TY - GEN
T1 - Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power
AU - Dong, Jin
AU - Kuruganti, Teja
AU - Djouadi, Seddik
AU - Olama, Mohammed
AU - Xue, Yaosuo
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/31
Y1 - 2018/12/31
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85061501874&partnerID=8YFLogxK
U2 - 10.1109/eGRID.2018.8598663
DO - 10.1109/eGRID.2018.8598663
M3 - Conference contribution
AN - SCOPUS:85061501874
T3 - Conference Record of the 3rd IEEE International Workshop on Electronic Power Grid, eGrid 2018
BT - Conference Record of the 3rd IEEE International Workshop on Electronic Power Grid, eGrid 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Workshop on Electronic Power Grid, eGrid 2018
Y2 - 12 November 2018 through 14 November 2018
ER -
