Solar irradiance forecasting using ground-based sky images

SIOG 236

06/07/2018

Cristian Cortes A.

1

Contents

• Motivation

• Sky cameras

• Data Processing

• Limitations

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Motivation• Why we need forecast?

• Electricity generated from solar energy is related with weather conditions (variability)

• Higher levels of solar power on the electric grid can be problematic (curtailment)

[R. Escobar, 2016]

[ T. Stoffel & S. Wilcox, 2004]3

Samples of sky images

[Kleissl, 2013]4TSI USI

Sky cameras available

Name Developer Image formatCommercially

available

TSI-800 YES JPG ???

USI UCSD PNG Yes

SW-02 All sky imager Steady-Sun JPG Yes

ASI-16 All Sky Imager Eko Instruments HDR JPG Yes

[Chow et al., 2011] [steadysun] [Chow et al., 2015] [EKO] 5

• Developed specifically for solar forecasting

• High Dynamic Range images

• Shadowband is not necessary

Fisheye LensSigma 4.5mm

Microprocessor Arduino MEGA 2560

with data acquisition breakout board

ComputerDual core 1.8 Ghz Intel

Atom, 4GB RAM

GPS

Harddrive MountFor removable hard drive

IMU

Power Distribution Board24, 12, & 5 VDC

Power Supply120 VAC in, 24VDC out

Camera Heatsink

High Current MOSFETsfor duty cycling coolers/heaters

CameraAllied Vision GE 2040C

UCSD Sky Imager (USI)

[Chow et al., 2015]6

Sky Imager Forecast Procedure

Calibrated Imagery

Detect Cloud

Ray trace shadows

Data of power or

irradiance

Determine cloud

attenuation

Power Forecast

Compute MotionForecast future

position

Cloud Base Height

Data

[Chow et al., 2015]7

Data processing

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2017-12-22 17:01:30 UTC

Red Channel

Blue Channel

Red - Blue ratio

Clear sky images for a whole day were processed

A Clear Sky Library is built

=

-Clear Sky Library

Cloud Decision

0: clear sky1: thin cloud2: thick cloud

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Preliminary Results

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Sky Imager

ray tracing of

direct solar beam

cloudmap

forecast domain

cloudmap legend

cloudclear sky

Cloud Decision

Projection

[Chow et al., 2015]

Deployment

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𝜖ℎ =1

𝑛

𝑖=1

𝑛

(𝑠1𝑖 − 𝑠2

𝑖 )2

Estimating Cloud Base Height (CBH)

[Nguyen and Kleissl, 2014]

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Final Results

[Kleissl, 2013]13

Limitations

• Solar region issues

• Estimating cloud base height is not easy

• Clouds behavior• Shape is not constant

• More than one layer of clouds

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References

• Chow, C. W., Urquhart, B., Lave, M., Dominguez, A., Kleissl, J., Shields, J., & Washom, B. (2011). Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed. Solar Energy, 85(11), 2881–2893.

• Chow, C., Gohari, S., Urquhart, B., Yang, H., Kurtz, B., Nguyen, D., Ghonima, M., Mejia F., and Kleissl, J. (2015). Design and application of a high dynamic range sky imaging system for solar forecasting.

• Escobar, R. (2016). Fraunhofer Chile Seminar: Pronóstico de Irradiación y Producción en Plantas Solares

• Kleissl, J. (2013). Solar Energy Forecasting and Resource Assessment.

• Nguyen, D., & Kleissl, J. (2014). Stereographic methods for cloud base height determination using two sky imagers. Solar Energy, 107, 495–509.

• Stoffel, T. and Wilcox, S. (2004). Solar Radiation Measurement: A Workshop For The National Association of States Universities and Land Grant Colleges

• Urquhart, B., Kurtz, B., Dahlin, E., Ghonima, M., Shields, J. E., & Kleissl, J. (2015). Development of a sky imaging system for short-term solar power forecasting. Atmospheric Measurement Techniques, 8(2), 875–890.

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Solar irradiance forecasting using ground-based sky

images

SIOG 236

06/06/2018

Cristian Cortes A.

16