High-impact Low-probability Events Research Articles

Developing resilience pathways for interdependent infrastructure networks: A simulation-based approach with consideration to risk preferences of decision-makers

In this study, we propose a methodological framework to identify and evaluate cost-effective pathways for enhancing resilience in large-scale interdependent infrastructure systems, considering decision-makers’ risk preferences. We focus on understanding how decision-makers with varying risk preferences perceive the benefits from infrastructure resilience investments and compare them with upfront costs in the context of high-impact low-probability (HILP) events. First, we compute the costs of interventions as the sum of their capital costs and maintenance costs. The benefits of the interventions include the reduction in physical damage costs and business disruption losses resulting from the improved resilience of the network. In the final stage, we develop statistical models to predict the perceived net benefits of different network resilience configurations in power, water, and transport networks. These models are employed in an optimization framework to identify optimal resilience investment pathways. By incorporating Cumulative Prospect Theory (CPT) in the optimization framework, we show that decision-makers who assign higher weights to low probability events tend to allocate more resources towards post-disaster recovery strategies leading to increased resilience against HILP events, like earthquakes. We illustrate the methodology using a case study of the interdependent infrastructure network in Shelby County, Tennessee.

Unlocking responsive flexibility within local energy communities in the presence of grid-scale batteries

The transition towards a decentralized, decarbonized, and distributed energy infrastructure necessitates techno-economic initiatives to empower local energy communities (LECs) to achieve self-reliance and evolve into self-sustained electricity networks. It is crucial to underscore the significance of network resilience, especially in the context of local power generation, battery storage, and the radial topology of low-voltage (LV) networks. While contemporary LV networks have made significant attempts to integrate distributed energy resources (DERs), the notable deficiency lies in their lack of network redundancy, posing a substantial challenge in the occurrence of high-impact, low-probability (HILP) events. Therefore, to enhance LV network resilience and leverage its capability to withstand unexpected disruptions, the network operator needs to unlock the potential contributions of end-users within the active distribution networks (ADNs). In this paper, a comprehensive model is developed based on multi-temporal optimal power flow (MTOPF) for unbalanced LV networks addressing the technical issues in islanded microgrid operational planning. The contributions of the grid-scale batteries in forming islanded microgrids and the flexibility that can be provided by the end-users in the LEC have been considered in this paper. To demonstrate the performance of the proposed model, the simulation studies have been carried out on a part of medium and low voltage networks, consisting of network reconfiguration and load transferring capability to reduce the service interruptions during HILP events. The energy-not-served (ENS) is chosen as one of the key performance indicators (KPIs) in this study. With the unlocking flexibility potentials and contribution of the DERs, including grid-scale energy storage (GES) units and Photovoltaic (PV) panels, the ENS has been reduced from 700.8 kWh to 447.5 kWh by activating the local resources, proper switching action, and contribution of the flexible loads, for one of the severe HILP events, i.e., the main grid outage. In this case, the full load curtailment index is reduced from 180 to 106 client hours.

Risk-constrained strategic resilience enhancement of power distribution and natural gas systems through G2P and P2G planning and distributed load Restoration against flood

Floods, categorized as high-impact low-probability (HILP) events, pose significant risks to power distribution systems (PDS) and natural gas systems (NGS). Ensuring mutual support during emergencies through energy sharing, facilitated by coupling devices, highlights the necessity for optimal integration. However, managing these systems separately by different operators calls for coordinated yet decentralized operation. Hence, this paper introduces a risk-constrained bi-level model to integrate PDS and NGS. The upper level deals with siting and sizing power to gas (P2G) and gas to power (G2P) units. Meanwhile, the lower level employs decentralized load restoration using the alternating direction method of multipliers (ADMM) algorithms, with each system individually minimizing its own risk. To account for the unpredictable nature of HILP events surpassing a predefined risk threshold (α), this study defines Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) for each system as resilience criteria. Validation of the model is demonstrated using an IEEE 33-bus PDS and a 20-node NGS, and applied to a real-world large-scale PDS in Khuzestan province, Iran. Across various scenarios, higher system risks prompt an increase in the number and size of planned coupling devices, particularly to address the most severe 5% of scenarios. This optimization leads to enhanced energy sharing, resulting in a resilience improvement of nearly 35% in specific cases, along with a notable 25% reduction in both active and reactive power loss. Additionally, the sensitivity analysis explores the impact of varying risk levels and thresholds on outcomes, demonstrating how investors can utilize the proposed model across diverse risk perspectives.

