Spatial and performance optimality in power distribution networks

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Complex network theory has been widely used in vulnerability analysis of power networks, especially for power transmission ones. With the development of the smart grid concept, power distribution networks are becoming increasingly relevant. In this paper, we model power distribution systems as spatial networks. Topological and spatial properties of 14 European power distribution networks are analyzed, together with the relationship between geographical constraints and performance optimization, taking into account economic and vulnerability issues. Supported by empirical reliability data, our results suggest that power distribution networks are influenced by spatial constraints which clearly affect their overall performance.Peer ReviewedPostprint (author's final draft

Vulnerability analysis in complex networks under a flood risk reduction point of view

The measurement and mapping of transportation network vulnerability to natural hazards constitute subjects of global interest for a sustainable development agenda and as means of adaptation to climate change. During a flood, some elements of a transportation network can be affected, causing the loss of lives. Furthermore, impacts include damage to vehicles, streets/roads, and other logistics services - sometimes with severe economic consequences. The Network Science approach may offer a valuable perspective considering one type of vulnerability related to network-type critical infrastructures: the topological vulnerability. The topological vulnerability index associated with an element is defined as reducing the network’s average efficiency due to removing the set of edges related to that element. In this paper, we present the results of a systematic literature overview and a case study applying the topological vulnerability index for the highways in Santa Catarina (Brazil). We produce a map considering that index and areas susceptible to urban floods and landslides. Risk knowledge, combining hazard and vulnerability, is the first pillar of an Early Warning System and represents an important tool for stakeholders of the transportation sector in a disaster risk reduction agenda.Peer Reviewe

Unique Opportunities of Island States to Transition to a Low-Carbon Mobility System

Small islands developing states (SIDS) contribute minuscule proportions to global greenhouse gas (GHG) emissions and energy consumption, but are highly exposed to climate change impacts, in particular to extreme weather events and sea-level rise. However, there is little research on potential decarbonization trajectories unique to SIDS. Here, we argue that insular topology, scale, and economy are distinctive characteristics of SIDS that facilitate overcoming carbon lock-in. We investigate these dimensions for the three islands of Barbados, Fiji, and Mauritius. We find that insular topologies and small scale offer an opportunity for both public transit corridors and rapid electrification of car fleets. The tourism sector enables local decision-makers and investors to experiment with shared mobility and to induce spillover effects by educating tourists about new mobility options. Limited network effects, and the particular economy thus enables to overcome carbon lock-in. We call for targeted investments into SIDS to transition insular mobility systems towards zero carbon in 2040. The decarbonization of SIDS is not only needed as a mitigation effort, but also as a strong signal to the global community underlining that a zero-carbon future is possible.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

A Quantitative Framework for Assessing Vulnerability and Redundancy of Freight Transportation Networks

Freight transportation networks are an important component of everyday life in modern society. Disruption to these networks can make peoples’ daily lives extremely difficult as well as seriously cripple economic productivity. This dissertation develops a quantitative framework for assessing vulnerability and redundancy of freight transportation networks. The framework consists of three major contributions: (1) a two- stage approach for estimating a statewide truck origin-destination (O-D) trip table, (2) a decision support tool for assessing vulnerability of freight transportation networks, and (3) a quantitative approach for measuring redundancy of freight transportation networks.The dissertation first proposes a two-stage approach to estimate a statewide truck O-D trip table. The proposed approach is supported by two sequential stages: the first stage estimates a commodity-based truck O-D trip table using the commodity flows derived from the Freight Analysis Framework (FAF) database, and the second stage uses the path flow estimator (PFE) concept to refine the truck trip table obtained from the first stage using the truck counts from the statewide truck count program. The model allows great flexibility of incorporating data at different spatial levels for estimating the truck O- D trip table. The results from the second stage provide us a better understanding of truck flows on the statewide truck routes and corridors, and allow us to better manage the anticipated impacts caused by network disruptions.A decision support tool is developed to facilitate the decision making system through the application of its database management capabilities, graphical user interface, GIS-based visualization, and transportation network vulnerability analysis. The vulnerability assessment focuses on evaluating the statewide truck-freight bottlenecks/chokepoints. This dissertation proposes two quantitative measures: O-D connectivity (or detour route) in terms of distance and freight flow pattern change in terms of vehicle miles traveled (VMT). The case study adopts a “what-if” analysis approach by generating the disruption scenarios of the structurally deficient bridges in Utah due to earthquakes. In addition, the potential impacts of disruptions to multiple bridges in both rural and urban areas are evaluated and compared to the single bridge failure scenarios.This dissertation also proposes an approach to measure the redundancy of freight transportation networks based on two main dimensions: route diversity and network spare capacity. The route diversity dimension is used to evaluate the existence of multiple efficient routes available for users or the degree of connections between a specific O-D pair. The network spare capacity dimension is used to quantify the network- wide spare capacity with an explicit consideration of congestion effect. These two dimensions can complement each other by providing a two-dimensional characterization of freight transportation network redundancy. Case studies of the Utah statewide transportation network and coal multimodal network are conducted to demonstrate the features of the vulnerability and redundancy measures and the applicability of the quantitative assessment methodology

