High Impact Low Probability Research Articles

Seismic-Resilient Electric Power Distribution Systems: Harnessing the Mobility of Power Sources

Mobile power sources (MPSs), including mobile emergency generators, truck-mounted mobile energy storage systems, and electric vehicles, have great potentials to be employed as grid-support resources during power grid emergency operating conditions to supply the critical loads and enhance the resilience of distribution system (DS) via a swift disaster restoration. We here investigate the MPS dispatch (i.e., routing and scheduling) in coordination with DS dynamic network reconfiguration. We propose a two-stage restoration scheme to facilitate the DS restoration following the high-impact low-probability (HILP) seismic disasters. In the first stage, a seismic hazard is simulated through a Monte Carlo simulation engine to estimate the unavailability of power distribution branches under a suite of seismic force scenarios. In the second stage, a mixed-integer nonlinear programming (MINLP) optimization model is formulated for DS restoration that cooptimizes the routing and scheduling of MPSs and DS dynamic network reconfiguration. The MINLP model is then linearized to a mixed-integer linear programming model to reduce the computation complexity, where the seismic-resilience recovery strategies are generated at different timescales. The efficacy of the proposed method is evaluated on the IEEE 33-node test system and the results verify a significant reduction in the load outages and an improved power system resilience to HILP earthquakes.

Enhancing Power Grid Resilience Through an IEC61850-Based EV-Assisted Load Restoration

Contrary to reliability analysis in power systems with the main mission on safely and securely withstanding credible contingencies in day-to-day operations, resilience assessments are centered on high-impact low probability (HILP) events in the grid. This paper proposes an autonomous load restoration architecture founded on IEC 61850-8-1 GOOSE communication protocol to engender an enhanced feeder-level resilience in active power distribution grids. Different from the past research on outage management solutions, most of which 1) are not resilience-driven; 2) are reactive solutions to local single-fault events; and 3) do not address both network built-in flexibilities and flexible resources. The proposed solution harnesses 1) the imported power and flexibility from the neighboring networks; 2) distributed energy resources; and 3) vehicle to grid capacity of electric vehicles aggregations to enhance the feeder-level resourcefulness for agile response and recovery. Through real-time self-reconfiguration strategies, the suggested solution is capable of coping both single and subsequent outage events, and will engender a heightened resilience before and during the contingency period. Moreover, a resilience evaluation framework, which quantifies the contribution of all resources involved in service restoration, is developed. Real-time performance of the designed architecture is evaluated on a real-world power distribution grid using a real-time hardware-in-the-loop platform. Numerical case studies through a number of diverse scenarios demonstrate the efficacy of the proposed restoration solution in practicing an enhanced resilience in power distribution systems in response to HILP scenarios.

Seismic-Resilient Bulk Power Grids: Hazard Characterization, Modeling, and Mitigation

The operation of the electricity delivery infrastructure is environmentally driven and vulnerable to a wide range of high-impact low-probability (HILP) hazards. Among different classes of HILP disasters, earthquakes are one of the most unpredictable hazards which may lead to widespread disruptions of mission-critical services and infrastructures. This article introduces a comprehensive framework for modeling and characterization of seismic hazards, vulnerability assessment of electric systems to the earthquake, and corrective actions and mitigation strategies ensuring operational resilience. The Monte Carlo simulation is employed to produce a realistically large set of possible earthquake scenarios to capture the stochastic nature of seismic hazards. An inclusive approach is then introduced based on the fundamental principles of fragility curves to assess the vulnerability of power generation facilities in the face of HILP earthquakes. A new seismic risk metric is suggested that takes into account both hazard and vulnerability probabilities, as well as the financial consequences due to postquake disruptions in power generation stations. Along with the generation redispatch strategy as a conventional mitigation solution following a nontrivial contingency, a new mitigation strategy centered on corrective network topology control is formulated to maximize the load outage recovery following HILP disruptions. The proposed decision support tool enables a swift restoration and improved resilience in dealing with the aftermath of the HILP earthquakes. Efficacy of the proposed framework is numerically analyzed and verified on both the IEEE 57-bus and IEEE 118-bus test systems.

