Designing microgrid energy markets : A case study : The Brooklyn Microgrid

Review of photovoltaic power forecasting

http://coimbra.ucsd.edu/publications/papers/2013_Inman_Pedro_Coimbra.pdf

Solar forecasting methods for renewable energy integration

Solar photovoltaic generation forecasting methods: A review

On recent advances in PV output power forecast

Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego

. Digital images of the sky obtained using a total sky imager (TSI) are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR) to the RBR of a clear sky library (CSL) generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD) measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF) to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

/pdf/a-method-for-cloud-detection-and-opacity-classification-3acqasyrnf.pdf

A method for cloud detection and opacity classification based on ground based sky imagery

Evaluating business models for microgrids: Interactions of technology and policy

. To facilitate the development of solar power forecasting algorithms based on ground-based visible wavelength remote sensing, we have developed a high dynamic range (HDR) camera system capable of providing hemispherical sky imagery from the circumsolar region to the horizon at a high spatial, temporal, and radiometric resolution. The University of California, San Diego Sky Imager (USI) captures multispectral, 16 bit, HDR images as fast as every 1.3 s. This article discusses the system design and operation in detail, provides a characterization of the system dark response and photoresponse linearity, and presents a method to evaluate noise in high dynamic range imagery. The system is shown to have a radiometrically linear response to within 5% in a designated operating region of the sensor. Noise for HDR imagery is shown to be very close to the fundamental shot noise limit. The complication of directly imaging the sun and the impact on solar power forecasting is also discussed. The USI has performed reliably in a hot, dry environment, a tropical coastal location, several temperate coastal locations, and in the great plains of the United States.

/pdf/development-of-a-sky-imaging-system-for-short-term-solar-pxnnqxc63e.pdf

Development of a sky imaging system for short-term solar power forecasting

This chapter discusses the use of ground-based visible wavelength imagery for short-term forecasting of solar-power output. It begins with selected applications of short-term forecasting to both transmission- and distribution-scale generation. The hardware used in sky-imaging systems is next presented, providing technical and historical information on system optics and sensory equipment (e.g., charge-coupled devices). A high-quality sky-imaging system designed by the authors (the USI) is then briefly examined. The core of the chapter consists of describing sky-imagery analysis techniques, followed by their application to a large solar-power plant. The techniques used to detect clouds, determine their height, and estimate their motion are explained. Application of these methods to generate a power forecast is presented in a case study of a 48 MW solar-power plant, for which a week of data was analyzed. The results are discussed and ideas for improvement are presented. Finally, recommendations for advancing ground-based visible-wavelength imagery and forecast performance are presented.

M. S. Ghonima

Papers

Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego

A method for cloud detection and opacity classification based on ground based sky imagery

Evaluating business models for microgrids: Interactions of technology and policy

Development of a sky imaging system for short-term solar power forecasting

Chapter 9 – Sky-Imaging Systems for Short-Term Forecasting