Aggregate load forecast with payback model of the electric water heaters for a direct load control program

M. Shaad, R. Errouissi, C. P. Diduch, M. E. Kaye, L. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Domestic electric water heaters (DEWH) hold a large share of residential load in North America. The aggregated load profile of electric water heaters follows a similar pattern to the total household load profile, which means that changing the profile of DEWH load can significantly change the shape of the aggregated load profile. To change the load profile, the controller requires an estimation of future load profile and the payback effect of the control action on the forecasted load. This paper presents a load forecast module that uses a Kalman filtered neural network to forecast the aggregated controllable load combined with a statistical payback model to identify the impact of the control action on the load forecast. The proposed method was used by the University of New Brunswick as part of a pilot project named Power Shift Atlantic that aims to provide more than 11MW of ancillary services by controlling more than 1200 controllable loads. The experimental results on the real pilot project shows that the forecast method can be adapted with the dynamic behaviour of the customers. The payback model was also verified by applying various control signals on the pilot project.

Original languageEnglish
Title of host publicationProceedings - 2014 Electrical Power and Energy Conference, EPEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages214-219
Number of pages6
ISBN (Electronic)9781479960385
DOIs
Publication statusPublished - Feb 27 2014
Externally publishedYes
Event2014 Electrical Power and Energy Conference, EPEC 2014 - Calgary, Canada
Duration: Nov 12 2014Nov 14 2014

Publication series

NameProceedings - 2014 Electrical Power and Energy Conference, EPEC 2014

Conference

Conference2014 Electrical Power and Energy Conference, EPEC 2014
Country/TerritoryCanada
CityCalgary
Period11/12/1411/14/14

Keywords

  • Demand-Side Management
  • Kalman Filter
  • Load Forecast
  • Neural Network
  • Payback Effect
  • Smart Grid

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Fingerprint

Dive into the research topics of 'Aggregate load forecast with payback model of the electric water heaters for a direct load control program'. Together they form a unique fingerprint.

Cite this