Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Li, Baojie; Chen, Xin; Jain, Anubhav

doi:10.1016/j.renene.2024.121493

Title: Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Journal Article · 29 September 2024 · Renewable Energy

DOI: https://doi.org/10.1016/j.renene.2024.121493 · OSTI ID:2459359

Li, Baojie ^[1];

^[1];

^[1]

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Power modeling, widely applied for health monitoring and power prediction, is crucial for the efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power modeling uses a physical equivalent circuit model, with the core challenge being the estimation of model parameters. Traditional parameter estimation either relies on datasheet information, which does not reflect the system's current health status, especially for degraded PV systems, or requires additional I-V characterization, which is generally unavailable for large-scale PV systems. Thus, we build upon our previously developed tool, PV-Pro (originally proposed for degradation analysis), to enhance its application for power modeling of degraded PV systems. PV-Pro extracts model parameters from production data without requiring I-V characterization. This dynamic model, periodically updated, can closely capture the actual degradation status, enabling precise power modeling. PV-Pro is compared with popular power modeling techniques, including persistence, nominal physical, and various machine learning models. The results indicate that PV-Pro achieves outstanding power modeling performance, with an average nMAE of 1.4 % across four field-degraded PV systems, reducing error by 17.6 % compared to the best alternative technique. Furthermore, PV-Pro demonstrates robustness across different seasons and severities of degradation. The tool is available as a Python package at https://github.com/DuraMAT/pvpro.

View Journal Article

Cite

Export

Save

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: AC02-05CH11231; NA0003525

OSTI ID:: 2459359

Journal Information:: Renewable Energy, Journal Name: Renewable Energy Vol. 236; ISSN 0960-1481

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (46)

