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Online Residential Demand Reduction Estimation Through Control Group Selection

  • Leslie HattonEmail author
  • Philippe Charpentier
  • Eric Matzner-Løber
Conference paper
  • 1.7k Downloads
Part of the Lecture Notes in Statistics book series (LNS, volume 217)

Abstract

Demand response levers, as tariff incentive or direct load control on residential electrical appliances, are potential solutions to efficiently manage peak consumption and aid in grid security. The major objective is to estimate the consumption that would have been used in the absence of demand reduction: the baseline. This is an important issue to enhance demand response for electricity markets and to allow the grid operators to efficiently manage the grid. For these reasons, baseline estimation methods have to satisfy the following operational objectives: highly accurate, computationally efficient, cost-effective and flexible to the demand response customer turnover. In general, methods using available data from the control group give the best results, but current control group methods do not satisfy the aforementioned operational objectives. Having a real control group is highly costly because it requires to meter thousands customers who will not be used in the demand response offer. So there is a need to find new methods to select a control group. The advancement of smart meters can now provide a wealth of data to construct this group. This paper proposes the use of individual smart meter loads to select a control group. The method satisfies the aforementioned operational objectives since the selected control group is adaptable in operations even to demand response customers changes. The methodology developed is based on a selection algorithm and constraint regression approach. These new methods have been successfully tested in an online environment.

Keywords

Demand Response Load Curve Demand Reduction Individual Load Lasso Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank the editor and two anonymous referees for their valuable comments which helped in improving the paper.

The online DR estimation solution is the result of a common work realized with Benoît Grossin, EDF R&D, Dept. ICAME, we thank him for the accomplished work.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leslie Hatton
    • 1
    Email author
  • Philippe Charpentier
    • 2
  • Eric Matzner-Løber
    • 3
  1. 1.EDF R&D, Dept. ICAME and agrocampus RennesRennesFrance
  2. 2.EDF R&D, Dept. ICAMEClamartFrance
  3. 3.Universite Rennes 2RennesFrance

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