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Research on power system resilience has increased due to frequent natural hazards. Many reviews have been performed on power system resilience, focusing on specific topics such as the application of energy storage systems and microgrids for resilience enhancement. To comprehensively analyze the work carried out on power system resilience, this paper presents a methodology to perform quantitative analyses that combines bibliometric and correlation analyses. For this reason, 851 research publications from popular databases, namely Scopus and Web of Science, spanning from 2001 to 2022, were obtained for analysis. In the quantitative analysis, bibliometric measures are evaluated and analyzed to identify the most productive elements (e.g., countries, papers, journals, and institutions), keyword co-occurrences, and track the thematic evolution of authors' keywords. Additionally, hazard metrics such as risk against natural hazards and word count of natural hazards are proposed to reflect the event-specific risk level of a country and its progress in event-specific resilience research. These metrics are later utilized to perform correlation analyses along with bibliometric measures. This correlation aims to identify lesser-explored natural hazards and countries with the potential for event-specific resilience studies. Overall, this study offers insights into power system resilience trends and emerging areas. It emphasizes less-attended, highly exposed natural hazards, helping prioritize research for future resilient models to address their unique challenges. • Bibliometric and Correlation Analyses were performed using 851 resources. • Identify top countries, institutions, journals, and cited papers. • Perform keyword co-occurrence, its evolution, and visualize trends using diagrams. • Correlation analysis between resilience publications and risk of natural hazards. [ABSTRACT FROM AUTHOR]

Highlights • Microgrids provision of reliability and resilience is quantified at system-level. • Multiple sources of uncertainty are modelled using probabilistic capacity tables. • Long supporting duration differentiates resilience services from reliability ones. • Probabilistic metrics are defined to indicate microgrids' capacity contribution. • Aggregation of microgrids at system-level demonstrates significant synergic effect. Abstract Microgrids are emerging to coordinate distributed energy resources and locally increase reliability to expected events and resilience to extreme events. Furthermore, by deploying their inherent flexibility, grid-connected microgrids are capable to provide different services at the system-level too. However, these are often assessed independently and a comprehensive integrated framework that can assess benefits for the whole power system is missing. In this outlook, this paper introduces a system-level assessment framework based on the concept of different duration's reserve services that can be provided by microgrids to the main electricity grid in response to both credible (reliability-oriented) and extreme, possibly unforeseen (resilience-oriented) contingencies. Probabilistic capacity tables accounting for different sources of uncertainty related to both microgrids' operation and occurrences of unfavorable events are built to assess the microgrids' potential capacity contribution to a particular reserve service. Case studies based on representative microgrids and a British test system clearly illustrate and quantify how aggregation of microgrids could provide significant contribution to both short-term reliability and longer-duration resilience services far beyond the simple summation of the individual contributions, thus demonstrating a clear synergic effect, as well as the key role played by different forms of energy storage. The proposed framework can assist policy makers and regulators on the strategic role of microgrids for energy system planning and policy developments, including design of ancillary services markets not only to enhance system reliability but also resilience. [ABSTRACT FROM AUTHOR]

Power systems are prone to cascading outages leading to large-area blackouts with significant social and economic consequences. Intentional controlled islanding (i.e. the separation of the system into sustainable islands) is an effective strategy to mitigate these catastrophic events. To ensure a correct separation, nonetheless, it is crucial to define a suitable time to split the system (i.e. to answer the when to island question). To consider the probability of the event, the reliability of the system components, the reliability of the information and communication technologies, and the potential economic costs of the event, answering the above question within a risk-based framework becomes critical. To date, however, this has not been done. This study proposes a risk-based methodology to define, in an adaptive manner, a suitable time to split the system following an event. This methodology complements the well-studied where to island question, resulting in an integral solution of the islanding problem. To illustrate the approach, the IEEE 118-bus dynamic system is adopted considering realistic security criteria. Simulation results demonstrate the effectiveness and flexibility of the methodology in identifying a suitable time for the creation of islands, which, in turn, results in the prevention of blackouts that would otherwise be obtained. [ABSTRACT FROM AUTHOR]

