Pub Date : 2025-03-13DOI: 10.1109/JPHOTOV.2025.3545825
Dylan J. Colvin;Andrew M. Gabor;William C. Oltjen;Philip J. Knodle;Ange Dominique Yao;Brent A. Thompson;Nadia Khan;Sina Lotfian;Joseph Raby;Albert Jojo;Xuanji Yu;Max Liggett;Hubert P. Seigneur;Roger H. French;Laura S. Bruckman;Mengjie Li;Kristopher O. Davis
As the photovoltaics (PV) industry grows in sophistication, so must the extent to which systems are characterized. UV Fluorescence (UVF) imaging is a valuable, easy-to-perform, high-throughput, nonintrusive technique for characterizing modules in the field and in the lab. However, UVF is still a relatively new technique, and many in the PV industry are still unaware of its potential. We provide a guideline for obtaining, processing, and interpreting UVF images. We have provided a list of considerations for imaging hardware and settings, a suggested pipeline for image processing, and details on a survey of features shown in UVF images. A new database with UVF images of 7190 modules and another database curated by BrightSpot Automation are publicly available.
{"title":"Ultraviolet Fluorescence Imaging for Photovoltaic Module Metrology: Best Practices and Survey of Features Observed in Fielded Modules","authors":"Dylan J. Colvin;Andrew M. Gabor;William C. Oltjen;Philip J. Knodle;Ange Dominique Yao;Brent A. Thompson;Nadia Khan;Sina Lotfian;Joseph Raby;Albert Jojo;Xuanji Yu;Max Liggett;Hubert P. Seigneur;Roger H. French;Laura S. Bruckman;Mengjie Li;Kristopher O. Davis","doi":"10.1109/JPHOTOV.2025.3545825","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3545825","url":null,"abstract":"As the photovoltaics (PV) industry grows in sophistication, so must the extent to which systems are characterized. UV Fluorescence (UVF) imaging is a valuable, easy-to-perform, high-throughput, nonintrusive technique for characterizing modules in the field and in the lab. However, UVF is still a relatively new technique, and many in the PV industry are still unaware of its potential. We provide a guideline for obtaining, processing, and interpreting UVF images. We have provided a list of considerations for imaging hardware and settings, a suggested pipeline for image processing, and details on a survey of features shown in UVF images. A new database with UVF images of 7190 modules and another database curated by BrightSpot Automation are publicly available.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 3","pages":"465-477"},"PeriodicalIF":2.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1109/JPHOTOV.2025.3545820
Rajiv Daxini;Kevin S. Anderson;Joshua S. Stein;Marios Theristis
Understanding the impact of variation in the solar spectrum on photovoltaic (PV) device output is critical for accurate and reliable PV performance modeling. While previous studies have examined these spectral effects extensively at the module level, this study examines the spectral impact at the cell level and how subsequent current mismatch can influence module-level output. Cell-level external quantum efficiency (EQE) data from 11 new commercial PV modules are analyzed. The module power output, as determined by the spectral mismatch factor of the module-limiting cell, is computed using the measured cell EQE data in conjunction with gridded meteorological and spectral irradiance data simulated at an approximately 20 $mathbf{mathrm{km}}$ resolution across the contiguous USA over one year. This study finds only a small variation in annualized module output of around 0.2% as a result of intramodule EQE variation. However, these losses exhibit significant seasonality, varying by up to around four times the annualized energy difference on a month-to-month basis. The seasonality of the energy loss has implications for subannual PV performance analysis applications such as capacity testing.
