High Impact Low Probability Research Articles

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

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.

A resilience-oriented pre-positioning approach for electric vehicle routing and scheduling in coupled energy and transport sectors

High-impact low-probability (HILP) events have occurred more frequently than before and caused severe damage to conventional power systems. Energy hubs (EHs) consist of various distributed energy resources (DERs) for energy generation, conversion and storage across different sectors. The high energy integration of EH systems makes them more robust under extreme events than traditional power systems. In addition, the large penetration of electric vehicles (EVs) has been witnessed in modern energy systems due to their significant benefits in accelerating transport electrification and reducing carbon emissions. Due to the characteristics of mobility and flexibility, EVs can be utilised as mobile energy resources in coupled energy and transport sectors and shift energy between different regions via effective routing and scheduling behaviours. In this context, this paper proposes a resilient-oriented pre-positioning approach for the routing and scheduling of multiple EVs in a coupled energy-transport system, where the energy system includes multiple EHs across both electricity and heat sectors. To simulate real-world scenarios, uncertainties associated with renewable generation, load profiles, and EV commuting time as well as contingencies related to component failures are incorporated into the proposed pre-positioning approach via stochastic programming. Extensive case studies are carried out based on an EH system including three EHs and five EVs, which illustrate that EVs can coordinate with static DERs in the system and perform energy shifting between different EHs via appropriate routing and scheduling behaviours. Additionally, results demonstrate that the proposed pre-positioning approach can achieve higher resilience level and ensure the supply continuity of critical loads.

Analysis of grid flexibility in 100% electrified urban energy community: A year-long empirical study

Achieving 'carbon neutrality' in the building sector involves high penetration of renewables, electrification of energy use, and improved flexible interactions with the grid to balance energy supply and demand. This study analyzed one year in an urban energy community powered entirely by electricity, using high levels of renewable sources. By facilitating energy sharing between residential and non-residential buildings, the community redirected surplus energy to meet demand, achieving 57.6% self-consumption, 38.4% self-sufficiency, and 126.3% energy independence rate. This achievement involved an annual electricity consumption of 107 MWh, sevenfold that of previous simulation-based studies, marking the first real-world demonstration. Using machine learning clustering with domain-based interpretation, the study unraveled complex interactions between building operations and the power grid, influenced by external factors (weather, occupant behavior), system design, and controls. Managing low-probability high-impact (LPHI) events of severe demand or production peaks, accounting for only 0.5% of the time and 1.8% of net energy annually, was crucial to grid stability and cost reduction. The study confirmed the limitations of increasing battery energy storage capacity in certain environments, highlighting the importance of demand-side management (DSM) with thermal systems. The DSM effectively handled LPHI events, reducing grid stress with an 18.3% decrease in annual electricity cost .

Temporal assessment of operational resilience of transmission network and adaptation measures for a high-impact long duration cyclonic windstorm

The exposure to High-Impact Low-Probability (HILP) events can have significant impact on the performance of transmission networks. Under such conditions, the power system components must be resilient and robust to meet the uninterrupted load demand. This paper proposes a quantitative framework for temporal assessment of operational resilience of transmission network and suggests suitable adaptation measures when the system is subjected to high impact cyclonic windstorm lasting for a long duration. The fragility curves of transmission lines are correlated with wind profiles during cyclone and failure probability each transmission line is determined using the Monte Carlo Simulation (MCS) The operational resilience of transmission networks is quantified by computing Total Transfer Capability (TTC), Available Transfer Capability (ATC), Existing Transmission Uses (ETU), Total Reliability Margin (TRM) and unserved load. To improve the operational resilience, adaptation measures with modifications in the robustness of the structural strength is proposed and investigated on standard IEEE 57 bus system and IEEE 118 bus system. Sensitivity analysis is performed to understand how changes in the percentage increase of robustness affect the overall system performance. The findings give valuable insights for evaluating the operational resilience of transmission line infrastructure during such extreme weather events.

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.