Investment planning framework for mitigating cascading failures

Critical component outages can lead to widespread cascading propagation, which is however typically ignored in existing investment planning approaches. To address this gap, this paper seamlessly integrates advanced cascading failure analysis into resilient investment planning. It first deploys a stochastic simulator to generate spatiotemporal high-impact low-probability (HILP) events, which are then assessed using a cascading failure model, generating various cascading quantification metrics (CQMs). The framework explicitly quantifies tail risks (i.e., HILP events) using Conditional Value-at-Risk (CVaR) with a confidence level determined by unsupervised clustering, instead of using a predetermined confidence level. This enables the more tailored identification of a set of worst-case scenarios for the system under investigation, improving its practicality. An optimization model then utilizes the outputs of the cascading analysis and the defined CVaR confidence level to identify investment portfolios that provide a hedge against cascading failures. The proposed work is demonstrated on the IEEE 39-bus system, revealing reduced cascading propagation.

A Resilient Integrated Resource Planning Framework for Transmission Systems: Analysis and Optimization

This article presents a resilient Integrated Resource Planning (IRP) framework designed for transmission systems, with a specific focus on analyzing and optimizing responses to High-Impact Low-Probability (HILP) events. The framework aims to improve the resilience of transmission networks in the face of extreme events by prioritizing the assessment of events with significant consequences. Unlike traditional reliability-based planning methods that average the impact of various outage durations, this work adopts a metric based on the proximity of outage lines to generators to select HILP events. The system’s baseline resilience is evaluated by calculating load curtailment in different parts of the network resulting from HILP outage events. The transmission network is represented as an undirected graph. Graph-theoretic techniques are used to identify islands with or without generators, potentially forming segmented grids or microgrids. This article introduces Expected Load Curtailment (ELC) as a metric to quantify the system’s resilience. The framework allows for the re-evaluation of system resilience by integrating additional generating resources to achieve desired resilience levels. Optimization is performed in the re-evaluation stage to determine the optimal placement of distributed energy resources (DERs) for enhancing resilience, i.e., minimizing ELC. Case studies on the IEEE 24-bus system illustrate the effectiveness of the proposed framework. In the broader context, this resilient IRP framework aligns with energy sustainability goals by promoting robust and resilient transmission networks, as the optimal placement of DERs for resilience enhancement not only strengthens the system’s ability to withstand and recover from disruptions but also contributes to efficient resource utilization, advancing the overarching goal of energy sustainability.

Optimizing safety barrier allocation to prevent domino effects in large-scale chemical clusters using graph theory and optimization algorithms

Domino effects are high-impact low-probability events that can have catastrophic consequences. To prevent and to reduce risks related to such events, safety barriers (SBs) are crucial. However, the initiation, propagation, and stopping processes of domino effects are characterized with complexity and uncertainties and hence they are unpredictable. This makes it challenging to allocate SBs based on predicted probabilities. In this study, a multi-objective optimization model which integrates graph theory with Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to allocate add-on SBs effectively. Graph metrics were used to quantify the escalation risks related to storage tanks and to optimize the allocation of add-on SBs, thereby minimizing the consequences of a domino effect under a budget constraint. The results of the case study demonstrate great efficiency in finding globally optimal strategies with a largest reduction of 94.3% in the out-closeness score due to the implementation of add-on SBs, allowing decision-makers to choose the most preferable investment strategy in face of domino effect risk. Our study therefore provides a novel approach to achieve an optimal allocation of add-on SBs globally and can be useful in preventing domino effects in large-scale chemical clusters equipped with a large number of storage tanks.

Resiliency Assessment and Enhancement of Renewable Dominated Edge of Grid Under High-Impact Low-Probability Events—A Review

Resiliency Assessment and Enhancement of Renewable Dominated Edge of Grid Under High-Impact Low-Probability Events—A Review

A Scalable Approach to Large Scale Risk-Averse Distribution Grid Expansion Planning

Distribution grid reliability and resilience has become a major topic of concern for utilities and their regulators. In particular, with the increase in severity of extreme events, utilities are considering major investments in distribution grid assets to mitigate the damage of highly impactful outages. Communicating the overall economic and risk-mitigation benefits of these investments to regulators is an important element of the approval process. Today, industry reliability and resilience planning practices are based largely on methods that do not take explicit account of risk. This paper proposes a practical method for identifying optimal combinations of investments in new line segments and storage devices while considering the balance between the risk associated with high impact low probability events and the reliability related to routine failures. We show that this method can be scaled to address large scale networks and demonstrate its benefits using a Target Feeder from the Commonwealth Edison Reliability Program.

