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Title: Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting

Abstract

Irradiance received on the earth's surface is the main factor that affects the output power of solar PV plants, and is chiefly determined by the cloud distribution seen in a ground-based sky image at the corresponding moment in time. It is the foundation for those linear extrapolation-based ultra-short-term solar PV power forecasting approaches to obtain the cloud distribution in future sky images from the accurate calculation of cloud motion displacement vectors (CMDVs) by using historical sky images. Theoretically, the CMDV can be obtained from the coordinate of the peak pulse calculated from a Fourier phase correlation theory (FPCT) method through the frequency domain information of sky images. The peak pulse is significant and unique only when the cloud deformation between two consecutive sky images is slight enough, which is likely possible for a very short time interval (such as 1?min or shorter) with common changes in the speed of cloud. Sometimes, there will be more than one pulse with similar values when the deformation of the clouds between two consecutive sky images is comparatively obvious under fast changing cloud speeds. This would probably lead to significant errors if the CMDVs were still only obtained from the single coordinate of themore » peak value pulse. However, the deformation estimation of clouds between two images and its influence on FPCT-based CMDV calculations are terrifically complex and difficult because the motion of clouds is complicated to describe and model. Therefore, to improve the accuracy and reliability under these circumstances in a simple manner, an image-phase-shift-invariance (IPSI) based CMDV calculation method using FPCT is proposed for minute time scale solar power forecasting. First, multiple different CMDVs are calculated from the corresponding consecutive images pairs obtained through different synchronous rotation angles compared to the original images by using the FPCT method. Second, the final CMDV is generated from all of the calculated CMDVs through a centroid iteration strategy based on its density and distance distribution. Third, the influence of different rotation angle resolution on the final CMDV is analyzed as a means of parameter estimation. Simulations under various scenarios including both thick and thin clouds conditions indicated that the proposed IPSI-based CMDV calculation method using FPCT is more accurate and reliable than the original FPCT method, optimal flow (OF) method, and particle image velocimetry (PIV) method.« less

Authors:
ORCiD logo [1];  [2];  [3];  [2];  [4];  [5];  [6]
+ Show Author Affiliations
  1. North China Electric Power Univ., Baoding (China); Univ. of Illinois, Urbana-Champaign, IL (United States)
  2. North China Electric Power Univ., Baoding (China)
  3. China Electric Power Research Inst., Beijing (China)
  4. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  5. Univ. of Beira Interior, Covilha (Portugal)
  6. Univ. of Beira Interior, Covilha (Portugal); Univ. of Lisbon (Portugal); Univ. of Porto (Portugal)
Publication Date:
Research Org.:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1424581
Report Number(s):
NREL/JA-5D00-70164
Journal ID: ISSN 0196-8904
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Energy Conversion and Management
Additional Journal Information:
Journal Volume: 157; Journal Issue: C; Journal ID: ISSN 0196-8904
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; phase correlation; Fourier transform; cloud motion displacement vector; sky image; solar forecasting

Citation Formats

Wang, Fei, Zhen, Zhao, Liu, Chun, Mi, Zengqiang, Hodge, Bri-Mathias, Shafie-khah, Miadreza, and Catalao, Joao P. S. Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting. United States: N. p., 2017. Web. doi:10.1016/j.enconman.2017.11.080.
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Wang, Fei, Zhen, Zhao, Liu, Chun, Mi, Zengqiang, Hodge, Bri-Mathias, Shafie-khah, Miadreza, & Catalao, Joao P. S. Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting. United States. https://doi.org/10.1016/j.enconman.2017.11.080
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Wang, Fei, Zhen, Zhao, Liu, Chun, Mi, Zengqiang, Hodge, Bri-Mathias, Shafie-khah, Miadreza, and Catalao, Joao P. S. Mon . "Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting". United States. https://doi.org/10.1016/j.enconman.2017.11.080. https://www.osti.gov/servlets/purl/1424581.
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@article{osti_1424581,
title = {Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting},
author = {Wang, Fei and Zhen, Zhao and Liu, Chun and Mi, Zengqiang and Hodge, Bri-Mathias and Shafie-khah, Miadreza and Catalao, Joao P. S.},
abstractNote = {Irradiance received on the earth's surface is the main factor that affects the output power of solar PV plants, and is chiefly determined by the cloud distribution seen in a ground-based sky image at the corresponding moment in time. It is the foundation for those linear extrapolation-based ultra-short-term solar PV power forecasting approaches to obtain the cloud distribution in future sky images from the accurate calculation of cloud motion displacement vectors (CMDVs) by using historical sky images. Theoretically, the CMDV can be obtained from the coordinate of the peak pulse calculated from a Fourier phase correlation theory (FPCT) method through the frequency domain information of sky images. The peak pulse is significant and unique only when the cloud deformation between two consecutive sky images is slight enough, which is likely possible for a very short time interval (such as 1?min or shorter) with common changes in the speed of cloud. Sometimes, there will be more than one pulse with similar values when the deformation of the clouds between two consecutive sky images is comparatively obvious under fast changing cloud speeds. This would probably lead to significant errors if the CMDVs were still only obtained from the single coordinate of the peak value pulse. However, the deformation estimation of clouds between two images and its influence on FPCT-based CMDV calculations are terrifically complex and difficult because the motion of clouds is complicated to describe and model. Therefore, to improve the accuracy and reliability under these circumstances in a simple manner, an image-phase-shift-invariance (IPSI) based CMDV calculation method using FPCT is proposed for minute time scale solar power forecasting. First, multiple different CMDVs are calculated from the corresponding consecutive images pairs obtained through different synchronous rotation angles compared to the original images by using the FPCT method. Second, the final CMDV is generated from all of the calculated CMDVs through a centroid iteration strategy based on its density and distance distribution. Third, the influence of different rotation angle resolution on the final CMDV is analyzed as a means of parameter estimation. Simulations under various scenarios including both thick and thin clouds conditions indicated that the proposed IPSI-based CMDV calculation method using FPCT is more accurate and reliable than the original FPCT method, optimal flow (OF) method, and particle image velocimetry (PIV) method.},
doi = {10.1016/j.enconman.2017.11.080},
journal = {Energy Conversion and Management},
number = C,
volume = 157,
place = {United States},
year = {Mon Dec 18 00:00:00 EST 2017},
month = {Mon Dec 18 00:00:00 EST 2017}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1016/j.enconman.2017.11.080
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Cited by: 85 works
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Figures / Tables:

Figure 1 Figure 1: Sky image based UST-SPPF process.
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    Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting
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    • Wang, Fei; Zhen, Zhao; Wang, Bo
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    • DOI: 10.3390/app8010028

    A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features
    journal, July 2018

    • Wang, Fei; Li, Kangping; Wang, Xinkang
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    • DOI: 10.3390/en11071750

    Study on the Incentives Mechanism for the Development of Distributed Photovoltaic Systems from a Long-Term Perspective
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    • Sun, Chenjun; Mi, Zengqiang; Ren, Hui
    • Energies, Vol. 11, Issue 5
    • DOI: 10.3390/en11051291
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