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Title: Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power

Abstract

Cybersecurity and privacy are of the utmost importance for safe, reliable operation of the electric grid. It is well known that the increased connectivity/interoperability between all stakeholders (e.g., utilities, suppliers, and consumers) will enable personal information collection. Significant advanced metering infrastructure (AMI) deployment and demand response (DR) programs across the country, while enable enhanced automation, also generate energy data on individual consumers that can potentially be used for exploiting privacy. Inspired by existing works which consider DR, battery-based perturbation, and differential privacy noise adding, we novelly consider the aggregator (cluster) level privacy issue in the DR framework of solar photovoltaic (PV) generation following. Different from most of the existing works which mainly rely on the charging/discharging scheduling of rechargeable batteries, we utilize controllable building loads to serve as virtual storage devices to absorb a large portion of the PV genertaion while delicately keeping desired noisy terms to satisfy the differential privacy for the raw load profiles at the aggregator level. This not only ensures differential privacy, but also improves the DR efficiency in load following since part of the noisy signal in solar PV generation has been filtered out. In particular, a mixed integer quadratic optimization problem is formulated tomore » optimally dispatch a population of on/off controllable loads to achieve this privacy-preserving DR service.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1494017
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 3rd IEEE Workshop on the Electronic Grid (eGrid 2018) - Charleston, South Carolina, United States of America - 11/12/2018 2:00:00 PM-11/14/2018 2:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Dong, Jin, Kuruganti, Teja, Djouadi, Seddik M., Olama, Mohammed M., and Xue, Yaosuo. Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power. United States: N. p., 2018. Web. doi:10.1109/eGRID.2018.8598663.
Dong, Jin, Kuruganti, Teja, Djouadi, Seddik M., Olama, Mohammed M., & Xue, Yaosuo. Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power. United States. doi:10.1109/eGRID.2018.8598663.
Dong, Jin, Kuruganti, Teja, Djouadi, Seddik M., Olama, Mohammed M., and Xue, Yaosuo. Thu . "Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power". United States. doi:10.1109/eGRID.2018.8598663. https://www.osti.gov/servlets/purl/1494017.
@article{osti_1494017,
title = {Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power},
author = {Dong, Jin and Kuruganti, Teja and Djouadi, Seddik M. and Olama, Mohammed M. and Xue, Yaosuo},
abstractNote = {Cybersecurity and privacy are of the utmost importance for safe, reliable operation of the electric grid. It is well known that the increased connectivity/interoperability between all stakeholders (e.g., utilities, suppliers, and consumers) will enable personal information collection. Significant advanced metering infrastructure (AMI) deployment and demand response (DR) programs across the country, while enable enhanced automation, also generate energy data on individual consumers that can potentially be used for exploiting privacy. Inspired by existing works which consider DR, battery-based perturbation, and differential privacy noise adding, we novelly consider the aggregator (cluster) level privacy issue in the DR framework of solar photovoltaic (PV) generation following. Different from most of the existing works which mainly rely on the charging/discharging scheduling of rechargeable batteries, we utilize controllable building loads to serve as virtual storage devices to absorb a large portion of the PV genertaion while delicately keeping desired noisy terms to satisfy the differential privacy for the raw load profiles at the aggregator level. This not only ensures differential privacy, but also improves the DR efficiency in load following since part of the noisy signal in solar PV generation has been filtered out. In particular, a mixed integer quadratic optimization problem is formulated to optimally dispatch a population of on/off controllable loads to achieve this privacy-preserving DR service.},
doi = {10.1109/eGRID.2018.8598663},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Conference:
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