Rethinking disaster risk for ecological risk assessment

While disaster events are consequential, they are rare. Ecological risk assessment processes tend to estimate risk through an “expected value” lens that focuses on the most probable events, which can drastically underappreciate the importance of rare events. Here, we show that expected value and average risk-based calculations underappreciate disaster events through questionable assumptions about equally weighing high probability low impact events with low probability high impact events, and in modeling probability as a chance among an ensemble of possible futures when many contexts of ecological risk assessment are focused on a single entity over time. We propose an update to ecological risk assessment that is specifically inclusive of disaster risk potential by adopting analytical processes that estimate the maximum hazard or impact that might be experienced in the future, borrowing from the practice of modeling “Value at Risk” in financial risk contexts. We show how this approach can be adopted in a variety of data contexts, including situations where no quantitative data is available and risk assessment is based on expert judgement, which is common for ecological risk assessment. Increased exposure to environmental variation requires assessment tools to better prepare for, mitigate, and respond to disasters.

Equilibrium Allocation of ESSs in Multiple UIESs-Accessed Distribution Networks Considering the Resilience and Economic Benefits

Recently, extreme disasters considered as high-impact low-probability (HILP) events have caused severe power outages. User-level integrated energy systems (UIESs) is being widely formed and accessed to distribution networks (DNs), which has the potential to enhance the resilience of DNs. Therefore, multiple UIESs-accessed DNs against such HILP events are required in order to enhance the resilience of the distribution network. In this paper, from a resilience-oriented perspective, an equilibrium allocation method of multiple energy storage systems (ESSs) is proposed considering the planning of distribution network with multiple UIES for improving the resilience of distribution network. Firstly, the topology and operation modes of multiple UIESs-accessed DNs are analyzed. Secondly, a quantitative evaluation method of UIES power support capability is formulated, in which the differences in type, quantity and scale of coupling equipment in different scenarios are considered. An allocation model of multiple ESSs is then established using the Nash equilibrium method to balance the distribution network resilience and economics. Finally, simulation tests verify the superiority of the proposed equilibrium allocation model of multiple ESSs compared with other resilience enhancement strategies.

Enabling resiliency using microgrids with dynamic boundaries

With the increased frequency of adverse weather and manmade events in recent years, the issue of the power distribution system (PDS) resiliency has become drastically important. Microgrids with various types of distributed energy resources (DERs) have capabilities to enhance the PDS’s resiliency against high impact low probability (HILP) events. Resiliency is defined as the ability of the system to keep supplying critical loads even during and after extreme contingencies. This paper presents a systematic method to segmentize parts of a PDS into microgrids with flexible boundaries to enhance resiliency and mitigate negative impacts from anticipated threats. The method is aimed at anticipation and preparation to HILP events in advance: alternative flexible boundaries of the microgrids must be preplanned during normal operation and dynamically changed prior to or during disturbances. The Mixed-Integer Linear Programming (MILP) is formulated and applied to select switching actions to adjust microgrids’ boundaries as well as to supply critical loads, while meeting system constraints. Networked microgrids are merged and reconfigured as necessary to maximize supply to critical loads, driven by a factor-based resiliency metric, which is obtained using the Analytical Hierarchy Process (AHP). The modified IEEE 123 bus system was utilized for validation of the proposed method. Compared with the traditional fixed-boundary microgrids, this approach determines the most resilient network configuration to supply high-priority critical loads. The developed method can be employed in power system planning, operation, and in decision-making to enhance operational resiliency and invest in system upgrades appropriately.

Probabilistic Assessment of Power Systems Resilience Under Natural Disasters

Extreme weather can have a substantial influence on a power system's operational resilience since they are high-impact, low-probability (HILP) events. Therefore, Power systems must be resilient to HILP incidents in addition to being reliable against widely spread and credible threats. Despite the rarity of such events, the severity of their potential impact necessitates the development of appropriate resilience assessment tools to capture their implications and enhance the resilience of energy infrastructure systems. In this paper, a probabilistic strategy is proposed to assess and evaluate the operational resilience of power distribution networks against the impacts of HILP events depending on value-at-risk and conditionally value-at-risk quantitative risk-based assessments. With several scenarios built on sequentially Monte Carlo simulations, the consequence of a windstorm on a distribution network can be assessed using a probability-based resilience assessment methodology. The presented method is examined on an IEEE 37-bus system. Different case studies based on detailed data are presented and analyzed to demonstrate the proposed method's usefulness.

