Abstract
This paper describes new aggregate load models of batteries, electric vehicles (EVs) and deferrable appliances (DAs), for use in demand response (DR). Compared to other models that have previously appeared in the literature, the low order models we propose aggregate large populations of devices that share certain parameters. The models also reveal various characteristics of populations of DR devices, such as aggregate energy demand, flexibility and ramping potential. We further look at the different ways the aggregate model consumes energy, identify strategies that bound all others in terms of stored energy and use those to predict the future feasible operating region.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 926-930 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509045457 |
| DOIs | |
| State | Published - Apr 19 2017 |
| Event | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States Duration: Dec 7 2016 → Dec 9 2016 |
Other
| Other | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 12/7/16 → 12/9/16 |
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Keywords
- Demand Response
- Energy Storage
- Load Aggregation
- Load Shifting
- Reserve Capacity
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications
Cite this
Aggregate load models for demand response : Exploring flexibility. / Hreinsson, Kari; Scaglione, Anna; Vittal, Vijay.
2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 926-930 7905978.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Aggregate load models for demand response
T2 - Exploring flexibility
AU - Hreinsson, Kari
AU - Scaglione, Anna
AU - Vittal, Vijay
PY - 2017/4/19
Y1 - 2017/4/19
N2 - This paper describes new aggregate load models of batteries, electric vehicles (EVs) and deferrable appliances (DAs), for use in demand response (DR). Compared to other models that have previously appeared in the literature, the low order models we propose aggregate large populations of devices that share certain parameters. The models also reveal various characteristics of populations of DR devices, such as aggregate energy demand, flexibility and ramping potential. We further look at the different ways the aggregate model consumes energy, identify strategies that bound all others in terms of stored energy and use those to predict the future feasible operating region.
AB - This paper describes new aggregate load models of batteries, electric vehicles (EVs) and deferrable appliances (DAs), for use in demand response (DR). Compared to other models that have previously appeared in the literature, the low order models we propose aggregate large populations of devices that share certain parameters. The models also reveal various characteristics of populations of DR devices, such as aggregate energy demand, flexibility and ramping potential. We further look at the different ways the aggregate model consumes energy, identify strategies that bound all others in terms of stored energy and use those to predict the future feasible operating region.
KW - Demand Response
KW - Energy Storage
KW - Load Aggregation
KW - Load Shifting
KW - Reserve Capacity
UR - http://www.scopus.com/inward/record.url?scp=85019214853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019214853&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2016.7905978
DO - 10.1109/GlobalSIP.2016.7905978
M3 - Conference contribution
SP - 926
EP - 930
BT - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -