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Abstract Extensive inverter‐based power sources (IPS) impose significant challenges on the restoration of high renewable penetrated power systems (HRPPS). To enhance HRPPS resilience, the proper utilization of IPSs must be implemented. Combining frequency dynamics of IPSs and synchronous generators, this paper proposes a coordinated restoration method for multi‐type power sources after a major blackout. First, interactions between synchronous generators and IPSs are systematically analyzed. Based on this, output characteristics and constraints of IPSs in the power sources restoration process are quantified. Second, the dynamic frequency regulation capability (DFRC) of restored systems is quantified based on a unified transfer function structure model. Then the maximum power disturbance that restored systems can bear is derived based on DFRC indices including the maximum frequency deviation and the rate of change of frequency. Third, considering interactions between power sources and the DFRC of restored systems, a coordinated restoration optimization model of multi‐type power sources is proposed. Finally, case studies based on a modified IEEE 39‐bus system are simulated to verify the applicability and superiority of the proposed method. Meanwhile, results show that the proposed method for quantifying DFRC is more suitable for HRPPSs than traditional inertia‐based methods.

Extensive inverter‐based power sources (IPS) impose significant challenges on the restoration of high renewable penetrated power systems (HRPPS). To enhance HRPPS resilience, the proper utilization of IPSs must be implemented. Combining frequency dynamics of IPSs and synchronous generators, this paper proposes a coordinated restoration method for multi‐type power sources after a major blackout. First, interactions between synchronous generators and IPSs are systematically analyzed. Based on this, output characteristics and constraints of IPSs in the power sources restoration process are quantified. Second, the dynamic frequency regulation capability (DFRC) of restored systems is quantified based on a unified transfer function structure model. Then the maximum power disturbance that restored systems can bear is derived based on DFRC indices including the maximum frequency deviation and the rate of change of frequency. Third, considering interactions between power sources and the DFRC of restored systems, a coordinated restoration optimization model of multi‐type power sources is proposed. Finally, case studies based on a modified IEEE 39‐bus system are simulated to verify the applicability and superiority of the proposed method. Meanwhile, results show that the proposed method for quantifying DFRC is more suitable for HRPPSs than traditional inertia‐based methods. [ABSTRACT FROM AUTHOR]

When a rainy day affects the power grid, instead of enjoying the weather, many consumers face unplanned blackouts worldwide. Approximately 80% of blackouts in the US are weather-induced power outages. Through the amalgamation and meticulous preprocessing of diverse public datasets, encompassing variables such as maximum temperature, solar exposure, and precipitation levels, the study aims to unravel the intricate dynamics through which weather influences power resilience. We utilize over ten years of data from 47 local government areas. The analysis focuses on predicting future power outages using a state-of-the-art deep learning Long Short-Term Memory (LSTM) model. The results show a promising area under the Receiver Operating Characteristic (AUC ROC) curve of approximately 90% and a mean precision exceeding 96%. The experiments utilize a 5-fold cross-validation methodology to ensure robustness and reliability in the predictive model. It reveals the nexus between weather patterns and power systems and offers practical insights. The proposed work can serve as a valuable resource for all stakeholders in the energy sector, fostering informed decision-making and contributing to the ongoing dialogue on enhancing power resilience, improving cyber-physical infrastructure, and disaster preparedness.

Climate change is causing an increase in extreme weather events that can stress the electric grid as it was not originally designed to withstand them, and at the same time, new unexpected threats emerge such as cyberattacks making the grid more vulnerable. On top of that, situations such as the new pandemic of Covid-19 pose the grid under new scenarios of large load reduction where renewable energies gain importance and traditional sources are strongly curtailed. On this basis, the resilience of the electrical grid turns into a key feature to be analysed and strongly enhanced. Thus, the power grid is being analysed by researchers in recent years to assess such hazards along with what measures or resources are effective against them, such as distributed energy resources based on renewable energies. Based on these studies, this paper aims to explore which power grid resilience KPIs fit more to describe the scenarios of the disruptive events for the consequent discussion of the installation of renewable sources to prioritize investments in order to minimize impacts. Furthermore, a geographic information system based model is developed to analyse a static study case of flooding in the city of Barcelona. Results show that the city’s power grid is vulnerable to the scenario, but it has the potential of renewable energies to mitigate its consequences.

