DocumentCode :
185116
Title :
Dynamic estimation of the price-response of deadline-constrained electric loads under threshold policies
Author :
Ohannessian, Mesrob I. ; Roozbehani, Mardavij ; Materassi, Donatello ; Dahleh, Munther A.
Author_Institution :
Dept. de Math., Univ. Paris-Sud, Orsay, France
fYear :
2014
fDate :
4-6 June 2014
Firstpage :
2798
Lastpage :
2803
Abstract :
The paper presents a consistent and unbiased estimator for dynamic, one-step-ahead prediction of the aggregate response of a large number of individual loads to a common price signal, using only aggregate past response data. The price per unit of consumption is an exogenous signal which is updated at discrete time intervals. It is assumed that individual loads arrive in the system at random times with random demands and random consumption deadlines, and may defer their consumption up to the deadline in order to minimize their total cost. It is further assumed that the individual loads adopt a threshold policy in the sense that they only consume when the price is below a certain threshold. A dynamic aggregate model is constructed from models of independent individual loads. A consistent and unbiased estimator which only uses aggregate data, i.e., the price and aggregate consumption time-series is presented for estimating the aggregate consumption as a function of price.
Keywords :
estimation theory; power markets; pricing; aggregate consumption time-series; consistent estimator; deadline-constrained electric loads; discrete time intervals; dynamic aggregate model; dynamic one-step-ahead prediction; electricity market; exogenous signal; price signal; price-response dynamic estimation; random consumption deadlines; random demands; random times; threshold policy; unbiased estimator; Aggregates; Computational modeling; Equations; History; Mathematical model; Real-time systems; Vectors; Estimation; Load Scheduling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
ISSN :
0743-1619
Print_ISBN :
978-1-4799-3272-6
Type :
conf
DOI :
10.1109/ACC.2014.6859473
Filename :
6859473
Link To Document :
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