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

Journal Article · · Energy Conversion and Management
ORCiD logo [1];  [2];  [3];  [2];  [4];  [5];  [6]
  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)
+ Show Author Affiliations

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.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1424581
Report Number(s):
NREL/JA-5D00-70164
Journal Information:
Energy Conversion and Management, Vol. 157, Issue C; ISSN 0196-8904
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 94 works
Citation information provided by
Web of Science

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Cited By (6)

Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting journal August 2018
An Alternative Internet-of-Things Solution Based on LoRa for PV Power Plants: Data Monitoring and Management journal March 2019
Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting journal December 2017
A guideline to solar forecasting research practice: Reproducible, operational, probabilistic or physically-based, ensemble, and skill (ROPES) journal March 2019
A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features journal July 2018
Study on the Incentives Mechanism for the Development of Distributed Photovoltaic Systems from a Long-Term Perspective journal May 2018

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14 SOLAR ENERGY
24 POWER TRANSMISSION AND DISTRIBUTION
phase correlation
Fourier transform
cloud motion displacement vector
sky image
solar forecasting