Developing an advanced dynamic risk analysis method for fire-related domino effects

Domino effects are typically high impact low probability (HILP) accidents, whereby escalation effects triggered by fires are most frequent. The evolution of fire-related domino effects depends on synergistic effects and the performance of safety barriers, but those factors usually are time-dependent. In the present study, a methodology is developed to provide more accurate probabilities related to domino effects, by considering the temporal evolution of escalation vectors caused by time-dependent factors. The Dynamic Bayesian Network (DBN) approach is applied both to model the spatial-temporal propagation pattern of domino effects and to estimate the dynamic probabilities of domino chains. The methodology is illustrated with a case study to determine the dynamic aspect of the probabilities of domino effects considering the impact of add-on (active and passive) safety barriers and taking into account synergistic effects. The critical units for facilitating domino propagation have been identified by the analysis of posterior probabilities, and further validated using graph theory. The methodology will be helpful for risk management and emergency decision-making of any chemical industrial area.

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks, but also evaluate the potential improvements offered by different planning measures. In this article, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to high-impact low-probability (HILP) events. Specifically, we define two risk-based measures: value-at-risk ( $VaR_\alpha$ ) and conditional value-at-risk ( $CVaR_\alpha$ ) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively, for the events beyond a prespecified risk threshold, $\alpha$ . Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics.

Electrical Power System Resilience Assessment: A Comprehensive Approach

Extreme weather events such as earthquake and hurricane have disastrous consequences on power systems. Due to the inherent nature of these events, as high-impact low-probability (HILP) events, selection of an appropriate method that can provide the effects of weather conditions on the power system behavior still remains a significant challenge. Resilience is a new concept that focuses on mitigating the destructive effects of such disastrous events on power systems. This article provides a fundamental framework for quantifying and modeling of power systems resilience, with focus on high wind incidence. The algorithm composes of four steps. In the first step, the prerequisites of the analysis are described. Disaster modeling is carried out in step second and the transmission line status and system topology obtaining are outlined in the third step. In the last step, based on optimal power flow tracing, a novel resilience index is introduced based on axiomatic design concepts. The core of the main algorithm involves a novel matrix-based approach to count automatically all possible routes from generators to loads, considering load importance, before and after a disruption. The effectiveness of the proposed approach is illustrated by the IEEE 14-bus system. The proposed methodology is original and a comprehensive approach.

Striking a balance between the costs and benefits of increasing response capability: A microworld study of the effect of capability assessments

The Hyogo and Sendai frameworks highlight the key role of disaster risk management (DRM). However, the guidance is limited on how to best conduct DRM activities, such as capability assessments, to reduce the impact of disasters. In this study, we examine the effectiveness of different ways of presenting capability assessments, both when dealing with so-called high impact/low probability (HILP) events and low impact/high probability (LIHP) events. More specifically, we introduce microworld experiments as a new approach to studying and developing DRM knowledge. This approach allows the researcher to retain some of the dynamics and complexity of the real DRM context while providing greater experimental control compared to field studies. We report the results from such a study in which forty-nine DRM professionals assumed the role of municipal decision-makers who were asked to balance investments in public services and goods with increasing response capability (in order to reduce losses from potential future adverse events), with or without support from capability assessments. The results show, as expected that it was harder to manage the trade-off between the cost of increasing response capability and benefits in terms of reduced losses in HILP situations compared to LIHP. Also, the results show that presenting capability assessments to the decision-makers increased the investments in capability and they became better at balancing the cost of investment with reduced losses. The results thus suggest that capability assessments can be useful to support decision-making for proactively reducing the impacts of disasters.

