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[Seminars]
Generating Long-Term Wind Scenarios Condtioned on Sequential Short-Term Forecasting Using LTM
Mr Kyle Perline
School of Civil & Environmental Engineering, Cornell University
 
Monday 14 August 2017, 04:00pm - 05:00pm
S16-06-118, DSAP Seminar Room
 

In this paper a new Long Term Generation (LTG) method is developed that generates long-term synthetic
wind scenarios conditioned on sequential short-term forecasts, and the Joint
Distribution Comparison (JDC) method is developed to evaluate these scenarios.
Scenarios generated using the LTG method can help to better evaluate the
operation of power systems with wind integration by providing additional
forecast and outcome scenarios on which the systems can be backtested. The LTG
method is distinct from the existing wind scenario generation methods, which
either generate short-term scenarios conditioned on a single short-term
forecast or generate long-term scenarios that are not conditioned on any
forecast information. The LTG algorithm is a generalization of the short-term
scenario generation methods, where the marginal distributions of wind power at
each time step conditioned on the forecasts are first estimated and then the
full joint distribution is constructed. The JDC is a statistical test that
compares the historical forecast and synthetic scenario joint distribution to
the historical forecast and historical outcome joint distribution. These two
new methods are applied to a dataset of historical wind forecasts and outcomes
from Bonneville Power Administration.