Security Aspects of Internet of Things aided Smart Grids: a Bibliometric Survey

The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.Comment: The paper is published in Elsevier's Internet of Things journal. 25 pages + 20 pages of reference

The Resilience Of Smart Energy Systems Against Adversarial Attacks, Operational Degradation And Variabilities

The presented research investigates selected topics concerning resilience of critical energy infrastructures against certain types of operational disturbances and/or failures whether natural or man-made. A system is made resilient through the deployment of physical devices enabling real-time monitoring, strong feedback control system, advanced system security and protection strategies or through prompt and accurate man-made actions or both. Our work seeks to develop well-planned strategies that act as a foundation for such resiliency enabling techniques.The research conducted thus far addresses three attributes of a resilient system, namely security, efficiency, and robustness, for three types of systems associated with current or future energy infrastructures. First (chapter 1), we study the security aspect of cyber-physical systems which integrate physical system dynamics with digital cyberinfrastructure. The smart electricity grid is a common example of this system type. In this work, an abstract theoretical framework is proposed to study data injection/modification attacks on Markov modeled dynamical systems from the perspective of an adversary. The adversary is capable of modifying a temporal sequence of data and the physical controller is equipped with prior statistical knowledge about the data arrival process to detect the presence of an adversary. The goal of the adversary is to modify the arrivals to minimize a utility function of the controller while minimizing the detectability of his presence as measured by the K-L divergence between the prior and posterior distribution of the arriving data. The trade-off between these two metrics– controller utility and the detectability cost is studied analytically for different underlying dynamics.Our second study (chapter 2) reviews the state of the art ocean wave generation technologies along with system level modeling while providing an initial study of the impacts of integration on a typical electrical grid network as compared to the closest related technology, wind energy extraction. In particular, wave power is computed from high resolution measured raw wave data to evaluate the effects of integrating wave generation into a small power network model. The system with no renewable energy sources and the system with comparable wind generation have been used as a reference for evaluation. Simulations show that wave power integration has good prospects in reducing the requirements of capacity and ramp reserves, thus bringing the overall cost of generation down.Our third study(chapter 3) addresses the robustness of resilient ocean wave generation systems. As an early-stage but rapidly developing technology, wave power extraction systems must have strong resilience requirements in harsh, corrosive ocean environments while enabling economic operation throughput their lifetime. Such systems are comprised of Wave Energy Converters (WECs) that are deployed offshore and that derive power from rolling ocean waves. The Levelized Cost of Electricity (LCOE) for WECs is high and one important way to reduce this cost is to employ strategies that minimize the cost of maintenance of WECs in a wave farm. In this work, an optimal maintenance strategy is proposed for a group of WECs, resulting in an adaptive scheduling of the time of repair, based on the state of the entire farm. The state-based maintenance strategy seeks to find an optimal trade-off between the moderate revenue generated from a farm with some devices being in a deteriorated or failed state and the high repair cost that typifies ocean wave farm maintenance practices. The formulation uses a Markov Decision Process (MDP) approach to devise an optimal policy which is based on the count of WECs in different operational states.Our fourth study (chapter 4) focuses on enabling resilient electricity grids with Grid Scale Storage (GSS). GSS offers resilient operations to power grids where the generation, transmission, distribution and consumption of electricity has traditionally been ``just in time . GSS offers the ability to buffer generated energy and dispatch it for consumption later, e.g., during generation outage and shortages. Our research addresses how to operate GSS to generate revenue efficiency in frequency regulation markets. Operation of GSS in frequency regulation markets is desirable due to its fast response capabilities and the corresponding revenues. However, GSS health is strongly dependent on its operation and understanding the trade-offs between revenues and degradation factors is essential. This study answers whether or not operating GSS at high efficiency regularly reduces its long-term performance (and thereby its offered resilience to the power grid).Our fifth study (chapter 5) focuses on the resilience of Wide Area Measurement Systems (WAMS) which is an integral part of modern electrical grid infrastructure. The problem of the global positioning system (GPS) spoofing attacks on smart grid endowed with phasor measurement units (PMUs) is addressed, taking into account the dynamical behavior of the states of the system. It is shown how GPS spoofing introduces a timing synchronization error in the phasor readings recorded by the PMU and alters the measurement matrix of the dynamical model. A generalized likelihood ratio-based hypotheses testing procedure is devised to detect changes in the measurement matrix when the system is subjected to a spoofing attack. Monte Carlo simulations are performed on the 9-bus, 3-machine test grid to demonstrate the implication of the spoofing attack on dynamic state estimation and to analyze the performance of the proposed hypotheses test. Asymptotic performance analysis of the proposed test, which can be used for large-scale smart grid networks, is also presented