Steering through the storm: a process framework to guide purchasing and supply managers in navigating low-probability-high-impact disruptions

PurposeThis study seeks to explore the strategies purchasing and supply managers can employ to navigate the challenges presented by low-probability-high-impact (LPHI) disruptions. The core aim is to create a process framework that provides a systematic, step-by-step method to help purchasing and supply managers effectively deal with the chaos triggered by LPHI events.Design/methodology/approachThe study draws on qualitative data collected from eight firms operating within different industries (healthcare, fishing, food retail and manufacturing), where two firms represented each industry. The data underwent a thorough analytical process involving open coding, axial coding and aggregation of categories, resulting in the identification and formulation of overarching themes.FindingsThe analysis unveiled five primary challenges purchasing and supply management (PSM) encountered during the COVID-19 pandemic. These include supply shortages, supplier opportunism, the imperative to build a new supply base, price volatility and the need to make critical decisions based on limited information. It also identified contingent factors that influenced the magnitude of these challenges and approaches applied to address them. Additionally, it identified five responses to the challenges and two contingent factors that affected the responses.Originality/valueThis study extends the existing body of knowledge in purchasing and supply management by developing a process framework tailored to assist purchasing and supply managers in effectively addressing LPHI disruptions. To the best of our knowledge, this is one of the first studies to offer a structured, step-by-step approach that guides PSM professionals in navigating the chaos likely to be caused by such events.

A MILP model for optimal switch placement to enhance power distribution system performance in normal and hurricane conditions

Severe hurricanes can inflict significant damages in the order of several times the common network failures on both the distribution companies and consumers. Network reconfiguration and condition-based switching are the common actions for enhancing distribution network performance in both normal and emergency conditions. In this paper, a novel approach is presented for simultaneous improvement in resiliency, reliability, and power losses. A practical strategy is proposed by considering the coordination between load restoration and repair of damaged lines in optimal switch placement problems. The use of both remote-controlled switches (RCS) and manual switches (MS) as well as considering practical constraints related to line repair and load restoration leads to the increased number of decision variables and consequently the degraded performance of meta-heuristic methods to obtain the global optimum solutions, especially for larger scale networks. Therefore, the proposed approach uses the exact method of MILP to solve the optimization problem. In this context, the uncertainty related to damage status and repair time of lines during High-Impact Low-Probability (HILP) events is the main challenge in linear modeling of the problem for which new methods are provided in the study. The programming is coded in MATLAB and tested on a modified IEEE 33-bus network.

A decentralized framework for economic‐resilient operation of networked microgrids, considering demand response program

AbstractThe occurrence of natural disasters can disrupt the normal functioning of distribution networks. In this situation, the activeness of distribution networks in the sense of benefiting from energy sources and related structures such as microgrids (MGs) makes them benefit from the capability of structures such as MGs, in case of High‐Impact Low‐Probability (HILP) events, so that consumers are able to disconnect from main grid and trade with other MGs. Thus, the energy management of networked MGs needs a new operation structure to encourage them providing each other with the required energy by creating the necessary incentives. This paper presents a novel cooperative framework aimed at enhancing the resilience of MGs through the establishment of peer‐to‐peer (P2P) energy trading among their constituents. In the present model, MGs engage in energy transactions with one another, aiming to minimize costs through the cooperative operation of networked MGs. The utilization of the suggested model enables MGs to enhance network resilience within the framework of peer‐to‐peer energy transactions, while also achieving cost reductions in comparison to islanding and non‐cooperative operation. The numerical results obtained demonstrate the efficacy of the proposed methodology in enhancing the resilience of MGs and mitigating their operational expenses.

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.

Strategic dispatch of electric buses for resilience enhancement of urban energy systems

The increasing frequency of the occurrence of high impact low probability (HILP) disruptive events has posed huge threats to the power system. Therefore, power system resilience improvement has drawn world-wide attention. Electric buses (EB) are equipped with a large capacity of batteries which could provide sufficient energy and capacity values to run islanded micro-grids (MG) for hours, with a particular focus on V2G, which will deliver system resilience during extreme operational conditions. In this paper, an innovative reciprocal transport-power system coordination scheme is proposed to simultaneously mitigate the adverse impacts on both systems. By using this economic incentive-free approach, EBs are endogenously motivated to provide resilience-oriented V2G service to the power system, while increasing the transport service survivability during HILP disruptive events. Additionally, a novel EB dispatch rescheduling method considering the trade-off of both the requirement of energy supply security and transport service fulfilment is proposed. By using this method, the essential load curtailment of local MGs can be further alleviated, while the impacts of reduced charging service on transport service can also be further mitigated. Through a series of case studies, we illustrate that EBs have significant potential to provide resilience enhancement to the urban energy system based on appropriate dispatch strategies. Meanwhile, the interests of the power system and the transport system can be well reconciled by using the proposed approach during HILP disruptive events.