Energy Storage Planning for Enhanced Resilience of Power Distribution Networks Against Earthquakes

Energy infrastructures are perceived continuously vulnerable to a range of high-impact low-probability (HILP) incidents-e.g., earthquakes, tsunamis, floods, windstorms, etc.- the resilience to which is highly on demand. Specifically suited to battery energy storage system (BESS) solutions, this paper presents a new resilience-driven framework for hardening power distribution systems against earthquakes. The concept of fragility curve is applied to characterize an earthquake hazard, assess its impact on power distribution systems, and estimate the unavailability of the network elements when exposed to extreme earthquakes. A new metric is defined to quantify the network resilience taking into account the uncertain nature of such HILP events. A linear programming optimization problem is formulated to determine the capacity and location of the BESSs for enhanced resilience against earthquakes. Efficacy of the proposed framework is numerically analyzed and verified through application to a real-world distribution power grid.

Risk-based probabilistic-possibilistic self-scheduling considering high-impact low-probability events uncertainty

In recent years, examining the ruinous consequence of extreme weather events on the power system is one of the most challenging issues that researchers have confronted to. Considerable extreme conditions are generally the missing part of a realistic self-scheduling problem. Considering high-impact low-probability (HILP) events into the model have at least two benefits: first, generation companies (GenCos) can elude from financial disadvantages of upcoming HILP events and then the ISO can better clear energy and reserve markets with a preventive-oriented process to enhance power system resilience. This paper provides a pre-extreme condition self-scheduling for a price-taker generation company with renewable generation units which participates in the day-ahead energy and spinning reserve markets. Uncertainties associated with electricity prices and wind power production are characterized by multiple stochastic scenarios. The stochastic behavior of wind power is presented by using the Beta probability density function (PDF). In order to model the uncertainty of forced outages of generating units due to HILP events and the probability of being called for reserve deployment, a possibilistic approach is proposed. By comparing the generation scheduling under different risk factors and according to the financial disadvantages of HILP events, the conditional value-at-risk (CVaR) risk-averse strategy is considered into the model.

Exploiting network topology optimization and demand side management to improve bulk power system resilience under windstorms

Natural hazards are great threats to the safe and reliable operation of electric power systems. Some high-impact low-probability (HILP) events usually cause severe load losses to the power grid. Therefore, the enhancement of power system resilience appears to be particularly important. Nowadays, novel smart grid technologies provide more flexible ways for the power system operation. However, these technologies are not comprehensively incorporated in the resilience assessment framework to quantify their contribution for resilience enhancement. In this paper, fragility models for transmission lines and towers and sequential wind data are applied to simulate the operational states of power system under windstorm events. Then, a demand side management (DSM) program termed weather condition based real time pricing (WCRTP) framework is proposed to regulate the customer's electricity consumption behavior according to the extreme weather conditions. A network topology optimization (NTO) operation strategy is applied to mitigate the transmission congestion and realize the potential of transmission capacity by optimizing the network topology. The sequential Monte Carlo Simulation (MCS) method based resilience enhancement evaluation framework is developed to incorporate WCRTP and NTO. Numerical case studies are performed on modified RTS-79 systems. Both methods are proved to be effective self-adaptive measures for power systems in both customer behavior regulation aspect and transmission strategy aspect to boost the system resilience, which could eventually help power system operators to deal with the natural hazards in a more flexible, resourceful, and reliable manner.

Critical Load Restoration Using Distributed Energy Resources for Resilient Power Distribution System

Extreme weather events have a significant impact on the aging and outdated power distribution infrastructures. These high-impact low-probability (HILP) events often result in extended outages and loss of critical services, thus, severely affecting customers' safety. This calls for the need to ensure resilience in distribution networks by quickly restoring the critical services during a disaster. This paper presents an advanced feeder restoration method to restore critical loads using distributed energy resources (DERs). A resilient restoration approach is proposed that jointly maximizes the amount of restored critical loads and optimizes the restoration times by optimally allocating grid's available DER resources. The restoration problem is modeled as a mixed-integer linear program with the objective of maximizing the resilience to post-restoration failures while simultaneously satisfying grid's critical connectivity and operational constraints and ensuring a radial operation for a given open-loop feeder configuration. Simulations are performed to demonstrate the effectiveness of the proposed approach using IEEE 123-node feeder with 5 DERs supplying 11 critical loads and IEEE 906-bus feeder with 3 DERs supplying 17 critical loads. The impacts of DER availability and fuel reserve on restored networks are assessed and it is shown that the proposed approach is successfully able to restore a maximum number of critical loads using available DERs.

