Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

Logothetis, Stavros-Andreas; Salamalikis, Vasileios; Nouri, Bijan; Remund, Jan; Zarzalejo, Luis F.; Xie, Yu; Wilbert, Stefan; Ntavelis, Evangelos; Nou, Julien; Hendrikx, Niels; Visser, Lennard; Sengupta, Manajit; Pó, Mário; Chauvin, Remi; Grieu, Stephane; Blum, Niklas; Sark, Wilfried van; Kazantzidis, Andreas

doi:10.3390/en15176191

Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

Journal Article · Thu Aug 25 00:00:00 EDT 2022 · Energies

DOI:https://doi.org/10.3390/en15176191· OSTI ID:1891280

Logothetis, Stavros-Andreas ^[1]; ^[1]; Nouri, Bijan ^[2]; Remund, Jan ^[3]; Zarzalejo, Luis F. ^[4]; Xie, Yu ^[5]; Wilbert, Stefan ^[2]; ^[6]; Nou, Julien ^[7]; Hendrikx, Niels ^[8]; ^[8]; ^[5]; ^[9]; Chauvin, Remi ^[10]; ^[11]; ^[2]; ^[8]; Kazantzidis, Andreas ^[1]

University of Patras (Greece)
German Aerospace Center (DLR), Almeria (Spain)
Meteotest, Bern (Switzerland)
Research Centre for Energy, Environment and Technology (CIEMAT), Madrid (Spain)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
CSEM Center Alpnach (Switzerland)
Centre National de la Recherche Scientifique (CNRS) (France)
Utrecht University (Netherlands)
EKO Instruments, The Hague (Netherlands)
PROMECA Ingénierie, Domène (France)
University of Perpignan (France)

Solar forecasting constitutes a critical tool for operating, producing and storing generated power from solar farms. In the framework of the International Energy Agency’s Photovoltaic Power Systems Program Task 16, the solar irradiance nowcast algorithms, based on five all-sky imagers (ASIs), are used to investigate the feasibility of ASIs to foresee ramp events. ASIs 1–2 and ASIs 3–5 can capture the true ramp events by 26.0–51.0% and 49.0–92.0% of the cases, respectively. ASIs 1–2 provided the lowest (<10.0%) falsely documented ramp events while ASIs 3–5 recorded false ramp events up to 85.0%. On the other hand, ASIs 3–5 revealed the lowest falsely documented no ramp events (8.0–51.0%). ASIs 1–2 are developed to provide spatial solar irradiance forecasts and have been delimited only to a small area for the purposes of this benchmark, which penalizes these approaches. These findings show that ASI-based nowcasts could be considered as a valuable tool for predicting solar irradiance ramp events for a variety of solar energy technologies. The combination of physical and deep learning-based methods is identified as a potential approach to further improve the ramp event forecasts.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: European Union (EU); German Federal Ministry for Economic Affairs and Technology Climate Action; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1891280

Report Number(s):: NREL/JA-5D00-84191; MainId:84964; UUID:d8def530-8968-463b-a6c3-0cad908aa476; MainAdminID:67709

Journal Information:: Energies, Journal Name: Energies Journal Issue: 17 Vol. 15; ISSN 1996-1073

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

References (24)