To enhancing a small fraction of critical components in the face to extreme disturbances can effectively improve the power grid resilience. Identifying the set of vulnerable components in the power system that most affect its resilience was investigated. First, based on the resilience process curve, the largest amount of power supply of a given topology was proposed to quantify power grid resilience, which was calculated with the optimal power flow model and simplex method. Then, a multi-objective optimization model was built, and two indicators, the number of damaged components and the maximum power that the grid can provide, were used as objective functions. Second, three intelligent optimization algorithms were selected to solve the established multi-objective optimization model. Simulation results show that the proposed method can effectively identify the critical components that have a significant impact on power system resilience, compared with commonly used methods. [ABSTRACT FROM AUTHOR]

Controlled islanding is an important defense mechanism for avoiding blackouts by dividing the system into several stable islands. Sustainable systems that incorporate a high proportion of renewable energy are prone to frequency instability or even severe blackout events due to extreme weather conditions. Thus, it is critical to investigate controlled islanding considering frequency stability constraints to reduce the risk of a sustainable system collapse in extreme weather conditions. Here, the frequency constraint of islands is derived based on the law of energy conservation, and the island connectivity constraint is proposed based on the idea of network flow. A controlled island second-order cone programming model with frequency stability constraints is proposed for the islanding strategy. The consideration of frequency constraints can help to avoid islands with too low inertia generated by the islanding strategies, ensuring that the frequency nadir of the island remains within a safe range after disturbance. Connectivity constraints can ensure connectivity within the island and no connectivity between different islands. The model also meets the reactive power balance and voltage limits in the system. Simulations of the three test systems show that this island model, which takes frequency stability into account, is effective in reducing the risk of sustainable power system collapse in extreme weather conditions. [ABSTRACT FROM AUTHOR]

We develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities. • Stochastic optimization for generator winterization considering social equity. • Power outages' impacts on individuals are modeled by Adverse Effect Probabilities. • Case study with winter storm scenarios based on 2021 Texas winter storm in Texas. • Equitable outcomes are achievable with no added load shed. [ABSTRACT FROM AUTHOR]

Extreme weather events are one of the main causes of large-scale power outages in distribution systems. The changing climate has led to an increase in the frequency and severity of these events, which, if not mitigated, are expected to lead to more instances of widespread outages and the severe societal and economic damages that ensue. Protecting the power grid against such events, which are high impact yet low frequency, requires a paradigm shift in grid design practices. In recent years, many researchers have focused on the resilience of the power grid against extreme weather events by proposing various grid hardening and/or redundancy solutions. The goal of this paper is to provide a survey of the literature related to the infrastructural resilience of the power grid against extreme events. Currently, no standard definitions or metrics exist for power grid resilience, and researchers adopt various models for quantifying and assessing it. Hence, a review of the most commonly used definitions and metrics for resilience is provided first, with a discussion of their advantages and disadvantages. Next, the paper presents an extensive and critical review of the solution methodologies proposed in the literature for improving the infrastructural resilience of the power grid. The shortcomings of the current solution methods and gaps in research are identified, followed by a discussion of the future directions in research. [ABSTRACT FROM AUTHOR]

This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

The data obtained from storm-induced damage to power grids possesses an inherent skewness distribution, which impedes the development of the damage forecasting model. An inaccurate damage forecasting model may fail to accurately forecast the damages and hinder the planning, preventive measures, and restorative actions for a storm event. This study investigates the challenges that must be overcome to yield an accurate model and proposes a machine learning-based damage forecasting method. A robust forecasting model was developed by identifying the key explanatory variables using the G-mean values. The method combines the application of the weighted extreme learning machine (ELM) and long short-term memory model (LSTM) to forecast power grid damage in response to storm events. The weighted ELM is used to classify the grid state for a storm in advance and the LSTM is subsequently used to forecast the number of grid damage cases. The actual storm event data were used to verify the efficacy of the proposed method using the root mean square error. The results demonstrate that the proposed method outperforms the regular forecasting method as it is more robust and accurate.

