  Demand response (DR) programs have emerged as a potential key enabling
ingredient in the context of smart grid (SG). Nevertheless, the rising concerns
over privacy issues raised by customers subscribed to these programs constitute
a major threat towards their effective deployment and utilization. This has
driven extensive research to resolve the hindrance confronted, resulting in a
number of methods being proposed for preserving customers' privacy. While these
methods provide stringent privacy guarantees, only limited attention has been
paid to their computational efficiency and performance quality. Under the
paradigm of differential privacy, this paper initiates a systematic empirical
study on quantifying the trade-off between privacy and optimality in
centralized DR systems for maximizing cumulative customer utility. Aiming to
elucidate the factors governing this trade-off, we analyze the cost of privacy
in terms of the effect incurred on the objective value of the DR optimization
problem when applying the employed privacy-preserving strategy based on Laplace
mechanism. The theoretical results derived from the analysis are complemented
with empirical findings, corroborated extensively by simulations on a 4-bus MG
system with up to thousands of customers. By evaluating the impact of privacy,
this pilot study serves DR practitioners when considering the social and
economic implications of deploying privacy-preserving DR programs in practice.
Moreover, it stimulates further research on exploring more efficient approaches
with bounded performance guarantees for optimizing energy procurement of MGs
without infringing the privacy of customers on demand side.
