Assessing the Privacy Cost in Centralized Event-Based Demand Response for Microgrids

Abstract

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.

A Competitive Scheduling Algorithm for Online Demand Response in Islanded Microgrids

“…2 as well as in Table I in the Appendix. As for the latter, the feeder is a portion of a 12.47 kV radial system, practically deployed in Canada, whose particulars can be consulted in [50]. For both MGs, the time-varying generation capacity (C t ) t∈T is sampled according to a Bernoulli process.…”

Realistic Differentially-Private Transmission Power Flow Data Release

“…Recent work demonstrates convergence for control of optimal power flow according to a differentially private projected subgradient method [22]. As a different line of work, Karapetyan et al [23] study the trade-off between privacy and fidelity in the context of micro-grid based on a privacy-preserving demand response optimization problem. Zhao et al [24] investigate the charging/discharging of household batteries in the differential privacy context to address privacy concern of smart meters and Eibl et al [25] study the use case of privacy preserving electric load forecasting.…”

Differential Privacy for Power Grid Obfuscation

“…Karapetyan et al [9] empirically quantify the trade-off between privacy and utility in demand response systems. The authors analyze the effects of a simple Laplace mechanism on the objective value of the demand response optimization problem.…”