Risk-Based Active Distribution System Planning for Resilience Against Extreme Weather Events

Enhancing the resilience of power distribution systems to extreme weather events is of critical concern. Upgrading the distribution system infrastructure by system hardening and investing in smart grid technologies effectively enhances grid resilience. Existing distribution system planning methods primarily consider the persistent cost of the expected events (such as faults and outages likely to occur) and aim at improving system reliability. The resilience to extreme weather events requires reducing the impacts of the high impact low probability (HILP) events that are characterized by the tail probability of the event impact distribution. Thus, the resilience-oriented system upgrades solutions need to be driven by the risks imposed by extreme weather events on the power grid infrastructure rather than persistent costs. This paper aims to develop a risk-based approach for the long-term resilience planning of active power distribution systems against extreme weather events. The proposed approach explicitly models (1) the impacts of HILP events using a two-stage risk-averse stochastic optimization framework, thus, explicitly incorporating the risks of HILP events in long-term planning decisions, and (2) the advanced distribution grid operations (in the aftermath of the event) such as intentional islanding into infrastructure planning problem. The inclusion of risk in the planning objective provides additional flexibility to the grid planners to analyze the trade-off between risk-neutral and risk-averse planning strategies.

Electrical energy systems resilience: A comprehensive review on definitions, challenges, enhancements and future proceedings

AbstractSecure energy providing is a critical feature of sustainable societies, where this subject requires reliable operation of generation, transmission and distribution infrastructures. In order to achieve the mentioned goal, the influence of uncertainties should be analyzed using appropriate methods, criteria and tools. The high‐impact low‐probability (HILP) events are special uncertainties that can affect the safe operation and lead to irreparable damages. This paper presents a comprehensive review of resilience in electrical energy systems consisting of fundamental concepts and definitions, challenges, enhancements and future proceedings. In this regard, a resilience evaluation framework is designed first in which all the necessary actions for a resilience study are defined. In the next step, various HILP events are introduced and then the resilience concept is defined accurately and related curves are investigated. Afterwards, the resilience assessment indices and quantitative measures are described and their details and applications are precisely explained. As well, the resilience enhancement strategies are discussed from short‐term and long‐term perspectives and eventually, resilience‐based objective functions and optimization approaches are classified in the last part. It is noteworthy that the main research gaps are also clarified according to the reviewed papers and further suggestions are expressed to cope with the declared problems.

A Petri net model-based resilience analysis of nuclear power plants under the threat of natural hazards

• Resilience of nuclear power plant against HILP events is assessed. • Petri net is effective in modelling the damage and maintenance of nuclear reactor. • The deterioration of the systems should not be neglected in the resilience study. • Periodic maintenance is critical for ensuring the resilience of nuclear reactor. Due to global climate change, nuclear power plants are increasingly exposed to the threats of extreme natural disasters. In this paper, a resilience engineering approach is applied to tackle all aspects of nuclear safety, spanning from design, operation, and maintenance to accident response and recovery, in the case of high-impact low-probability events. Petri net models are developed to simulate the losses caused by extreme events, the health states of relevant systems, mitigation processes, and the recovery and maintenance processes. The method developed is applied to assess the resilience of a single-unit pressurised heavy water reactor under the threat of three possible external events. Possible loss of coolant accidents and station blackout accidents caused by the events are considered. With the aid of the models developed, both the influence of stochastic deterioration and the impact of external events on the resilience of the reactor can be assessed quantitatively. The simulation results show that the method can comprehensively describe the resilience of nuclear power plants against various disruptive events. It is also found that the stochastic deterioration that does not directly affect the operation of nuclear reactors is critical to the resilience of reactors.

A novel framework for resilient overhead power distribution networks

The natural disasters that are classified as high impact low probability (HILP) events have a major effect on the power system infrastructure, in particular, the overhead power distribution networks (OPDNs) causing significant challenges in maintaining continuity of service to critical loads (CLs). A resilient OPDN should maintain operational normalcy by quickly absorbing, adapting and recovering from the HILP events resulting in an improved customer satisfaction through continuity of electricity supply and minimal safety hazards. Modern OPDS predominantly consists of overhead electric lines and poles with diverse characteristics by virtue of their age, maintenance, location etc. Unlike the normal operating conditions, quick and accurate decision making from the network operator becomes very critical in maintaining the resiliency of a OPDN during HILP events. This paper proposes a novel framework for resiliency assessment, quantification, enabling and enhancement that facilitates resilient reconfiguration of an OPDN during HILP events. Two novel physical system metrics with multiple sub- factors specific to the overhead networks are also proposed for resiliency assessment followed by resiliency quantification using diamond pairwise comparison, kth-order additive fuzzy measure and Choquet integral. The resiliency enabling and enhancement algorithms for contingency scenarios as well as cost-benefit analysis for network planning strategies are presented in this work. The novel resiliency framework presented in this work is used to enable the resiliency of a OPDN and provide the relevant information about the feasible network options with their resiliency value. The performance of the proposed framework is illustrated on the 415V OPDN in Nayabad area of the CESC Limited, Kolkata, India through simulation case studies.

