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The growing impact of weather-related power outages on economy and society in the last decades underlines the rising need for power system resilience. Power system resilience can be boosted through adoption of probabilistic approaches and preventive actions building on smart grid capabilities. Decisions on the best-performing preventive action, however, are nontrivial and must consider the expected impact of an upcoming event, weather forecasts, fault probabilities, and their corresponding uncertainties. This article presents a three-stage decision-making methodology that is based on assessing weighted preevent and postevent performance loss and considers spatial uncertainty of fault probabilities, modeled by probability distributions. The methodology is demonstrated using preventive actions, such as additional network constraints and islanding, aiming to mitigate cascading failures in transmission networks. Their performance loss is compared to the traditional N-1 criterion. Simultaneous faults of up to three lines are considered as initiating cause in the IEEE 30-bus network and the 489-bus German transmission network to verify potential and scalability of the methodology. Results show that the decision-making methodology effectively identifies the best-performing action to reduce the risk of cascading failures for any level of uncertainty. [ABSTRACT FROM AUTHOR]

Accounts of extreme power outages of transmission systems suggest the acceleration of cascading outage propagation over time and the splitting of the cascade into a slow phase and a subsequent fast phase. This is significant to network operators, as mitigation actions, such as load shedding, can only be effectively applied during the slow phase. During the fast phase, the network disintegrates too quickly for any manual intervention. To supplement the accounts of extreme outages, we describe the observed acceleration of smaller and more common cascading outages by analyzing transmission outage data published by one North American utility. Our results show that these common cascades accelerate much less than the extreme cascades. This justifies ongoing research in mitigation strategies. [ABSTRACT FROM AUTHOR]

Although extreme natural disasters have occurred all over the world throughout history, power systems planners do not usually recognize them within network investment methodologies. Moreover, planners had historically focused on reliability approaches based on average (rather than risk) performance indicators, undermining the effects of high impact and low probability events on investment decisions. To move towards a resilience centred approach, we propose a practical framework that can be used to identify network investments that offer the highest level of hedge against risks caused by natural hazards. In a first level, our framework proposes network enhancements and, in a second level, uses a simulation to evaluate the resilience level improvements associated with the network investment propositions. The simulator includes 4 phases: threat characterization, vulnerability of systems components, system response, and system restoration, which are simulated in a sequential Monte Carlo fashion. We use this modeling framework to find optimal portfolio solutions for resilient network enhancements. Through several case studies with applications to earthquakes, we distinguish the fundamental differences between reliability- and resilience-driven enhancements, and demonstrate the advantages of combining transmission investments with installation of backup distributed generation. [ABSTRACT FROM AUTHOR]

Power systems are prone to cascading outages leading to large-area blackouts, and intentional controlled islanding (ICI) can mitigate these catastrophic events by splitting the system into sustainable islands. ICI schemes are used as the last resort to prevent cascading events; thus, it is critical to evaluate the corresponding system risks to ensure their correct operation. This paper proposes a unified framework to assess the risk of ICI schemes. First, a novel ICI method to create islands with minimum power imbalance is presented. Further, a risk assessment methodology is used to assess the probability and impact of the main operational modes of the ICI scheme. The unified framework provides insights on the benefits of implementing ICI, considering the uncertainties related to its reliability. The ICI scheme is demonstrated using the IEEE 9-bus system. The proposed unified framework is then fully deployed on the actual power system of Cyprus. Multiple case studies on the real network are created to demonstrate the adaptability and robustness of the proposed scheme to different system conditions. The adoption of the unified framework highlights that the system risk significantly reduces with the ICI in service, even when the reliability uncertainties associated with the scheme are considered. [ABSTRACT FROM AUTHOR]

Historical electrical disturbances highlight the impact of extreme weather on power system resilience. Even though the occurrence of such events is rare, the severity of their potential impact calls for 1) developing suitable resilience assessment techniques to capture their impacts and 2) assessing relevant strategies to mitigate them. This paper aims to provide fundamentals insights on the modeling and quantification of power systems resilience. Specifically, a fragility model of individual components and then of the whole transmission system is built for mapping the real-time impact of severe weather, with focus on wind events, on their failure probabilities. A probabilistic multitemporal and multiregional resilience assessment methodology, based on optimal power flow and sequential Monte Carlo simulation, is then introduced, allowing the assessment of the spatiotemporal impact of a windstorm moving across a transmission network. Different risk-based resilience enhancement (or adaptation) measures are evaluated, which are driven by the resilience achievement worth index of the individual transmission components. The methodology is demonstrated using a test version of the Great Britain's system. As key outputs, the results demonstrate how, by using a mix of infrastructure and operational indices, it is possible to effectively quantify system resilience to extreme weather, identify and prioritize critical network sections, whose criticality depends on the weather intensity, and assess the technical benefits of different adaptation measures to enhance resilience. [ABSTRACT FROM PUBLISHER]

Electrical power systems have been traditionally designed to be reliable during normal conditions and abnormal but foreseeable contingencies. However, withstanding unexpected and less frequent severe situations still remains a significant challenge. As a critical infrastructure and in the face of climate change, power systems are more and more expected to be resilient to high-impact low-probability events determined by extreme weather phenomena. However, resilience is an emerging concept, and, as such, it has not yet been adequately explored in spite of its growing interest. On these bases, this paper provides a conceptual framework for gaining insights into the resilience of power systems, with focus on the impact of severe weather events. As quantifying the effect of weather requires a stochastic approach for capturing its random nature and impact on the different system components, a novel sequential Monte-Carlo-based time-series simulation model is introduced to assess power system resilience. The concept of fragility curves is used for applying weather- and time-dependent failure probabilities to system's components. The resilience of the critical power infrastructure is modeled and assessed within a context of system-of-systems that also include human response as a key dimension. This is illustrated using the IEEE 6-bus test system. [ABSTRACT FROM PUBLISHER]

Power systems have typically been designed to be reliable to expected, low-impact high-frequency outages. In contrast, extreme events, driven for instance by extreme weather and natural disasters, happen with low-probability, but can have a high impact. The need for power systems, possibly the most critical infrastructures in the world, to become resilient to such events is becoming compelling. However, there is still little clarity as to this relatively new concept. On these premises, this paper provides an introduction to the fundamental concepts of power systems resilience and to the use of hardening and smart operational strategies to improve it. More specifically, first the resilience trapezoid is introduced as visual tool to reflect the behavior of a power system during a catastrophic event. Building on this, the key resilience features that a power system should boast are then defined, along with a discussion on different possible hardening and smart, operational resilience enhancement strategies. Further, the so-called \Phi \Lambda E\Pi resilience assessment framework is presented, which includes a set of resilience metrics capable of modeling and quantifying the resilience performance of a power system subject to catastrophic events. A case study application with a 29-bus test version of the Great Britain transmission network is carried out to investigate the impacts of extreme windstorms. The effects of different hardening and smart resilience enhancement strategies are also explored, thus demonstrating the practicality of the different concepts presented. [ABSTRACT FROM AUTHOR]

INCREASING THE RESILIENCE of critical power infrastructures to high-impact, low-probability events, such as extreme weather phenomena driven by climate change, is of key importance for keeping the lights on. However, what does resilience really mean? Should we build a stronger and bigger grid or a smarter one? This article discusses a conceptual framework of power system resilience, its key features, and potential enhancement measures. [ABSTRACT FROM PUBLISHER]