Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency

Increased coupling between critical infrastructure networks, such as power and communication systems, will have important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several have studied interdependent network models and reported that increased coupling can increase system vulnerability. However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communication networks. This paper reports on two sets of experiments that compare the network vulnerability implications resulting from simple topological models and models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid shows a much higher level of vulnerability, relative to the contagion model. Second, we compare a model of topological cascades in coupled networks to three different physics-based models of power grids coupled to communication networks. Again, the more accurate models suggest very different conclusions. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the coupled topological model, in which zero coupling is optimal. Finally, an extreme case in which communication failures immediately cause grid failures, suggests that if systems are poorly designed, increased coupling can be harmful. Together these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems

Locating line and node disturbances in networks of diffusively coupled dynamical agents

A wide variety of natural and human-made systems consist of a large set of dynamical units coupled into a complex structure. Breakdown of such systems can have dramatic impact, as for instance neurons in the brain or lines in an electric grid. Preventing such catastrophic events requires in particular to be able to detect and locate the source of disturbances as fast as possible. We propose a simple method to identify and locate disturbances in networks of coupled dynamical agents, relying only on time series measurements and on the knowledge of the (Kron-reduced) network structure. The strength and the appeal of the present approach lies in its simplicity paired with the ability to precisely locate disturbances and even to differentiate between line and node disturbances. If we have access to measurement at only a subset of nodes, our method is still able to identify the location of the disturbance if the disturbed nodes are measured. If not, we manage to identify the region of the network where the disturbance occurs.Comment: 15 pages, 5 figure

Dynamical modeling of cascading failures in the Turkish power grid

A reliable supply of electricity is critical for our modern society and any large scale disturbance of the electrical system causes substantial costs. In 2015, one overloaded transmission line caused a cascading failure in the Turkish power grid, affecting about 75 million people. Here, we analyze the Turkish power grid and its dynamical and statistical properties. Specifically, we propose, for the first time, a model that incorporates the dynamical properties and the complex network topology of the Turkish power grid to investigate cascading failures. We find that the network damage depends on the load and generation distribution in the network with centralized generation being more susceptible to failures than a decentralized one. Furthermore, economic considerations on transmission line capacity are shown to conflict with stability.Comment: 8 pages, 5 figure

Securing Smart Grids to Address Environmental Issues in Regional Planning

This chapter examines regional planning and development in relation to sustainability and highlights sustainability challenges in various regional planning case studies. Creating smart cities addresses the problems that arise from rapid urbanisation and growth of the urban population. This chapter provides an overview of smart cities and discusses several global smart city efforts. It introduces the idea of smart energy highlighting the smart grid components and how it tackles environmental challenges in regional planning. Additionally, it analyses several threats to the smart grid that may hinder its efficient operation and makes suggestions on how to deal with them so that sustainable energy may be delivered to smart cities

Creating, Validating, and Using Synthetic Power Flow Cases: A Statistical Approach to Power System Analysis

abstract: Synthetic power system test cases offer a wealth of new data for research and development purposes, as well as an avenue through which new kinds of analyses and questions can be examined. This work provides both a methodology for creating and validating synthetic test cases, as well as a few use-cases for how access to synthetic data enables otherwise impossible analysis. First, the question of how synthetic cases may be generated in an automatic manner, and how synthetic samples should be validated to assess whether they are sufficiently ``real'' is considered. Transmission and distribution levels are treated separately, due to the different nature of the two systems. Distribution systems are constructed by sampling distributions observed in a dataset from the Netherlands. For transmission systems, only first-order statistics, such as generator limits or line ratings are sampled statistically. The task of constructing an optimal power flow case from the sample sets is left to an optimization problem built on top of the optimal power flow formulation. Secondly, attention is turned to some examples where synthetic models are used to inform analysis and modeling tasks. Co-simulation of transmission and multiple distribution systems is considered, where distribution feeders are allowed to couple transmission substations. Next, a distribution power flow method is parametrized to better account for losses. Numerical values for the parametrization can be statistically supported thanks to the ability to generate thousands of feeders on command.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Stochastic timeseries analysis in electric power systems and paleo-climate data

