  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 generation 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.
