Guest Editorial: Privacy and Security in Smart Grids

A smart grid is an advanced electricity network that tightly couples cyber and physical aspects through advanced monitoring, information processing and bi-directional communication technologies.While advanced monitoring and communication technologies make the grid 'smarter', they also introduce new vulnerabilities.Smart grids are complex and extensive critical infrastructures and they are under constant threat from a wide range of attacks, including data theft, false data injection, denial of service attacks, malware attacks, energy theft, as well as coordinated and distributed versions of these attacks.Successful attacks on smart grids can cause significant economic and social damage through power outages, damage to infrastructure, energy theft and waste.Therefore, it is essential to detect physical as well as cyber attacks before they result in cascading failures in the network.Privacy is another growing concern for the future of smart grids.The smart aspect of the grid requires near real time monitoring of the grid state, including consumer demand and response behaviour through smart meters, to increase the resilience and reliability of the grid.However, with advances in machine learning and data mining technologies, such fine-grained information about user activity can easily provide sensitive information about individual consumers, threatening their privacy.Consumer privacy must be protected not only against third-party attackers, but also against the legitimate energy providers and grid operators, making it a highly difficult challenge.This Special Section brings together papers that study security and privacy issues in smart grids with the goal of developing information technologies that can provide the necessary security and reliability to the grid without sacrificing consumer privacy.Aligned with the interdisciplinary nature of security and privacy challenges in smart grids, we bring together papers from a wide range of research areas including power systems, machine learning, statistical estimation and optimisation.

Abstract licence: CC BY-ND

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