configurations of the photovoltaic system at a specific location were analyzed in order to find the optimal solution in terms of the relationship between the price of the system, the electricity produced and the time required to return the investment. The program PVsyst was used for the design and optimization of photovoltaic systems. The obtained simulation results for both proposed photovoltaic systems are presented and compared.

Design of a photovoltaic performance prediction model using meteorological models and machine learning techniques

This study presents an advanced predictive model to estimate daily photovoltaic energy production using machine learning techniques. Artificial neural networks, support vector machines, and random forests were employed to capture complex relationships among meteorological variables such as solar radiation and temperature. The models were trained and evaluated using historical data, employing metrics like mean squared error and cross-validation techniques to ensure robustness and generalization of the results. Findings indicate that the implemented models effectively predict the performance of photovoltaic systems under diverse climatic conditions in the eastern region of Michoacán. This model not only enhances operational planning of solar installations but also facilitates more efficient integration of renewable energy into power grids. The results underscore the potential of photovoltaic technologies and the crucial role of artificial intelligence in transitioning towards more sustainable energy systems.

Mono‐disperse submicron silver powder for conductive elastomer

AbstractThe synthesis of superfine silver powder with good dispersity is of great significance for accelerating the development of high‐end electronics and photovoltaic products. Herein, a kind of quasi‐spherical submicron silver powder prepared by the introduction of gelatin during silver ions reduction is reported. The optimal scheme is determined that the initial pH of gelatin solution is 3 and the mass of gelatin (Mw 50,000—70,000) is 10% of silver nitrate, leading to a mono‐disperse silver powder with regular morphology and average particle size of 0.5 µm. In addition, the prepared silver powder can be further combined with polydimethylsiloxane (PDMS) to form a stretchable conductive elastomer (PDMS/Ag elastomer). In unstretched state, the minimum sheet resistance of the elastomer can reach 66 Ω/□ without annealing treatment. Even when the elongation reaches 100%, it still exhibits good conductivity with a sheet resistance of only 13 kΩ/□. The comprehensive performance, mainly referring to stretching and conductivity, is comparable to that of some flexible conductive materials involving silver nanowire or nanocarbon. Thus, this study will demonstrate a new class of silver powder with a potential in flexible electronics.

Quantifying Measurement Uncertainty in Photovoltaic Module Performance at Standard Test Conditions: A Machine-Based Approach

This paper presents an in-depth analysis of measurement uncertainty in the output parameters of photovoltaic (PV) devices, focusing specifically on measurements conducted under standard test conditions (STC). Accurate characterization of module I-V performance is crucial for PV manufacturers, researchers, and investors alike. The study provides a comprehensive overview of the measurement procedures for both performance and temperature coefficient of PV modules, with a strong emphasis on the detailed calculation of associated uncertainties. Notably, the research was conducted under real-world sunlight conditions, with special attention given to reference devices such as reference cells, modules, or pyranometers, which play a pivotal role in determining overall uncertainty components. By utilizing a diverse array of machines from various sources, the results obtained are applicable across a wide range of measurement configurations. Furthermore, adherence to the ISO/IEC 17025 series of standards ensures a standardized and reliable approach. The novelty of this research lies in its comprehensive approach to uncertainty analysis, encompassing both performance and temperature coefficient measurements under real-world conditions. By providing valuable guidelines for PV module characterization and reliability assessment, particularly in uncertainty estimation, this study contributes significantly to the advancement of photovoltaic technology.

A comparison between the ocean and offshore photovoltaic production system into microgrids: benefits and limits

The current work offers a detailed comparison of the advantages and disadvantages of microgrids concerning the developments of photovoltaic (PV) production installed near the shore and those installed offshore. As demand for renewable energy increases, integrating offshore and marine photovoltaic systems offers a promising approach to increase energy production while minimizing land use. This study explores the inherent advantages of offshore photovoltaic systems, including higher energy production due to the cooling effect of water, reduced reliance on land, and the ability to tap into sustained marine solar resources. On the other hand, this paper also addresses challenges associated with these systems, such as: B. Increased installation complexity, vulnerability to harsh ocean conditions, and potential impacts on marine ecosystems. The results in this paper show good performance for both offshore and floating PV systems, except that the offshore PV system excels over the other system by 3.13% in energy production. Moreover, the difference in the annual efficiency of the two PV systems reached 0.55%. These values are considered low because both systems are installed in water, given that both systems benefit from lower temperature and solar irradiation values. Nevertheless, these two systems equally present their own unique challenges including, but not limited to, operational and maintenance cost increase, effect on marine ecology and the technical hindrances on installation and grid interconnectivity. The aim of this review is to disentangle the achievements made regarding the current state of the art in floating photovoltaic technologies. When dealing with performance metrics, two solutions are examined in order to demonstrate the feasibility of providing the energy needs in an ecological way.