Real-time monitoring and diagnosis of photovoltaic system degradation only using maximum power point-the Suns-Vmp method Sun, Xingshu; Chavali, Raghu Vamsi Krishna; Alam, Muhammad Ashraful Progress in Photovoltaics: Research and Applications, Vol. 27, Issue 1 https://doi.org/10.1002/pip.3043	journal	July 2018
On the limited memory BFGS method for large scale optimization Liu, Dong C.; Nocedal, Jorge Mathematical Programming, Vol. 45, Issue 1-3 https://doi.org/10.1007/BF01589116	journal	August 1989
A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network Wang, Kejun; Qi, Xiaoxia; Liu, Hongda Applied Energy, Vol. 251 https://doi.org/10.1016/j.apenergy.2019.113315	journal	October 2019
Extensive comparison of physical models for photovoltaic power forecasting Mayer, Martin János; Gróf, Gyula Applied Energy, Vol. 283 https://doi.org/10.1016/j.apenergy.2020.116239	journal	February 2021
Photovoltaic power forecast based on satellite images considering effects of solar position Si, Zhiyuan; Yang, Ming; Yu, Yixiao Applied Energy, Vol. 302 https://doi.org/10.1016/j.apenergy.2021.117514	journal	November 2021
Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods AlShafeey, Mutaz; Csáki, Csaba Energy Reports, Vol. 7 https://doi.org/10.1016/j.egyr.2021.10.125	journal	November 2021
Trends and gaps in photovoltaic power forecasting with machine learning Alcañiz, Alba; Grzebyk, Daniel; Ziar, Hesan Energy Reports, Vol. 9 https://doi.org/10.1016/j.egyr.2022.11.208	journal	December 2023
A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework Wang, Fei; Xuan, Zhiming; Zhen, Zhao Energy Conversion and Management, Vol. 212 https://doi.org/10.1016/j.enconman.2020.112766	journal	May 2020
Random forest solar power forecast based on classification optimization Liu, Da; Sun, Kun Energy, Vol. 187 https://doi.org/10.1016/j.energy.2019.115940	journal	November 2019
Multi-scale solar radiation and photovoltaic power forecasting with machine learning algorithms in urban environment: A state-of-the-art review Tian, Jia; Ooka, Ryozo; Lee, Doyun Journal of Cleaner Production, Vol. 426 https://doi.org/10.1016/j.jclepro.2023.139040	journal	November 2023
Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest Larson, David P.; Nonnenmacher, Lukas; Coimbra, Carlos F. M. Renewable Energy, Vol. 91 https://doi.org/10.1016/j.renene.2016.01.039	journal	June 2016
Machine learning methods for solar radiation forecasting: A review Voyant, Cyril; Notton, Gilles; Kalogirou, Soteris Renewable Energy, Vol. 105 https://doi.org/10.1016/j.renene.2016.12.095	journal	May 2017
Physical and hybrid methods comparison for the day ahead PV output power forecast Ogliari, Emanuele; Dolara, Alberto; Manzolini, Giampaolo Renewable Energy, Vol. 113 https://doi.org/10.1016/j.renene.2017.05.063	journal	December 2017
Deep learning neural networks for short-term photovoltaic power forecasting Mellit, A.; Pavan, A. Massi; Lughi, V. Renewable Energy, Vol. 172 https://doi.org/10.1016/j.renene.2021.02.166	journal	July 2021
Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution Visser, Lennard; AlSkaif, Tarek; van Sark, Wilfried Renewable Energy, Vol. 183 https://doi.org/10.1016/j.renene.2021.10.102	journal	January 2022
Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study Pierro, Marco; Gentili, Damiano; Liolli, Fabio Romano Renewable Energy, Vol. 189 https://doi.org/10.1016/j.renene.2022.03.041	journal	April 2022
Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model Limouni, Tariq; Yaagoubi, Reda; Bouziane, Khalid Renewable Energy, Vol. 205 https://doi.org/10.1016/j.renene.2023.01.118	journal	March 2023
Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations Brester, Christina; Kallio-Myers, Viivi; Lindfors, Anders V. Renewable Energy, Vol. 207 https://doi.org/10.1016/j.renene.2023.02.130	journal	May 2023
Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models Sarmas, Elissaios; Spiliotis, Evangelos; Stamatopoulos, Efstathios Renewable Energy, Vol. 216 https://doi.org/10.1016/j.renene.2023.118997	journal	November 2023
Development of a novel solar PV module model for reliable power prediction under real outdoor conditions Kumar, Manish; Malik, Prashant; Chandel, Rahul Renewable Energy, Vol. 217 https://doi.org/10.1016/j.renene.2023.119224	journal	November 2023
Analysis and prediction of the performance and reliability of PV modules installed in harsh climates: Case study Iraq Hameed, Mohammed Adnan; Kaaya, Ismail; Al-Jbori, Mudhafar Renewable Energy, Vol. 228 https://doi.org/10.1016/j.renene.2024.120577	journal	July 2024
Solar photovoltaic system modeling and performance prediction Ma, Tao; Yang, Hongxing; Lu, Lin Renewable and Sustainable Energy Reviews, Vol. 36 https://doi.org/10.1016/j.rser.2014.04.057	journal	August 2014
Solar cell parameters extraction based on single and double-diode models: A review Humada, Ali M.; Hojabri, Mojgan; Mekhilef, Saad Renewable and Sustainable Energy Reviews, Vol. 56 https://doi.org/10.1016/j.rser.2015.11.051	journal	April 2016
Parameter estimation of solar photovoltaic (PV) cells: A review Jordehi, A. Rezaee Renewable and Sustainable Energy Reviews, Vol. 61 https://doi.org/10.1016/j.rser.2016.03.049	journal	August 2016
Monitoring system for photovoltaic plants: A review Madeti, Siva Ramakrishna; Singh, S. N. Renewable and Sustainable Energy Reviews, Vol. 67 https://doi.org/10.1016/j.rser.2016.09.088	journal	January 2017
