Simulating Resilient Urban Microgrids Using Distributed Energy Resources (DER) Deployment Scenarios and Economic Impact Modelling

Link-level resilience analysis for real-world networks using crowd-sourced data

Reliable power supply to the consumers is the key goal for power utilities. The two key reliability indices for power utilities are SAIDI and SAIFI. These indices indicate the average duration and frequency of supply interruptions for the consumer. A low value on these indices reflects a reliable and mostly uninterrupted power supply. Reliability of the power supply is most threatened by events such as incipient failures that often go undetected by conventional protection schemes and can significantly increase average outage duration. However, such events initially manifest in terms of distortions in current and voltage waveforms. In this paper, waveform distortions are leveraged to detect and locate such events before they cascade to cause further damage. Early detection and localization will help in planning a swift response and mitigation of risk. Such a timely action will reduce the outage duration and interruption frequency, eventually leading to improvement in grid reliability indices. The paper further investigates these concepts by evaluating case studies on an IEEE-34 Node Distribution feeder test system in Real Time.

The occurrence of natural disasters such as cyclones, earthquakes, and floods has increased worldwide, and their effect is most intense in localized regions. These events expose weaknesses in the power system infrastructures and show how well-prepared the system is to operate its services in a resilient way. During and after an extreme event, the outage and service disruption happens due to the inability of the affected part of the grid to cope with disruptions, which leads to insufficient resilience of the system. It is becoming more critical to enhance the resilience of electrical networks to extreme weather occurrences through suitable hardening processes and smart operational techniques. A reliable prevention approach necessitates a quantitative resilience metric that can estimate the effects of the future extreme events on distribution systems and assess the possible benefits of various planning strategies. This study aims to address the issue of lack of resilience metrics and proposes probabilistic metrics for evaluating the performance of distribution systems resilience in case of extreme weather events. Specifically, this study introduces active and passive resilience concepts to provide insights into system response. The proposed resilience metrics for various weather scenarios are quantified for the IEEE 33-bus system. The simulation framework also analyzes the effects of different operational and structural resilience improvement approaches on the proposed resilience metrics.

Cyber-physical systems are an integral component of weapons, sensors, and autonomous vehicles, as well as cyber assets directly supporting tactical forces. Mission resilience of tactical networks affects command and control, which is important for successful military operations. Traditional engineering methods for mission assurance will not scale during battlefield operations. Commanders need useful mission resilience metrics to help them evaluate the ability of cyber assets to recover from incidents to fulfill mission essential functions. We develop 6 cyber resilience metrics for tactical network architectures. We also illuminate how psychometric modeling is necessary for future research to identify resilience metrics that are both applicable to the dynamic mission state and meaningful to commanders and planners.

Critical Infrastructures are infrastructures that provide vital or critical services for the population such as supply of electricity, drinking water, food, and health care. Interruptions of these services may have a direct or indirect adverse impact on the population's welfare. I.e. failures of critical services can lead to injuries or even losses of life, property damages, social and economic disruptions and thus drastically reduce the overall security of supply. We propose a new resilience measure that can be applied together with other known graph-related measures in order to achieve resilient, efficient, and economic feasible critical supply networks. In contrast to pure graph measures, the resilience measure we propose, takes spatial information of infrastructures and their criticalities w.r.t. to the availability of e.g. power or water into account. To enable a comprehensive evaluation of network topologies, a composite resilience metric is introduced, which uses these aforementioned measures as weighted building blocks. For evaluating critical network designs, the proposed methodology applies this composite resilience metric and Multi-Criteria Decision Analysis. Based on this, a genetic algorithm is proposed for identifying disaster resilient water supply networks.

Transportation networks daily provide accessibility and crucial services to societies. However, they must also maintain an acceptable level of service to critical infrastructures in the case of disruptions, especially during natural disasters. We have developed a method for assessing the resilience of transportation network topology when exposed to environmental hazards. This approach integrates graph theory with stress testing methodology and involves five basic steps: (1) establishment of a scenario set that covers a range of seismic damage potential in the network, (2) assessment of resilience using various graph-based metrics, (3) topology-based simulations, (4) evaluation of changes in graph-based metrics, and (5) examination of resilience in terms of spatial distribution of critical nodes and the entire network topology. Our case study was from the city of Kathmandu in Nepal, where the earthquake on April 25, 2015, followed by a major aftershock on May 12, 2015, led to numerous casualties and caused significant damage. Therefore, it is a good example for demonstrating and validating the developed methodology. The results presented here indicate that the proposed approach is quite efficient and accurate in assisting stakeholders when evaluating the resilience of transportation networks based on their topology.

