Analysis for Effects of Temperature Rise of PV Modules upon Driving Distance of Vehicle Integrated Photovoltaic Electric Vehicles

The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO2 emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.

Comparison between the Energy Required for Production of PV Module and the Output Energy Througout the Product Life Time

Electrical energy consumption is growing and is necessary to improve the technologies related to energy production. We have carried out a pilot study about environmental impacts during the manufacturing process of PV （photovoltaic） modules and compared between the energy requirement for the production of PV cells and modules and generation throughout the life time of the finished good that is PV module. It was taken into account the generation of environmental aspects and impacts in the manufacture of monocrystalline silicon PV modules （consisting of three components： silicon cell, fiat tempered glass and aluminum frame）, and an analysis of a grid-connected PV system using an energetic alternative in residences was considered. Results show that, this kind of renewable energy is really clean and can be considered as a way to change the energy technology.

PV Array Reconfiguration Based on the Shaded Cells'Number for PV Modules

Reconfiguration can increase the output power for a PV array under partial shadows.However,traditional reconfiguration methods consider the PV module as either totally shaded or totally unshaded,and module-based simulation is employed to evaluate the reconfiguration effect.Actually,there is an unneglectable error when treating a partially shaded PV module as totally shaded,through using a more accurate cellbased simulation.Based on the analysis of the determinant factors on MPPs’power of a PV array,a new reconfiguration method is proposed based on the exact partial shadow shape projected on the PV array.This method restructures the electrical connection among PV modules of a PV array according to the shaded cells’number(SCN)of every PV module.Extensive cell-based simulations are carried out on a PV array to verify the effectiveness of the proposed SCN-based reconfiguration method.Comprehensive comparisons among various reconfiguration methods and shadow distributions clearly show its suitability to different irregular shadows and its superiority in PV output power enhancement.

Maximizing Solar Potential Using the Differential Grey Wolf Algorithm for PV System Optimization

Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions.This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer(DGWO).It dynamically adjusts the parameters of the MPPT controller,specifically the duty cycle of the SEPIC converter,to efficiently track the Maximum Power Point(MPP).The proposed system aims to enhance the energy harvesting capability of solar PV systems by optimizing their performance under varying solar irradiance,temperature and shading conditions.Simulation results demonstrate the effectiveness of the DGWO-based MPPT system in maximizing the power output of solar PV installations compared to conventional MPPT methods.This research contributes to the development of advanced MPPT techniques for improving the efficiency and reliability of solar energy systems.

Analysis of the Effect of Temperature and Relative Humidity on the Reliability of a Photovoltaic Module

Photovoltaic energy occupies a significant place in the renewable energy market, with photovoltaic (PV) modules playing a vital role in converting solar energy into electricity. However, their effectiveness is likely to be affected by variations in environmental conditions, including temperature and relative humidity. The study examines the impact of these major climatic factors on the reliability of PV modules, aiming to provide crucial information for optimizing and managing these systems under varying conditions. Inspired by Weibull’s law to model the lifespan of components, we proposed a mathematical model integrating a correction factor linked to temperature and relative humidity. Using this approach, simulations in Matlab Simulink reveal that increasing temperature and relative humidity have an adverse impact on the reliability and lifespan of PV modules, with a more pronounced impact on temperature. The results highlight the importance of considering these environmental parameters in the management and optimization of photovoltaic systems to ensure their long-term efficiency.

PV Capacity Evaluation Using ASTM E2848: Techniques for Accuracy and Reliability in Bifacial Systems

A variety of test methodologies are commonly used to assess if a photovoltaic system can perform in line with expectations generated by a computer simulation. One of the commonly used methodologies across the PV industry is an ASTM E2848. ASTM E2848-13, 2023 test method provides measurement and analysis procedures for determining the capacity of a specific photovoltaic system built in a particular place and in operation under natural sunlight. This test method is mainly used for acceptance testing of newly installed photovoltaic systems, reporting of DC or AC system performance, and monitoring of photovoltaic system performance. The purpose of the PV Capacity Test and modeled energy test is to verify that the integrated system formed from all components of the PV Project has a production capacity that achieves the Guaranteed Capacity and the Guaranteed modeled AEP under measured weather conditions that occur when each PV Capacity Test is conducted. In this paper, we will be discussing ASTM E2848 PV Capacity test plan purpose and scope, methodology, Selection of reporting conditions (RC), data requirements, calculation of results, reporting, challenges, acceptance criteria on pass/fail test results, Cure period, and Sole remedy for EPC contractors for bifacial irradiance.

