Emergency Response Strategy for Power System Resilience Improvement Based on Network Reconfiguration and Mobile Emergency Generator Deployment

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

The uncertain nature of natural, man-made, and complex phenomena poses a challenge to microgrid (MG) functioning. In the face of such unexpected events, the power supply to the customer degrades and it becomes necessary to manage the performance of MG components. Therefore, the resilience of MG should be a priority. Resilience prepares the system to handle the operational loss and recover quickly to its pre-disturbance state. It is the ability to adapt to changing conditions, withstand it, and rapidly recover from uncertain natural disasters, man-made interruptions, and complex events termed as high-impact low-probability (HILP) events. MG planning and operation strategy for such HILP events enhances resilience. To analyze this strategy, the uncertain nature of MGs needs to be addressed. However, resilience study can be extended throughout the power system but is more suitable for MGs. It is due to the location of MGs at different terrains that makes it more vulnerable to HILP events. Crisp value of resilience parameter fails to capture the wide range of variations in MG behavior. To incorporate these significant variations, fuzzy-based resilience is required. The fuzzy-based resilience planning and operation is flexible and allows variabilities associated with changing environment. This chapter provides a comprehensive analysis of fuzzy-based resilience assessment for MG planning and operation against windstorm. Weibull wind assessment estimates the maximum likelihood of wind speed distribution in a particular region. Distribution lines are the exposed component during windstorm, so the probability of impacting the MG connectivity is very high. Therefore, this chapter focuses on distribution line fragility. The fragility curve of distribution lines depicts the wind speed-dependent failure probability. The region-specific wind profile of windstorms is mapped to the fragility curve of lines to obtain the time and hazard-dependent operational status. The Monte-Carlo probabilistic assessment measures this disruption status of lines by comparing the failure of lines as a function of weather parameter. To evaluate the influence of uncertain parameters on the operation and planning of MG, fuzzy-based system average interruption frequency index (FSAIFI), fuzzy-based system average interruption duration index (FSAIDI), and fuzzy-based average service availability index (FASAI) are calculated. For MG resilience planning, it is essential to assess the time-varying nature of these indices. The characteristics of these indices are thus assessed using the resilience triangle. It describes the resilience level of a system during each specific phase of the windstorm, which are pre-disturbance, degraded, and restorative stages. This analysis is tested on IEEE 33-bus system. Also, a comparative assessment of the resilience triangle and trapezoid approach for the IEEE 33-bus system is provided. This graphical representation of fuzzy-based performance parameters provides an insight into the impact of uncertainties on the MG under HILP events.

Resilience to high impact low probability (HILP) weather events is an increasing concern to address the vulnerabilities of the aging power distribution infrastructures. It is essential to improve distribution system's resilience using appropriate planing measures such as infrastructure hardening or standby resources. Absence of regulatory policies have made it extremely challenging for distribution system operators (DSOs) to plan system upgrades for enhancing resilience. This paper presents a novel framework to plan investments for resilience-driven system upgrades. The framework quantifies the value of service for the upgrades in terms of the monetary value-at-risk due to customer interruption. The socio-economic optimal level for resilience-driven investments is computed through the marginal investment cost for system upgrades and the resulting marginal value of service. The proposed framework is validated for deploying distributed generators (DGs) as a planning measure to enhance resilience using a modified IEEE 123-bus test system. Results from the test-case demonstrate the effectiveness of the framework to facilitate DSOs in investment decisions for enhancing resilience that is driven by the risks posed by the HILP events and the socio-economic costs of planning measures.

High-impact low-probability (HILP) events such as hurricanes, snow storms, heat waves, and floods cause widespread power outages and blackouts. With the increasing challenges concerning the threats to the power systems and the growing need to mitigate the impacts of the HILP events, resilience has become a crucial desirable characteristic for the power grid infrastructures. The operators within the control center of the power grid need to analyze previous threats as well as predict other possible threats so that pre-planning can be done to mitigate its impact on the overall power system. This paper explains the development of planning and analysis tool for the resilient power grid with hydro generation and distributed energy resources (DERs). Hydro resources need to be coordinated with DERs to meet the grid resilience requirements while meeting water constraints. We discussed in detail the importance of the tool and its functionality taking isolated power system with hydro and DERs, and grid connected utility system as case studies. This tool facilitates the operator to identify the possible threats so that they can visualize and prepare strategies for assets safety, maintain inventory, alert customers beforehand, and reduce its impact on system resilience. Finally, the qualitative assessment of the tool's ability is presented to explain its contribution and effectiveness. It includes details about how investing in smart grid technologies such as remote-controlled switches, Distributed Energy Resources (DERs) and increased distribution automation can be beneficial for resilience enhancement.