{"title":"Photovoltaic Module Spectral Mismatch Losses Due to Cell-Level EQE Variation","authors":"Rajiv Daxini;Kevin S. Anderson;Joshua S. Stein;Marios Theristis","doi":"10.1109/JPHOTOV.2025.3545820","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3545820","url":null,"abstract":"Understanding the impact of variation in the solar spectrum on photovoltaic (PV) device output is critical for accurate and reliable PV performance modeling. While previous studies have examined these spectral effects extensively at the module level, this study examines the spectral impact at the cell level and how subsequent current mismatch can influence module-level output. Cell-level external quantum efficiency (EQE) data from 11 new commercial PV modules are analyzed. The module power output, as determined by the spectral mismatch factor of the module-limiting cell, is computed using the measured cell EQE data in conjunction with gridded meteorological and spectral irradiance data simulated at an approximately 20 <inline-formula><tex-math>$mathbf{mathrm{km}}$</tex-math></inline-formula> resolution across the contiguous USA over one year. This study finds only a small variation in annualized module output of around 0.2% as a result of intramodule EQE variation. However, these losses exhibit significant seasonality, varying by up to around four times the annualized energy difference on a month-to-month basis. The seasonality of the energy loss has implications for subannual PV performance analysis applications such as capacity testing.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 3","pages":"458-464"},"PeriodicalIF":2.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1109/JPHOTOV.2025.3562931
Brandon K. Byford;Laura E. Boucheron;Bruce H. King;Jennifer L. Braid
Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (I–V) curve analyzes for identification of damage and power loss. I–V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I–V curves for PV modules from their corresponding EL images. The predicted I–V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current $I_{text {sc}}$, and open-circuit voltage $V_{text {oc}}$ with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance $R_{text {s}}$ demonstrates a mean error of 5.19%, and the photocurrent $I$ a mean error of 0.197%. The shunt resistance $R_{text {sh}}$ and dark current $I_{text {o}}$ parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.
{"title":"Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images","authors":"Brandon K. Byford;Laura E. Boucheron;Bruce H. King;Jennifer L. Braid","doi":"10.1109/JPHOTOV.2025.3562931","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3562931","url":null,"abstract":"Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (<italic>I–V</i>) curve analyzes for identification of damage and power loss. <italic>I–V</i> curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict <italic>I–V</i> curves for PV modules from their corresponding EL images. The predicted <italic>I–V</i> curves allow the accurate prediction of the maximum power point (MPP), short-circuit current <inline-formula><tex-math>$I_{text {sc}}$</tex-math></inline-formula>, and open-circuit voltage <inline-formula><tex-math>$V_{text {oc}}$</tex-math></inline-formula> with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance <inline-formula><tex-math>$R_{text {s}}$</tex-math></inline-formula> demonstrates a mean error of 5.19%, and the photocurrent <inline-formula><tex-math>$I$</tex-math></inline-formula> a mean error of 0.197%. The shunt resistance <inline-formula><tex-math>$R_{text {sh}}$</tex-math></inline-formula> and dark current <inline-formula><tex-math>$I_{text {o}}$</tex-math></inline-formula> parameters are predicted with larger errors because of their sensitivity to small changes in the <italic>I–V</i> curve.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"557-565"},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11002587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-08DOI: 10.1109/JPHOTOV.2025.3563887
Khaled Alnuaimi;Ameena Saad Al-Sumaiti;Mohamad Alansari;Huai Wang;Khalifa Hassan Al Hosani
The transition to renewable energy sources like photovoltaic (PV) systems is essential for societal progress, counteracting the adverse effects of fossil fuels. However, managing PV systems entails significant challenges and economic implications. PV fault occurrence necessitates swift detection and resolution, exacerbating financial burdens. Effective fault diagnosis relies heavily on data from PV plant monitoring and energy management systems. Historically, PV monitoring relied on manual inspections, but autonomous aerial vehicle (UAV) technology provides a more efficient and comprehensive solution, enhancing safety and offering detailed imagery, scalability, environmental monitoring, and advanced data analytics. This study utilizes deep learning (DL) approaches to monitor the health of the PV, focusing on analyzing UAV-captured scenes. Specifically, this article presents an end-to-end two-stage DL-based health monitoring framework that consists of semantic segmentation model, SegFormer, for isolating solar panels and object detection model, YOLOv8, for identifying anomalies within the PV modules. The proposed framework is validated and compared with state-of-the-art (SOTA) models on a three publicly available UAV-captured datasets. Results show improvements of 25.8% and 1.5% in solar panel segmentation, and 26.6% in solar panel anomaly detection compared with recent SOTA models.
