TY - GEN
T1 - Demand response implementation for improved system efficiency in remote communities
AU - Wrinch, Michael
AU - Dennis, Greg
AU - El-Fouly, Tarek H.M.
AU - Wong, Steven
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Demand Response
KW - Energy Conservation
KW - Energy Control
KW - Energy Management
KW - Implementation Challenges
KW - Load Prediction
KW - Smart Grids
UR - https://www.scopus.com/pages/publications/84875584719
U2 - 10.1109/EPEC.2012.6474932
DO - 10.1109/EPEC.2012.6474932
M3 - Conference contribution
AN - SCOPUS:84875584719
SN - 9781467320801
T3 - 2012 IEEE Electrical Power and Energy Conference, EPEC 2012
SP - 105
EP - 110
BT - 2012 IEEE Electrical Power and Energy Conference, EPEC 2012
T2 - 2012 IEEE Electrical Power and Energy Conference, EPEC 2012
Y2 - 10 October 2012 through 12 October 2012
ER -