Improvement of the Resilience of a Microgrid Using Fragility Modeling and Simulation

The modern microgrid is designed to withstand various disruptive events that have a high probability of occurrence but have a low impact on the system. This improves the reliability of the system but does not take into consideration the disruptive events that have a low probability of occurrence but have a large impact on the system, such as extreme weather or natural disasters. Redesigning a microgrid to withstand low probability high impact events is very costly and is not a feasible solution to existing microgrids. This paper proposes a method to improve the resilience of an existing microgrid to quickly recover from low probability high impact events. The method used for this purpose is a combination of Monte Carlo simulation and prioritization of load of the microgrid. The efficacy of the method is examined by modeling microgrids using a fragility model. Using the proposed novel resilience index, the resilience of the IEEE 5-Bus system and IEEE 14-Bus system and the effect of load shedding on the resilience of the microgrid are analyzed and presented. The effect on smaller and larger grids and their resilience is examined. A novel resilience index is used to quantify the improvement of resilience of the proposed method when compared to other methods available in the literature.

Quantifying the Difference Between Resilience and Reliability in the Operation Planning of Mobile Resources for Power Distribution Grids

Modern power grids have high levels of distributed energy resources, automation, and inherent flexibility. Those characteristics have been proven to be favorable from an environmental, social and economic perspective. Despite the increased versatility, modern grids are becoming more vulnerable to high-impact low-probability (HILP) threats, particularly for the distribution networks. On one hand, this is due to the increasing frequency and severity of weather events and natural disasters. On the other hand, it is aggravated by the increased complexity of smart grids. Resilience is broadly defined as the capability of a system to mitigate the effects of and recover from HILP events, which is often confused with reliability that is concerned with low-impact high-probability (LIHP) ones. In this paper, a distribution system in Portugal is simulated to showcase how the utilization of flexibility and mobile energy resources (MERs) should be considered differently relative to HILP vs LIHP threats.

Enhancing Resilience of Distribution Networks by Coordinating Microgrids and Demand Response Programs in Service Restoration

In case of high-impact low-probability events, in order to restore the critical loads of the distribution network as much as possible, it is necessary to employ all available resources such as microgrids and distributed generations. This article presents a two-stage method for critical load restoration after an extreme event utilizing the coordination of all available distributed generations, microgrids, and demand response programs. In the first stage, the postdisaster reconfiguration of the network is determined, and in the second stage, the demand response outputs of the responsive loads and the restoration status of all critical loads are ascertained. In the first stage, an algorithm is proposed to determine electrical islands, and in the second stage, a new model based on the generalized Benders decomposition algorithm is proposed, which considers demand response programs and all operational constraints and aims to maximize the restored critical loads. The effectiveness of the proposed method is verified by simulating the 33- and 69-bus test systems.

Resilience enhancement for urban distribution network via risk-based emergency response plan amendment for ice disasters

High-impact low-probability (HILP) events, such as ice disasters, result in large losses of critical loads (CLs) in the urban distribution network (UDN). Thus, emergency response plans are usually made against various HILP events to improve the UDN resilience. However, these emergency plans are usually made under incomplete and imperfect information; thus, the execution of these plans may not be effective and even risky under the real situation. Therefore, in this paper, a multi-stage UDN resilience enhancement framework is proposed for tackling this challenge. At the first stage, the distribution system operator (DSO) forms typical failure scenarios based on historical data of damage to electric components under ice disasters. Under each scenario, the DSO designs a response plan to minimize the CLs’ loss and associated costs. Thanks to the updated information on the ice disaster, at the second stage, DSO makes a risk assessment on the planned emergency response. If the risk is unacceptable for any period of the ice disaster, at the third stage, DSO amends the response plan to alleviate the “second-order” impacts on CLs, distribution lines, and distributed generations (DGs). Finally, simulations on the modified IEEE-69 system, including 10 CLs and some DGs, show that the proposed framework can effectively reduce second-order impacts due to both the ice disaster and its impact on the execution of originally planned emergency response.