A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy Nouri, Bijan; Blum, Niklas; Wilbert, Stefan Solar RRL, Vol. 6, Issue 5 https://doi.org/10.1002/solr.202100442	journal	August 2021
Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting Kong, Weicong; Jia, Youwei; Dong, Zhao Yang Applied Energy, Vol. 280 https://doi.org/10.1016/j.apenergy.2020.115875	journal	December 2020
A new approach to the real-time assessment of the clear-sky direct normal irradiance Nou, Julien; Chauvin, Rémi; Thil, Stéphane Applied Mathematical Modelling, Vol. 40, Issue 15-16 https://doi.org/10.1016/j.apm.2016.03.022	journal	August 2016
Cloud Detection Methodology Based on a Sky-imaging System Chauvin, R.; Nou, J.; Thil, S. Energy Procedia, Vol. 69 https://doi.org/10.1016/j.egypro.2015.03.198	journal	May 2015
Intra-hour irradiance forecasting techniques for solar power integration: A review Chu, Yinghao; Li, Mengying; Coimbra, Carlos F. M. iScience, Vol. 24, Issue 10 https://doi.org/10.1016/j.isci.2021.103136	journal	October 2021
Identification of periods of clear sky irradiance in time series of GHI measurements Reno, Matthew J.; Hansen, Clifford W. Renewable Energy, Vol. 90 https://doi.org/10.1016/j.renene.2015.12.031	journal	May 2016
Characterizing and analyzing ramping events in wind power, solar power, load, and netload Cui, Mingjian; Zhang, Jie; Feng, Cong Renewable Energy, Vol. 111 https://doi.org/10.1016/j.renene.2017.04.005	journal	October 2017
Forecasting of solar power ramp events: A post-processing approach Abuella, Mohamed; Chowdhury, Badrul Renewable Energy, Vol. 133 https://doi.org/10.1016/j.renene.2018.09.005	journal	April 2019
Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements Caldas, M.; Alonso-Suárez, R. Renewable Energy, Vol. 143 https://doi.org/10.1016/j.renene.2019.05.069	journal	December 2019
Real-time forecasting of solar irradiance ramps with smart image processing Chu, Yinghao; Pedro, Hugo T. C.; Li, Mengying Solar Energy, Vol. 114 https://doi.org/10.1016/j.solener.2015.01.024	journal	April 2015
Towards a standardized procedure to assess solar forecast accuracy: A new ramp and time alignment metric Vallance, Loïc; Charbonnier, Bruno; Paul, Nicolas Solar Energy, Vol. 150 https://doi.org/10.1016/j.solener.2017.04.064	journal	July 2017
Cloud height and tracking accuracy of three all sky imager systems for individual clouds Nouri, B.; Kuhn, P.; Wilbert, S. Solar Energy, Vol. 177 https://doi.org/10.1016/j.solener.2018.10.079	journal	January 2019
A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique Kumler, Andrew; Xie, Yu; Zhang, Yingchen Solar Energy, Vol. 177 https://doi.org/10.1016/j.solener.2018.11.046	journal	January 2019
Determination of cloud transmittance for all sky imager based solar nowcasting Nouri, B.; Wilbert, S.; Segura, L. Solar Energy, Vol. 181 https://doi.org/10.1016/j.solener.2019.02.004	journal	March 2019
A way to increase parabolic trough plant yield by roughly 2% using all sky imager derived DNI maps Nouri, Bijan; Noureldin, Kareem; Schlichting, Tim SOLARPACES 2019: International Conference on Concentrating Solar Power and Chemical Energy Systems https://doi.org/10.1063/5.0028667	conference	January 2020
Identifying Wind and Solar Ramping Events Florita, Anthony; Hodge, Bri-Mathias; Orwig, Kirsten 2013 IEEE Green Technologies Conference (GreenTech 2013), 2013 IEEE Green Technologies Conference (GreenTech) https://doi.org/10.1109/GreenTech.2013.30	conference	April 2013
Forecasting Solar Power Ramp Events Using Machine Learning Classification Techniques Abuella, Mohamed; Chowdhury, Badrul 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG) https://doi.org/10.1109/PEDG.2018.8447599	conference	June 2018
Understanding wind ramp events through analysis of historical data Kamath, Chandrika IEEE PES T&D 2010 https://doi.org/10.1109/TDC.2010.5484508	conference	April 2010
A Computational Approach to Edge Detection Canny, John IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, Issue 6 https://doi.org/10.1109/TPAMI.1986.4767851	journal	November 1986
An Optimized Swinging Door Algorithm for Identifying Wind Ramping Events Cui, Mingjian; Zhang, Jie; Florita, Anthony R. IEEE Transactions on Sustainable Energy, Vol. 7, Issue 1 https://doi.org/10.1109/TSTE.2015.2477244	journal	January 2016
A Wind Energy Ramp Tool and Metric for Measuring the Skill of Numerical Weather Prediction Models Bianco, Laura; Djalalova, Irina V.; Wilczak, James M. Weather and Forecasting, Vol. 31, Issue 4 https://doi.org/10.1175/WAF-D-15-0144.1	journal	August 2016
Using Cloud Classification to Model Solar Variability Reno, Matthew J.; Stein, Joshua S. Unpublished https://doi.org/10.13140/2.1.3150.8488	text	January 2013
Applying self-supervised learning for semantic cloud segmentation of all-sky images Fabel, Yann; Nouri, Bijan; Wilbert, Stefan Atmospheric Measurement Techniques, Vol. 15, Issue 3 https://doi.org/10.5194/amt-15-797-2022	journal	February 2022
McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions Lefèvre, M.; Oumbe, A.; Blanc, P. Atmospheric Measurement Techniques, Vol. 6, Issue 9 https://doi.org/10.5194/amt-6-2403-2013	journal	January 2013

Similar Records

Benchmarking of Solar Irradiance Nowcast Performance Derived from All-Sky Imagers

Journal Article · Sat Sep 03 00:00:00 EDT 2022 · Renewable Energy · OSTI ID:1890144

Advanced Solar and Load Forecasting Incorporating HD Sky Imaging (Phase III)

Technical Report · Mon Apr 18 00:00:00 EDT 2022 · OSTI ID:1873655

Measuring the impact of additional instrumentation on the skill of numerical weather prediction models at forecasting wind ramp events during the first Wind Forecast Improvement Project (WFIP)

Journal Article · Wed May 29 20:00:00 EDT 2019 · Wind Energy · OSTI ID:1523668

Related Subjects

14 SOLAR ENERGY
all-sky imagers
forecasting
ramp events
solar irradiance ramp event forecasting

Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

Citation Formats

References (24)

Similar Records

Related Subjects