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.

High-Impact Low-Probability (HILP) Events are catastrophic events characterised by a lack of precedence and high levels of uncertainty due to their cascading and compounding dynamics. Over recent decades, these events have become more likely as the interconnectedness of social and ecological systems has grown, heightening the risk of cascading failures across sectors and amplifying the impacts of initial triggers. As traditional risk-based approaches often fail in analyzing complex risk interactions, a more holistic methodology is needed to identify cross-sectoral vulnerabilities, common failure points, and strategies to improve systemic resilience. The AGILE project aims to fill this gap by developing a methodology for understanding, assessing, managing, and communicating HILP events with a systemic, risk-agnostic and systemic resilience perspective. The proposed methodology is subdivided into 3 tiers with increasing levels of detail: the first tier is the scoping study, i.e. the mapping of the system critical functions and their relationships, and developing a guideline structure for table-top exercises; the second tier refers to the identification and parametrization of interdependences and feedback loops between critical functions to gauge single-points-of failure; finally the third tier aims to uses network analysis and technological innovations (e.g. artificial intelligence and machine learning) to create an asset-level representation of systemic performance, evaluating flows of information, resources, and energy through the system in real time.This paper describes the methodological approach under development for the third tier and its preliminary application to the metropolitan city of Venice. The system is conceptualized as a network, with nodes representing key elements that drive the city's functionality, such as transportation infrastructure, communication systems, ecosystems, households, and economic activities, while links represent the dependencies between these components.   The quantification of the links poses significant challenges, requiring a combination of available data, expert input, and, where possible, machine learning and artificial intelligence techniques. By drawing analogies between graph metrics and risk variables, this network analysis identifies key elements that could exacerbate system failure (for example, authority and closeness of a node can be associated with exposure and vulnerability of the corresponding critical function). Simulations are used to explore how risks might propagate through the network, offering valuable insights into potential consequences and strategies for resilience enhancement. Efforts are ongoing to define and refine Venice’s systemic network, with a focus on understanding and addressing vulnerabilities to enhance urban resilience in the face of future HILP events.

It is evident worldwide that high-impact, low-probability (HILP) events, such as associated to extreme weather, can have disastrous consequences on power systems resilience. In this paper, we propose a Severity Risk Index (SRI) that with the support of smart grid technologies (e.g., real-time monitoring) is capable of providing an indication of the evolving risk of power systems subject to HILP events in a smart and adaptive way, thus potentially contributing to effective decision-making to mitigate such risk. Specific applications considered here refer to windstorm events, for which purpose the proposed SRI is embedded in a Sequential Monte Carlo simulation for capturing the spatiotemporal effects of windstorms passing across transmission networks. Latin Hypercube Sampling and backward scenario reduction method are used to produce a computationally tractable number of representative scenarios for SRI computation. The IEEE 24-bus reliability test system is used to demonstrate the effectiveness of the proposed SRI.

Optimal resilient operation of hub energy system considering uncertain parameters

Metrics and quantitative framework for assessing microgrid resilience against windstorms

To deal with the issue of the distribution system (DS) resilience enhancement under extreme weather events, this paper presents a new methodological framework for enhancing the load restoration of DS during a hurricane. As distinct from existing studies, this approach integrates mobile emergency generators (MEGs) and prosumer communities (PCs) incorporating combined heat and power (CHP) units, electric boilers (EBs), photovoltaic (PV) sources, and demand response (DR) resources and comprehensively explores the coordination and flexibility of supply-side and demand-side resources to boost the post-disaster DS recovery. The discussed problem is formulated by using a two-stage robust optimization model. In the first stage, the MEGs are pre-positioned prior to the hurricane with the objective of minimizing the outage cost of the load to promote the capability of DS to resist extreme disturbance. The second stage determines the real-time allocation of MEGs, output power of CHP units and EBs, and the power consumption of electric loads after the hurricane to maximize DS’s load recovery considering the worst-case of the uncertainty realization. Since the fragility analysis of network elements has an essential impact on the efficacy of the optimal strategy, we introduce the Z-number-based approach to scientifically determine the failure probability of components considering the effect of the aging of components and the credibility of the failure information obtained from the fragility. The effectiveness of the proposed methodology is examined based on an IEEE 123-bus distribution test system, and the obtained results confirm the validity of the proposed approach in actual implementations.