{"title":"Deep Learning-Based Health Monitoring for Photovoltaic Systems","authors":"Khaled Alnuaimi;Ameena Saad Al-Sumaiti;Mohamad Alansari;Huai Wang;Khalifa Hassan Al Hosani","doi":"10.1109/JPHOTOV.2025.3563887","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3563887","url":null,"abstract":"The transition to renewable energy sources like photovoltaic (PV) systems is essential for societal progress, counteracting the adverse effects of fossil fuels. However, managing PV systems entails significant challenges and economic implications. PV fault occurrence necessitates swift detection and resolution, exacerbating financial burdens. Effective fault diagnosis relies heavily on data from PV plant monitoring and energy management systems. Historically, PV monitoring relied on manual inspections, but autonomous aerial vehicle (UAV) technology provides a more efficient and comprehensive solution, enhancing safety and offering detailed imagery, scalability, environmental monitoring, and advanced data analytics. This study utilizes deep learning (DL) approaches to monitor the health of the PV, focusing on analyzing UAV-captured scenes. Specifically, this article presents an end-to-end two-stage DL-based health monitoring framework that consists of semantic segmentation model, SegFormer, for isolating solar panels and object detection model, YOLOv8, for identifying anomalies within the PV modules. The proposed framework is validated and compared with state-of-the-art (SOTA) models on a three publicly available UAV-captured datasets. Results show improvements of 25.8% and 1.5% in solar panel segmentation, and 26.6% in solar panel anomaly detection compared with recent SOTA models.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"577-592"},"PeriodicalIF":2.5,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Among the recent developments in photovoltaic technologies, perovskite solar cells (PSCs) have drawn significant attention, owing to their exceptional power conversion efficiency (PCE), cost-effectiveness, and better optoelectronic characteristics. However, the stability and presence of lead (toxicity) in PSCs remains a major challenge to their commercialization. In this study, we experimentally investigated all-inorganic, lead-free CsGeI3-based PSCs in an n-i-p configuration. The CsGeI3 films were synthesized using a one-step spin-coating technique and their crystallographic characteristics were analyzed. Furthermore, we fabricated and tested different device architectures incorporating CsGeI3 as the absorber layer with various electron transport layers (ETLs), including TiO2, ZnO, and graphene oxide (GO), while employing MoS2 as the hole transport layer. The resulting device structure was Fluorine doped Tin oxide (FTO)/(TiO2/ZnO/GO)/CsGeI3/MoS2/Ni). All fabricated devices demonstrated excellent performance, with the TiO2-based ETL device achieving the highest PCE of 10.79%. In addition, incorporating reduced graphene oxide (rGO) as an interface layer on top of the absorber layer further enhanced photovoltaic performance by approximately 3% across all configurations (achieving outstanding efficiency of 13.57%). The hydrophobic nature and high conductivity of rGO suggest its potential as a promising strategy for improving the stability and efficiency of lead-free PSCs in future applications.
{"title":"CsGeI3 Perovskite-Based Solar Cells for Higher Efficiency and Stability: An Experimental Investigation","authors":"Dolly Kumari;Nilesh Jaiswal;Deepak Punetha;Satyendra Kumar Mourya;Saurabh Kumar Pandey","doi":"10.1109/JPHOTOV.2025.3563882","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3563882","url":null,"abstract":"Among the recent developments in photovoltaic technologies, perovskite solar cells (PSCs) have drawn significant attention, owing to their exceptional power conversion efficiency (PCE), cost-effectiveness, and better optoelectronic characteristics. However, the stability and presence of lead (toxicity) in PSCs remains a major challenge to their commercialization. In this study, we experimentally investigated all-inorganic, lead-free CsGeI<sub>3</sub>-based PSCs in an n-i-p configuration. The CsGeI<sub>3</sub> films were synthesized using a one-step spin-coating technique and their crystallographic characteristics were analyzed. Furthermore, we fabricated and tested different device architectures incorporating CsGeI<sub>3</sub> as the absorber layer with various electron transport layers (ETLs), including TiO<sub>2</sub>, ZnO, and graphene oxide (GO), while employing MoS<sub>2</sub> as the hole transport layer. The resulting device structure was Fluorine doped Tin oxide (FTO)/(TiO<sub>2</sub>/ZnO/GO)/CsGeI<sub>3</sub>/MoS<sub>2</sub>/Ni). All fabricated devices demonstrated excellent performance, with the TiO<sub>2</sub>-based ETL device achieving the highest PCE of 10.79%. In addition, incorporating reduced graphene oxide (rGO) as an interface layer on top of the absorber layer further enhanced photovoltaic performance by approximately 3% across all configurations (achieving outstanding efficiency of 13.57%). The hydrophobic nature and high conductivity of rGO suggest its potential as a promising strategy for improving the stability and efficiency of lead-free PSCs in future applications.