In this thesis a data science study of elementary stochastic processes is laid, aided with the development of two numerical software programmes, applied to power-grid frequency studies and Dansgaard--Oeschger events in paleo-climate data. Power-grid frequency is a key measure in power grid studies. It comprises the balance of power in a power grid at any instance. In this thesis an elementary Markovian Langevin-like stochastic process is employed, extending from existent literature, to show the basic elements of power-grid frequency dynamics can be modelled in such manner. Through a data science study of power-grid frequency data, it is shown that fluctuations scale in an inverse square-root relation with their size, alike any other stochastic process, confirming previous theoretical results. A simple Ornstein--Uhlenbeck is offered as a surrogate model for power-grid frequency dynamics, with a versatile input of driving deterministic functions, showing not surprisingly that driven stochastic processes with Gaussian noise do not necessarily show a Gaussian distribution. A study of the correlations between recordings of power-grid frequency in the same power-grid system reveals they are correlated, but a theoretical understanding is yet to be developed. A super-diffusive relaxation of amplitude synchronisation is shown to exist in space in coupled power-grid systems, whereas a linear relation is evidenced for the emergence of phase synchronisation. Two Python software packages are designed, offering the possibility to extract conditional moments for Markovian stochastic processes of any dimension, with a particular application for Markovian jump-diffusion processes for one-dimensional timeseries. Lastly, a study of Dansgaard--Oeschger events in recordings of paleoclimate data under the purview of bivariate Markovian jump-diffusion processes is proposed, augmented by a semi-theoretical study of bivariate stochastic processes, offering an explanation for the discontinuous transitions in these events and showing the existence of deterministic couplings between the recordings of the dust concentration and a proxy for the atmospheric temperature

Soft Computing approaches in ocean wave height prediction for marine energy applications

El objetivo de esta tesis consiste en investigar el uso de técnicas de Soft Computing (SC) aplicadas a la energía producida por las olas o energía undimotriz. Ésta es, entre todas las energías marinas disponibles, la que exhibe el mayor potencial futuro porque, además de ser eficiente desde el punto de vista técnico, no causa problemas ambientales significativos. Su importancia práctica radica en dos hechos: 1) es aproximadamente 1000 veces más densa que la energía eólica, y 2) hay muchas regiones oceánicas con abundantes recursos de olas que están cerca de zonas pobladas que demandan energía eléctrica. La contrapartida negativa se encuentra en que las olas son más difíciles de caracterizar que las mareas debido a su naturaleza estocástica. Las técnicas SC exhiben resultados similares e incluso superiores a los de otros métodos estadísticos en las estimaciones a corto plazo (hasta 24 h), y tienen la ventaja adicional de requerir un esfuerzo computacional mucho menor que los métodos numérico-físicos. Esta es una de las razones por la que hemos decidido explorar el uso de técnicas de SC en la energía producida por el oleaje. La otra se encuentra en el hecho de que su intermitencia puede afectar a la forma en la que se integra la electricidad que genera con la red eléctrica. Estas dos son las razones que nos han impulsado a explorar la viabilidad de nuevos enfoques de SC en dos líneas de investigación novedosas. La primera de ellas es un nuevo enfoque que combina un algoritmo genético (GA: Genetic Algorithm) con una Extreme Learning Machine (ELM) aplicado a un problema de reconstrucción de la altura de ola significativa (en un boya donde los datos se han perdido, por ejemplo, por una tormenta) utilizando datos de otras boyas cercanas. Nuestro algoritmo GA-ELM es capaz de seleccionar un conjunto reducido de parámetros del oleaje que maximizan la reconstrucción de la altura de ola significativa en la boya cuyos datos se han perdido utilizando datos de boyas vecinas. El método y los resultados de esta investigación han sido publicados en: Alexandre, E., Cuadra, L., Nieto-Borge, J. C., Candil-García, G., Del Pino, M., & Salcedo-Sanz, S. (2015). A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction. Ocean Modelling, 92, 115-123. La segunda contribución combina conceptos de SC, Smart Grids (SG) y redes complejas (CNs: Complex Networks). Está motivada por dos aspectos importantes, mutuamente interrelacionados: 1) la forma en la que los conversores WECs (wave energy converters) se interconectan eléctricamente para formar un parque, y 2) cómo conectar éste con la red eléctrica en la costa. Ambos están relacionados con el carácter aleatorio e intermitente de la energía eléctrica producida por las olas. Para poder integrarla mejor sin afectar a la estabilidad de la red se debería recurrir al concepto Smart Wave Farm (SWF). Al igual que una SG, una SWF utiliza sensores y algoritmos para predecir el olaje y controlar la producción y/o almacenamiento de la electricidad producida y cómo se inyecta ésta en la red. En nuestro enfoque, una SWF y su conexión con la red eléctrica se puede ver como una SG que, a su vez, se puede modelar como una red compleja. Con este planteamiento, que se puede generalizar a cualquier red formada por generadores renovables y nodos que consumen y/o almacenan energía, hemos propuesto un algoritmo evolutivo que optimiza la robustez de dicha SG modelada como una red compleja ante fallos aleatorios o condiciones anormales de funcionamiento. El modelo y los resultados han sido publicados en: Cuadra, L., Pino, M. D., Nieto-Borge, J. C., & Salcedo-Sanz, S. (2017). Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms. Energies, 10(8), 1097