Differential privacy for renewable energy resources based smart metering

Highlights

• We discuss integration of differential privacy in renewable energy based smart grid.

• We provide importance of privacy in renewable energy resources based smart meters.

• We provide analysis of privacy preservation strategies implemented in smart grid.

• We presented simulation work over real-time RER based smart metering data.

• We evaluated that our DPLM algorithm outperforms previously proposed strategies

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%.

Introduction

The increase in the cost of fossil fuel-based energy generation has developed an interest in the integration of different forms of Renewable Energy Resources (RERs) for domestic use [23]. RERs are energy resources that can be used to generate additional energy in order to serve the needs of a specific area or building. RERs can be of diverse nature, but the traditional RERs which are used over homes include solar panels, small wind turbines, and small biomass plants. This integration of RERs in smart grid is one of the future direction of energy systems because of their heterogeneous nature and dynamic capabilities [39]. In future, it is predicted that smart homes will be equipped with more than one RER including solar, and wind energy at the same time [11]. However, due to the intermittent nature of RERs (e.g. available only in specific hours), they are generally used in combination with traditional fossil fuel based energy from grid utility [39].

Along with this enhancement, advanced information and communication technologies (ICT) have become an essential part of smart grid architecture [5]. Two-way communication of energy and data in smart grid is paving its path towards more intelligent, secure, and automated control of smart grid [12]. Smart meters and Advanced Metering Infrastructure (AMI) networks are transmitting real time data of smart homes to grid utility after a specific interval of time for future demand response management and load forecasting strategies [20], [24]. This data can help utility to make an estimate regarding futuristic use of energy in order to overcome any load shedding or power cut. Similarly, this fine-grained data can also lead to an early detection of any sort of theft or tempering in smart meter.

However, this reported smart meter data can be used for several financial and commercial purposes. For instance, smart grid utility can increase prices of certain hours in which RERs are not available, or this data can be sold out to business corporates who can use this data to carry out targeted advertisement for their potential customers. Furthermore, the data collected by smart grid utility can also cause serious harm to privacy of grid users [5], [21]. Real time monitoring of energy data can reveal private information regarding daily routine, habits, and lifestyle of customers. This can simply be done from data by using basic Non-Intrusive Load Monitoring (NILM) techniques [1]. In the same way, real time monitoring can disclose private information about presence and absence of any particular RER in specific hours, hence utility can exploit this information by certain ways (e.g. may increase the prices of electricity in certain hours of the day). Therefore, preserving privacy of smart meter data along with real time monitoring is the need of future energy systems. Several privacy preserving techniques of smart meters are presented in previous literature. These techniques include battery-based load hiding (BLH) [50], cryptographic encryption [14], data perturbation [18], splitting of data into different chunks before transmission [10], trusted third party [27], and decentralized framework for data aggregation [5]. A comprehensive privacy protection strategy named as differential privacy was proposed by C. Dwork in 2006 [15]. Differential privacy works over the principle of perturbing data by adding adequate amount of noise [22], [45]. However, this scheme also proved to be fruitful in various real-time applications as well. Detailed information about differential privacy is presented in Section 3.1.

In this paper, we present a real time differential privacy load monitoring (DPLM) algorithm for smart meter users integrating RERs. This technique provides a desirable level of privacy to smart meter users by first accumulating all values of energies, adding noise, and finally restricting values to a specific peak limit before transmitting it to utility. Along with this, we integrate the functionality for monthly monitoring, this keeps the track of total usage of grid energy, solar energy, and wind energy till end of the month. These accumulated values provide statistical information about the total grid energy usage in the month (for billing purpose) and total solar and wind energy generated (for future demands response and load forecasting scenarios).

Many previous researches have been carried out to use differential privacy in order to protect privacy of smart meters. Few researches use the concept of battery load balancing and its integration with differential privacy to provide dual perturbation, such as the authors in [48], [50] used battery load hiding along with differential privacy to protect mutual information sharing. Similarly, another approach is to use differential privacy directly to perturb smart metering data, researchers such as in  [3], [7], [17], [49] used various mechanisms of differential privacy noise addition to protect smart meter user privacy according to their requirement. A detailed description about these previously proposed strategies and their comparison with DPLM is provided in Section 2. To conclude, differential privacy is a well-researched topic in the field of smart grid, however the researches have not yet considered the integration of differential privacy in renewable energy resources based load monitoring domain. Our proposed DPLM mechanism will be the pioneering step towards integration of differential privacy based renewable energy resource based smart meter reporting.

The novelty and motivation of our proposed strategy is first described here:

• Privacy preservation for smart meters in literature have been carried out mainly by considering battery load balancing, data perturbation, and encryption strategies [14], [50]. These schemes have considered and optimized various applications of smart grid to benefit smart meter users as mentioned in Table 1. However, privacy protection strategy have never been employed for RERs based smart grid scenario. To the best of our knowledge, our novel contribution is the development of differential privacy strategy in renewable energy resources based smart grid.

• To optimize privacy a bit further, we introduced a peak factor in our differential privacy based privacy preservation strategy. Selecting the optimal peak-threshold value according to environmental scenario is difficult in smart grid. This selection of optimal peak-threshold becomes even more difficult in availability of multiple RERs. Owning to these limitations, we have developed differential privacy preservation strategy with suitable peak-threshold value on which our strategy protects RERs real-time data efficiently.

• In literature, various analytical differential privacy models for other smart grid scenarios have been formulated, but analytical modeling for RERs based smart metering considering peak-threshold preservation has merely been touched. We formulated analytical model by developing the algorithm to protect reporting of real-time smart meter and RERs values.

The main contributions of this paper are as follows:

• We provide a detailed overview and analysis of privacy preserving strategies of smart meters.

• We preserve privacy of daily routine, habits, and lifestyle of RERs integrated smart meter users.

• We preserve information about intermittent (only available for specific hours) availability or unavailability of renewable energy resources.

• We develop an algorithm for monthly accumulation of grid and renewable energy that only transmits the values for monthly billing and transmit preserved energy value after every 10 min.

To the best of our knowledge, no literature work that protects the intermittent readings of RERs along with protecting peak-threshold by using differential privacy have been discussed in the past.

The remaining sections of the paper are organized according to following pattern, in Section 2, we give an overview of related work that is carried out so far. The contents of Section 3 contain the description of our proposed DPLM strategy in RERs based smart grid. In Section 4, performance evaluation of DPLM strategy is presented with help of experimental results. Finally, in Section 5, conclusion and future research directions are described.