Algorithms for Replica Placement in High-Availability Storage

A new model of causal failure is presented and used to solve a novel replica placement problem in data centers. The model describes dependencies among system components as a directed graph. A replica placement is defined as a subset of vertices in such a graph. A criterion for optimizing replica placements is formalized and explained. In this work, the optimization goal is to avoid choosing placements in which a single failure event is likely to wipe out multiple replicas. Using this criterion, a fast algorithm is given for the scenario in which the dependency model is a tree. The main contribution of the paper is an $O(n + \rho \log \rho)$ dynamic programming algorithm for placing $\rho$ replicas on a tree with $n$ vertices. This algorithm exhibits the interesting property that only two subproblems need to be recursively considered at each stage. An $O(n^2 \rho)$ greedy algorithm is also briefly reported.Comment: 22 pages, 7 figures, 4 algorithm listing

Interrelation of structure and operational states in cascading failure of overloading lines in power grids

As the modern power system is expected to develop to a more intelligent and efficientversion, i.e. the smart grid, or to be the central backbone of energy internet for freeenergyinteractions,securityconcernsrelatedtocascadingfailureshavebeenraisedwithconsideration of catastrophic results. The researches of topological analysis based oncomplex networks have made great contributions in revealing structural vulnerabilitiesof power grids including cascading failure analysis. However, existing literature withinappropriate assumptions in modeling still cannot distinguish the effects between thestructure and operational state to give meaningful guidance for system operation. Thispaper is to reveal the interrelation between network structure and operational statesin cascading failure and give quantitative evaluation by integrating both perspectives.For structure analysis, cascading paths will be identified by extended betweenness andquantitatively described by cascading drop and cascading gradient. Furthermore, theoperational state for cascading paths will be described by loading level. Then, the riskof cascading failure along a specific cascading path can be quantitatively evaluatedconsideringthesetwofactors.Themaximumcascadinggradientofallpossiblecascadingpaths can be used as an overall metric to evaluate the entire power grid for its featuresrelated to cascading failure. The proposed method is tested and verified on IEEE30-bussystem and IEEE118-bus system, simulation evidences presented in this paper suggest

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

A data-driven method to assess the causes and impact of delay propagation in air transportation systems

Air transportation systems are exposed to disruptions, which have significant impact on operations. Airlines operate tight schedules to maximise resource utilisation, however, the lack of sufficient buffers often result in propagating delays. Thus, understanding how likely it is to experience delays, why they keep happening and what is their impact on airline operations are important steps for the management of the disruptions they cause. In this paper, we propose a data-driven method to empirically analyse how delays propagate and their impact on an airline schedule. Our multi-layer network method captures different variables that are influenced by schedule disruption, namely aircraft (tail), crew, passengers and their interfaces. The method is tested on the schedule disruptions of a hub-and-spoke airline where we empirically demonstrate that incorporating information in this multi-layered manner results in a more robust assessment of delay propagation. The method along with the empirical results of this study can support aviation system planners gain additional insights into flight delay propagation patterns and consequently support their resource allocation decisions while improving overall system performance

Intelligent Novel Methods for Identifying Critical Components and Their Combinations for Hypothesized Cyber-physical Attacks Against Electric Power Grids

As a revolutionary change to the traditional power grid, the smart grid is expected to introduce a myriad of noteworthy benefits by integrating the advanced information and communication technologies in terms of system costs, reliability, environmental impacts, operational flexibility, etc. However, the wider deployment of cyber networks in the power grid will bring about important issues on power system cyber security. Meanwhile, the power grid is becoming more vulnerable to various physical attacks due to vandalism and probable terrorist attacks. In an envisioned smart grid environment, attackers have more entry points to various parts of the power grid for launching a well-planned and highly destructive attack in a coordinated manner. Thus, it is important to address the smart grid cyber-physical security issues in order to strengthen the robustness and resiliency of the smart grid in the face of various adverse events. One key step of this research topic is to efficiently identify the vulnerable parts of the smart grid. In this thesis, from the perspective of smart grid cyber-physical security, three critical component combination identification methods are proposed to reveal the potential vulnerability of the smart grid. First, two performance indices based critical component combination recognition methods are proposed for more effectively identifying the critical component combinations in the multi-component attack scenarios. The optimal selection of critical components is determined according to the criticality of the components, which can be modeled by various performance indices. Further, the space-pruning based enumerative search strategy is investigated to comprehensively and effectively identify critical combinations of multiple same or different types of components. The pruned search space is generated based on the criticality of potential target component which is obtained from low-order enumeration data. Specifically, the combinatorial line-generator attack strategy is investigated by exploring the strategy for attacking multiple different types of components. Finally, an effective, novel approach is proposed for identifying critical component combinations, which is termed search space conversion and reduction strategy based intelligent search method (SCRIS). The conversion and reduction of the search space is achieved based on the criticality of the components which is obtained from an efficient sampling method. The classic intelligent search algorithm, Particle Swarm Optimization (PSO), is improved and deployed for more effectively identifying critical component combinations. MATLAB is used as the simulation platform in this study. The IEEE 30, 39, 118 and Polish 2383-bus systems are adopted for verifying the effectiveness of the proposed attack strategies. According to the simulation results, the proposed attack strategies turn out to be effective and computationally efficient. This thesis can provide some useful insight into vulnerability identification in a smart grid environment, and defensive strategies can be developed in view of this work to prevent malicious coordinated multi-component attacks which may initiate cascading failures in a cyber-physical environment