Power systems are prone to cascading outages leading to large‐area blackouts with significant social and economic consequences. Intentional controlled islanding (i.e. the separation of the system into sustainable islands) is an effective strategy to mitigate these catastrophic events. To ensure a correct separation, nonetheless, it is crucial to define a suitable time to split the system (i.e. to answer the when to island question). To consider the probability of the event, the reliability of the system components, the reliability of the information and communication technologies, and the potential economic costs of the event, answering the above question within a risk‐based framework becomes critical. To date, however, this has not been done. This study proposes a risk‐based methodology to define, in an adaptive manner, a suitable time to split the system following an event. This methodology complements the well‐studied where to island question, resulting in an integral solution of the islanding problem. To illustrate the approach, the IEEE 118‐bus dynamic system is adopted considering realistic security criteria. Simulation results demonstrate the effectiveness and flexibility of the methodology in identifying a suitable time for the creation of islands, which, in turn, results in the prevention of blackouts that would otherwise be obtained. [ABSTRACT FROM AUTHOR]

Resilience to high impact low probability events is becoming of growing concern, for instance to address the impacts of extreme weather on critical infrastructures worldwide. However, there is, as yet, no clear methodology or set of metrics to quantify resilience in the context of power systems and in terms of both operational and infrastructure integrity. In this paper, the resilience “trapezoid” is therefore introduced which extends the resilience “triangle” that is traditionally used in existing studies, in order to consider the different phases that a power system may experience during an extreme event. The resilience trapezoid is then quantified using time-dependent resilience metrics that are specifically introduced to help capture the critical system degradation and recovery features associated to the trapezoid for different temporal phases of an event. Further, we introduce the concepts of operational resilience and infrastructure resilience to gain additional insights in the system response. Different structural and operational resilience enhancement strategies are then analyzed using the proposed assessment framework, considering single and multiple severe windstorm events that hit the 29-bus Great Britain transmission network test case. The results clearly highlight the capability of the proposed framework and metrics to quantify power system resilience and relevant enhancement strategies. [ABSTRACT FROM PUBLISHER]

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]

In this article, we examine the need for integrating resilience against natural disasters in power system planning through a review of the recent literature including guidance notes prepared by multilateral development banks, US Department of Energy and the wider research community. We present the challenges faced by practitioners in implementing resilience-centric analyses, especially in developing countries where data availability is a critical constraint and development of bespoke models for each system/country is an arduous if not an impossible task. This is followed by a discussion on how such planning analysis may still be constructed within the limitation of data, methodology and tools. [ABSTRACT FROM AUTHOR]

Extreme weather hazards, as high-impact low-probability events, have catastrophic consequences on critical infrastructures. As a direct impact of climate change, the frequency and severity of some of these events is expected to increase in the future, which highlights the necessity of evaluating their impact and investigating how can systems withstand a major disruption with limited degradation and recover rapidly. This paper first presents a multi-phase resilience assessment framework that can be used to analyze any natural threat that may have a severe single, multiple and/or continuous impact on critical infrastructures, such as electric power systems. Namely, these phases are (i) threat characterization, (ii) vulnerability assessment of the system's components, (iii) system's reaction and (iv) system's restoration. Second, multi-phase adaptation cases, i.e. making the system more robust, redundant and responsive are explained to discuss different strategies to enhance the resilience of the electricity network. To illustrate the above, this time-dependent framework is applied to assess the impact of potential future windstorms and floods on a reduced version of the Great Britain's power network. Finally, the adaptation cases are evaluated to conclude in what situations a stronger, bigger or smarter grid is preferred against the uncertain future. [ABSTRACT FROM AUTHOR]

A key driver for developing more sustainable energy systems is to decrease the effects of climate change, which could include an increase in the frequency, intensity and duration of severe weather events. Amongst others, extreme weather has a significant impact on critical infrastructures, and is considered one of the main causes of wide-area electrical disturbances worldwide. In fact, weather-related power interruptions often tend to be of high impact and sustained duration, ranging from hours to days, because of the large damage on transmission and distribution facilities. Hence, enhancing the grid resilience to such events is becoming of increasing interest. In this outlook, this paper first discusses the influence of weather and climate change on the reliability and operation of power system components. Since modelling the impact of weather is a difficult task because of its stochastic and unpredicted nature, a review of existing methodologies is provided in order to get an understanding of the key modelling approaches, challenges and requirements for assessing the effect of extreme weather on the frequency and duration of power system blackouts. Then, the emerging concept of resilience is discussed in the context of power systems as critical infrastructure, including several defense plans for boosting the resilience of power systems to extreme weather events. A comprehensive modelling research framework is finally outlined, which can help understand and model the impact of extreme weather on power systems and how this can be prevented or mitigated in the future. [ABSTRACT FROM AUTHOR]

