TY - GEN
T1 - A dynamic simulation tool for estimating demand response potential from residential loads
AU - Johnson, Brandon J.
AU - Starke, Michael R.
AU - Abdelaziz, Omar A.
AU - Jackson, Roderick K.
AU - Tolbert, Leon M.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/23
Y1 - 2015/6/23
N2 - This paper presents a MATLAB based dynamic simulation tool for estimating demand response potential from residential loads. First, a review of residential demand response strategies is conducted. Next, the modeling approach used during the development of this tool is described. Markov chain based occupant behavior models constructed using data gathered by the U.S. Census Bureau in the American Time Use Survey (ATUS) are used in conjunction with models of the most common residential loads to predict the dynamic changes in residential power demand on a one-minute time scale. Separate control schemes are used along with these models to simulate different demand response strategies. Finally, simulation results showing the benefits and trade-offs associated with residential demand response programs are presented. Future work will involve using this tool to examine specific utility areas and the development of real-time pricing and incentive program components.
AB - This paper presents a MATLAB based dynamic simulation tool for estimating demand response potential from residential loads. First, a review of residential demand response strategies is conducted. Next, the modeling approach used during the development of this tool is described. Markov chain based occupant behavior models constructed using data gathered by the U.S. Census Bureau in the American Time Use Survey (ATUS) are used in conjunction with models of the most common residential loads to predict the dynamic changes in residential power demand on a one-minute time scale. Separate control schemes are used along with these models to simulate different demand response strategies. Finally, simulation results showing the benefits and trade-offs associated with residential demand response programs are presented. Future work will involve using this tool to examine specific utility areas and the development of real-time pricing and incentive program components.
KW - Demand response
KW - Dynamic load modeling
KW - Occupant behavior modeling
KW - Residential power demand
UR - http://www.scopus.com/inward/record.url?scp=84939149204&partnerID=8YFLogxK
U2 - 10.1109/ISGT.2015.7131867
DO - 10.1109/ISGT.2015.7131867
M3 - Conference contribution
AN - SCOPUS:84939149204
T3 - 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2015
BT - 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2015
Y2 - 18 February 2015 through 20 February 2015
ER -