The National Solar Radiation Data Base (NSRDB) Sengupta, Manajit; Xie, Yu; Lopez, Anthony Renewable and Sustainable Energy Reviews, Vol. 89 https://doi.org/10.1016/j.rser.2018.03.003	journal	June 2018
A comprehensive review on protection challenges and fault diagnosis in PV systems Pillai, Dhanup S.; Rajasekar, N. Renewable and Sustainable Energy Reviews, Vol. 91 https://doi.org/10.1016/j.rser.2018.03.082	journal	August 2018
A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization Ahmed, R.; Sreeram, V.; Mishra, Y. Renewable and Sustainable Energy Reviews, Vol. 124 https://doi.org/10.1016/j.rser.2020.109792	journal	May 2020
Irradiance-to-power conversion based on physical model chain: An application on the optimal configuration of multi-energy microgrid in cold climate Wang, Wenting; Yang, Dazhi; Huang, Nantian Renewable and Sustainable Energy Reviews, Vol. 161 https://doi.org/10.1016/j.rser.2022.112356	journal	June 2022
Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction Markovics, Dávid; Mayer, Martin János Renewable and Sustainable Energy Reviews, Vol. 161 https://doi.org/10.1016/j.rser.2022.112364	journal	June 2022
Benefits of physical and machine learning hybridization for photovoltaic power forecasting Mayer, Martin János Renewable and Sustainable Energy Reviews, Vol. 168 https://doi.org/10.1016/j.rser.2022.112772	journal	October 2022
Improvement and validation of a model for photovoltaic array performance De Soto, W.; Klein, S. A.; Beckman, W. A. Solar Energy, Vol. 80, Issue 1 https://doi.org/10.1016/j.solener.2005.06.010	journal	January 2006
Comparison of different physical models for PV power output prediction Dolara, Alberto; Leva, Sonia; Manzolini, Giampaolo Solar Energy, Vol. 119 https://doi.org/10.1016/j.solener.2015.06.017	journal	September 2015
Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data Wolff, Björn; Kühnert, Jan; Lorenz, Elke Solar Energy, Vol. 135 https://doi.org/10.1016/j.solener.2016.05.051	journal	October 2016
Review of photovoltaic power forecasting Antonanzas, J.; Osorio, N.; Escobar, R. Solar Energy, Vol. 136 https://doi.org/10.1016/j.solener.2016.06.069	journal	October 2016
Methodology of Köppen-Geiger-Photovoltaic climate classification and implications to worldwide mapping of PV system performance Ascencio-Vásquez, Julián; Brecl, Kristijan; Topič, Marko Solar Energy, Vol. 191 https://doi.org/10.1016/j.solener.2019.08.072	journal	October 2019
Modeling of PV system and parameter extraction based on experimental data: Review and investigation Humada, Ali M.; Darweesh, Salih Y.; Mohammed, Khalid G. Solar Energy, Vol. 199 https://doi.org/10.1016/j.solener.2020.02.068	journal	March 2020
Determining circuit model parameters from operation data for PV system degradation analysis: PVPRO Li, Baojie; Karin, Todd; Meyers, Bennet E. Solar Energy, Vol. 254 https://doi.org/10.1016/j.solener.2023.03.011	journal	April 2023
A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods Pedro, Hugo T. C.; Larson, David P.; Coimbra, Carlos F. M. Journal of Renewable and Sustainable Energy, Vol. 11, Issue 3 https://doi.org/10.1063/1.5094494	journal	May 2019
Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison Gaboitaolelwe, Jwaone; Zungeru, Adamu Murtala; Yahya, Abid IEEE Access, Vol. 11 https://doi.org/10.1109/ACCESS.2023.3270041	journal	January 2023
pvlib python: a python package for modeling solar energy systems F. Holmgren, William; W. Hansen, Clifford; A. Mikofski, Mark Journal of Open Source Software, Vol. 3, Issue 29 https://doi.org/10.21105/joss.00884	journal	September 2018
Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements Massaoudi, Mohamed; Chihi, Ines; Sidhom, Lilia Energies, Vol. 14, Issue 13 https://doi.org/10.3390/en14133992	journal	July 2021
Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection Niccolai, Alessandro; Ogliari, Emanuele; Nespoli, Alfredo Energies, Vol. 15, Issue 24 https://doi.org/10.3390/en15249433	journal	December 2022
Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity Monteiro, Claudio; Santos, Tiago; Fernandez-Jimenez, L. Energies, Vol. 6, Issue 5 https://doi.org/10.3390/en6052624	journal	May 2013
Real-Time Monitoring System for a Utility-Scale Photovoltaic Power Plant Moreno-Garcia, Isabel; Palacios-Garcia, Emilio; Pallares-Lopez, Victor Sensors, Vol. 16, Issue 6 https://doi.org/10.3390/s16060770	journal	May 2016
Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM Wang, Jidong; Ran, Ran; Song, Zhilin Journal of Electrical Engineering and Technology, Vol. 12, Issue 1 https://doi.org/10.5370/JEET.2017.12.1.064	journal	January 2017

Similar Records

Determining circuit model parameters from operation data for PV system degradation analysis: $\mathrm{PVPRO}$

Journal Article · 2023 · Solar Energy · OSTI ID:1968239

Li, Baojie; Karin, Todd; Meyers, Bennet E.; +6 more

Fault detection in PV systems

Journal Article · 2017 · Applied Solar Energy (Online) · OSTI ID:22793184

Immanuel Selvakumar, A.

Outdoor PV Module Degradation of Current-Voltage Parameters: Preprint

Conference · 2012 · OSTI ID:1038302

Smith, Ryan M.; Jordan, Dirk C.; Kurtz, Sarah R.

Related Subjects

14 SOLAR ENERGY
Health monitoring
Machine learning
PV system
Photovoltaic
Physical model
Power modeling
Power prediction

Title: Power modeling of degraded PV systems: Case studies using a dynamically updated physical model (PV-Pro)

Citation Formats

References (46)

Similar Records

Related Subjects