High-impact Low-probability Events Research Articles

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.

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.

Network hardening and optimal placement of microgrids to improve transmission system resilience: A two-stage linear program

This paper aims to develop a linear two-stage optimization problem based on an attacker-defender resilient planning (AD-RP) model to improve the power system's operational and infrastructural resilience in the face of low-probability high-impact events. In the developed model, attackers are natural phenomena that can cause the most severe damage to system performance, and defenders are actions that minimize system vulnerabilities. In the first stage, a stochastic model depending on Monte-Carlo simulation is developed to present a new index for selecting the most vulnerable transmission system components. This index is designed based on combining the worst possible case of attack, disaster statistical analysis, system structure and fragility curves. In the second stage, as defense operations, the hardening of vulnerable lines and microgrids placement in the proper places are carried out considering investment budget constraints. Minimizing load shedding and ensuring the resilience of the transmission network are the main objectives behind the second stage. In this regard, a comprehensive metric for the evaluation of the transmission system resilience is introduced. Thanks to a mixed-integer programming problem, the effectiveness of the proposed AD-RP model in increasing system resilience is demonstrated in the IEEE 30-bus and 118-bus test systems.

Microgrid resilience: a holistic and context-aware resilience metric

Microgrids present an effective solution for the coordinated deployment of various distributed energy resources and furthermore provide myriad additional benefits such as resilience, decreased carbon footprint, and reliability to energy consumers and the energy system as a whole. Boosting the resilience of distribution systems is another major benefit of microgrids. This is because they can also serve as a backup power source when the utility grid operations are interrupted due to either high-probability low-impact events like a component failure or low-probability high-impact events - be it a natural disaster or a planned cyberattack. However, the degree to which any particular system can defend, adapt, and restore normal operation depends on various factors including the type and severity of events to which a microgrid is subjected. These factors, in turn, are dependent on the geographical location of the deployed microgrid as well as the cyber risk profile of the site where the microgrid is operating. Therefore, in this work, we attempt to capture this multi-dimensional interplay of various factors in quantifying the ability of the microgrid to be resilient in these varying aspects. This paper, thus, proposes a customized site-specific quantification of the resilience strength for the individual microgrid capability to absorb, restore, and adapt to the changing circumstances for sustaining the critical load when a low-probability high-impact event occurs - termed as - context-aware resilience metric. We also present a case study to illustrate the key elements of our integrated analytical approach.

Post-event electric taxis dispatch for enhancing resilience of distribution systems

With the frequent occurrences of high-impact-low-probability (HILP) events such as typhoons, lightning strikes, and cyber attacks, people have the increasingly need of raising distribution network’s (DN) resilience. Deploying backup sources into the system can effectively improve the resilience of DNs. With the development of transportation electrification, the number of electric taxis (ETs) in urban transit is now growing. Besides, Vehicle-to-Grid (V2G) technology has also developed vigorously. Therefore, ETs can be dispatched to charging stations under unified command to feed power back to the grid when HILP events happen. To this end, we establish an optimization model for the post-event recovery of the DN using ETs. In this model, we combine the energy provided by V2G stations with the existing distributed generators and energy storage in the grid to restore the outage areas jointly. Topology reconfiguration technique and power flow balance of DN have also been fully considered in the modeling process. The whole grid is divided into several microgrids and powered by different sources. The multi-source multi-region power restoration method improves the stability of the DN restoration. It is easier to control the voltage and power quality. The method we proposed is tested on the modified IEEE 33-bus system. The simulation results verify the effectiveness and efficiency of our work.

Developing an innovating optimization framework for enhancing the long-term energy system resilience against climate change disruptive events

The energy system is one of the major sub-sectors of the economy in every society in which both supply with high reliability and compatibility of the energy sector with the environment is a necessity in the process of economic reconstruction and sustainable development. The energy system has been more susceptible to various disruptive events in recent years. Thus, designing a resilient energy system capable of resisting and recovering rapidly from high-impact low-probability (HILP) disruptive events is essential. This paper focuses on developing a two-stage long-term optimization framework for evaluating the energy system resilience as a crucial Critical Infrastructure (CI) system against HILP caused by climate change. The results show the share of electricity generation shift from coal and hydropower to PV in different scenarios based on the maximization of energy system resiliency. The faster occurring this shift, the greater vulnerability of the system. Furthermore, the sensitivity analysis of the optimum solution shows that the vulnerability of energy systems against HILP events depends on planner flexibility.

Multi-stage and resilience-based distribution network expansion planning against hurricanes based on vulnerability and resiliency metrics

Distribution networks should be expanded to supply reliable and cost-effective services for new and existing customers to respond to the rising demand. On the other hand, there is an increasing awareness of resilience in the face of high impact low probability events. Therefore, this paper presents a multistage, dynamic, and resilient distribution network expansion planning framework to expand a distribution network resiliently. The proposed model is constructed as a six-step structure. The first step develops a network expansion topology. The hurricane occurrence model is executed in the second one that introduces a novel vulnerability index to recognize the most vulnerable facilities. Resilient planning based on the resilience resources such as distributed generations, hardening actions, and distributed automation systems is performed as the third step to reinforce the network preventive capabilities. In the fourth step, a resilient operation is considered to decrease unserved loads and restore the distribution system rapidly. Resiliency evaluation based on technical, financial, and social welfare metrics is employed as the fifth step. Finally, the non-dominated sorting genetic algorithm II optimization method operators are implemented in the sixth step to optimize the problem. Numerical simulations are performed on a 20 nodes distribution system and a real network. The results demonstrate the effectiveness of the proposed method.