Power grids are vulnerable to extreme events that may cause the failure of multiple components and lead to severe power outages. It is of practical importance to design effective restoration strategies to enhance the power grid resilience. In this brief, we consider different time scales of various restoration methods and propose an integrated strategy to maximize the total amount of electricity supplied to the loads in the recovery process. The strategy properly combines the slow restoration method of component repair and the fast restoration method of optimal power dispatch. The Q-learning algorithm is used to generate the sequential order of repairing damaged components and update the network topology. Linear optimization is used to obtain the largest amount of power supply on given network topology. Simulation results show that our proposed method can coordinate the available resources and manpower to effectively restore the power grid after extreme events. [ABSTRACT FROM AUTHOR]

Extreme weather events are one of the main causes of large-scale power outages in distribution systems. The changing climate has led to an increase in the frequency and severity of these events, which, if not mitigated, are expected to lead to more instances of widespread outages and the severe societal and economic damages that ensue. Protecting the power grid against such events, which are high impact yet low frequency, requires a paradigm shift in grid design practices. In recent years, many researchers have focused on the resilience of the power grid against extreme weather events by proposing various grid hardening and/or redundancy solutions. The goal of this paper is to provide a survey of the literature related to the infrastructural resilience of the power grid against extreme events. Currently, no standard definitions or metrics exist for power grid resilience, and researchers adopt various models for quantifying and assessing it. Hence, a review of the most commonly used definitions and metrics for resilience is provided first, with a discussion of their advantages and disadvantages. Next, the paper presents an extensive and critical review of the solution methodologies proposed in the literature for improving the infrastructural resilience of the power grid. The shortcomings of the current solution methods and gaps in research are identified, followed by a discussion of the future directions in research.

The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare.

Managing the risk of wildfires has been arguably the biggest recent challenge of electric utilities with infrastructure located in the wildland-urban interface. Utilities are deploying solutions for wildfire risk mitigation, such as public safety power shutoffs, which are counter-intuitive from a reliability-centric operation paradigm. This article presents an overview of the challenges, implications, and potential strategies for wildfire risk mitigation in power systems, and introduces the vision for a wildfire-resilient power system. The wildfire risk management strategies presented in this article range from fault prevention methods such as structural hardening, vegetation management and implementing advanced protection systems, to arc-suppression and ignition prevention methods. This article also identifies relevant research opportunities associated with implementing wildfire mitigation techniques on power systems.

As an important type of load-side resources, the distributed generation (DG) is widely used in smart buildings to provide new possibilities for rapid power supply recovery and load transfer after extreme disasters. This paper proposed a comprehensive evaluation framework for the smart building load restoration, which is used to quantitatively analyze and calculate the transfer capacity of the DG supply at the end of the smart buildings to the critical load in the distribution system after extreme disaster.On the basis of modeling different types of physical equipment in smart buildings, the multi-energy complementary and energy coupling characteristics were considered. Firstly, three quantitative evaluation indexes including electric power transfer amount, heat transfer amount and cooling transfer amount were proposed. The contribution of smart building power to system load recovery after extreme disasters was accurately quantified. On this basis, the specific calculation method for the above evaluation indicators was further proposed via using the random mixed integer programming method. Finally, taking the load of an industrial park as an example, the validity of the evaluation framework proposed was verified. The simulation results demonstrate that the proposed method can fully explore the energy supply potential of smart buildings with ensuring the normal operation of smart buildings, and effectively improve the reliability of power distribution systems under extreme disasters.

