A model of assessment of collateral damage on power grids based on complex network theory

As power grids are gradually adjusted to fit into a smart grid paradigm, a common problem is to identify locations where it is most beneficial to introduce distributed generation. In order to assist in such a decision, we work on a graph model of a regional power grid, and propose a method to assess collateral damage to the network resulting from a localized failure. We perform complex network analysis on multiple instances of the network, looking for correlations between estimated damages and betweenness centrality indices, attempting to determine which model is best suited to predict features of our network

A Topological Investigation of Phase Transitions of Cascading Failures in Power Grids

Cascading failures are one of the main reasons for blackouts in electric power transmission grids. The economic cost of such failures is in the order of tens of billion dollars annually. The loading level of power system is a key aspect to determine the amount of the damage caused by cascading failures. Existing studies show that the blackout size exhibits phase transitions as the loading level increases. This paper investigates the impact of the topology of a power grid on phase transitions in its robustness. Three spectral graph metrics are considered: spectral radius, effective graph resistance and algebraic connectivity. Experimental results from a model of cascading failures in power grids on the IEEE power systems demonstrate the applicability of these metrics to design/optimize a power grid topology for an enhanced phase transition behavior of the system

Criteria for Modification of Complex Infrastructure Networks

Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network.Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network

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

Investigation Of Multi-Criteria Clustering Techniques For Smart Grid Datasets

The processing of data arising from connected smart grid technology is an important area of research for the next generation power system. The volume of data allows for increased awareness and efficiency of operation but poses challenges for analyzing the data and turning it into meaningful information. This thesis showcases the utility of clustering algorithms applied to three separate smart-grid data sets and analyzes their ability to improve awareness and operational efficiency. Hierarchical clustering for anomaly detection in phasor measurement unit (PMU) datasets is identified as an appropriate method for fault and anomaly detection. It showed an increase in anomaly detection efficiency according to Dunn Index (DI) and improved computational considerations compared to currently employed techniques such as Density Based Spatial Clustering of Applications with Noise (DBSCAN). The efficacy of betweenness-centrality (BC) based clustering in a novel clustering scheme for the determination of microgrids from large scale bus systems is demonstrated and compared against a multitude of other graph clustering algorithms. The BC based clustering showed an overall decrease in economic dispatch cost when compared to other methods of graph clustering. Additionally, the utility of BC for identification of critical buses was showcased. Finally, this work demonstrates the utility of partitional dynamic time warping (DTW) and k-shape clustering methods for classifying power demand profiles of households with and without electric vehicles (EVs). The utility of DTW time-series clustering was compared against other methods of time-series clustering and tested based upon demand forecasting using traditional and deep-learning techniques. Additionally, a novel process for selecting an optimal time-series clustering scheme based upon a scaled sum of cluster validity indices (CVIs) was developed. Forecasting schemes based on DTW and k-shape demand profiles showed an overall increase in forecast accuracy. In summary, the use of clustering methods for three distinct types of smart grid datasets is demonstrated. The use of clustering algorithms as a means of processing data can lead to overall methods that improve forecasting, economic dispatch, event detection, and overall system operation. Ultimately, the techniques demonstrated in this thesis give analytical insights and foster data-driven management and automation for smart grid power systems of the future

Measuring Decentrality in Blockchain Based Systems

Blockchain promises to provide a distributed and decentralized means of trust among untrusted users. However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin. This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality. In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems. First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer. Then, we quantify decentrality in these layers using various metrics. At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback-Leibler divergence, etc. Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality. At the storage layer, we apply a distribution index to define centrality. Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations. We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network