Maintaining Electric System Safety Through An Enhanced Network Resilience

Global environmental variations in the past two decades have contributed to a significant deviation of classic ecological patterns, leading to severe electricity outages triggered by extreme weather-driven phenomena. This has highlighted an urgent need for enhancing the resilience and robustness of the interconnected electricity grid against such high impact low probability (HILP) incidents. From the electrical safety point of view, it is essential to increase the operators’ awareness on a better understanding of such hazards and grid vulnerabilities, and enhance their preparedness on how to respond or mitigate the probable outages. This paper proposes a temporary, yet agile, restoration strategy in response to the forecasted HILP events, founded based on efficient utilization of the grid existing infrastructure, and aimed at improving its resilience against such extreme emergencies. The applied concept of reconfiguration is proactively planned to recover the electricity outages in a timely manner. In the meantime, two sets of metrics are proposed to quantify both the grid operational and infrastructure resilience. The presented framework aids the system operator to evaluate the outage recovery plans considering their impacts on system resilience and decide on the final implementation. The proposed approach is applied to the IEEE 118-bus test system facing an HILP event, where results reveal its applicability and efficiency.

Metrics and quantitative framework for assessing microgrid resilience against windstorms

Recent extreme weather events have emphasized the need for new methods and metrics to assess the power system resilience in response to high-impact low-probability (HILP) events. Microgrids (MGs) have been instrumental in such occasions for maintaining the power supply continuity to local customers. This paper provides a quantitative framework for assessing the MG resilience in response to HILP windstorms. The proposed framework jointly employs fragility curves of overhead distribution branches and windstorm profile to quantify the degradation in the MG performance (particularly supplied load in this work). The proposed analytical method is simple and computationally efficient which offers a quick means for getting knowledge about adverse impacts of an approaching windstorm and taking preventive measures accordingly. A set of normalized metrics is defined which provides a comparable tool for assessing the resilience in various operating conditions and power systems. The impacts of restorative actions, the system reinforcement, and the event severity on resilience curves and metrics are also investigated. The effectiveness of the proposed approach in response to an extreme windstorm is examined on a real-scale MG test bed.

Analysis of pipe-bursting construction risks using probability-impact model

PurposeCivil utility projects, both open-trench and trenchless, are subject to risk. These risks have both direct and indirect effect on project cost, schedule, quality and safety. It is therefore critical for the project management team to include risk management as an integral part of their project planning and execution. The purpose of this study is to identify the pipe-bursting construction risks and determine their probability of occurrence and cost impact and provide the appropriate responses to mitigate the identified risks.Design/methodology/approachThis is an exploratory design using an industry-wide questionnaire survey to collect data on the probability of occurrence and impact of risks on cost of pipe-bursting projects. A probability-impact model was used to categorize the risks to determine their criticality and the appropriate risk responses.FindingsThe model revealed that majority of the analyzed risks have low impact-low probability of occurrence and high impact-low probability of occurrence. Undocumented repairs to host pipe was the only risk identified as having high probability of occurrence and high impact on cost. The risk responses suggest a combination of risk transfer, reduction and acceptance to be appropriately applied to mitigate the risks. A discussion on the good practices indicates that most pipe-bursting operations can be done safely and successfully if site and project conditions are known before bursting and the appropriate measures are taken to address those conditions.Research limitations/implicationsAlthough the identified risks may apply to other utility construction methods, the focus of this research is limited to risks that occur during the construction phase of a pipe-bursting construction project.Practical implicationsRisk management is very critical to the success of any construction project. Identification and assessment of risks alone will not serve the purpose of risk management unless meaningful ways to mitigate those risks in a structured way are planned. The probability-impact model for the pipe-bursting construction risks with the mitigation strategies will help owners, engineers and contractors plan for and adequately respond to these risks. Additionally, a logical assessment of the risks will aid in effective decision-making regarding the management of the project.Originality/valueExtensive literature review indicates that there is no existing literature on the probability of occurrence and impact on cost of risks in pipe-bursting projects. This paper presents the results of a wide-ranging analysis on construction risks in pipe-bursting projects. This is the first analysis incorporating the use of the probability-impact model to determine the criticality of various pipe-bursting construction risks.