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"533-540"},"PeriodicalIF":2.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate modeling of photovoltaic (PV) systems is critical for the design, financial analysis, and monitoring of solar PV plants. For bifacial PV applications, models must additionally offer robust rear-side irradiance algorithms. However, bifacial PV irradiance models have yet to be sufficiently validated for east–west vertically oriented systems, where direct beam solar irradiation swaps at solar noon. Here, we validate five bifacial irradiance models with field data collected in Golden, CO, USA (40°N) and Fairbanks, AK, USA (65°N) for east–west vertical, north–south vertical, and south-tilted arrays. There is no clear best performer among subhourly models; Bifacial_radiance, bifacialVF, the System Advisor Model, and dual-sided energy tracer (DUET) comparably predict seasonal and daily changes in PV production, with root-mean-squared error (RMSE) falling in the range of 11–28% depending on the location and system orientation. PVSyst (v7.4.8), limited by hourly resolution, demonstrates RMSE in the range of 33–45%. The primary causes of high RMSE are similar for all models; using an irradiance cutoff of >100 W/m2, using clear-sky filtering, and removing time stamps with snow, lowers model RMSE to 4–13% for subhourly models and 12–25% for PVSyst. Regular meteorological station servicing is found to further decrease model RMSE by up to 3% abs. in Alaska. Finally, we model bifacial PV systems in over 250 locations between 15 and 85°N, finding that deviations between model-predicted annual insolation tend to be 2–3× higher for vertical PV systems than south-facing fixed-tilt systems. We discuss potential methods for improving vertical PV modeling and provide recommendations for high-quality field data collection in northern environments.
光伏（PV）系统的精确建模对于太阳能光伏电站的设计、财务分析和监测至关重要。对于双面光伏应用，模型必须另外提供强大的后侧辐照度算法。然而，双面PV辐射模型尚未充分验证东西向垂直定向系统，其中直接光束太阳辐射在太阳正午交换。在这里，我们用在美国科罗拉多州Golden（40°N）和美国AK州Fairbanks（65°N）收集的五种双面辐照度模型验证了东西垂直、南北垂直和南倾斜阵列的现场数据。在亚小时模型中没有明确的最佳表现；Bifacial_radiance、bifacialVF、System Advisor Model和dual-sided energy tracer （DUET）可以比较地预测PV产量的季节和每日变化，根据位置和系统方向的不同，均方根误差（RMSE）在11-28%的范围内下降。PVSyst （v7.4.8）受每小时分辨率的限制，显示RMSE在33-45%的范围内。高均方根误差的主要原因在所有模型中都是相似的；使用bbb100 W/m2的辐照度截止值，使用晴空过滤，并去除有雪的时间戳，将亚小时模型的RMSE降低到4-13%，PVSyst降低到12-25%。发现定期的气象站服务使阿拉斯加的模型均方根误差进一步降低了3%。最后，我们在北纬15°至85°之间的250多个地点对双面光伏系统进行了建模，发现垂直光伏系统的模型预测年日照量之间的偏差往往比朝南固定倾斜系统高2 - 3倍。我们讨论了改进垂直PV建模的潜在方法，并为北方环境中高质量的现场数据收集提供了建议。
{"title":"Vertical Bifacial Photovoltaic System Model Validation: Study With Field Data, Various Orientations, and Latitudes","authors":"Erin Tonita;Silvana Ovaitt;Henry Toal;Karin Hinzer;Christopher Pike;Chris Deline","doi":"10.1109/JPHOTOV.2025.3561395","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3561395","url":null,"abstract":"Accurate modeling of photovoltaic (PV) systems is critical for the design, financial analysis, and monitoring of solar PV plants. For bifacial PV applications, models must additionally offer robust rear-side irradiance algorithms. However, bifacial PV irradiance models have yet to be sufficiently validated for east–west vertically oriented systems, where direct beam solar irradiation swaps at solar noon. Here, we validate five bifacial irradiance models with field data collected in Golden, CO, USA (40°N) and Fairbanks, AK, USA (65°N) for east–west vertical, north–south vertical, and south-tilted arrays. There is no clear best performer among subhourly models; Bifacial_radiance, bifacialVF, the System Advisor Model, and dual-sided energy tracer (DUET) comparably predict seasonal and daily changes in PV production, with root-mean-squared error (RMSE) falling in the range of 11–28% depending on the location and system orientation. PVSyst (v7.4.8), limited by hourly resolution, demonstrates RMSE in the range of 33–45%. The primary causes of high RMSE are similar for all models; using an irradiance cutoff of >100 W/m<sup>2</sup>, using clear-sky filtering, and removing time stamps with snow, lowers model RMSE to 4–13% for subhourly models and 12–25% for PVSyst. Regular meteorological station servicing is found to further decrease model RMSE by up to 3% abs. in Alaska. Finally, we model bifacial PV systems in over 250 locations between 15 and 85°N, finding that deviations between model-predicted annual insolation tend to be 2–3× higher for vertical PV systems than south-facing fixed-tilt systems. We discuss potential methods for improving vertical PV modeling and provide recommendations for high-quality field data collection in northern environments.