Assessing Survivability of the Beijing Subway System

The assessment of survivability is a common topic in critical network infrastructure research. In order to examine the critical components whose disruptions can cause huge system degradation, many measures have been approached to depict the characteristics of network systems. Serving more than ten million passengers a day, the Beijing subway system, which ranks third in the world for its length and annual ridership, raises survivability issues in the face of potential disruptions of network components along with its constantly increasing complexity. In this research, we provide an accessibility-based survivability measure with which to explore how potential outages of network components might affect the overall functionality of the Beijing subway system. System survivability is measured from two perspectives: [1] connectivity under various simulated failures of stations and [2] variations in passenger flows in response to a disruptive influence. Plausible scenarios are constructed using local demographic data and daily ridership reports from subway management companies. To assess the possible range of influences, we develop a weighted rank-based simulation algorithm to approximate the extreme combinatorial disruption instances. The range of the potential effect highlights the best and worst-case scenarios so as to identify the critical components and help to prepare corresponding contingency plans. This research will enable the more legitimate allocation of limited emergency response resources and highlight the way of improving the survivability of the system

An investigation to improve community resilience using network graph analysis of infrastructure systems

PhD ThesisDisasters can have devastating effects on our communities and can cause great suffering to the people who reside within them. Critical infrastructure underpins the stable functioning of these communities and the severity of disasters is often linked to failure of these systems. Traditionally, the resilience of infrastructure systems is assessed by subjecting physically based models to a range of hazard scenarios. The problem with this approach is that it can only inform us of inadequacies in the system for the chosen scenarios, potentially leaving us vulnerable to unforeseen events. This thesis investigates whether network graph theory can be used to give us increased confidence that the system will respond well in untested scenarios by developing a framework that can identify generic system characteristics and hence describe the underlying resilience of the network. The novelty in the work presented in this thesis is that it overcomes a shortcoming in existing network graph theory by including the effects of the spatial distribution of geographically dispersed systems. To consider spatial influence, a new network generation algorithm was developed which incorporated rules that connects system components based on both their spatial distribution and topology. This algorithm was used to generate proxy networks for the European, US and China air traffic networks and demonstrated that the inclusion of this spatial component was crucial to form the highly connected hub airports observed in these networks. The networks were then tested for hazard tolerance and in the case of the European air traffic network validated using data from the 2010 Eyjafjallajökull eruption. Hazard tolerance was assessed by subjecting the networks to a series of random, but spatially coherent, hazards and showed that the European air traffic network was the most vulnerable, having up to 25% more connections disrupted compared to a benchmark random network. This contradicts traditional network theory which states that these networks are resilient to random hazards. To overcome this shortcoming, two strategies were employed to improve the resilience of the network. One strategy ‘adaptively’ modified the topology (crises management) while the other ‘permanently’ modified it (hazard mitigation). When these modified networks were subjected to spatial hazards the ‘adaptive’ approach Page i produced the most resilient network, having up to 23% fewer cancelled air routes compared to the original network, for only a 5% change in airport capacity. Finally, as many infrastructure networks are flow based systems, an investigation into whether graph theory could identify vulnerabilities in these systems was conducted. The results demonstrated that by using a combination of both physically based and graph theory metrics produced the best predictive skill in identifying vulnerable nodes in the system. This research has many important implications for the owners and operators of infrastructure systems. It has demonstrated the European air traffic network to be vulnerable to spatial hazard and shown that, because many infrastructure networks possess similar properties, may therefore be equally vulnerable. It also provides a method to identify generic system vulnerabilities and strategies to reduce these. It is argued that as this research has considered generic networks it can not only increase infrastructure resilience to known threats but also to previously unidentified ones and therefore is a useful method to help protect these systems to large scale disasters and reduce the suffering for the people in the communities who rely upon them.EPSR

Enhancing OpenStreetMap for the Assessment of Critical Road Infrastructure in a Disaster Context