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged periods.Thus,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules.To address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images.Furthermore,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition process.This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process.The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures.The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules.In conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules.This breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice.By proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

Research on the potential-induced degradation (PID) of PV modules running in two typical climate regions

In order to accurately select photovoltaic modules under different climatic conditions,three kinds of polycrystalline silicon photovoltaic modules were prepared for this study using different properties of packaging materials and two typical climatic zones of China were selected for installation and operation of these photovoltaic(PV)modules.The photoelectric parameters(maximum power,open-circuit voltage,short-circuit current,etc.)and electroluminescence images of these modules were analysed before and after their operation for 6 months.The study found that the performance of PV modules in different climatic regions shows different decay tendency and degradation mechanism.There was a significant difference in the degradation of the three different types of PV modules in the sub-humid-hot region(Suzhou,Jiangsu);two kinds of photovoltaic modules using relatively poorly performing package materials showed significant potential-induced degradation effects.However,the degradation trend of the three different types of PV modules in the warm-temperate region(Kenli,Shandong)was consistent and no significant potential-induced degradation effect was observed.

A novel porous channel to optimize the cooling performance of PV modules

This paper dealt with a series of numerical investigations on a new porous cooling channel applied to PV/T systems in order to improve the insufficient heat transfer in the conventional channel.The proposed porous cooling channel based on field synergy theory had a higher overall heat transfer coefficient,which enhanced the total efficiency of the PV/T system.The numerical model was validated with experimental data.The results showed that holes distributed non-uniformly near the outlet of the cooling water led to a better cooling effect,and a hole diameter of 0.005 m led to an optimal performance.The total efficiency of the PV module with the new cooling channel was 4.17%higher than the conventional one at a solar irradiance of 1000 W/m^(2)and an inlet mass flow rate of 0.006 kg/s.In addition,as the solar irradiance increased from 300 to 1200 W/m^(2),the total efficiency of the new PV/T system dropped by 5.07%,which included reductions in both the electrical and thermal efficiency.The total efficiency was improved by 18.04%as the inlet mass flow rate of cooling water increased from 0.002 to 0.02 kg/s.

Resiliency oriented control of a smart microgrid with photovoltaic modules

The resiliency of a standalone microgrid is of considerable issue because the available regulation measures and capabilities are limited.Given this background,this paper presented a new mathematical model for a detailed photovoltaic(PV)module and the application of new control techniques for efficient energy extraction.The PV module employs a single-stage conversion method to integrate it with the utility grid.For extraction the maximum power from PV and integrate it to power grid,a three-phase voltage source converter is used.For obtaining the maximum power at a particular irradiance a maximum power point tracking(MPPT)scheme is used.The fuzzy logic control and adaptive network-based fuzzy inference system are proposed for direct current(DC)link voltage control.The proposed model and control scheme are validated through a comparison with the standard power-voltage and current-voltage charts for a PV module.Simulation results demonstrate that the system stability can be maintained with the power grid and in the island mode,in contrast with the MPPT.

Modeling of the Photovoltaic Module Operating Temperature for Various Weather Conditions in the Tropical Region

The operating temperature is a critical factor affecting the performances of photovoltaic(PV)modules.In this work,relevant models are proposed for the prediction of this operating temperature using data(ambient temperature and solar irradiance)based on real measurements conducted in the tropical region.For each weather condition(categorized according to irradiance and temperature levels),the temperatures of the PV modules obtained using the proposed approach is compared with the corresponding experimentally measured value.The results show that the proposed models have a smaller Root Mean Squared Error than other models developed in the literature for all weather conditions,which confirms the reliability of the proposed framework.

