Power Grid Network Research Articles

STPA-SafeSec: Safety and security analysis for cyber-physical systems

Cyber-physical systems tightly integrate physical processes and information and communication technologies. As today's critical infrastructures, e.g., the power grid or water distribution networks, are complex cyber-physical systems, ensuring their safety and security becomes of paramount importance. Traditional safety analysis methods, such as HAZOP, are ill-suited to assess these systems. Furthermore, cybersecurity vulnerabilities are often not considered critical, because their effects on the physical processes are not fully understood. In this work, we present STPA-SafeSec, a novel analysis methodology for both safety and security. Its results show the dependencies between cybersecurity vulnerabilities and system safety. Using this information, the most effective mitigation strategies to ensure safety and security of the system can be readily identified. We apply STPA-SafeSec to a use case in the power grid domain, and highlight its benefits.

Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid.

Due to the advantage of avoiding upstream disturbance and voltage fluctuation from a power transmission system, Islanded Micro-Grids (IMG) have attracted much attention. In this paper, we first propose a novel self-sufficient Cyber-Physical System (CPS) supported by Internet of Things (IoT) techniques, namely “archipelago micro-grid (MG)”, which integrates the power grid and sensor networks to make the grid operation effective and is comprised of multiple MGs while disconnected with the utility grid. The Electric Vehicles (EVs) are used to replace a portion of Conventional Vehicles (CVs) to reduce CO emission and operation cost. Nonetheless, the intermittent nature and uncertainty of Renewable Energy Sources (RESs) remain a challenging issue in managing energy resources in the system. To address these issues, we formalize the optimal EV penetration problem as a two-stage Stochastic Optimal Penetration (SOP) model, which aims to minimize the emission and operation cost in the system. Uncertainties coming from RESs (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by the Monte Carlo-based method. To enable the reasonable deployment of EVs in each MGs, we develop two scheduling schemes, namely Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS), respectively. An extensive simulation study based on a modified 9 bus system with three MGs has been carried out to show the effectiveness of our proposed schemes. The evaluation data indicates that our proposed strategy can reduce both the environmental pollution created by CO emissions and operation costs in UCS and LCS.

Taming instabilities in power grid networks by decentralized control

Renewables will soon dominate energy production in our electric power system. And yet, how to integrate renewable energy into the grid and the market is still a subject of major debate. Decentral Smart Grid Control (DSGC) was recently proposed as a robust and decentralized approach to balance supply and demand and to guarantee a grid operation that is both economically and dynamically feasible. Here, we analyze the impact of network topology by assessing the stability of essential network motifs using both linear stability analysis and basin volume for delay systems. Our results indicate that if frequency measurements are averaged over sufficiently large time intervals, DSGC enhances the stability of extended power grid systems. We further investigate whether DSGC supports centralized and/or decentralized power production and find it to be applicable to both. However, our results on cycle-like systems suggest that DSGC favors systems with decentralized production. Here, lower line capacities and lower averaging times are required compared to those with centralized production.

Electromigration assessment for power grid networks considering temperature and thermal stress effects

With technology scaling, reliability has emerged as a major design constraint for very-large-scale integrated circuits. Many prior works have investigated electromigration (EM) on full-chip power grid interconnects. However, most of the published results were obtained under the assumption of uniformly distributed temperature and/or residual stress across interconnects. In this paper, we demonstrate the implementation of novel methodology and flow for full-chip EM assessment on the multi-layered power grid networks of a 32nm test-chip and investigate the impacts of the within-die temperature and thermal stress variations on the failure rate. The proposed approach is based on recently developed physics-based EM models and the EM-induced IR-drop degradation criterion that replaces the traditional conservative weakest segment method. The cross-layout temperature distribution caused by power dissipations in devices and by interconnect Joule heating has been characterized and taken into account in the full-chip EM assessment methodology. Results of the simulations performed on the analyzed multi-layered power/ground nets show that traditional assumption of the uniform average temperature leads to inaccurate predictions of the time-to-failure. Furthermore, the consideration of thermal stress variation results in a retarded EM induced degradation.

