DocumentCode :
2387879
Title :
Load forecasting and demand response
Author :
Luh, Peter B. ; Michel, Laurent D. ; Friedland, Peter ; Guan, Che ; Wang, Yuting
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Connecticut, Storrs, CT, USA
fYear :
2010
fDate :
25-29 July 2010
Firstpage :
1
Lastpage :
3
Abstract :
Demand response is a reduction in the consumption of electricity by customers from their expected consumption in response to reliability or price triggered signals. Enabled by advanced metering infrastructure and other smart grid technologies, it is expected to be a crucial mechanism to compensate system uncertainties and the associated risks including those related to intermittent renewable generation such as wind or solar. In this paper, challenges associated with load forecasting and demand response are discussed. To address the challenges, our key ideas are to (1) forecast load together with corresponding confidence intervals having additional price-related input variables; and (2) use online stochastic optimization together with very-short-term load forecasting results to find effective and robust solutions to manage both dispatchable and non-dispatchable demand response.
Keywords :
load forecasting; stochastic programming; advanced metering infrastructure; dispatchable demand response; intermittent renewable generation; load forecasting; nondispatchable demand response; online stochastic optimization; price triggered signals; price-related input variables; smart grid technologies; solar; wind; Load forecasting; advanced metering infrastructure; demand response; multi-level wavelet neural network methods; online stochastic optimization; smart grid;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting, 2010 IEEE
Conference_Location :
Minneapolis, MN
ISSN :
1944-9925
Print_ISBN :
978-1-4244-6549-1
Electronic_ISBN :
1944-9925
Type :
conf
DOI :
10.1109/PES.2010.5590062
Filename :
5590062
Link To Document :
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