Resilience-oriented distribution network optimal planning to improve the continuity of power supply

ABSTRACTA widely dispersed asset, electric grid has enormous impacts on people’s lives. Recently occurred various high-impact low-probability (HILP) threats have raised the concerns about the classical reliability-oriented view. Therefore, withstanding unexpected and less frequent severe situations still remain a significant challenge. As a critical infrastructure (CI), power systems are more and more expected to be resilient to HILPs. So, utilities put their endeavours to harden enough their networks against HILPs. In this regard, there is a great need to a methodology able to optimise the hardening programme investments. This scheme could potentially save a large amount of money, as well as increase the resilience of the programme. So, this paper is organised to provide a comprehensive study on optimal resilient planning of distribution networks aiming to find an optimal solution for simultaneous optimal feeder routing problem and substation allocation, finding types of installed conductors and cost-effective hardening of the lines considering the deliberate attacks on urban CIs, costs and operational parameters in normal and resilient modes of distribution networks. Voltage limitation and thermal limit of feeders are modelled using fuzzy set theory. To validate the effectiveness of the proposed scheme, simulations are carried out on a relatively large-scale distribution network.

From risk to WEF security in the city: The influence of interdependent infrastructural systems

Across the planet, interacting threats are converging in urban areas beset with pressures brought on by global processes such as urbanization and climate change, and the challenges of creating water, energy and food (WEF) security for their populations. With an increased probability of floods and other extremes, goes a heightened potential for cascading effects as WEF security is at risk from an array of tightly bound interdependencies undergirding the WEF nexus. Such interdependencies heighten risk for generalized disruptions, as, for instance, when heavy precipitation triggers a breakdown of transportation infrastructure, leading to failures in energy generation, and provision of food and water. In this paper, we apply a framework to examine how interdependent WEF infrastructural systems mediate the risks that climate extremes pose to urban WEF security. Given that urban WEF security often hinges on dynamics that take place in regions outside city boundaries, we also examine the effect of this dependence on urban FEW security risk. We compare the pre- and post-event governance and infrastructural conditions shaping WEF security in four cities: Boulder Colorado and New York (USA) illustrative of WEF security risks posed by low probability high impact extreme events; and Accra (Ghana) and Mexico City (Mexico), illustrative of governance and infrastructural arrangements that can fail even under low risk high probability extreme events. We find that complex technological and governance failures can amplify negative impacts from extremes. Conversely, institutional actions and infrastructural supports can mitigate these impacts. By understanding interdependencies, cities can anticipate and avoid cascading effects on WEF systems. We reflect on how commonalities and differences in sociodemographic, economic, technological, environmental, and governance configurations relate to different capacities to mitigate risks and adapt.

