Characterizing Infrastructure Resilience in Disasters Using Dynamic Network Analysis of Consumers’ Service Disruption Patterns

This study proposes a network analysis framework for characterizing infrastructure resilience in the aftermath of disasters through the use of consumers’ service disruption information. In the presented framework, the notion of “peers” is used to construct the network models of consumers experiencing service disruption in the aftermath of a disaster to understand the type and extent of infrastructure damages and specify disruption patterns. Data related to electricity disruption in Bhaktapur, Nepal, in the aftermath of the 2015 Gorkha Earthquake is used to construct the network models of consumers’ networks at different points in time in the aftermath of the disaster. The created models are then used in network analysis for examining the network topological characteristics (such as clustering) and specifying the attributes of service disruption. The contribution of this paper lie in: (i) the development and validation of a novel network from disruption information, (ii) identify the extent of infrastructure disruption, type of damage, and recoverability from changes in network topology over time

Reliability Modeling and Improvement of Critical Infrastructures: Theory, Simulation, and Computational Methods

This dissertation presents a framework for developing data-driven tools to model and improve the performance of Interconnected Critical Infrastructures (ICIs) in multiple contexts. The importance of ICIs for daily human activities and the large volumes of data in continuous generation in modern industries grant relevance to research efforts in this direction. Chapter 2 focuses on the impact of disruptions in Multimodal Transportation Networks, which I explored from an application perspective. The outlined research directions propose exploring the combination of simulation for decision-making with data-driven optimization paradigms to create tools that may provide stakeholders with optimal policies for a wide array of scenarios and conditions. The flexibility of the developed simulation models, in combination with cutting-edge technologies, such as Deep Reinforcement Learning (DRL), sets the foundation for promising research efforts on the performance, analysis, and optimization of Inland Waterway Transportation Systems. Chapter 3 explores data-driven models for condition monitoring and prognostics, with a focus on using Deep Learning (DL) to predict the Remaining Useful Life of turbofan engines based on sequential sensor measurements. A myriad of approaches exist for this type of problems, and the main contribution for future efforts might be centered around combining this type of data-driven methods with simulation tools and computational methods in the context of network resilience optimization. Chapter 4 revolves around developing data-driven methods for estimating all-terminal reliability of networks with arbitrary structures and outlines research directions for data-driven surrogate models. Furthermore, the use of DRL for network design optimization and maximizing all-terminal network reliability is presented. This poses a promising research venue that has been extended to network reliability problems involving dynamic decision-making on allocating new resources, maintaining and/or improving the edges already in the network, or repairing failed edges due to aging. The outlined research presents various data-driven tools developed to collaborate in the context of modeling and improvement for Critical Infrastructures. Multiple research venues have been intertwined by combining various paradigms and methods to achieve this goal. The final product is a line of research focused on reliability estimation, design optimization, and prognostics and health management for ICIs, by combining computational methods and theory

Improved coordinated automatic voltage control in power grids through complex network analysis

PhD ThesisAutomatic and Co-ordinated Voltage Regulation (CVR) contributes significantly to economy and security of transmission grids. The role of CVR will become more critical as systems are operated closer to their capacity limits due to technical, economic and environmental reasons. CVR has 1 min resolution and owing to the inherent complexity of the task, CVR is enabled through zoning-based Reduced Control Models (RCM) i.e. simplified models of the network suitable for Voltage Control. RCM not only enables CVR bus also affects its performance and robustness. This thesis contributes towards improved CVR through thorough investigation of the RCM. As a starting point, with current power systems structure in mind, this work investigates static RCM schemes (i.e. fixed Reduced Control Model for all network configurations). To that end this thesis develops: (1) a novel generic framework for CVR modelling and evaluation and (2) new zoning-based RCM approaches using Complex Network Analysis. The evaluation of CVR in conjunction with both static and adaptive RCM schemes are based on a novel framework for CVR modelling and evaluation. This framework is generic and can be used to facilitate the selection and design of any of the CVR components. As a next step, due to the fact that a single RCM cannot be optimal for all network configurations, adaptive RCM (i.e. RCM determined in an online event driven fashion) is investigated using the proposed framework. This concerns future transmission grids where RCM is driven by the need for reliability rather than economy of measurement points at a planning phase. Lastly, this thesis examines zone division in an interconnected system ranging from EHV down to MV, and assesses the required degree of co-ordination for the voltage control of these zones. Essentially, this last item extends the scope of this work’s contributions beyond a single transmission-level Independent System Operator (ISO).EPSRC for funding my Research and the Consortium of the “Autonomic Power System” project

Protection des Infrastructures Essentielles par Advanced Modélisation, simulation et optimisation pour l’atténuation et résilience de défaillance en cascade

