The DNICast team

A.Kazantzidis

Laboratory of Atmospheric Physics, University of Patras, Greece

Solar resource and forecasting needs

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)

DNICast: Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies

DNICast at a glance

DNICast at a glance

DNICast at a glance: the testbed

The basic steps for solar resource and

forecasting

Water vapor?

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

Difference (%) between the daily DNI and the corresponding monthly mean, for 5 areas in Europe

Aerosol optical properties: why needed?

What aerosol properties can we get?

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%

Trend Analysis

Highest Trend

Sig. Trend for AERONETNon Sig. Trend for MACC

Estimation of impact on clear-sky DNI

Relative MBE (%)

Estimation of impact on clear-sky DNI

Relative MBE (%) Including corrections on AODs

23/7/2014 24/7/2014

AOD(500) = 0.09 AOD(500) = 0.43

The DNICast approach to estimate AOD

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

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).

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

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

• Meteotest method using GFS based Weather Research and Forecasting model for wind fields

DNICast: Cloud properties for solar resource

and forecasting

• 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

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).

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

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

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!