Supply Chain Tsunamis: Research on Low Probability High Impact Disruptions

This paper introduces supply chain tsunamis as a major strategic supply chain phenomenon. Like their ecological counterparts, supply chain tsunamis occur at relatively long intervals and are therefore easily mistaken for unique events, rather than recurring phenomena. In contrast to ocean tsunamis, they can in principle be prevented through timely and adequate managerial action. However, their immediate impact is just as sudden and disruptive, and their ability to reshape supply chains of companies and even industries equally long-lasting. They are fundamentally different from phenomena like the “bullwhip effect” (Lee et al., 1997) and “black swan events” (Taleb, 2007). This paper further explores a preliminary typology of supply chain tsunamis suggested in an earlier publication (Akkermans and Van Wassenhove, 2013). Each type of tsunami focuses on a very different part of the supply chain periphery where the first signals of a developing tsunami can be observed. In this paper we use a detailed example from the high-tech electronics industry to describe how a supply chain tsunami unfolds over time. This is done both from an external and an internal perspective. The external perspective shows the sequence of events visible to the outside observer. The internal perspective focuses on the managerial decisionmaking processes that cause and (sometimes) resolve supply chain tsunamis. We link the notion of supply chain tsunamis to the broader need to revive strategic operations management research. Supply chain tsunamis affect corporate strategy and have a profound impact on business and management. They are living proof that operations remain, after almost half a century, the “missing link in corporate strategy” (Skinner, 1969). Therefore, we argue that business tsunamis deserve deeper research and suggest some avenues.

Metrics and Quantification of Operational and Infrastructure Resilience in Power Systems

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.

Dynamic risk analysis for Seveso sites

The accident that occurred in Seveso in 1976 changed our approach to risk assessment forever. For instance, it led to drafting specific European directives to prevent major accident hazards. Their focus is on industrial sites handling dangerous substances and denominated “Seveso sites”. Their operators have the obligation to provide specific information to the competent authorities, such as safety reports. However, risk analysis studies usually provide a static picture of the site status, while the system constantly evolves or degrades. For this reason, this contribution suggests a dynamic risk analysis approach aiming to continuously calibrate and improve, based on new related evidence and lessons learned. The work suggests focusing on such early deviations to lower the probability of high impact low probability (HILP) events. Three complementary methods may be used to process such information: dynamic hazard identification, dynamic analysis of initiating events, and dynamic analysis of consequences. A representative example of their potential is provided by comparing their capabilities with the causes that led to the Seveso catastrophe. Despite its limitations, Dynamic Risk Analysis represents an opportunity for improved decision-making support and critical risk communication.

Public Utility Commissions to Foster Resilience Investment in Power Grid Infrastructure

In the United States, power system works at 99.9 percent reliability but it does not imply that it is safe and reliable against all types of hazards whether natural, technological or man-made. Especially the high impact low probability (HILP) events also called “black-swan events” offer a complex risk environment for power grid infrastructure. On the other hand, the utility companies have to work in a very competitive and regulated environment maintaining a balance between their investments on infrastructure, rate of returns, serviceability and cost to the customers. Hence, it is not economically feasible and justifiable to harden the entire infrastructure owing to budget constraint and regulatory issues. In such a scenario, enhancing resilience selectively within the power grid infrastructure would serve the purpose of achieving optimum security against any type of hazards, including HILP events. Resilience is the property that incorporates robustness and rapid recovery of infrastructure in face of any type of unprecedented havoc hazards. Enhancing resilience is cost intensive and future return on such investments are also risky and uncertain. Again, “resilience investments” i.e. investments needed to enhance resilience of power grid need approval from Public Utility Commissions (PUC) for cost recovery from public consumers and also must involve other investment agencies to invest in the power infrastructure by offering them good rates of return on investment (ROI). This paper will discuss the various key issues that hinder adequate investments in resilience enhancement of power grid infrastructure. Some of the key issues identified are (i) lack of knowledge about the Black Swan Events and their impact on the power grid infrastructure; (ii) strict regulatory restrictions imposed by PUC within the purview of which the utilities work; (iii) lack of strong value proposition for resilience investment business cases as presented by the utilities to the PUC, and (iv) lack of adequate incentives for the investors to invest in the power infrastructure. A conceptual strategic decision making framework for the PUC is also proposed that would help in approving or incentivizing adequate resilience investments for power grid infrastructure.