Market value of differentially-private smart meter data
File(s)
Accepted version
Author(s)
Chhachhi, Saurab
Teng, Fei
Type
Conference Paper
Abstract
This paper proposes a framework to investigate the value of sharing privacy-protected smart meter data between domestic consumers and load serving entities. The framework consists of a discounted differential privacy model to ensure individuals cannot be identified from aggregated data, a ANN-based short-term load forecasting to quantify the impact of data availability and privacy protection on the forecasting error and an optimal procurement problem in day-ahead and balancing markets to assess the market value of the privacy-utility trade-off. The framework demonstrates that when the load profile of a consumer group differs from the system average, which is quantified using the Kullback-Leibler divergence, there is significant value in sharing smart meter data while retaining individual consumer privacy.
Date Issued
2021-03-16
Date Acceptance
2021-03-01
Citation
2021 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2021, pp.1-5
ISSN
2167-9665
Publisher
IEEE
Start Page
1
End Page
5
Journal / Book Title
2021 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT)
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (E
National Grid Electricity Transmission Plc
Economic and Social Research Council
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000662927400077&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
RES/0560/7615/205
PO: 3400015516
2113082
Source
IEEE-Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
data markets
differential privacy
load forecasting
smart grid
smart meters
Publication Status
Published
Start Date
2021-02-16
Finish Date
2021-02-18
Coverage Spatial
Washington, DC
Date Publish Online
2021-03-16