149. SKY IMAGER FORECASTING FOR MICROGRID OPTIMIZATION

Department: Mechanical & Aerospace Engineering
Faculty Advisor(s): Jan Kleissl

Primary Student
Name: Chi Wai Chow
Email: cwchow@ucsd.edu
Phone: 626-215-5587
Grad Year: 2014

Student Collaborators
Bryant Urquhart, burquhar@ucsd.edu | Anders Nottrot, anottrot@ucsd.edu | Jenny Luoma, jluoma@ucsd.edu

Abstract
Accurate solar forecasting has become increasingly important as solar energy supply is growing and demand management is applied to primarily use green power in data centers and microgrids. The ability to predict the fluctuating nature of the solar resource affects the optimal management of the electric grid. Improved monitoring and instrumentation of the solar resource on smaller spatial and temporal scales is required to provide this intra-hour information. A ground-based total sky imager can provide continuous monitoring of the sky hemisphere. We present a method to forecast global horizontal irradiance using fractional sky cover and cloud motion vectors. Sky images taken every 30 seconds are processed to determine sky cover. Cloud motion vectors are generated by cross-correlating two consecutive sky images. Future cloud locations are then computed by applying the vector field to the sky scene. A Master Controller for the UC San microgrid takes the solar and utility pricing forecasts to execute demand response and ramping of fossil generation to alternatively optimize for economics or green power. Experiences in the implementation at UC San Diego and case studies will be discussed.

Related Links:

  1. solar.ucsd.edu

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