دانلود Consumer profiling for demand response programs in smart grids

Abstract

In this paper, we propose new models for consumer demand  response  estimation  in  a  smart  energy  environment, where  consumers  have  access  to  real  time  electricity  pricing information  and can respond to  price signals by  changing  their energy  consumption  through  a  two-way  communication  system. We introduce  a  stochastic  model that differentiates  and characterizes two principal constituents of consumers demand response behavior: a long-term steady behavior and a short-term dynamic response  behavior.  We  further  propose  a  method  to  estimate conditional  probability  distributions  of  future  demand  given current  demand  and price  information,  which  gives  a  complete probabilistic characterization of the short-term dynamic response behavior.  This  approach  extracts  much  more  information  on consumer behavior from a given set of data than the traditional approach  which  estimates  statistics  such  as  demand  elasticity directly.  We  demonstrate  our  methodology  with  the  residential demand  response  experimental  data  taken  from  the  Olympic Peninsula project, and discuss in detail the results of the proposed approach.