Parallel power system restoration (PPSR) restores isolated sections (islands) of the network in parallel, thus the overall restoration process is accelerated. These islands are defined during the preparation stage of PPSR as part of a sectionalising strategy (SS). During this process, it is important that the operators only use updated post‐blackout system information. This study proposes a new methodology based on the 'cut‐set' matrix defined in graph theory, which can identify a shortlist of suitable SSs that satisfy the critical PPSR constraints in a few minutes. This short list of SSs, not identified in previous works, can be presented to the operators to help them select a restoration plan that is tailored to the specific changes in topology and asset availability that the blackout has caused. The methodology is illustrated using the IEEE 9‐bus system, and validated using the IEEE 118‐bus and the Polish 3375‐bus system to demonstrate the efficiency of the new approach for large‐scale networks. Multiple case studies are developed to demonstrate the adaptability of the methodology to different system conditions, for example, the unavailability of assets. In every case, the methodology quickly identified a number of SSs that create suitable islands for parallel restoration. [ABSTRACT FROM AUTHOR]

System Integrity Protection Schemes (SIPS) are traditionally designed with an emphasis on dependability. This ensures they operate when required to preserve system integrity and as a result, most SIPS are implemented as fully duplicated schemes. However, as the complexity and uncertainty of power systems increase, enhancing the security of SIPS becomes vitally important. This prevents spurious operations, which have a detrimental impact on system reliability. A procedure for designing SIPS that achieve an effective tradeoff between dependability and security is proposed in this paper. The proposed method uses fault tree analysis and the theory of minimal cut sets to break down the reliability analysis of the complete SIPS into the analysis of the individual operational phases of SIPS, which simplifies the analysis. Then, this study determines the minimum reliability requirements of each component, i.e. Mean Time To Failure (MTTF) and Mean Time To Fail Spurious (MTTF spurious ) and the optimum design of SIPS for realizing the desired level of dependability and security. It is illustrated using the Dinorwig Intertrip Scheme, which is located in North Wales and operated by National Grid (Great Britain transmission system operator). [ABSTRACT FROM AUTHOR]

Due to the increasing size and operational complexity of modern power systems, transmission system operators often have difficulties forming a complete and accurate picture of the state of the part of the system for which they are responsible. These difficulties may prevent them from achieving the level of situation awareness (SA) that they need to make the right decisions and respond effectively to an incident. Inadequate SA has indeed been identified as one of the contributing factors in several recent large electrical disturbances worldwide. This paper first reviews the fundamentals of SA and then discusses the main sources of operator errors due to insufficient SA in power systems and how these affect the operational decision-making process. It then discusses tools and standards that can help system operators improve their level of SA. Finally, a generic procedure for achieving sufficient SA is presented, which aims to guide the design of an information system that is both technology- and user-oriented. [ABSTRACT FROM AUTHOR]

System integrity protection schemes (SIPS) are a widely used solution to the challenges in operating electrical power transmission systems during the last few decades. Since these protection schemes have become an integral part of the system, it must be ensured that their performance satisfies the reliability requirements of electrical utilities, when expressed in terms of dependability and security. This study proposes a method based on Markov modelling and fault tree analysis for assessing the reliability of a generic SIPS, but it is illustrated using the Dinorwig intertrip scheme, located in North Wales and operated by National Grid (Great Britain system operator). In addition, two reliability indices, widely used in the process control industry, are suggested for quantifying the reliability level of SIPS: (i) safety integrity level and (ii) spurious trip level. Many operators tend to have SIPS permanently in service; this reduces the probability of a 'failure to operate' because of a problem in the arming software or an error by a human operator that prevented the scheme being armed when required. Therefore, the impact of having SIPS always armed on SIPS reliability is compared with the impact of switching IN the scheme only when the arming conditions are fulfilled. [ABSTRACT FROM AUTHOR]