Distributed Production-Sharing Optimization and Application to Power Grid Networks

Based on recent works on asynchronous versions of the distributed alternating direction method of multipliers (ADMM) algorithm, we develop and prove the convergence of a distributed asynchronous method for production-sharing problems over networks. The asynchronous nature of the algorithm not only allows both for the relaxation of the synchronization constraint often inherent to distributed ADMM-based methods and distributed optimization methods at large, but also allows for random local failures to occur in fully centralized methods. These two considerations motivate the application of the method to the direct-current optimal power flow (DC-OPF) problem in power transmission networks. Applied to the DC-OPF, this method leads to an overall network optimal production obtained through a sequence of local computations in subareas of the network (each area waking up randomly while the rest of the network is nonoperational) and neighboring data exchanges. In another scenario, the DC-OPF is performed via iterations of a centralized network-wide ADMM method, which may contain disconnected nodes (in general with low probability and for a short duration). In both cases, this method still converges and thus provides additional flexibility to classical DC-OPF algorithms. The proposed algorithm, inherently designed for networks of overlapping subareas, is then extended to networks of nonoverlapping areas. Simulations are carried out on the IEEE-30 and IEEE-118 bus test systems, which illustrate the convergence, scalability, and effectiveness of the proposed algorithms.

Exploring the topological sources of robustness against invasion in biological and technological networks.

For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent’s rule are applied to show a subtle trade-off between topological and wiring complexity.

The Current and Solution of Wind Power Consumption for Gansu Province

The average wind power in China increases year by year, obviously wind power output volatility and uncertainty that makes the large-scale wind power grid to bring a lot of problem, such as peaking and frequency modulation, voltage regulator, stability, power quality problems, etc. The power structure in China is given priority with coal-fired thermal power, peaking power is relatively scarce. Lack of wind power on a large scale grid has intensified. The paper briefly analyzes the power grid network frame structure, load and power characteristics in Gansu province. Considering the measured data in 2014 of Gansu power network, intuitive shows Gansu power grid fluctuation characteristics of wind power output in different period of time, discussed the influence of wind power grid peak shaving capability. It improving the large-scale wind power access after the measures of Gansu power grid peak shaving capability.

Sparse Causal Temporal Modeling to Inform Power System Defense

The accurate estimation of system state variables at buses in the power-grid is crucial for determining the operational state of the power system. Spoofing attacks on meters at buses can bypass bad data detectors in Supervisory Control and Data Acquisition (SCADA) systems and undetectably manipulate state estimates. Existing methods for protection of the state estimate of critical buses against spoofing attacks assume the existence of a given set of set of critical buses/meters to be protected. Given budget constraints, determining the critical set of meters/buses to be protected against spoofing attacks is a crucial problem. In this paper, we address the issue of how best to determine the set of meters to be protected. We suggest the use of two sparse temporal modeling methods from the machine learning literature to evaluate the influence of each meter measurement on the power grid network. Based on the influence distribution as indexed by these methods we can populate a set of meter measurements which serves a dual purpose. First, this set can serve as the initial collection of nodes that is a required input for methods developed to defend against false injection attacks on power system state estimation. Second, the high influential power of meters in the set incentivizes their protection since they are pivotal for making real-time prediction in the absence of complete real-time data from other meters. Thus, we introduce influence as one of the primary criteria to be considered in the process of selecting nodes to protect. We also suggest a novel way to measure influence based on sparse structure learning. We provide results on a publicly available simulated dataset and discuss how to use the notion of the N50 statistic to calibrate the number of meter measurements that should be protected under a specified budget.

New centrality measures for assessing smart grid vulnerabilities and predicting brownouts and blackouts

This paper proposes mathematical models based on the electrical properties of smart grids for conducting vulnerability analyses and predicting brownouts and blackouts. Definitions of pseudo-Laplacian, pseudo-adjacency and pseudo-degree matrices for smart grids are introduced to specify new centrality measures with electrical interpretations. The centrality measures are used to rank the relative importance of nodes (e.g., generating stations or substations) and edges (e.g., transmission lines or buses) of a graph corresponding to a power grid network and to assess the overall vulnerability of the network. The reliability of using the centrality measures to predict brownouts and blackouts is demonstrated in the face of random and targeted attacks. Monte-Carlo simulations are used to analyze attacks on smart grid networks and to assess the performance of the centrality measures. The simulations employ the IEEE 30-bus, IEEE 57-bus and IEEE 300-bus networks as well as the WSCC 4941-bus real power grid. Every scenario in the Monte-Carlo simulations involves the removal of a subset of buses and performing a complete nonlinear Newton–Raphson power flow analysis to compute the power traffic matrix and the corresponding centrality. The Monte-Carlo simulations conclusively demonstrate that electrical centrality measures based on the power traffic matrix are reliable indicators of the total unsatisfied load ratio (i.e., the load taken offline due to an attack divided by the total load demand). A key result is that, if the total centrality score of the removed buses exceeds a threshold estimated via Monte-Carlo simulation, then a sudden and dramatic jump to a blackout situation is ensured.

