An activity-based spatial-temporal community electricity vulnerability assessment framework

The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial-temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, occupant activity patterns, and urban building characteristics. To integrate factors in these aspects, we also proposed a process as activity simulation-mapping-evaluation-visualization to apply the framework and visualize results. This framework can help planners make an effective first-time response by identifying the most vulnerable areas when a massive power outage happens during natural disasters. It can also be integrated into community resilience analysis models and potentially contributes to effective disaster risk managementComment: to be published in Proceedings of the 5th International Conference on Building Energy and Environmen

Digital Supply Chain Vulnerabilities in Critical Infrastructure: A Systematic Literature Review on Cybersecurity in the Energy Sector

The main purpose of this paper is to identify the current state of the art on digital supply chain cybersecurity risks in critical infrastructure and how the term resilience is used in this context. To achieve this objective, the authors applied a systematic literature review method that summarises and analyses the studies relevant for the research topic. In total 33 papers were identified. The results show that limited research is done on supply chain risks in critical infrastructure. Relevant frameworks and methods for resilience of supply chains have also been identified. These frameworks and methods could be very beneficial for a more holistic management of cybersecurity risks in the increasingly complex supply chains within critical infrastructure.publishedVersionPaid open acces

Power-Law Distributions of Dynamic Cascade Failures in Power-Grid Models

Power-law distributed cascade failures are well known in power-grid systems. Understanding this phenomena has been done by various DC threshold models, self-tuned at their critical point. Here we attempt to describe it using an AC threshold model, with a second-order Kuramoto type equation of motion of the power-flow. We have focused on the exploration of network heterogeneity effects, starting from homogeneous 2D lattices to the US power-grid, possessing identical nodes and links, to a realistic electric power-grid obtained from the Hungarian electrical database. The last one exhibits node dependent parameters, topologically marginally on the verge of robust networks. We show that too weak quenched heterogeneity, coming solely from the probabilistic self-frequencies of nodes (2D lattice) is not sufficient to find power-law distributed cascades. On the other hand too strong heterogeneity destroys the synchronization of the system. We found agreement with the empirically observed power-law failure size distributions on the US grid, as well as on the Hungarian networks near the synchronization transition point. We have also investigated the consequence of replacing the usual Gaussian self-frequencies to exponential distributed ones, describing renewable energy sources. We found a drop in the steady state synchronization averages, but the cascade size distribution both for the US and Hungarian systems remained insensitive and have kept the universal tails, characterized by the exponent $\tau\simeq 1.8$. We have also investigated the effect of an instantaneous feedback mechanism in case of the Hungarian power-grid.Comment: Extended version with minor changes, accepted in Entropy 22 pages, 13 figure

Heavy-Tail Analysis of Network Theory-Based Critical Asset Identification Metrics for Bulk Transmission Power Systems

Large-scale blackouts present a significant threat to the reliable delivery of electricity expected of utilities. Often these blackouts are precipitated on a small set of failures, whether through component failures or operator error as a result of insufficient real-time system awareness. In response, a wide array of power system modeling methods has emerged to identify critical assets in electric power systems. This work seeks to study a select grouping of network theory metrics proposed in literature to identify critical power system assets. In total, two standard network theory metrics and eight “extended” complex network betweenness and degree centrality metrics across six synthetic power systems of varying size will be examined. These extended complex network representations of power systems account for structural (e.g. system impedances and susceptance) and operational (e.g. power flow and line losses) properties of power systems not readily captured by standard network theory metrics. All ten metrics, evaluated for each of the six networks, are calculated and tested for heavy-tailed, and more specifically power-law tail, distributions to determine potential connections to blackout size distributions. These heavy-tail tests have shown scaling parameters for power-law fits less than 2 for extended betweenness metrics, closely matching blackout data. System operation metrics more broadly have also show consistent power-law identification among different network sizes over the five metrics tested. Comprehensive system analysis to determine which metrics are most powerful in identifying mechanisms underlying blackout size distributions is recommended as a primary direction to extend this work