Power grid vulnerability is a critical issue in power industry. In order to understand and mitigate power grid vulnerability under threats, existing research often makes use of defender–attacker–defender (DAD) models to derive effective protection plans and evaluate grid performances under various contingencies. Corrective topology control (also known as corrective line switching) can be used as an operation to mitigate outages or attacks. In this paper, we propose to extend the traditional defender–attacker–defender model with the post-contingency corrective line switching operations as an effective post-contingency mitigation method. The proposed new model cannot be solved by existing algorithms used to solve the traditional defender–attacker–defender models because of the introduced binary transmission switching variables in the inner level problem. To solve this complicated DAD with transmission line switching (DAD-TLS) model, we customized and developed an exact algorithm based on the nested column-and-constraint generation (NCCG) algorithm, to compute global optimal solutions. A set of numerical studies on IEEE RTS one-area system demonstrate the significant improvements of power system resilience under malicious attacks due to line switching, and highlight that hardening plans derived from this new DAD model are much more cost-effective compared with those obtained from traditional DAD models. [ABSTRACT FROM AUTHOR]

Power grid resilience has been a major concern as extreme weather events become more frequent recently. Particularly, wind power generation has constituted a significant body of generation portfolio worldwide. Under stormy weather conditions, wind power generation would be curtailed due to the over‐speed protection of wind turbines. Subsequently, the sharply reduced generation resources would significantly endanger the reliability of power supply. In this study, the authors propose to apply the risk‐limiting methodology to improve the efficient utilisation of wind power prior to reluctant wind curtailment on the advent of wind stormy events. The proposed method integrates three programs, namely, day‐ahead unit commitment (UC), hourly‐ahead UC, and real‐time load shedding into one single model along with risk‐limiting constraints. The first program provides the baseline of hourly dispatch; whereas, the latter two programs serve as recourse means while wind stormy event unfolds. The proposed model is cast into a multi‐stage mixed‐integer linear programming model and solved by commercial solvers. Illustrative examples demonstrate that the proposed method can reduce the total dispatch cost over the time‐horizon including the wind stormy event, by postponing the timing of proactively starting quick‐start generating units and shedding loads, in contrast to conventional two‐stage stochastic dispatch methods. [ABSTRACT FROM AUTHOR]

Although long-duration power outages can lead to significant damages to the local economy, the human catastrophe that can potentially unfold due to the failure of essential services can far outweigh the financial damages incurred. Furthermore, power outages do not impact individuals equally, and access to proper resources (or lack thereof) can significantly affect how individuals deal with long-duration outages. Various socioeconomic and demographic characteristics have been shown in the literature to correlate with increased health risks, levels of power outage preparedness, and willingness and means to evacuate if necessary. This highlights the need to identify socially vulnerable groups and communities so that during such events information, assistance, and resources can be provided in a more targeted manner. This study presents a three-dimensional metric of social vulnerability to quantify the degree to which a person's life or livelihood is put at risk by a long-duration power outage. Dimensions of vulnerability include health, preparedness, and evacuation. Principal component analysis and an L2 norm model are applied to produce a single metric for each of the three dimensions of vulnerability. These three scores are then aggregated using Pareto ranking to determine an overall vulnerability score. A case study is presented for the state of Colorado using data from the 2020 US Census as well as other relevant federal and state datasets. The index put forth in this paper can be used by power utilities and other federal, state, or local authorities to plan and manage their operations during long-duration power outages in a more equitable way. [ABSTRACT FROM AUTHOR]

Recently, the academic and industrial literature has coalesced around an enhanced vision of the electric power grid that is responsive, dynamic, adaptive and flexible. As driven by decarbonization, reliability, transportation electrification, consumer participation and deregulation, this future grid will undergo technical, economic and regulatory changes to bring about the incorporation of renewable energy and incentivized demand side management and control. As a result, the power grid will experience fundamental changes in its physical system structure and behavior that will consequently require enhanced and integrated control, automation, and IT-driven management functions in what is called enterprise control. While these requirements will open a plethora of opportunities for new control technologies, many of these solutions are largely overlapping in function. Their overall contribution to holistic techno-economic control objectives and their underlying dynamic properties are less than clear. Piece-meal integration and a lack of coordinated assessment could bring about costly-overbuilt solutions or even worse unintended reliability consequences. This work, thus, reviews these existing trends in the power grid evolution. It then motivates the need for holistic methods of integrated assessment that manage the diversity of control solutions against their many competing objectives and contrasts these requirements to existing variable energy resource integration studies. The work concludes with a holistic framework for “enterprise control” assessment of the future power grid and suggests directions for future work. [ABSTRACT FROM AUTHOR]