고고도핵폭발 전자기펄스 피해 분석을 위한 전산모사 연구

This paper describes computer simulation program of high-altitude electromagnetic pulse (HEMP). The HEMP is produced by the gamma rays form high-altitude nuclear explosion. The gamma rays generate a current of compton electron that leads to the production of electromagnetic fields. In case of high altitude nuclear burst, the electrical fields at the earth's surface are strong enough to be damaged for electrical and electronic device over a very much larger area. Therefore, national infrastructure will be serious damage such as power grid and communication network. In this paper introduce simulation program for calculation of HEMP and present to simulation study results of high altitude nuclear explosion experiment from U.S. and U.S.S.R.

Link Prediction via More Information and Evaluation Index

Link prediction is an important content in the related fields of computer and network. Recently, the link prediction algorithm based on node similarity is the research hotspot. However, it is found that the outcome of single predictor has large deviations with the actual network. For this question, in this paper, we propose a similarity index by combining more information of network from ten kinds of existed algorithm. We compare ten well-known local similarity index on five kinds of real network datasets like USAir network, Power Grid network and so on. The experimental results show that the new index has a better accuracy and university than the current algorithms. Simultaneously, we also found that the evaluation measures of link prediction are partial and one-sided. We therefore design another new evaluation index considering the factors of time and accuracy, which can evaluate algorithm more comprehensively and scientifically. © 2015 by Binary Information Press

GridPACKTM: A framework for developing power grid simulations on high-performance computing platforms

This paper describes the GridPACKTM framework, which is designed to help power grid engineers develop software capable of running on high-performance computers. The framework makes extensive use of software templates to provide high-level functionality while still providing flexibility to easily implement a broad range of models and algorithms. GridPACKTM contains modules for setting up distributed power grid networks, supporting application-specific bus and branch models, creating distributed matrices and vectors and using parallel linear and non-linear solvers. It also provides mappers to create matrices and vectors based on properties of the network and functionality to support Input/Output (IO) and to manage errors. The goal of GridPACKTM is to substantially reduce the complexity of writing software for parallel computers while still providing efficient and scalable software solutions. The use of GridPACKTM is illustrated for a simple powerflow example and performance results for the powerflow and dynamic simulations are discussed.

Maximizing the Strength of Fiber Bundles under Uniform Loading.

The collective strength of a system of fibers, each having a failure threshold drawn randomly from a distribution, indicates the maximum load carrying capacity of different disordered systems ranging from disordered solids, power-grid networks, to traffic in a parallel system of roads. In many of the cases where the redistribution of load following a local failure can be controlled, it is a natural requirement to find the most efficient redistribution scheme, i.e., following which system can carry the maximum load. We address the question here and find that the answer depends on the mode of loading. We analytically find the maximum strength and corresponding redistribution schemes for sudden and quasistatic loading. The associated phase transition from partial to total failure by increasing the load has been studied. The universality class is found to be dependent on the redistribution mechanism.

Robust Multiobjective Controllability of Complex Neuronal Networks.

This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

Parallel GMRES solver for fast analysis of large linear dynamic systems on GPU platforms

In this paper, we propose an efficient parallel dynamic linear solver, called GPU-GMRES, for transient analysis of large linear dynamic systems such as large power grid networks. The new method is based on the preconditioned generalized minimum residual (GMRES) iterative method implemented on heterogeneous CPU–GPU platforms. The new solver is very robust and can be applied to power grids with different structures as well as for general analysis problems for large linear dynamic systems with asymmetric matrices. The proposed GPU-GMRES solver adopts the very general and robust incomplete LU based preconditioner. We show that by properly selecting the right amount of fill-ins in the incomplete LU factors, a good trade-off between GPU efficiency and convergence rate can be achieved for the best overall performance. Such tunable feature can make this algorithm very adaptive to different problems. GPU-GMRES solver properly partitions the major computing tasks in GMRES solver to minimize the data traffic between CPU and GPUs to enhance performance of the proposed method. Furthermore, we propose a new fast parallel sparse matrix–vector (SpMV) multiplication algorithm to further accelerate the GPU-GMRES solver. The new algorithm, called segSpMV, can enjoy full coalesced memory access compared to existing approaches. To further improve the scalability and efficiency, segSpMV method is further extended to multi-GPU platforms, which leads to more scalable and faster multi-GPU GMRES solver. Experimental results on the set of the published IBM benchmark circuits and mesh-structured power grid networks show that the GPU-GMRES solver can deliver order of magnitudes speedup over the direct LU solver, UMFPACK. The resulting multi-GPU-GMRES can also deliver 3–12×speedup over the CPU implementation of the same GMRES method on transient analysis.