Resiliency-oriented operation of distribution networks under unexpected wildfires using multi-horizon information-gap decision theory

Extreme events may trigger cascading outages of different components in power systems and cause a substantial loss of load. Forest wildfires, as a common type of extreme events, may damage transmission/distribution lines across the forest and disconnect a large number of consumers from the electric network. Hence, this paper presents a robust scheduling model based on the notion of information-gap decision theory (IGDT) to enhance the resilience of a distribution network exposed to wildfires. Since the thermal rating of a transmission/distribution line is a function of its temperature and current, it is assumed that the tie-line connecting the distribution network to the main grid is equipped with a dynamic thermal rating (DTR) system aiming at accurately evaluating the impact of a wildfire on the ampacity of the tie-line. The proposed approach as a multi-horizon IGDT-based optimization problem finds a robust operation plan protected against the uncertainty of wind power, solar power, load, and ampacity of tie-lines under a specific uncertainty budget (UB). Since all uncertain parameters compete to maximize their robust regions under a specific uncertainty budget, the proposed multi-horizon IGDT-based model is solved by the augmented normalized normal constraint (ANNC) method as an effective multi-objective optimization approach. Moreover, a posteriori out-of-sample analysis is used to find (i) the best solution among the set of Pareto optimal solutions obtained from the ANNC method given a specific uncertainty budget, and (ii) the best resiliency level by varying the uncertainty budget and finding the optimal uncertainty budget. The proposed approach is tested on a 33-bus distribution network under different circumstances. The case study under different conditions verifies the effectiveness of the proposed operation planning model to enhance the resilience of a distribution network under a close wildfire. © 2022 The Author(s

Modelling Automation–Human Driver Handovers Using Operator Event Sequence Diagrams

This research aims to show the effectiveness of Operator Event Sequence Diagrams (OESDs) in the normative modelling of vehicle automation to human drivers’ handovers and validate the models with observations from a study in a driving simulator. The handover of control from automation to human operators has proved problematic, and in the most extreme circumstances catastrophic. This is currently a topic of much concern in the design of automated vehicles. OESDs were used to inform the design of the interaction, which was then tested in a driving simulator. This test provided, for the first time, the opportunity to validate OESDs with data gathered from videoing the handover processes. The findings show that the normative predictions of driver activity determined during the handover from vehicle automation in a driving simulator performed well, and similar to other Human Factors methods. It is concluded that OESDs provided a useful method for the human-centred automation design and, as the predictive validity shows, can continue to be used with some confidence. The research in this paper has shown that OESDs can be used to anticipate normative behaviour of drivers engaged in handover activities with vehicle automation in a driving simulator. Therefore, OESDs offer a useful modelling tool for the Human Factors profession and could be applied to a wide range of applications and domains.</jats:p

Cyber-Physical Vulnerability Assessment in Smart Grids Based on Multilayer Complex Networks

This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart GridIn the last decade, the main attacks against smart grids have occurred in communication networks (ITs) causing the disconnection of physical equipment from power networks (OTs) and leading to electricity supply interruptions. To deal with the deficiencies presented in past studies, this paper addresses smart grids vulnerability assessment considering the smart grid as a cyber-physical heterogeneous interconnected system. The model of the cyber-physical system is composed of a physical power network model and the information and communication technology network model (ICT) both are interconnected and are interrelated by means of the communication and control equipment installed in the smart grid. This model highlights the hidden interdependencies between power and ICT networks and contains the interaction between both systems. To mimic the real nature of smart grids, the interconnected heterogeneous model is based on multilayer complex network theory and scale-free graph, where there is a one-to-many relationship between cyber and physical assets. Multilayer complex network theory centrality indexes are used to determine the interconnected heterogeneous system set of nodes criticality. The proposed methodology, which includes measurement, communication, and control equipment, has been tested on a standardized power network that is interconnected to the ICT network. Results demonstrate the model’s effectiveness in detecting vulnerabilities in the interdependent cyber-physical system compared to traditional vulnerability assessments applied to power networks (OT).This research was funded by Fundación Iberdrola España, within the 2020 research support scholarship program