Assessing the privacy cost in centralized event-based demand response for microgrids

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 formidable hurdle 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, the privacy cost is evaluated in terms of changes in objective value of the DR optimization problem when effecting the employed privacy-preserving strategy based on Laplace mechanism. The analytical results presented herein are complemented with empirical findings, corroborated extensively by simulations with up to thousands of customers on a realistic 4-bus microgrid (MG) model embracing the underlying power distribution network properties and AC power flow constraints. By evaluating the privacy impact, 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 to explore more efficient privacy solutions for energy procurement of MGs with bounded constant optimality guarantees.