Differential privacy for renewable energy resources based smart metering

Abstract

The increasing energy costs and increase in losses in traditional power grid system triggered the integration of Renewable Energy Resources (RERs) in smart homes. The global desire of consumers to rely on RERs such as solar energy, and wind energy has increased dramatically. Similarly, the IT technologies are also playing their part in smart grid development, such as real time data monitoring. On the other hand, with the advancement of these IT technologies in smart meters, the privacy of customers is also at risk Smart grid utility knows the exact generation of any specific renewable resource in a specific interval of time. Utility need to monitor this real time data for load forecasting and implementation of demand response scenarios. However, the utility may misuse the data and may increase the prices for specific time slots when RERs are not present. Similarly, real time monitoring of data can lead to estimation of life routines of users such as sleeping habits, time of usage of heavy appliances, and lifestyle. In this paper, a Differential Privacy based real time Load Monitoring approach (DPLM) is proposed that preserve the privacy of users by masking the values of load in such a way that utility will not be able to judge the usage of specific RER and the daily routine of any smart meter user. We compare our scheme with Gaussian Noise Differential Privacy (GNDP) strategy. Experimental results validate that our DPLM approach provides a desirable solution to protect smart grid user’s privacy by efficient noise addition and peak value protection along with having an error rate of only 1.5%.