136. POWER LOAD FORECASTING FOR HIGH SOLAR PENETRATION COMMUNITIES AND ITS APPLICATIONS

Department: Mechanical & Aerospace Engineering
Research Institute Affiliation: Center for Energy Research (CER)
Faculty Advisor(s): Carlos Coimbra

Primary Student
Name: Amanpreet Kaur
Email: a3kaur@ucsd.edu
Phone: 858-822-1367
Grad Year: 2014

Abstract
UC San Diego and UC Merced meet substantial parts of their power demand from highly variable solar energy annually. High solar penetration communities replicate the future grid scenario for CA, aiming at 33% renewable energy integration by 2020. The major challenge with this level of penetration of renewable integration is that the variability in solar power production has direct impact on the load demand from the grid especially during the daylight times when the energy prices and demand is at the peak. In this research we present the forecasting techniques for such communities and its applications for reliable and cost efficient integration. Along with load forecasting results for UC San Diego and UC Merced campuses, its applications in Automated Demand Response (ADR), Fast Demand Response (FDR) and load shifting will be presented.

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