Situation awareness is a key factor in preserving power system security, as it enables effective and timely decision-making and reactions by the operators to an incident. Insufficient situation awareness results in a delayed, incorrect or deficient response, endangering power system stability. This factor was actually identified as one of the main causes of several electrical disturbances in the last decade. This paper identifies numerous factors that govern the formation of situation awareness in a power system control center. A multi-state model based on Markov modeling is proposed for assessing the impact of insufficient situation awareness on the probability of power system blackouts. The proposed model considers the level of situation awareness and the state of the information infrastructure. The workings of this model are illustrated using the IEEE 24-bus Reliability Test System. [ABSTRACT FROM PUBLISHER]

Power outages due to cascading failures which are triggered by extreme weather pose an increasing risk to modern societies and draw attention to an emerging need for power network resilience. Machine learning (ML) is used for a real‐time selection process on preventive actions, such as topology reconfiguration and islanding, aiming to reduce the risk of cascading failures. Training data is obtained from Monte Carlo simulations of cascading failures triggered by extreme events. The trained ML‐based decision‐making process uses only predictors that are readily available prior to an extreme event, such as event location and intensity, network topology and load, and requires no further time‐consuming simulations.The proposed decision‐making process is compared to time‐consuming but ideal decision‐making and fast but trivial decision‐making. Demonstrations on the German transmission network show that the proposed ML‐based selection process efficiently prevents the uncontrolled propagation of cascading failures and performs similarly to an ideal decision‐making process whilst being computationally three orders of magnitude faster. [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]

This paper examines the socio-economic and geopolitical outcomes associated with infrastructure development across multiple scales. Starting from the premise that planetary socio-technical transformations in this vein have distinctly national drivers, we focus on the urban agency of Chinese-led investment. The paper explores how different forms of infrastructural development generated by the Belt and Road Initiative (BRI) affects transformations in the political and material fabric of cities and their host regions. We approach BRI-related infrastructural practice through three interconnected optics – discourse, instruments, and politics – so as to interrogate the articulation of projects linked to the BRI within the material site of the urban. Based on theorisations of infrastructure from an urban perspective and a critical review of literature on the BRI itself, we develop three illustrative case studies at different spatial scales and within different geographic contexts – in Pakistan, Central Europe and the UK. To examine the cases as well as their embeddedness in broader debates on the topic, we use a systematic review methodology relying on a wide variety of sources. We offer comparative and relational perspectives on the manner in which these relatively diverse cases demonstrate China's role as a global urban actor. [ABSTRACT FROM AUTHOR]

• A novel dynamic model of cascading failures in power systems is established. • Increased wind penetration and fast frequency responses are obtained. • Impact of increased wind penetration on cascading risks is quantified. • Behaviour characteristics of fast frequency responses are demonstrated. The ever-growing penetration of renewable based generation is leading to significant increases in the risk of cascading failures in low-inertia, and interconnection-rich, power systems. This paper proposes a framework for quantifying the risk of cascading failures in renewable-rich power systems with fast frequency response (FFR) services. This is achieved by developing a novel time-based dynamic model of cascading failures to capture the transient behaviours of the system response and quantify the risk of blackout. This dynamic cascading failure model is complemented by unit commitment for representative dispatch and includes the provision of FFR. The proposed framework is illustrated using a modified version of the Illinois 200-bus synthetic system. As key outputs, the results emphasize the importance of accurate modelling of ancillary services associated with frequency regulation in cascading failure analysis, and indicate that increased wind penetration can lead to a higher probability of power outages. Adopting FFR services can help to mitigate the cascading risk but may introduce rotor angle instability issues. [ABSTRACT FROM AUTHOR]

