Privacy-cost trade-offs in demand-side management with storage
File(s)
Accepted version
Author(s)
Tan, O
Gomez-Vilardebo, J
Gunduz, D
Type
Journal Article
Abstract
Demand-side energy management (EM) is studied
from a
privacy-cost trade-off
perspective, considering time-of-use
pricing and the presence of an energy storage unit. Privacy i
s
measured as the variation of the power withdrawn from the gri
d
from a fixed target value. Assuming non-causal knowledge of t
he
household’s aggregate power demand profile and the electric
ity
prices at the energy management unit (EMU), the privacy-cos
t
trade-off is formulated as a convex optimization problem, a
nd
a low-complexity
backward water-filling algorithm
is proposed to
compute the optimal EM policy. The problem is studied also in
the online setting assuming that the power demand profile is
known to the EMU only causally, and the optimal EM policy is
obtained numerically through dynamic programming (DP). Du
e
to the high computational cost of DP, a low-complexity heuri
stic
EM policy with a performance close to the optimal online solu
tion
is also proposed, exploiting the water-filling algorithm ob
tained
in the offline setting. As an alternative, information theor
etic
leakage rate is also evaluated, and shown to follow a similar
trend as the load variance, which supports the validity of th
e
load variance as a measure of privacy. Finally, the privacy-
cost
trade-off, and the impact of the size of the storage unit on th
is
trade-off are studied through numerical simulations using
real
smart meter data in both the offline and online settings.
from a
privacy-cost trade-off
perspective, considering time-of-use
pricing and the presence of an energy storage unit. Privacy i
s
measured as the variation of the power withdrawn from the gri
d
from a fixed target value. Assuming non-causal knowledge of t
he
household’s aggregate power demand profile and the electric
ity
prices at the energy management unit (EMU), the privacy-cos
t
trade-off is formulated as a convex optimization problem, a
nd
a low-complexity
backward water-filling algorithm
is proposed to
compute the optimal EM policy. The problem is studied also in
the online setting assuming that the power demand profile is
known to the EMU only causally, and the optimal EM policy is
obtained numerically through dynamic programming (DP). Du
e
to the high computational cost of DP, a low-complexity heuri
stic
EM policy with a performance close to the optimal online solu
tion
is also proposed, exploiting the water-filling algorithm ob
tained
in the offline setting. As an alternative, information theor
etic
leakage rate is also evaluated, and shown to follow a similar
trend as the load variance, which supports the validity of th
e
load variance as a measure of privacy. Finally, the privacy-
cost
trade-off, and the impact of the size of the storage unit on th
is
trade-off are studied through numerical simulations using
real
smart meter data in both the offline and online settings.
Date Issued
2017-01-20
Date Acceptance
2017-01-09
Citation
IEEE Transactions on Information Forensics & Security, 2017, 12 (6), pp.1458-1469
ISSN
1556-6013
Start Page
1458
End Page
1469
Journal / Book Title
IEEE Transactions on Information Forensics & Security
Volume
12
Issue
6
Copyright Statement
© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Sponsor
Engineering & Physical Science Research Council (E
Grant Number
EP/N021738/1
Subjects
Strategic, Defence & Security Studies
08 Information And Computing Sciences
09 Engineering
Publication Status
Published