CAMS GA Solar resource and forecasting needs by Kazantzidis
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Transcript of CAMS GA Solar resource and forecasting needs by Kazantzidis
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The DNICast team
A.Kazantzidis
Laboratory of Atmospheric Physics, University of Patras, Greece
Solar resource and forecasting needs
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Presentation outlook
• DNICast at glance
• Needs for solar resource and forecasting: Aerosol optical depth, Cloud properties, Enhancements due to cloudiness
• A tip for future use of ultraviolet radiation products (in liaison with health scientific community)
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DNICast: Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies
DNICast at a glance
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DNICast at a glance
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DNICast at a glance: the testbed
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The basic steps for solar resource and
forecasting
Water vapor?
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Aerosol optical properties: why needed?Concentrated Solar Technologies are dependent from DNI. Aerosols variability can be the major source ofDNI variability.
Differences (%) between the mean DNI for each year (2000-2012) and the average for the 13-year period. Only data for May-September are considered
Nikitidou et al., Renewable Energy, 2014
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Difference (%) between the daily DNI and the corresponding monthly mean, for 5 areas in Europe
Aerosol optical properties: why needed?
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What aerosol properties can we get?
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Goodness of Fit Statistics (AERONET vs MACC)
AOD values from AERONET and MACC are compared in terms ofMBE, RMSE and CC (550nm)
|MBE|<20%, RMSE<30%
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Trend Analysis
Highest Trend
Sig. Trend for AERONETNon Sig. Trend for MACC
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Estimation of impact on clear-sky DNI
Relative MBE (%)
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Estimation of impact on clear-sky DNI
Relative MBE (%) Including corrections on AODs
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23/7/2014 24/7/2014
AOD(500) = 0.09 AOD(500) = 0.43
The DNICast approach to estimate AOD
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440nm 500nm 675nm
Mean difs -0.009 0 -0.01
Median difs -0.004 0 -0.01
Std 0.03 0.02 0.02
The DNICast approach to estimate AOD
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Cloud properties for solar forecasting
Previous study has determined homogenous spatial clusters ofsimilar CCI variability using cluster analysis and cluster validityassessment methodologies (Zagouras et al., Solar Energy,2013,2014).
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CCI forecast: Seasonal Analysis (1)
Spatial distribution of the seasonal average mean MSE error (per pixel) of CMF between the predicted and the measured CCI values during winter
∑=
−=
n
i
iiPP
nMSE
1
2)ˆ(1
• Measures “fit-quality”
• Squaring emphasizes larger differences
Mean Squared Error
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CCI forecast: Seasonal Analysis (2)Spatial distribution of the seasonal average mean MSE error (per pixel) of CCI between the predicted and the measured CMF values during summer
• Smaller error than in winter
• Error distinguished between land-sites and sea
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• Meteotest method using GFS based Weather Research and Forecasting model for wind fields
DNICast: Cloud properties for solar resource
and forecasting
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• DLR-PA method using Meteosat Rapid-Scan-Modus HRV channel
• DLR-DFD method using a sectoral method based on MeteosatSecond Generation imagery
DNICast: Cloud properties for solar resource
and forecasting
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Enhancements due to cloudiness
A typical sky image (left panel) and the three selected areas (upper, middle, low) that correspond to different parts of the sky and solar zenith angles (right panel).
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The CRE as a function of the cosine of the solar zenith angle and the ratio of upper cloud cover to the total one. The upper clouds correspond to zenith angles 0 to 45o (cos45o=0.707) and this area is highlighted.
Tzoumanikas et al., Renewable Energy, 2016
Enhancements due to cloudiness
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Calculate end summer 25(OH)D (A)
required for 95% to remain ≥ 25 nmol/L
by end winter
Calculate monthly spend of 25(OH)D
(B)
Calculate UV dose (C) required to increase
25(OH)D from winter low to A, account for
spend B
Determine safe midday exposure time (no
sunburn), D
Calculate dose in time D for every day March
- Sept. Integrate to summer total E.
Is E ≥ C across UK?
A tip for future use of ultraviolet radiation products
(in liaison with health scientific community)
• UV risks and benefits are highly correlated to ambient UV exposure• UV effects are dependent of human behavior, skin type and age
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Acknowledgements
All Colleagues from the DNICast project (www.dnicast-project.net/)
The Hellenic Network of Solar Energy
Lab. of Atmospheric Physics
University of Patras, Greece
www.atmosphere-upatras.grThank you!