784 research outputs found
Analyzing Cascading Failures in Smart Grids under Random and Targeted Attacks
We model smart grids as complex interdependent networks, and study targeted
attacks on smart grids for the first time. A smart grid consists of two
networks: the power network and the communication network, interconnected by
edges. Occurrence of failures (attacks) in one network triggers failures in the
other network, and propagates in cascades across the networks. Such cascading
failures can result in disintegration of either (or both) of the networks.
Earlier works considered only random failures. In practical situations, an
attacker is more likely to compromise nodes selectively.
We study cascading failures in smart grids, where an attacker selectively
compromises the nodes with probabilities proportional to their degrees; high
degree nodes are compromised with higher probability. We mathematically analyze
the sizes of the giant components of the networks under targeted attacks, and
compare the results with the corresponding sizes under random attacks. We show
that networks disintegrate faster for targeted attacks compared to random
attacks. A targeted attack on a small fraction of high degree nodes
disintegrates one or both of the networks, whereas both the networks contain
giant components for random attack on the same fraction of nodes.Comment: Accepted for publication in 28th IEEE International Conference on
Advanced Information Networking and Applications (AINA) 201
MATCASC: A tool to analyse cascading line outages in power grids
Blackouts in power grids typically result from cascading failures. The key
importance of the electric power grid to society encourages further research
into sustaining power system reliability and developing new methods to manage
the risks of cascading blackouts. Adequate software tools are required to
better analyze, understand, and assess the consequences of the cascading
failures. This paper presents MATCASC, an open source MATLAB based tool to
analyse cascading failures in power grids. Cascading effects due to line
overload outages are considered. The applicability of the MATCASC tool is
demonstrated by assessing the robustness of IEEE test systems and real-world
power grids with respect to cascading failures
A Critical Review of Robustness in Power Grids using Complex Networks Concepts
Complex network theory for analyzing robustness in energy gridsThis paper reviews the most relevant works that have investigated robustness in power grids using Complex Networks (CN) concepts. In this broad field there are two different approaches. The first one is based solely on topological concepts, and uses metrics such as mean path length, clustering coefficient, efficiency and betweenness centrality, among many others. The second, hybrid approach consists of introducing (into the CN framework) some concepts from Electrical Engineering (EE) in the effort of enhancing the topological approach, and uses novel, more efficient electrical metrics such as electrical betweenness, net-ability, and others. There is however a controversy about whether these approaches are able to provide insights into all aspects of real power grids. The CN community argues that the topological approach does not aim to focus on the detailed operation, but to discover the unexpected emergence of collective behavior, while part of the EE community asserts that this leads to an excessive simplification. Beyond this open debate it seems to be no predominant structure (scale-free, small-world) in high-voltage transmission power grids, the vast majority of power grids studied so far. Most of them have in common that they are vulnerable to targeted attacks on the most connected nodes and robust to random failure. In this respect there are only a few works that propose strategies to improve robustness such as intentional islanding, restricted link addition, microgrids and smart grids, for which novel studies suggest that small-world networks seem to be the best topology.This work has been partially supported by the project TIN2014-54583-C2-2-R from the Spanish Ministerial Commission of Science and Technology (MICYT), by the project S2013/ICE-2933 from Comunidad de Madrid and by the project FUTURE GRIDS-2020 from the Basque Government
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
Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration
To reveal fault propagation paths is one of the most critical studies for the analysis of
power system security; however, it is rather dif cult. This paper proposes a new framework for the fault
propagation path modeling method of power systems based on membrane computing.We rst model the fault
propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter
concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models
and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability
and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP
system and make them suitable for large-scale power systems, we propose a model reduction method
for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output
neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE
14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the
SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise
investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction,
and the simulation results show a new success and promising approach to the engineering domain
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