The increasing frequency of high-impact, low-probability (HILP) events, such as natural disasters and cyberattacks, poses significant risks to the energy sector, highlighting the urgent need for resilient distribution grids. This paper proposes a novel framework to enhance power grid resilience following HILP events by strategically deploying mobile battery energy storage systems (MBESSs) and dynamically forming microgrids. This approach addresses the shortcomings of strategies that often focus on individual resilience aspects. The framework leverages both pre-event optimization and post-event dynamic resource allocation to achieve robust and efficient grid recovery. A novel quantitative metric is developed to evaluate grid resilience, enabling informed decision-making for MBESS deployment and scheduling. Before HILP events occur, the framework optimizes MBESS charge/discharge schedules to improve operational performance and enhance grid resilience. Following an event, the framework rapidly forms microgrids and dynamically relocates MBESSs to prioritize the restoration of loads, accelerating recovery efforts. By combining MBESS capabilities with microgrid formation and optimized resource allocation, this work provides a comprehensive and adaptable framework for bolstering grid resilience in the face of unforeseen disruptions, ultimately contributing to a more resilient and reliable power grid.

The increasing frequency and severity of extreme weather events pose significant threats to power systems, particularly at the distribution level. The most detrimental consequence of such events is observed in critical loads due to high outage costs. As a result, there is a pressing need for utilities to invest in enhancing system resilience, which requires a comprehensive resilience investment framework and metrics to evaluate system performance. This paper proposes a distribution system resilience assessment framework to guide strategic investment decisions. The framework incorporates a mathematical model that estimates system restoration time after an extreme event, considering the criticality of loads, the interdependence of component failures and repair sequences, and the availability of repair crews. In addition, two new resilience metrics—disconnected load point hours (DLH) and normalized DLH (NDLH)—are introduced, which provide a more comprehensive view of system resilience by reflecting both vulnerability and the ability to withstand and recover from extreme events. Case studies are performed on a modified IEEE 69-bus test system utilizing the developed framework. The results evaluate the effectiveness of different resilience investment strategies, including infrastructure hardening, distributed energy resources management, and repair process coordination, in improving the system resilience for maintaining the critical loads and the overall distribution system.

Resilience has been defined as the ability of a system to withstand stressors while preserving its structure and functions. Various resilience assessment frameworks and metrics have been developed for understanding individual water system behaviour. However, in coupled human-water systems, the increased complexity presents new challenges in the application of these frameworks. This exploratory study first conducted a literature review on system performance indicators, failure thresholds, and resilience metrics, across urban water supply, drainage, wastewater, groundwater, and river systems. Challenges are identified in intercomparison between system performance indicators, robustness of thresholds selection, and resilience metrics synthesis as well as their applicability to inform water management. Based on the insights, a bottom-up resilience assessment framework for coupled human-water systems is developed. This framework sets double thresholds to characterise the vulnerable and critical systems state during a disruptive period. Four shape-based resilience metrics are designed and uniformly applied to various performance indicators to facilitate intercomparison between subsystems. The application of the metrics crosses temporal scales, from event-level assessments for understanding system behaviour to annual-level evaluations of system reliability, which are ultimately synthesised at the system level for multi-stakeholder decision-making. The efficacy of this framework is demonstrated through its application with the integrated water system model (WSIMOD) in Luton, UK, serving as a case study. The findings highlight river water quality as the least resilient subsystem that needs prioritised management. Sensitivity analysis is conducted to examine the robustness of results, with subsequent interpretation linking these metrics to specific design variables for enhanced management. This framework can be further applied with stakeholder engagement and multi-criteria analysis for more effective decision-making to achieve better system performance under deep uncertainties.   

This study proposes a two‐stage stochastic optimisation model for jointly wind turbine (WT) allocation and network reconfiguration (NR) so as to increase the resiliency of distribution system in face of natural disasters. In this regard, in the first level, a possibilistic‐scenario method is proposed to select the line outage scenarios. The proposed model is capable with distribution systems and considers different failure probabilities for system components subject to the intensity of natural disaster in its associated zone. After selecting the line outage scenarios, in the second level, a multi‐stage optimisation framework is proposed for jointly NR and WT allocation in a multi‐zone and multi‐fault system, considering the uncertainty of system load and wind power generation. This strategy makes an interconnection between NR and islanded WTs to increase the resiliency of system and decreases the load shedding. Different economic objectives including, costs of load shedding and power generation are considered in the model. In addition, hardening budget is taken into consideration for the transmission lines, which is minimised during the optimisation process. The simulation results demonstrate the capability and necessity of proposed resiliency‐oriented method and prove the importance of hardening budgets.

Artificial intelligence for resilience enhancement of power distribution systems