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

## TL;DR

This paper empirically investigates the privacy-utility trade-off in centralized demand response systems for microgrids, analyzing the impact of differential privacy on optimization performance through simulations.

## Contribution

It provides a systematic empirical analysis of privacy costs in demand response, highlighting the trade-offs and factors affecting system optimality under differential privacy constraints.

## Key findings

- Privacy preservation incurs a measurable cost on the DR optimization objective.
- The Laplace mechanism's impact varies with privacy parameters and system size.
- Simulation results validate theoretical trade-off analysis.

## 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.