Bifacial PV modules have unique advantages in low-carbon building applications such as BIPV systems but often suffer from the shading problem resulting from higher surrounding objects or building facades.Point-blank q...Bifacial PV modules have unique advantages in low-carbon building applications such as BIPV systems but often suffer from the shading problem resulting from higher surrounding objects or building facades.Point-blank quantitative studies of PV performance of bifacial modules operating in actual environments as affected by shading on PV cells are lacking due to the difficulties of analysis caused by the existing multiple variable factors.By constructing an experimental comparison system on a flat roof of a building,we experimentally tested and analyzed the comparative variation characteristics of PV performance of bifacial and mono-facial modules under different shading area fractions.The results show that from the viewpoint of photoelectric efficiency,the PV performance of both bifacial and mono-facial PV modules clearly varied with the shading fraction of PV cell in some linear rules,though it is difficult to find regularity from the perspective of output power which was also affected by dynamic solar radiation intensity.An abnormal phenomenon emerged that the photoelectric efficiencies of the bifacial modules with small shading fraction were higher compared to the case without shading.Based on the findings of the experimental results,a regression approximation method based on shading test results(RAST Method)is further proposed to analyze and calculate the bifacial gain of bifacial modules.In the case of the existing roof installation,the mean bifacial gains of the two bifacial modules with different inclination angles were 8.86%and 11.30%,respectively.展开更多
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 indus...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.展开更多
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 ad...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.展开更多
The power generation of bifacial photovoltaic modules is greatly related to the diffuse solar radiation component received by the rear side,but radiation component data are scarce in China,where bifacial solar market ...The power generation of bifacial photovoltaic modules is greatly related to the diffuse solar radiation component received by the rear side,but radiation component data are scarce in China,where bifacial solar market is large.Radiation components can be estimated from satellite data,but sufficient ground truth data are needed for calibrating empirical methods or training machine learning methods.In this work,a data-augmented machine learning method was proposed to estimate radiation components.Instead of using observed ground truth,far more abundant radiation component data derived from sunshine duration measured at 2,453 routine weather stations in China were used to augment samples for training a machine-learning-based model.The inputs of the model include solar radiation(either from ground observation or satellite remote sensing)and surface meteorological data.Independent validation of the model at Chinese stations and globally distributed stations demonstrates its effectiveness and generality.Using a state-of-the-art satellite product of solar radiation as input,the model is applied to construct a satellite-based radiation component dataset over China.The new dataset not only outperforms mainstream radiation component datasets,but also has significantly higher accuracy than satellite-based datasets derived from other machine learning methods trained with limited observations,indicating the superiority of our data-augmented method.In principle,this model can be applied on the global scale without additional training with local data.展开更多
基金the National Natural Science Foundation of China(NSFC)(No.51408278)the Jiangxi Provincial Key Research and Development Program,China(No.20202BBEL53033)the Nature Science Foundation of Fujian Province(No.2022J01233269).
文摘Bifacial PV modules have unique advantages in low-carbon building applications such as BIPV systems but often suffer from the shading problem resulting from higher surrounding objects or building facades.Point-blank quantitative studies of PV performance of bifacial modules operating in actual environments as affected by shading on PV cells are lacking due to the difficulties of analysis caused by the existing multiple variable factors.By constructing an experimental comparison system on a flat roof of a building,we experimentally tested and analyzed the comparative variation characteristics of PV performance of bifacial and mono-facial modules under different shading area fractions.The results show that from the viewpoint of photoelectric efficiency,the PV performance of both bifacial and mono-facial PV modules clearly varied with the shading fraction of PV cell in some linear rules,though it is difficult to find regularity from the perspective of output power which was also affected by dynamic solar radiation intensity.An abnormal phenomenon emerged that the photoelectric efficiencies of the bifacial modules with small shading fraction were higher compared to the case without shading.Based on the findings of the experimental results,a regression approximation method based on shading test results(RAST Method)is further proposed to analyze and calculate the bifacial gain of bifacial modules.In the case of the existing roof installation,the mean bifacial gains of the two bifacial modules with different inclination angles were 8.86%and 11.30%,respectively.
文摘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.
基金the Jiangsu provincial key research and development program,China[grant number BE2023821]the Fundamental Research Funds for the Central Universities[grant number 30923011037]+1 种基金the National Natural Science Foundation of China(NSFC)[grant number 51408278]the Jiangxi provincial key research and development program,China[grant number 20202BBEL53033].
文摘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.
基金supported by the Sustainable Development International Cooperation Program of National Science Found-ation of China(Grant No.42361144875)the National Natural Science Foundation of China(Grant No.42171360).
文摘The power generation of bifacial photovoltaic modules is greatly related to the diffuse solar radiation component received by the rear side,but radiation component data are scarce in China,where bifacial solar market is large.Radiation components can be estimated from satellite data,but sufficient ground truth data are needed for calibrating empirical methods or training machine learning methods.In this work,a data-augmented machine learning method was proposed to estimate radiation components.Instead of using observed ground truth,far more abundant radiation component data derived from sunshine duration measured at 2,453 routine weather stations in China were used to augment samples for training a machine-learning-based model.The inputs of the model include solar radiation(either from ground observation or satellite remote sensing)and surface meteorological data.Independent validation of the model at Chinese stations and globally distributed stations demonstrates its effectiveness and generality.Using a state-of-the-art satellite product of solar radiation as input,the model is applied to construct a satellite-based radiation component dataset over China.The new dataset not only outperforms mainstream radiation component datasets,but also has significantly higher accuracy than satellite-based datasets derived from other machine learning methods trained with limited observations,indicating the superiority of our data-augmented method.In principle,this model can be applied on the global scale without additional training with local data.