Techno-economic evaluation of solar photovoltaic power production in China for sustainable development and the environment

Techno-economic evaluation of solar photovoltaic power production in China for sustainable development and the environment

Toward Reuse‐Ready PV: A Perspective on Recent Advances, Practices, and Future Challenges

Photovoltaic (PV) industry is facing challenges in end‐of‐life (EoL) management, as cumulative PV waste is expected to increase exponentially over the next two decades. The increase in revamping and repowering activities, especially of older PV fleets, underscores the need to refine EoL strategies and increase the reuse readiness of PV modules and other components. As PV systems transition from operational to the EoL regime, streamlined PV qualification and repair strategies become indispensable. This is the only way to ensure reusable PV systems and a bankable second‐life PV market. This perspective study sheds light on current research, innovations, practices, and future challenges toward higher PV reuse readiness in the PV industry, highlighting qualification methods, repair strategies, and standardization for PV reuse. An exemplary triage‐for‐reuse framework is discussed, involving four steps: 1) off‐site eligibility checks; 2) on‐site inspections and functionality tests; 3) collection and transportation; 4) deeper technical checks. The study also reviews recent advances and research in repair strategies for reuse—addressing notably reliability issues of backsheet, glass and interconnection components—as well as ongoing standardization efforts. Conclusions underscore the the pressing need for circularity in the entire PV products’ lifecycle: from design/material level to system, operations and maintenance, and EoL.

Assessment of the Influence of the Life Cycle of Solar Power Plant Materials and Components on Ecosystem Quality.

Currently, silicon is the most often utilized material for photovoltaic cell manufacturing, as it has the potential to convert solar energy directly into electricity. The silicon used in photovoltaic solutions must be highly pure. Large amounts of power, raw materials, and fossil fuels are consumed in the production process. Post-consumer treatment of polymers, materials, and components also requires energy and matter. These processes have a significant influence on the environment. As a result, the primary purpose of this article is to evaluate the influence of a photovoltaic power plant's material and component life cycle on ecosystem quality. The research focuses on an actual photovoltaic power plant with a capacity of 2 MW located in northern Poland. According to the findings, photovoltaic modules are the part that has the most negative environmental impact, since their manufacturing requires a substantial amount of materials and energy (primarily from conventional sources). Post-consumer management, in the form of recycling after use, would provide major environmental advantages and reduce detrimental environmental consequences throughout the course of the solar power plant's full life cycle.

An AI-Based Approach for the Assessment of the Lost Efficiencies of a PVT Plant

Developments in solar technologies are making it possible to effectively exploit solar resources in several regions of Earth, including urban areas and regions with low solar irradiation intensity. Since power and heat are fundamental necessities for all users, f users, photovoltaic/thermal (PVT) technology has emerged as a promising solution for sustainably-minded communities. Generally, the output electrical generation of a PVT system depends on the intermittent solar insolation, efficiency, and operating temperature of the photovoltaic (PV) cell. At the same time, thermal power also depends on collector thermal insulation and the temperature difference with the surroundings. Consequently, it is essential to predict and diagnose the PVT system's power to optimally utilize the collected solar energy. In recent years, intelligent techniques and approaches have been introduced to evaluate the performance of PVT systems. Artificial Intelligence (AI)-based methods have become prominent due to their capability of producing accurate predictions against the uncertainty and nonlinearity phenomena. This work presents an innovative application of AI for both the forecasting and the diagnostic of the performance of a PVT plant. The analyses performed are based on the experimental investigation conducted on the PVT system installed at the University of Catania campus. The outcomes of this study showed that the AI accurately predicted photovoltaic energy production and consequently, it can be used to detect periods of performance loss. From the test carried out in the real plant, it was seen that the use of the AI allows for prompt identification of malfunctioning in PVT plants can translate into avoid loss of over 5% of the electricity produced, and the total loss of thermal energy.