Privacy Preservation in Smart Grid Environment

ABSTRACTSmart grid is a new type of power grid that integrates advanced information communication technology and measurement technology into conventional power grids. It provides an efficient and intelligent energy service. However, the entities in the smart grid are easily hacked and sometimes even collapsed; then the whole grid system could be jeopardized. Therefore, the security of user privacy and communication in smart grids is still facing great challenges. In this paper, we propose a robust, distributed, and privacy-preserving aggregation scheme, RDPA for short, which uses the homomorphic Paillier cryptosystem technique combined with signature technique to ensure the confidentiality and integrality of users’ electricity consumption. In addition, we employ the threshold secret sharing technology and secret factors injection method to improve the robustness of smart grid systems. For example, RDPA can still run well even when a gateway is hacked. The experimental results and analysis demonstrate that RDPA can resist various attacks and protect user privacy, and its security and performance are better than other related schemes.

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

A smart grid is an intelligent power grid. In recent years, the smart grid environment and its applications are incorporated into a variety of areas. The smart grid environment, however, can expose much more information than the old environments. In the environment, smart devices can be media in the exposure of various and specific pieces of information as well as energy consumption. This poses a huge risk in that it, combined with other pieces of information, may expose much more information. The current smart grid environment raises a need to develop anonymous signature and authentication techniques to prevent privacy breaches. Trying to meet this need, the principal investigator conducted research for three years. This paper discusses both the research trends investigated by him and the limitations of the development research and future research in need. Smart grid security requires the development of encrypted anonymous authentication that is applicable to power plant security, including nuclear power plants as well as expandable test beds.

The smart grid provides a platform for thirdparty service providers to remotely monitor and manage energy usage for consumers. At the same time, the involvement of service providers brings a new set of security threats to the smart grid. In this article, we first identify the cyber security challenges on service provision in the smart grid. Then we present two main security issues related to service provision and provide potential solutions. The first one is to establish a secure communication procedure among the electric utility, consumers, and service providers. The second one is to provide a privacy-preserving yet accountable authentication framework among the smart grid entities without relying on any trusted third party. Finally, we suggest directions of future work on secure service provision by describing several open issues. 1979-2012 IEEE.

Evolution of Communication Technologies for Smart Grid applications

As a critical infrastructure, the electric power grid is vulnerable with respect to malicious physical and cyber attacks. Indeed, the importance of physical and cyber security for smart grids has been highlighted by recent intrusion incidents worldwide. Various types of sensors, instrumentation, information and communications technology (ICT), computational methodologies, and security controls have been developed to enhance the physical and cyber security of the smart grid. In this paper, critical vulnerabilities of a smart grid that can be exploited for physical and cyber intrusions are discussed. A comprehensive survey is conducted on the state‐of‐the‐art research to enhance the physical and cyber security in a smart grid environment. Furthermore, the interdependency of physical and cyber security is illustrated with an intrusion scenario. The emphasis is on the physical and cyber security of power substations. Computer simulation results for a case study are presented. WIREs Energy Environ 2016, 5:519–542. doi: 10.1002/wene.202This article is categorized under: Energy Infrastructure > Systems and Infrastructure

Key performance aspects of an LTE FDD based Smart Grid communications network

Developments in information and communication technologies (ICT) achieved new trends such as smart grid (SG). Innovations in ICT bring challenges to our security and expectations of privacy. ICT has such a big attack surface that possess increased risks from cyber-attacks in smart grid. Actually, as a critical infrastructure, the smart grid is more sensitive to cyber-attacks, as monitoring and control of smart grid can be realized over standard internet-based protocols. Therefore, an attacker may achieve financial cost to the service and create loss of electric resources, through breaching the real-time balance between energy production and consumption, by controlling data produced by the smart entities. Therefore, in this study, bagging ensemble classifier is evaluated for intrusion detection in a smart grid with a benchmark data. Experimental results have revealed the practicality of Bagging ensemble classifier by realizing a better performance for the cyber security threats in a smart grid environment. The results indicated that the suggested Bagging ensemble classifier with REP Tree achieved a total classification accuracy of 99.94%.

A new model of power system to identify critical elements for smart and bulk power transmission grid environment is presented in this paper. Proposed bidirectional power flow model under complex network framework can quantify the criticality of nodes whose removal would trigger cascading failure. The proposed model based centrality, which is an extension of nominal power flow based model, is able to find nodes whose removal exerts more stress on the system. Several measures of impact like path length increase, boosted connectivity loss, raised load loss and increase in number of overloaded lines justify the use of bidirectional power flow based model to evaluate the criticality in smart grid environment. Change in direction of power flow in various random lines of the test system has a very little effect on the rank of nodes in the critical list.

Identifying household electricity consumption patterns: A case study of Kunshan, China

MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things

Correcting “PALK: Password-based anonymous lightweight key agreement framework for smart grid”

A systematic review of data protection and privacy preservation schemes for smart grid communications

High-data-rate mobile multimedia applications can greatly increase energy consumption, leading to an emerging trend of addressing the “energy efficiency” aspect of mobile networks. Cognitive mobile networks with small cells are important techniques for meeting the high-data-rate requirements and improving the energy efficiency of mobile multimedia communications. However, most existing works do not consider the power grid, which provides electricity to mobile networks. Currently, the power grid is experiencing a significant shift from the traditional grid to the smart grid. In the smart grid environment, only considering energy efficiency may not be sufficient since the dynamics of the smart grid will have significant impacts on mobile networks. In this paper, we study green cognitive mobile networks with small cells in the smart grid environment. Unlike most existing studies on cognitive networks, where only the radio spectrum is sensed, our cognitive networks sense not only the radio spectrum environment but also the smart grid environment, based on which power allocation and interference management for multimedia communications are performed. We formulate the problems of electricity price decision, energy-efficient power allocation, and interference management as a three-stage Stackelberg game. A homogeneous Bertrand game with asymmetric costs is used to model price decisions made by the electricity retailers. A backward induction method is used to analyze the proposed Stackelberg game. Simulation results show that our proposed scheme can significantly reduce operational expenditure and CO2 emissions in cognitive mobile networks with small cells for multimedia communications.

Electric power grids are lagging in flexibility and time-response. A smart grid is an improved version of electrical grids that leverages Internet of Things (IoT) based devices to improve the overall infrastructure from the grid stations to intelligent appliances. It provides better understanding of supply and demand and overall flow of data depending based upon the requirements. Modern approach towards Smart grid envisions to provide electricity consumers with the opportunity to manage their respective power usage. Population increase has played a major role in the adoption of smart grid as a lot of electrical energy is consumed in the residential sector and a lot of architectures have been proposed for better flow of information from the smart meter to connectors and devices for improved customer participation. Customer needs have been very important in the smart grid. However, the customers have never been provided with the ease of choosing their own kind of benefits from the smart grid. In this work, we propose an enhanced architecture working effectively for multiple users based on their requirements. The users would be able to choose their type of scheduling techniques based on their requirements. These requirements may include cost reduction and increasing user comfort for better consumption of electricity and reliable systems. These requirements can be achieved using different Bio inspired computing based scheduling algorithms. Furthermore, in this work, we provide a comparison of these bio inspired scheduling techniques, i.e., Enhanced Differential Evolution, Bacterial Foraging Algorithm and Grey Wolf Optimization integrated in smart grid architecture for providing better consumption of electricity and achieving reliable systems. These algorithms mainly aim to schedule load, minimize electricity bills and maximize the user comfort depending on user demand.