CPIndex: Cyber-Physical Vulnerability Assessment for Power-Grid Infrastructures

To protect complex power-grid control networks, power operators need efficient security assessment techniques that take into account both cyber side and the power side of the cyber-physical critical infrastructures. In this paper, we present CPINDEX, a security-oriented stochastic risk management technique that calculates cyber-physical security indices to measure the security level of the underlying cyber-physical setting. CPINDEX installs appropriate cyber-side instrumentation probes on individual host systems to dynamically capture and profile low-level system activities such as interprocess communications among operating system assets. CPINDEX uses the generated logs along with the topological information about the power network configuration to build stochastic Bayesian network models of the whole cyber-physical infrastructure and update them dynamically based on the current state of the underlying power system. Finally, CPINDEX implements belief propagation algorithms on the created stochastic models combined with a novel graph-theoretic power system indexing algorithm to calculate the cyber-physical index, i.e., to measure the security-level of the system's current cyber-physical state. The results of our experiments with actual attacks against a real-world power control network shows that CPINDEX, within few seconds, can efficiently compute the numerical indices during the attack that indicate the progressing malicious attack correctly.

Improving the robustness of complex networks with preserving community structure.

Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are ‘robust yet fragile’, which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks.

Slow poisoning and destruction of networks: edge proximity and its implications for biological and infrastructure networks.

We propose a network metric, edge proximity, P(e), which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high P(e) might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by P(e) leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. P(e) can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by P(e) causes notable efficiency loss in U.S. and European power grid networks. P(e) identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by P(e) form the backbone of long food web chains. Finally, we scrutinize the potential of P(e) in edge controllability dynamics of directed networks.

A method for designing power supply chain networks accounting for failure scenarios and preventive maintenance

A power grid is vulnerable and failures are inevitable. Failures decrease the power supply with an adverse effect on meeting the demand for electricity. Therefore, there is a need for a method to design power grid networks that result in the least possible disruption to the power supply when a failure occurs. In the literature, the focus has been on the design of the generation system without considering the transmission system or failures in the transmission system. Since power grids are integrated generation and transmission systems, each system will affect the other, so both generation and transmission systems need to be considered, as they are in this article. Methods developed for the structural modelling and analysis of supply chains are shown to be useful. The focus in this article is on describing a method using the supply chain construct for designing power grids that are relatively insensitive to failure in the integrated generation and transmission system. The efficacy of the method is illustrated using data from the Tehran Regional Electric Company. One of the findings is that targeted failures have a higher impact on decreasing the performance of the power grid than random failures. However, the focus is on the method rather than the results per se.

Comparative analysis of existing models for power-grid synchronization

The dynamics of power-grid networks is becoming an increasingly active area of research within the physics and network science communities. The results from such studies are typically insightful and illustrative, but are often based on simplifying assumptions that can be either difficult to assess or not fully justified for realistic applications. Here we perform a comprehensive comparative analysis of three leading models recently used to study synchronization dynamics in power-grid networks—a fundamental problem of practical significance given that frequency synchronization of all power generators in the same interconnection is a necessary condition for a power grid to operate. We show that each of these models can be derived from first principles within a common framework based on the classical model of a generator, thereby clarifying all assumptions involved. This framework allows us to view power grids as complex networks of coupled second-order phase oscillators with both forcing and damping terms. Using simple illustrative examples, test systems, and real power-grid datasets, we study the inherent frequencies of the oscillators as well as their coupling structure, comparing across the different models. We demonstrate, in particular, that if the network structure is not homogeneous, generators with identical parameters need to be modeled as non-identical oscillators in general. We also discuss an approach to estimate the required (dynamical) system parameters that are unavailable in typical power-grid datasets, their use for computing the constants of each of the three models, and an open-source MATLAB toolbox that we provide for these computations.