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 4","pages":"600-609"},"PeriodicalIF":2.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10985871","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Methods for fully characterizing the harmonic injection amount of distributed photovoltaic grid connection and combining harmonic constraints with other constraints to accurately evaluate the hosting capacity of photovoltaic integration into distribution networks are of great significance as they ensure the safe and stable operation of distribution networks. Therefore, a novel hosting capacity evaluation method for distributed photovoltaics (PVs) connected in a power system based on the maximum likelihood estimation of harmonics (MLE) is proposed in this study. First, using the likelihood function from the MLE method, the harmonic parameters of distributed photovoltaic injections are optimally estimated, allowing for the accurate assessment of harmonic outputs during photovoltaic grid connections. Furthermore, a harmonic partitioning method is devised; it characterizes the connection degree between nodes in the grid-connected system, and it divides the distribution network into regions. The scenario number in the estimation of hosting capacities is effectively reduced. Finally, a comparison is carried out relative to the conventional hosting capacity. The assessment method proposed in this study considers the harmonic access in the actual distributed PV grid-connected system. An improved harmonic partitioning method is established based on the harmonic injection amount. The evaluation of PV hosting capacities in the region ensures accuracy and reduces calculation times. They provide references for the access capacity of the distribution network.
{"title":"A Novel Hosting Capacity Evaluation Method for Distributed PV Connected in Power System Based on Maximum Likelihood Estimation of Harmonic","authors":"Hongtao Shi;Jiahao Zhu;Yuchao Li;Zhenyang Yan;Tingting Chen;Bai Zhang","doi":"10.1109/JPHOTOV.2025.3541402","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3541402","url":null,"abstract":"Methods for fully characterizing the harmonic injection amount of distributed photovoltaic grid connection and combining harmonic constraints with other constraints to accurately evaluate the hosting capacity of photovoltaic integration into distribution networks are of great significance as they ensure the safe and stable operation of distribution networks. Therefore, a novel hosting capacity evaluation method for distributed photovoltaics (PVs) connected in a power system based on the maximum likelihood estimation of harmonics (MLE) is proposed in this study. First, using the likelihood function from the MLE method, the harmonic parameters of distributed photovoltaic injections are optimally estimated, allowing for the accurate assessment of harmonic outputs during photovoltaic grid connections. Furthermore, a harmonic partitioning method is devised; it characterizes the connection degree between nodes in the grid-connected system, and it divides the distribution network into regions. The scenario number in the estimation of hosting capacities is effectively reduced. Finally, a comparison is carried out relative to the conventional hosting capacity. The assessment method proposed in this study considers the harmonic access in the actual distributed PV grid-connected system. An improved harmonic partitioning method is established based on the harmonic injection amount. The evaluation of PV hosting capacities in the region ensures accuracy and reduces calculation times. They provide references for the access capacity of the distribution network.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 3","pages":"500-508"},"PeriodicalIF":2.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-20DOI: 10.1109/JPHOTOV.2025.3540337
{"title":"Call for Papers for a Special Issue of IEEE Transactions on Electron Devices","authors":"","doi":"10.1109/JPHOTOV.2025.3540337","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3540337","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 2","pages":"375-376"},"PeriodicalIF":2.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10897236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-20DOI: 10.1109/JPHOTOV.2025.3540335
{"title":"Call for Papers for a Special Issue of IEEE Transactions on Materials for Electron Devices","authors":"","doi":"10.1109/JPHOTOV.2025.3540335","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3540335","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 2","pages":"373-374"},"PeriodicalIF":2.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10897241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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