Die Häufigkeit von Naturkatastrophen nimmt weltweit zu, was zu immensen Schäden an kritischer Straßeninfrastruktur und deren Funktionalität führen kann. Daher ist es von entscheidender Bedeutung, die Funktionalität kritischer Straßeninfrastruktur vor, während und nach einer Katastrophe zu beurteilen. Dazu werden globale Straßendaten benötigt, die für die Routenplanung nutzbar sind. OpenStreetMap (OSM) stellt globale Straßennetzdaten zur Verfügung, die kostenlos und frei zugänglich sind. Allerdings ist die Verwendung der OSM Straßendaten für Routenplanungsanwendungen oft eine Herausforderung. Das übergeordnete Ziel dieser Arbeit ist die Entwicklung eines generischen, mehrskaligen Konzepts zur Analyse kritischer Straßeninfrastrukturen im Kontext von Naturgefahren unter Verwendung von OSM Daten. Dafür werden zwei aufeinander folgende Forschungsziele aufgestellt: (i) die Verbesserung der Routingfähigkeit von OSM Daten und (ii) die Bewertung kritischer Straßeninfrastruktur im Kontext von Naturgefahren. Daraus resultiert die Gliederung dieser Arbeit in zwei Hauptteile, die jeweils den Forschungszielen entsprechen. Im ersten Teil dieser Arbeit wird die Nutzbarkeit von OSM Daten für Routing Anwendungen verbessert. Zunächst wird dafür die Qualität des OSM Straßennetzwerks im Detail analysiert. Dabei werden zwei große Herausforderungen im Bereich der Anwendbarkeit von OSM Daten für die Routenplanung identifiziert: fehlende Geschwindigkeitsangaben und Fehler in der Straßenklassifizierung. Um die erste Herausforderung zu bewältigen, wird ein FuzzyFramework zur Geschwindigkeitsschätzung (Fuzzy-FSE) entwickelt, welches eine Fuzzy Regelung zur Schätzung der Durchschnittsgeschwindigkeit einsetzt. Diese Fuzzy Regelung basiert auf den Parametern Straßenklasse, Straßenneigung, Straßenoberfläche und Straßenlänge einsetzt. Das Fuzzy-FSE besteht aus zwei Teilen: einer Regel- und Wissensbasis, die über die Zugehörigkeitsfunktionen für den Ausgangsparameter Geschwindigkeit entscheidet, und mehrere Fuzzy-Regelsysteme, welche die resultierende Durchschnittsgeschwindigkeit berechnen. Die Ergebnisse zeigen, dass das Fuzzy-FSE auch bei ausschließlicher Verwendung von OSM Daten eine bessere Leistung erbringt als bestehende Methoden. Die Herausforderung der fehlerhaften Straßenklassifizierung wird durch die Entwicklung eines neuartigen Ansatzes zur Erkennung von Klassifizierungfehlern in OSM angegangen. Dabei wird sowohl nach nicht verbundenen Netzwerkteilen als auch nach Lücken im Straßennetzwerk gesucht. Verschiedene Parameter werden in einem Bewertungssystem kombiniert, um eine Fehlerwahrscheinlichkeit zu erhalten. Auf Basis der Fehlerwahrscheinlichkeit kann ein menschlicher Nutzer diese Fehler überprüfen und korrigieren. Die Ergebnisse deuten einerseits darauf hin, dass an Lücken mehr Klassifizierungsfehler gefunden werden als an nicht verbundenen Netzwerkteilen. Andererseits zeigen sie, dass das entwickelte Bewertungssystem bei einer benutzergesteuerten Suche nach Lücken zu einem schnellen Aufdecken von Klassifizierungsfehlern verwendet werden kann. Aus dem ersten Teil dieser Arbeit ergibt sich somit ein erweiterter OSM Datensatz mit verbesserter Routingfähigkeit. Im zweiten Teil dieser Arbeit werden die erweiterten OSM Daten zur Bewertung der kritischen Straßeninfrastruktur im Katastrophenkontext verwendet. Dazu wird der zweite Teil des generischen, mehrskaligen Konzepts entwickelt, das aus mehreren, miteinander verbundenen Modulen besteht. Ein Modul implementiert zwei Erreichbarkeitsindizes, welche verschiedene Aspekte der Erreichbarkeit im Straßennetzwerk hervorheben. In einem weiteren Modul wird ein grundlegendes Modell der Verkehrsnachfrage entwickelt, welches den täglichen interstädtischen Verkehr ausschließlich auf der Grundlage von OSM Daten schätzt. Ein drittes Modul verwendet die oben beschriebenen Module zur Schätzung verschiedener Arten von Auswirkungen von Naturkatastrophen auf das Straßennetzwerk. Schließlich wird in einem vierten Modul die Vulnerabilität des Straßennetzes gegenüber weiteren Schäden bei Langzeitkatastrophen analysiert. Das generische Konzept mit allen Modulen wird exemplarisch in zwei verschiedenen Regionen für zwei Waldbrandszenarien angewendet. Die Ergebnisse der Fallstudien zeigen, dass das Konzept ein wertvolles, flexibles und global anwendbares Instrument für Regionalplaner und Katastrophenmanagement darstellt, das länder- bzw. regionenspezifische Anpassungen ermöglicht und gleichzeitig wenig Daten benötigt