TY - GEN
T1 - Incentive design for demand-response based on building constraints – a utility perspective
AU - Kandhway, Kundan
AU - Vasan, Arunchandar
AU - Nagarathinam, Srinarayana
AU - Sarangan, Venkatesh
AU - Sivasubramaniam, Anand
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/8
Y1 - 2017/11/8
N2 - Electrical utilities offer incentives to their customers to reduce their demand during temporary supply-demand mismatches. While customers would prefer a higher incentive to participate, utilities would prefer to minimize the incentive while achieving a target reduction. Because the incentive affects the bottomline of the utility, identifying the optimal incentive reflecting this trade-off is important. Several works have focused on how to implement DR in a building, but there has been little work on identifying the optimal incentive from the utility’s perspective. We complement existing work with an approach on how a utility can identify the optimal incentive for a set of buildings that it serves, while meeting individual buildings’ constraints. To this end, we build Demand-Response Potential (DRP) models that give the economically rational demand reduction of a building as a function of the utility’s offered incentive. For handling scalability at the utility level, we approximate the DRP using regression based approach. We evaluate our approach on the PLUTO dataset of building types and sizes. We find that the DRP varies with building types (from 19% for restaurants to 54% for warehouses). It is typically low for buildings with high thermal inertia; and building types with low individual DRP can contribute significantly in aggregate due to their numbers. Offering non-uniform incentives to different buildings can improve the utility’s DR benefit by up to 19% compared to offering uniform incentives.
AB - Electrical utilities offer incentives to their customers to reduce their demand during temporary supply-demand mismatches. While customers would prefer a higher incentive to participate, utilities would prefer to minimize the incentive while achieving a target reduction. Because the incentive affects the bottomline of the utility, identifying the optimal incentive reflecting this trade-off is important. Several works have focused on how to implement DR in a building, but there has been little work on identifying the optimal incentive from the utility’s perspective. We complement existing work with an approach on how a utility can identify the optimal incentive for a set of buildings that it serves, while meeting individual buildings’ constraints. To this end, we build Demand-Response Potential (DRP) models that give the economically rational demand reduction of a building as a function of the utility’s offered incentive. For handling scalability at the utility level, we approximate the DRP using regression based approach. We evaluate our approach on the PLUTO dataset of building types and sizes. We find that the DRP varies with building types (from 19% for restaurants to 54% for warehouses). It is typically low for buildings with high thermal inertia; and building types with low individual DRP can contribute significantly in aggregate due to their numbers. Offering non-uniform incentives to different buildings can improve the utility’s DR benefit by up to 19% compared to offering uniform incentives.
UR - http://www.scopus.com/inward/record.url?scp=85052024227&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052024227&partnerID=8YFLogxK
U2 - 10.1145/3137133.3137142
DO - 10.1145/3137133.3137142
M3 - Conference contribution
AN - SCOPUS:85052024227
T3 - BuildSys 2017 - Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments
BT - BuildSys 2017 - Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments
A2 - Eskicioglu, Rasit
PB - Association for Computing Machinery, Inc
T2 - 4th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2017
Y2 - 8 November 2017 through 9 November 2017
ER -