Continuously increasing complexity and interconnectedness of modern critical infrastructures, together with increasingly complex risk environments, pose unique challenges for their secure, reliable, and efficient operation. The focus of the present dissertation is on the modelling, simulation and optimization of critical infrastructures (CIs) (e.g., power transmission networks) with respect to their vulnerability and resilience to cascading failures. This study approaches the problem by firstly modelling CIs at a fundamental level, by focusing on network topology and physical flow patterns within the CIs. A hierarchical network modelling technique is introduced for the management of system complexity. Within these modelling frameworks, advanced optimization techniques (e.g., non-dominated sorting binary differential evolution (NSBDE) algorithm) are utilized to maximize both the robustness and resilience (recovery capacity) of CIs against cascading failures. Specifically, the first problem is taken from a holistic system design perspective, i.e. some system properties, such as its topology and link capacities, are redesigned in an optimal way in order to enhance system’s capacity of resisting to systemic failures. Both topological and physical cascading failure models are applied and their corresponding results are compared. With respect to the second problem, a novel framework is proposed for optimally selecting proper recovery actions in order to maximize the capacity of the CI network of recovery from a disruptive event. A heuristic, computationally cheap optimization algorithm is proposed for the solution of the problem, by integrating foundemental concepts from network flows and project scheduling. Examples of analysis are carried out by referring to several realistic CI systems.Sans cesse croissante complexité et l'interdépendance des infrastructures critiques modernes, avec des environs de risque plus en plus complexes, posent des défis uniques pour leur exploitation sûre, fiable et efficace. L'objectif de la présente thèse est sur la modélisation, la simulation et l'optimisation des infrastructures critiques (par exemple, les réseaux de transmission de puissance) à l'égard de leur vulnérabilité et la résilience aux défaillances en cascade. Cette étude aborde le problème en modélisant infrastructures critiques à un niveau fondamental, en se concentrant sur la topologie du réseau et des modèles de flux physiques dans les infrastructures critiques. Un cadre de modélisation hiérarchique est introduit pour la gestion de la complexité du système. Au sein de ces cadres de modélisation, les techniques d'optimisation avancées (par exemple, non-dominée de tri binaire évolution différentielle (NSBDE) algorithme) sont utilisés pour maximiser à la fois la robustesse et la résilience (capacité de récupération) des infrastructures critiques contre les défaillances en cascade. Plus précisément, le premier problème est pris à partir d'un point de vue de la conception du système holistique, c'est-à-dire certaines propriétés du système, tels que ses capacités de topologie et de liaison, sont redessiné de manière optimale afin d'améliorer la capacité de résister à des défaillances systémiques de système. Les deux modèles de défaillance en cascade topologiques et physiques sont appliquées et leurs résultats correspondants sont comparés. En ce qui concerne le deuxième problème, un nouveau cadre est proposé pour la sélection optimale des mesures appropriées de récupération afin de maximiser la capacité du réseau d’infrastructure critique de récupération à partir d'un événement perturbateur. Un algorithme d'optimisation de calcul pas cher heuristique est proposé pour la solution du problème, en intégrant des concepts fondamentaux de flux de réseau et le calendrier du projet. Exemples d'analyse sont effectués en se référant à plusieurs systèmes de CI réalistes

Structural Vulnerability Analysis of Electric Power Distribution Grids

Power grid outages cause huge economical and societal costs. Disruptions in the power distribution grid are responsible for a significant fraction of electric power unavailability to customers. The impact of extreme weather conditions, continuously increasing demand, and the over-ageing of assets in the grid, deteriorates the safety of electric power delivery in the near future. It is this dependence on electric power that necessitates further research in the power distribution grid security assessment. Thus measures to analyze the robustness characteristics and to identify vulnerabilities as they exist in the grid are of utmost importance. This research investigates exactly those concepts- the vulnerability and robustness of power distribution grids from a topological point of view, and proposes a metric to quantify them with respect to assets in a distribution grid. Real-world data is used to demonstrate the applicability of the proposed metric as a tool to assess the criticality of assets in a distribution grid

A review on economic and technical operation of active distribution systems

© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods

Vulnerability of critical infrastructures : identifying critical nodes.

The objective of this research was the development of tools and techniques for the identification of critical nodes within critical infrastructures. These are nodes that, if disrupted through natural events or terrorist action, would cause the most widespread, immediate damage. This research focuses on one particular element of the national infrastructure: the bulk power system. Through the identification of critical elements and the quantification of the consequences of their failure, site-specific vulnerability analyses can be focused at those locations where additional security measures could be effectively implemented. In particular, with appropriate sizing and placement within the grid, distributed generation in the form of regional power parks may reduce or even prevent the impact of widespread network power outages. Even without additional security measures, increased awareness of sensitive power grid locations can provide a basis for more effective national, state and local emergency planning. A number of methods for identifying critical nodes were investigated: small-world (or network theory), polyhedral dynamics, and an artificial intelligence-based search method - particle swarm optimization. PSO was found to be the only viable approach and was applied to a variety of industry accepted test networks to validate the ability of the approach to identify sets of critical nodes. The approach was coded in a software package called Buzzard and integrated with a traditional power flow code. A number of industry accepted test networks were employed to validate the approach. The techniques (and software) are not unique to power grid network, but could be applied to a variety of complex, interacting infrastructures

Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations

This comprehensive review paper explores power system resilience, emphasizing its evolution, comparison with reliability, and conducting a thorough analysis of the definition and characteristics of resilience. The paper presents the resilience frameworks and the application of quantitative power system resilience metrics to assess and quantify resilience. Additionally, it investigates the relevance of complex network theory in the context of power system resilience. An integral part of this review involves examining the incorporation of data-driven techniques in enhancing power system resilience. This includes the role of data-driven methods in enhancing power system resilience and predictive analytics. Further, the paper explores the recent techniques employed for resilience enhancement, which includes planning and operational techniques. Also, a detailed explanation of microgrid (MG) deployment, renewable energy integration, and peer-to-peer (P2P) energy trading in fortifying power systems against disruptions is provided. An analysis of existing research gaps and challenges is discussed for future directions toward improvements in power system resilience. Thus, a comprehensive understanding of power system resilience is provided, which helps in improving the ability of distribution systems to withstand and recover from extreme events and disruptions

Nuevas técnicas para modelizar y analizar la vulnerabilidad de infraestructuras críticas de energía interdependientes

La interdependencia entre las redes de gas y electricidad es motivo de preocupación debido a la creciente utilización del gas para la generación de electricidad en centrales de ciclo combinado y al uso de energía eléctrica de los compresores en la red de gas. Estas redes están sujetas a riesgos de interrupción del suministro derivados de posibles problemas técnicos o amenazas intencionadas. Por lo tanto, resulta conveniente modelizar y analizar la vulnerabilidad de estas infraestructuras críticas de energía interdependientes.En esta tesis doctoral se presenta, en primer lugar, una metodología para analizar conjuntamente los flujos de electricidad y gas. El conjunto de ecuaciones no lineales que representan la operación del sistema de potencia se resuelve utilizando el método de Newton-Raphson, mientras que las ecuaciones en la red de gas se resuelven utilizando el enfoque de transformada análoga-lineal. Se presentan dos casos de estudio para demostrar la simplicidad de la metodología propuesta. Los resultados obtenidos se verifican contra el método Newton-Raphson tradicional con el fin de comprobar la solución alcanzada, encontrando un buen desempeño de la metodología conjunta aplicada. La aplicación del enfoque propuesto permite el análisis de la vulnerabilidad de las infraestructuras energéticas interdependientes. También, se desarrolla una metodología para evaluar la vulnerabilidad estructural de las redes de energía eléctrica y gas acopladas, considerando interdependencias en el proceso de fallos en cascada. La vulnerabilidad se evalúa empleando el índice de desconexión de carga y las medidas de centralidad de vulnerabilidad geodésica e impacto en la conectividad. El estudio muestra una elevada correlación entre el índice de desconexión de carga y el índice de vulnerabilidad geodésica. De esta manera, la teoría de grafos puede usarse como sustituto de los enfoques de flujos de carga que demandan un conocimiento detallado de los parámetros eléctricos e hidráulicos de los sistemas bajo estudio y son computacionalmente más intensivos que los métodos estadísticos de grafos. Como resultado, se propone un nuevo método para estimar la vulnerabilidad de las redes de energía eléctrica y gas conjuntas utilizando el índice de vulnerabilidad geodésica. Asimismo, se estudia el comportamiento de las redes de electricidad y gas natural de España, tanto de manera separada como conjunta. Los resultados muestran que la red de gas natural es menos robusta que la red eléctrica y que la red acoplada es más vulnerable que la red eléctrica ante fallos aleatorios y deliberados. Además, eliminar los nodos más fuertemente conectados de los dos sistemas independientes resultaría una estrategia de ataque eficaz para el rápido colapso de las infraestructuras acopladas interdependientes. Por último, se evalúa la robustez estructural de los planes de expansión de las infraestructuras de electricidad y gas natural en España. Los casos de estudio corresponden a las principales inversiones propuestas por los operadores de los sistemas en 2015-2020. Los resultados demuestran que la construcción de algunas instalaciones para la expansión de ambas redes no mejora la robustez estructural de la red acoplada; sin embargo, cuando se tiene en cuenta todo el programa de inversión se produce una mejora relativa de hasta un 6% con respecto al caso base. La metodología propuesta en esta tesis corrobora que la aplicación de la teoría de grafos es adecuada para analizar la planificación de activos de una infraestructura energética crítica, requiriendo únicamente la topología y el programa de inversiones para evaluar el desempeño de la red acoplada en caso de fallos en cascada. En suma, esta tesis doctoral pone de relieve la importancia de que los sistemas energéticos se aborden como redes acopladas debido a sus fuertes interacciones. Una perturbación en un sistema puede no ser crítica si las infraestructuras están separadas, pero dado que ambas redes son interdependientes, el impacto resultante podría causar fallos en el otro sistema. En otras palabras, las interdependencias aumentan el impacto de las perturbaciones.<br /