• Electric utility experience and challenges during the COVID-19 pandemic documented. • Practical conceptual framework is presented, for thinking about pandemic resilience. • CIGRE C4.47 definition of Power System Resilience applied to COVID-19 pandemic. • Case study using graph theory illustrates the risk of pandemics to utility assets. • Probabilistic epidemic spread study confirms effectiveness of mitigating measures. Resilience is a vital concept in engineering, business, and natural sciences, and is a measure of the ability of an entity to withstand High Impact Low Probability (HILP) events. During the COVID-19 pandemic, which started in late 2019/early 2020, power system utilities around the globe have responded in effective and efficient ways to enhance the resilience of their organisations, both in terms of real-time operations and prudent management of its infrastructure, in order to continue their mandate in providing reliable supply to meet customer demands. This paper presents the CIGRE definition for power system resilience, established by the C4.47 Working Group in 2018, and demonstrates the application of resilience-oriented thinking within the electrical sector. The response and recovery efforts are described, with respect to the key actionable measures integral to the power system resilience definition, taken before, during and after the COVID-19 pandemic. A practical conceptual framework is also presented for thinking about resilience in terms of three key components of resilience strategies: organisational, infrastructure and operational resilience. The paper also discusses the different strategies adopted in response to COVID-19, based on the C4.47 members' experiences during the pandemic. Finally, a case study is presented, which proves the effectiveness of a set of response measures, using graph theory and the characteristics of the staff-asset interactions. [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]

Cascading failure in electric power systems is a complicated problem for which a variety of models, software tools, and analytical tools have been proposed but are difficult to verify. Benchmarking and validation are necessary to understand how closely a particular modeling method corresponds to reality, what engineering conclusions may be drawn from a particular tool, and what improvements need to be made to the tool in order to reach valid conclusions. The community needs to develop the test cases tailored to cascading that are central to practical benchmarking and validation. In this paper, the IEEE PES working group on cascading failure reviews and synthesizes how benchmarking and validation can be done for cascading failure analysis, summarizes and reviews the cascading test cases that are available to the international community, and makes recommendations for improving the state of the art. [ABSTRACT FROM PUBLISHER]

• A multi-objective water-energy system design framework under uncertainty is presented. • Implications of spatial topology and interdependencies in multi-sector systems are explored. • The approach enables water-energy systems design given complex regional and sectoral trade-offs. The design of water and energy systems has traditionally been done independently or considering simplified interdependencies between the two systems. This potentially misses valuable synergies between them and does not consider in detail the distribution of benefits between different sectors or regions. This paper presents a framework to couple integrated water-power network simulators with multi-objective optimisation under uncertainty to explore the implications of explicitly including spatial topology and interdependencies in the design of multi-sector integrated systems. A synthetic case study that incorporates sectoral dependencies in resource allocation, operation of multi-purpose reservoirs and spatially distributed infrastructure selection in both systems is used. The importance of explicitly modelling the distribution of benefits across different sectors and regions is explored by comparing different spatially aggregated and disaggregated multi-objective optimisation formulations. The results show the disaggregated formulation identifies a diverse set of non-dominated portfolios that enables addressing the spatial and sectoral distribution of benefits, whilst the aggregated formulations arbitrarily induce unintended biases. The proposed disaggregated approach allows for detailed spatial design of interlinked water and energy systems considering their complex regional and sectoral trade-offs. The framework is intended to assist planners in real resource systems where diverse stakeholder groups are mindful of receiving their fair share of development benefits. [ABSTRACT FROM AUTHOR]

Historically, water and energy systems infrastructure planning and operation have been undertaken in isolation or with limited coupling. These approaches do not adequately recognize the complex interrelations between these systems, and using them introduces a risk of deploying inefficient planning and operational interventions. This risk will become more pronounced in the coming decades due to increasing pressures on water and energy resources, and opportunities could be lost regarding the integration of renewable energy sources. In this work, we present a new generalised and integrated system simulation and multi-objective optimisation framework that simulates water-energy interactions, and links the simulators to multi-objective evolutionary algorithms (MOEAs). The framework is generic and can be used to undertake planning level optimisation of interventions of integrated water and energy systems. The framework is applied to a synthetic case study that incorporates multidimensional interrelations among water and energy systems, exploring different trade-offs between water and energy system users. Results show the approach is capable of tackling complex links between both systems and guide decisions such that they consider the whole integrated system's performance. [ABSTRACT FROM AUTHOR]