A data-driven approach for quantifying the resilience of railway networks

Resilience is regarded as an essential design objective of a wide range of systems in modern society. This work is based on a vision that networks of mobile energy storage systems could provide an alternative off-grid power system design for rural and underdeveloped regions. To evaluate the resiliency of networked energy storage systems under overload failure, a model of concurrent cascading failure and healing processes is developed and demonstrated. Two resilience metrics are used to evaluate the resilience of a real-world network, namely the recovery level at a specified time and the recovery time. The simulations generate system trajectories at each time step. We explore the dependence of the system behavior on different model parameters that capture key recovery strategies. The success probability of the recovery of a failed node needs to be high enough for the network to restore its original functionality. Similarly, the increase in recovery budget parameter also leads to faster and higher recovery levels. However, in most cases, there appears to be upper limits for both parameters, beyond which any further increase could not improve the recovery performance. There is an optimum portion of the loads of the active neighboring nodes that will be carried by the newly recovered node that results in the shortest recovery times or highest recovery levels. Our work sheds light on how to enhance networked systems resiliency by considering the optimization of various model parameters.

We automatically extract resilience events and novel outage and restore processes from standard transmission utility detailed outage data. This new processing is applied to the outage data collected in NERC's Transmission Availability Data System to introduce and analyze statistics that quantify resilience of the transmission system against extreme weather events. These metrics (such as outage rate and duration, number of elements outaged, rated capacity outaged, restore duration, maximum simultaneous outages, and element-days lost) are calculated for all large weather-related events on the North American transmission system from 2015 to 2020 and then by extreme weather type that caused them such as hurricanes, tornadoes, and winter storms. Finally, we study how performance of the system changed with respect to the resilience metrics by season and year.

This paper presents the rationale behind performing an analysis of Internet resilience in the sense of maintaining a connection of autonomous systems in the presence of failures or attacks — on a level of a single country. Next, the graph of a network is constructed that represents interconnections between autonomous systems. The connectivity of the graph is examined for cases of link or node failure. Resilience metrics are proposed, focusing on a single autonomous system or on overall network reliability. The process of geographic location of networking infrastructure is presented, leading to an analysis of network resilience in the case of a joint failure of neighboring autonomous systems.

Accurate and fast machine learning algorithm for systems outage prediction

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.

The current era of structural design prioritizes safety, performance, and compliance with evolving standards. While these advancements have undoubtedly improved structural integrity, safety is no longer sufficient. The increasing frequency and intensity of natural disasters, including seismic events and tornadoes, along with human-induced hazards, such as blasts, demand a broader and more adaptive design philosophy. This paradigm is resilience, a concept that not only addresses immediate structural survival but also considers recovery, functionality, and the broader physical, social, and economic impacts of disasters. Resilience-based design surpasses conventional approaches by accounting for indirect consequences, such as downtime and cascading effects, and emphasizing recovery and long-term societal well-being. This paper presents a state-of-the-art review of resilience assessment for steel structures subjected to lateral loads from seismic, blast, and tornado hazards. Which impose significant demands on structural integrity and resilience due to the extreme forces they exert, making them critical in resilience assessment. The limitations of current design codes, such as the Eurocode, are critically examined, focusing on their emphasis on life safety over recovery and their lack of multi-hazard frameworks. Future directions are explored, including the adaptation of design codes to incorporate multi-hazard resilience, the integration of advanced materials, and the development of quantifiable resilience metrics. By addressing these challenges, this paper emphasizes the need to transform structural engineering to ensure that steel structures can coexist with disasters. The insights presented aim to promote the development of novel approaches and methodologies that enhance resilience as a core principle in structural steel design.

A Flow-based Vulnerability Measure for the Resilience of Urban Road Network

Designing resilient and economically viable water distribution systems: a multi-dimensional approach