• DocumentCode
    3509008
  • Title

    Demand response implementation for improved system efficiency in remote communities

  • Author

    Wrinch, M. ; Dennis, G. ; EL-Fouly, Tarek H. M. ; Wong, Simon

  • Author_Institution
    Smart Syst. & Analytics Group, Pulse Energy Inc., Vancouver, BC, Canada
  • fYear
    2012
  • fDate
    10-12 Oct. 2012
  • Firstpage
    105
  • Lastpage
    110
  • Abstract
    This paper evaluates the performance of a demand response (DR) system, installed in the remote community of Hartley Bay, British Columbia, which is used to reduce fuel consumption during periods of peak loads and poor fuel efficiency. The DR system, installed to shed load during these periods, is capable of shedding up to 15 per cent of maximum demand by adjusting wireless variable thermostats and load controllers on hot water heaters and ventilation systems in commercial buildings. The system was found to be successful in reducing demand by up to 35 kW during the DR event period, but caused a new, time-shifted “rebound” peak of 30 to 50 per cent following each event. A DR “staggering” method is introduced as a tool for reducing and delaying rebound without affecting occupant comfort and safety. In this work, load prediction models based on linear regression and averaging of historical data were also developed for measuring DR shed and rebound, with models based on averaging found to produce more accurate baselines.
  • Keywords
    load regulation; load shedding; regression analysis; space heating; thermostats; ventilation; British Columbia; DR event period; DR shed-rebound; DR staggering method; DR system; Hartley Bay; commercial buildings; demand response implementation; fuel consumption reduction; fuel efficiency; historical data averaging; hot water heaters; improved system efficiency; linear regression; load controllers; load prediction model; load shedding; remote communities; time-shifted rebound peak; ventilation systems; wireless variable thermostats; Buildings; Communities; Data models; Fuels; Generators; Load modeling; Predictive models; Demand Response; Energy Conservation; Energy Control; Energy Management; Implementation Challenges; Load Prediction; Smart Grids;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Power and Energy Conference (EPEC), 2012 IEEE
  • Conference_Location
    London, ON
  • Print_ISBN
    978-1-4673-2081-8
  • Electronic_ISBN
    978-1-4673-2079-5
  • Type

    conf

  • DOI
    10.1109/EPEC.2012.6474932
  • Filename
    6474932