Revealing cascading failure vulnerability in power grids using risk-graph

Security issues related to power grid networks have attracted the attention of researchers in many fields. Recently, a new network model that combines complex network theories with power flow models was proposed. This model, referred to as the extended model, is suitable for investigating vulnerabilities in power grid networks. In this paper, we study cascading failures of power grids under the extended model. Particularly, we discover that attack strategies that select target nodes (TNs) based on load and degree do not yield the strongest attacks. Instead, we propose a novel metric, called the risk graph, and develop novel attack strategies that are much stronger than the load-based and degree-based attack strategies. The proposed approaches and the comparison approaches are tested on IEEE 57 and 118 bus systems and Polish transmission system. The results demonstrate that the proposed approaches can reveal the power grid vulnerability in terms of causing cascading failures more effectively than the comparison approaches

Application of Complex Network Theory in Power System Security Assessment

The power demand increases every year around the world with the growth of population and the expansion of cities. Meanwhile, the structure of a power system becomes increasing complex. Moreover, increasing renewable energy sources (RES) has linked to the power network at different voltage levels. These new features are expected to have a negative impact on the security of the power system. In recent years, complex network (CN) theory has been studied intensively in solving practical problems of large-scale complex systems. A new direction for power system security assessment has been provided with the developments in the CN field. In this thesis, we carry out investigations on models and approaches that aim to make the security assessment from an overview system level with CN theory. Initially, we study the impact of the renewable energy (RE) penetration level on the vulnerability in the future grid (FG). Data shows that the capacity of RE has been increasing over by 10% annually all over the world. To demonstrate the impact of unpredictable fluctuating characteristics of RES on the power system stability, a CN model given renewable energy integration for the vulnerability analysis is introduced. The numerical simulations are investigated based on the simplified 14-generator model of the South Eastern Australia power system. Based on the simulation results, the impact of different penetrations of RES and demand side management on the Australian FG is discussed. Secondly, the distributed optimization performance of the communication network topology in the photovoltaic (PV) and energy storage (ES) combined system is studied with CN theory. A Distributed Alternating Direction Method of Multipliers (D-ADMM) is proposed to accelerate the convergence speed in a large dimensional communication system. It is shown that the dynamic performance of this approach is highly-sensitive to the communication network topology. We study the variation of convergence speed under different communication network topology. Based on this research, guidance on how to design a relatively more optimal communication network is given as well. Then, we focus on a new model of vulnerability analysis. The existing CN models usually neglect the detailed electrical characteristics of a power grid. In order to address the issue, an innovative model which considers power flow (PF), one of the most important characteristics in a power system, is proposed for the analysis of power grid vulnerability. Moreover, based on the CN theory and the Max-Flow theorem, a new vulnerability index is presented to identify the vulnerable lines in a power system. The comparative simulations between the power flow model and existing models are investigated on the IEEE 118-bus system. Based on the PF model, we improve a power system cascading risk assessment model. In this research the risk is defined by the consequence and probabilities of the failures in the system, which is affected by both power factors and the network structure. Furthermore, a cascading event simulation module is designed to identify the cascading chain in the system during a failure. This innovation can form a better module for the cascading risk assessment of a power system. Finally, we argue that the current cyber-physical network model have their limitations and drawbacks. The existing “point-wise” failure model is not appropriate to present the interdependency of power grid and communication network. The interactions between those two interdependent networks are much more complicated than they were described in some the prior literatures. Therefore, we propose a new interdependency model which is based on earlier research in this thesis. The simulation results confirm the effectiveness of the new model in explaining the cascading mechanism in this kind of networks