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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Electricity demand forecasting and bidding via dat
 a-driven inverse optimization - Juan Miguel Morale
 s González (Universidad de Málaga)
DTSTART;TZID=Europe/London:20190108T133000
DTEND;TZID=Europe/London:20190108T143000
UID:TALK116761AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/116761
DESCRIPTION:A method to predict the aggregate demand of a clus
 ter of price-responsive consumers of electricity i
 s discussed in this presentation. The price-respon
 se of the aggregation is modelled by an optimizati
 on problem whose defining parameters represent a s
 eries of marginal utility curves\, and minimum and
  maximum consumption limits. These parameters are\
 , in turn\, estimated from observational data usin
 g an approach inspired from duality theory. The re
 sulting estimation problem is nonconvex\, which ma
 kes it very hard to solve. In order to obtain good
  parameter estimates in a reasonable amount of tim
 e\, we divide the estimation problem into a feasib
 ility problem and an optimality problem. Furthermo
 re\, the feasibility problem includes a penalty te
 rm that is statistically adjusted by cross validat
 ion. The proposed methodology is data-driven and l
 everages information from regressors\, such as tim
 e and weather variables\, to account for changes i
 n the parameter estimates. The estimated price-res
 ponse model is used to forecast the power load of 
 a group of heating\, ventilation and air condition
 ing systems\, with positive results. We also show 
 how this method can be easily extended to be used 
 for demand-side bidding in electricity markets.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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