Among all city infrastructures affected by natural disaster events, electric power grid is the most critical one. Most services on which disaster relief efforts depend, rely entirely on the availability of dependable and continuous supply of power. To achieve power grid resiliency against natural disasters, it is first necessary to perform a thorough analysis of interdependencies within the energy delivery network. This paper puts forth a graph-theoretic methodology based on fuzzy cognitive maps that models and analyzes the grid as an interconnected system of elements (i.e., energy resources and loads) that are connected through weighted and directional edges (i.e., lines and feeders). The developed model provides a mathematical framework for the analysis of the power grid during natural disaster events, and is used to devise optimal reinforcement strategies for the grid infrastructure via capacity enhancements and component reinforcement. The problem has been formulated as a constrained quadratic optimization one. The analysis and optimization approach are performed using abstract models so as to ensure the generic nature of the proposed methodology. A case study is presented using the IEEE 34-bus test distribution system. The system is mapped onto the floodplain map of the city of Boulder, CO, and is used to verify the applicability of the proposed methodology for grid reinforcement against flood hazards. [ABSTRACT FROM AUTHOR]

Multiple physical failures and severe power disruptions occur in power grid under extreme operation conditions. In this paper, we study power grid resilience and derive quick recovery methods through adjusting the operating modes of available components and reconfiguring the remaining network. The largest amount of power that is available to the loads after reorganizing the remaining undamaged components in the post-disaster stage is identified as an important resilience indicator. An interior point method is firstly used to find the largest amount of power supply (LPS) of fixed topology. The post-disaster network should contain as many available components as possible in order to give the best topological connection. However, disconnecting some undamaged components proactively can further increase the LPS. This phenomenon can be interpreted as the Breass paradox and is effectively a combinatorial network reconfiguration. A double-loop optimization strategy is proposed to achieve the LPS available to the post-disaster network, where the interior point method serves the inner optimization loop and the outer optimization loop generates an optimal topology using a genetic algorithm. Simulation results verify the efficacy of the proposed method in achieving a quick power recovery in extreme events. Our work provides useful advice to power grid operators on how to effectively coordinate available resources after extreme events occur. • The grid resilience lifecycle is reviewed considering distinct time scales. • The Braess Paradox is observed which inspires the design of optimization method. • A double-loop optimization is designed to enlarge the amount of power supply. [ABSTRACT FROM AUTHOR]

• We evaluate the impact of a vegetation management standard (enhanced tree trimming) on power outage rates in a distribution network. • A purely statistical approach suggests a reduction of outage locations due to tree trimming between 49% and 65%. • Analysis based on an Outage Prediction Model that takes into account the variability of storm intensity suggests a 16% to 48% reduction of the number of outages after tree trimming. Enhanced Tree Trimming (ETT) is a vegetation management standard consisting in the trimming or removal of trees in proximity of overhead lines. We quantify the effects of this practice, designed to improve the resiliency of the overhead electrical power grid, using two independent methodologies. The first approach is a statistical study of the change of frequency of outage-free locations. The second approach uses an Outage Prediction Model (OPM) as a vulnerability assessment tool to evaluate the change in the number of outages before and after ETT. The OPM is a machine learning based framework that relates weather, soil, vegetation and electric grid characteristics to storm-related power outages. The two methods introduced in this work are compared against a straightforward assessment of the ETT impact on the number of outages. Applying both methods, the occurrence of power outages is studied for varying tree trimming amounts, for 144 storms occurred between 2005 and 2017 in the Northeastern United States. From the statistical approach we find a reduction of the outage-free grid cells during storms between 49% and 65% due to tree trimming. The OPM based analysis suggests that the number of outages during storms are reduced between 16% and 48% after performing ETT. [ABSTRACT FROM AUTHOR]