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Advanced demand response solutions based on fine-grained load control

Kaddah, R.; Kofman, D. and Pioro, Michal LU (2014) IEEE International Workshop on Intelligent Energy Systems (IWIES) In [Host publication title missing] p.38-45
Abstract
We consider demand response solutions having the capability to monitor different variables at users' premises, like presence and temperature, and to control individual appliances. We focus on the optimal control of the appliances during time periods where the available capacity is not enough to satisfy the demand generated by houses operating freely. We propose an approach to define the utility of appliances as a function of monitored variables, as well as control schemes to optimize this utility. Global optimums can be reached when a centralized entity (i.e., an aggregator) can gather information from each user and control each individual appliance. This may not be always possible, for example for privacy and/or scalability reasons. We... (More)
We consider demand response solutions having the capability to monitor different variables at users' premises, like presence and temperature, and to control individual appliances. We focus on the optimal control of the appliances during time periods where the available capacity is not enough to satisfy the demand generated by houses operating freely. We propose an approach to define the utility of appliances as a function of monitored variables, as well as control schemes to optimize this utility. Global optimums can be reached when a centralized entity (i.e., an aggregator) can gather information from each user and control each individual appliance. This may not be always possible, for example for privacy and/or scalability reasons. We therefore consider, in addition, a system where decisions are taken partially at a centralized site (global power allocation per home) and partially at customer premises (sharing of the allocated power among local appliances). Performances of proposed control mechanisms are evaluated and compared. We show the potential value of introducing demand response mechanisms at fine granularity. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
[Host publication title missing]
pages
38 - 45
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE International Workshop on Intelligent Energy Systems (IWIES)
external identifiers
  • Scopus:84916622927
DOI
10.1109/IWIES.2014.6957044
language
English
LU publication?
yes
id
b371d37c-7ba1-40cc-a5fc-39ca6d9553c8 (old id 5277393)
date added to LUP
2015-04-22 14:35:19
date last changed
2016-10-13 04:40:23
@misc{b371d37c-7ba1-40cc-a5fc-39ca6d9553c8,
  abstract     = {We consider demand response solutions having the capability to monitor different variables at users' premises, like presence and temperature, and to control individual appliances. We focus on the optimal control of the appliances during time periods where the available capacity is not enough to satisfy the demand generated by houses operating freely. We propose an approach to define the utility of appliances as a function of monitored variables, as well as control schemes to optimize this utility. Global optimums can be reached when a centralized entity (i.e., an aggregator) can gather information from each user and control each individual appliance. This may not be always possible, for example for privacy and/or scalability reasons. We therefore consider, in addition, a system where decisions are taken partially at a centralized site (global power allocation per home) and partially at customer premises (sharing of the allocated power among local appliances). Performances of proposed control mechanisms are evaluated and compared. We show the potential value of introducing demand response mechanisms at fine granularity.},
  author       = {Kaddah, R. and Kofman, D. and Pioro, Michal},
  language     = {eng},
  pages        = {38--45},
  publisher    = {ARRAY(0xa6d3cf8)},
  series       = {[Host publication title missing]},
  title        = {Advanced demand response solutions based on fine-grained load control},
  url          = {http://dx.doi.org/10.1109/IWIES.2014.6957044},
  year         = {2014},
}