Effect of Dust and Shadow on Performance of Solar Photovoltaic Modules: Experimental Analysis

This study presents an experimental performance of a solar photovoltaic module under clean,dust,and shadow conditions.It is found that there is a significant decrease in electrical power produced(40%in the case of dust panels and 80%in the case of shadow panels)and a decrease in efficiency of around 6%in the case with dust and 9%in the case with the shadow,as compared to the clean panel.From the results,it is clear that there is a substantial effect of a partial shadow than dust on the performance of the solar panel.This is due to the more obstruction of the sunlight by the shadowed area compared to the dust.The dust being finer particles for the given local experimental condition did not influence the panel than the shadow.The main outcome of this study is that the shadowing effect may cause more harm to the PV module than dust for the given experimental conditions.However,Further long-term studies on the effect of dust and shadow are needed to understand the effect on performance degradation and module life.

Comprehensive Examination of Solar Panel Design: A Focus on Thermal Dynamics

In the 21st century, the deployment of ground-based Solar Photovoltaic (PV) Modules has seen exponential growth, driven by increasing demands for green, clean, and renewable energy sources. However, their usage is constrained by certain limitations. Notably, the efficiency of solar PV modules on the ground peaks at a maximum of 25%, and there are concerns regarding their long-term reliability, with an expected lifespan of approximately 25 years without failures. This study focuses on analyzing the thermal efficiency of PV Modules. We have investigated the temperature profile of PV Modules under varying environmental conditions, such as air velocity and ambient temperature, utilizing Computational Fluid Dynamics (CFD). This analysis is crucial as the efficiency of PV Modules is significantly impacted by changes in the temperature differential relative to the environment. Furthermore, the study highlights the effect of airflow over solar panels on their temperature. It is found that a decrease in the temperature of the PV Module increases Open Circuit Voltage, underlining the importance of thermal management in optimizing solar panel performance.

Voltage-Based Hot-Spot Detection Method for Defective Cell in Photovoltaic Module Using Projector

This paper proposes a voltage-based hot-spot detection method for defective cells in PV module using projector. The presence of internal crystal defects is one of the main causes of hot-spot phenomenon in PV modules. Authors previously investigated the physical characteristics of hot-spot phenomenon referring to internal crystal defect. Based on it, a hot-spot detection method named as current-based SRC （self reverse current） detection method is developed. However, it becomes extraordinarily complicated to determine the defective cells under low illumination. In order to avoid this disadvantage, authors improve the SRC detection method by applying voltage. From the feasibility experiment results, it is confirmed that by calculating cell HSI （hotspots index） with voltage, the PV modules with defective cells can be prospectively excluded even under low illumination.

Numerical Analysis of an Industrial Polycrystalline Silicon Photovoltaic Module Based on the Single-Diode Model Using Lambert W Function

It is adopted the single-diode solar cell model and extended for a PV module. The current vs. voltage (I-V) characteristic based on the Lambert W-function was used. The estimation parameters for the simulation approach of the photovoltaic (PV) module make use of Levenberg-Marquardt method. It was considered an industrial polycrystalline silicon photovoltaic (PV) module and the simulated results were compared with the experimental ones extracted from a specific datasheet. The I-V characteristic for the analysed PV module and its maximum output power are investigated for different operating conditions of incident solar radiation flux and temperature, as well as parameters related to the solar cells material and technology (series resistance, shunt resistance and gamma factor). The analysis gives indications and limitations for design and optimization of the performance for industrial PV modules. This study can be implemented in any type of PV module.

Numerical modeling of all-day albedo variation for bifacial PV systems on rooftops and annual yield prediction in Beijing

Bifacial PV modules capture solar radiation from both sides,enhancing power generation by utilizing reflected sunlight.However,there are difficulties in obtaining ground albedo data due to its dynamic variations.To address this issue,this study established an experimental testing system on a rooftop and developed a model to analyze dynamic albedo variations,utilizing specific data from the environment.The results showed that the all-day dynamic variations in ground albedo ranged from 0.15 to 0.22 with an average of 0.16.Furthermore,this study evaluates the annual performance of a bifacial PV system in Beijing by considering the experimental conditions,utilizing bifacial modules with a front-side efficiency of 21.23%and a bifaciality factor of 0.8,and analyzing the dynamic all-day albedo data obtained from the numerical module.The results indicate that the annual radiation on the rear side of bifacial PV modules is 278.90 kWh/m^(2),which accounts for only 15.50%of the front-side radiation.However,when using the commonly default albedo value of 0.2,the rear-side radiation is 333.01 kWh/m^(2),resulting in an overestimation of 19.40%.Under dynamic albedo conditions,the bifacial system is predicted to generate an annual power output of 412.55 kWh/m^(2),representing a significant increase of approximately 12.37%compared to an idealized monofacial PV system with equivalent front-side efficiency.Over a 25-year lifespan,the bifacial PV system is estimated to reduce carbon emissions by 8393.91 kgCO_(2)/m^(2),providing an additional reduction of 924.31 kgCO_(2)/m^(2)compared to the idealized monofacial PV system.These findings offer valuable insights to promote the application of bifacial PV modules.

Power optimization of photovoltaic modules under varying environmental conditions based on current equalization collaborating with constant voltage control

The performance of photovoltaic(PV)modules is affected by environmental factors such as irradiance and temperature,which can lead to a decrease in output performance or even damage.This study proposes an improved formula for calculating the real maximum power of PV modules by analysing the influence of irradiance and temperature.A simulation model is developed using PLECS software to simulate the global maximum power of PV modules under different environmental conditions and the results are compared with the calculated real maximum power.A power optimization scheme for PV modules is then proposed based on current equalization and constant voltage control.This scheme employs a single-switch multi-winding forward-flyback converter to equalize the mismatched currents between cell strings,thereby enhancing the output performance.Traditional proportional-integral controllers are utilized to achieve constant voltage control and obtain the real maximum power of PV modules.Simulation models are built in the PLECS simulation platform to evaluate the performance of a global maximum power point tracking scheme based on the traditional perturb-and-observe(TPO)algorithm with current equalization,a segment perturb-and-observe algorithm without current equalization,and the proposed power optimization scheme.The simulation results demonstrate that the proposed constant voltage control has greater efficiency than the TPO algorithm.The proposed scheme achieves a significant improvement in efficiency,with a 27.87%increase compared with the segment perturb-and-observe algorithm without current equalization.

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.

Status quo on recycling of waste crystalline silicon for photovoltaic modules and its implications for China's photovoltaic industry

As a clean and efficient renewable energy source,solar energy has been rapidly applied worldwide.The growth rate of China's installed capacity ranks first in the world.However,the life span of photovoltaic(PV)modules is 25 to 30 years,and the rapid development of installed capacity indicates that a large number of PV modules will be decommissioned in the future.Therefore,the ongoing treatment of the scrapped PV waste cells in the near future requires urgent plans and countermeasures.Proper recycling and disposal of decommissioned PV modules is a practical requirement for the sustainable development of the country and industry.Crystalline silicon(c-Si)solar cells currently occupy 85%-90%of the market share,and some scholars have begun to seek the utilization pathways of the waste Si in and outside the PV industry.In this paper,the research status of the separation and recycling process of crystalline Si PV modules is reviewed,and the recycling ways of crystalline silicon are particularly focused on.In addition,the current bottlenecks in the PV recycling industry in China are analyzed and some suggestions on the sustainable development of the PV industry are proposed.

Short-Term Prediction of Photovoltaic Power Based on Fusion Device Feature-Transfer

To attain the goal of carbon peaking and carbon neutralization,the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy.However,this approach is hindered by the lack of training data for predicting new grid-connected PV power stations.To overcome this problem,this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations.The proposed model integrates a structure model,heat-dissipation conditions,and the loss coefficients of PV modules.Clear-Sky entropy,characterizes seasonal and weather data features,describes the main meteorological characteristics at the PV power station.Taking gate recurrent unit neural networks as the framework,the open and shared PV-power data as the source-domain training label,and a small quantity of power data from a new grid-connected PV power station as the target-domain training label,the neural network hidden layer is shared between the target domain and the source domain.The fully connected layer is established in the target domain,and the regularization constraint is introduced to fine-tune and suppress the overfitting in feature transfer.The prediction of PV power is completed by using the actual power data of PV power stations.The average measures of the normalized root mean square error(NRMSE),the normalized mean absolute percentage error(NMAPE),and the normalized maximum absolute percentage error(NLAE)for the model decrease by 15%,12%,and 35%,respectively,which reflects a much greater adaptability than is possible with other methods.These results show that the proposed method is highly generalizable to different types of PV devices and operating environments that offer insufficient training data.