A Bayesian Deep Learning-Based Probabilistic Risk Assessment and Early-Warning Model for Power Systems Considering Meteorological Conditions

The ongoing process of decarbonizing the power system and the frequent occurrence of extreme weather have led to a significant increase in operating uncertainty and greatly reduced the system controllability. An effective risk assessment and early-warning tool will significantly assist system operators in monitoring and controlling power systems. However, existing methods mainly rely on simplified analytical conditional probability models to describe the component failure probability under different operating conditions and are not suitable for low-probability risk assessment. Inspired by the idea of Bayesian statistics, a Bayesian deep learning-based probabilistic risk assessment and early-warning (BDL-PRAEW) model considering meteorological conditions is proposed in this paper. A new Bayesian neural network (BNN) is proposed which efficiently utilizes expert experience and domain knowledge as prior to model the contingency probability. And a hybrid neural network is developed to rationally utilize the multi-source heterogeneous data and comprehensively analyze the historical and forecast information. Finally, a novel risk assessment and early-warning model for high-impact, low-probability (HILP) extreme events is proposed, which can predict the risk for the next n time-steps. The proposed method is tested on a modified IEEE 118-bus system and a real-world provincial grid, and the results verify its effectiveness.

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.

On the Use of Mobile Power Sources in Distribution Networks Under Endogenous Uncertainty

Mobile power sources (MPSs) have promising potential for spatiotemporal flexibility exchange in power distribution systems (DS). They can be strategically employed to enhance the network resilience when facing the aftermath of high-impact low-probability (HILP) events. This paper proposes a novel service restoration formulation that models and accounts for the endogenous uncertainty in the MPSs routing and scheduling decision making. The proposed restoration model is formulated as a mixed-integer nonlinear programming (MINLP) problem with nonconvex continuous relaxation. We derive computationally tractable linearization procedures to reformulate the MINLP model as an equivalent mixed-integer linear programming (MILP) problem. Case studies on the IEEE 33-node and 123-node test systems demonstrate the role of incorporating endogenous uncertainties in the decision-making process and the effectiveness of the proposed restoration scheme in boosting the DS resilience.

A resilience-oriented multi-stage operation strategy for distribution networks considering multi-type resources

With the increasing severity of global warming and the rapid evolution of cyber-attack techniques, the enhancement of distribution network (DN) resilience that can effectively cope with natural disasters and man-made attacks has received extensive attention. Hence, in the context of a high impact low probability (HILP) event where attackers launch malicious attacks after a natural disaster occurs, a multi-stage DN operation strategy for resilience enhancement is proposed considering multi-type resources, such as gas turbines, stationary energy storage systems, mobile energy storage systems (MESSs), repair crews (RCs) and network reconfiguration. Firstly, the operation process of DN is divided into five stages based on the time lag between the natural disaster and the man-made attacks: the normal operation stage, the disaster disruption stage, the post-disaster degradation stage, the man-made attack stage and the defense and recovery stage. Secondly, under the background of a coupled power-transportation system, the optimal scheduling model for each stage of DN is established considering the effects of dynamic changes in traffic flow on the movement process of MESSs and RCs. Finally, the model is converted into a 5-layer mixed integer linear programming (MILP) problem that can be solved by sophisticated optimization software according to the time series relationship, and a case study is carried out with a modified IEEE 33-node system and the corresponding transportation network. The results demonstrate that under extreme scenarios, the proposed model can guarantee the load-side power supply as much as possible through flexible cooperation between multi-type resources, effectively enhancing the resilience of the DN.

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.