Power Grid Resilience Research Articles

Enhancing Infrastructure Resilience by Using Dynamically Updated Damage Estimates in Optimal Repair Planning: The Power Grid Case

AbstractBesides robustness, a crucial aspect of power grid resilience is the postdisruption restoration of transmission capacity. Conventionally, grid repair planning is initiated when damage asses...

A Novel Pre-Dispatching Strategy of Power System under Extreme Weather

Pre-dispatch is an important way for distribution networks to cope with typhoon weather, enhance resilience and reduce economic losses. In order to accurately describe the faults and consequences of components’ failure in the distribution network, this paper establishes a pre-dispatch model to cope with typhoon weather based on line failures consequence analysis. First, Monte Carlo simulation is used to sample the typical fault scenarios of vulnerable lines. According to the location of switchgear, the distribution network is partitioned and a block breaker correlation matrix is established. Combined with the line fault status, a fault consequence model of distribution lines related to the pre-dispatching strategy is established. Then, the objective function is given to minimize the sum of the cost of the pre-dispatch operation and the power outage, and then establish a pre-dispatch model for the distribution network. In order to reduce the computational complexity, PH (Progressive Hedging) algorithm is used to solve the model. Finally, the IEEE-69 test system is used to analyze the effectiveness of the method. The results show that the proposed dispatching model can effectively avoid potential risks, reduce system economic losses and improve the resilience of power grids.

Measurement data‐driven investigation of the actual power grid resilience with increasing renewable energy feed‐in

AbstractThe transformation of the energy system towards a sustainable reduction of CO2 emissions and the consequently rising contribution of fluctuating energy sources to the German energy system leads to new challenges. In particular, the sufficiency of the present powerline system is of interest. To evaluate the German network sufficiency, a data‐based modelling is used to measure the number of intervals with insufficient transport capabilities as well as the yearly sum of infeasible power transports. In order to do so, the actual residual loads are calculated from the power data in the database “GEOWISOL” and the data on the German power grid are obtained from the project “SciGrid.” The temporal resolution of power data is 15 min, and the spatial resolution refers to the 2‐digit ZIP code regions. The present transmission network is shown to be insufficient in a linearly increasing number of intervals for scenarios with increasing renewable power production. The uncertainties of the results presented are also investigated by means of a Monte Carlo simulation with the known uncertainties of the power data. The simulation results in a relative uncertainty for the number of intervals with insufficient transport capabilities of the network of roughly 1%. Thus, it can be used in the future either to investigate network expansion plans or to validate network expansion studies with simulated data or synthetic networks.

Mixed strategy for power grid resilience enhancement under cyberattack

ABSTRACT Power infrastructure networks continue to be at risk under natural and anthropogenic hazard events. Cyberattacks targeting power grids aim at magnifying the impacts through damage propagation to other dependent infrastructure network. In this respect, the current study focuses on the “draw-down” phase of power infrastructure network resilience considering different centrality measures to evaluate the robustness of power grids against cyberattacks. The study considers two network representations for the grid based on the network’s topological/connectivity (i.e., unweighted network) and the network’s power flow data (i.e., weighted network). Therefore, the study utilizes the evaluated measures to improve network robustness under cyberattacks, through considering (or not) the proposed mixed strategy. Based on the analyses, network-level vulnerability is quantified considering five different scenarios through evaluating two key performance metrics—Topology and Functionality indices. Nonetheless, applying the proposed mixed strategy to limit the attacker’s access to the network hubs would boost the overall grid robustness.

Application of Mobile Energy Storage for Enhancing Power Grid Resilience: A Review

Natural disasters can lead to large-scale power outages, affecting critical infrastructure and causing social and economic damages. These events are exacerbated by climate change, which increases their frequency and magnitude. Improving power grid resilience can help mitigate the damages caused by these events. Mobile energy storage systems, classified as truck-mounted or towable battery storage systems, have recently been considered to enhance distribution grid resilience by providing localized support to critical loads during an outage. Compared to stationary batteries and other energy storage systems, their mobility provides operational flexibility to support geographically dispersed loads across an outage area. This paper provides a comprehensive and critical review of academic literature on mobile energy storage for power system resilience enhancement. As mobile energy storage is often coupled with mobile emergency generators or electric buses, those technologies are also considered in the review. Allocation of these resources for power grid resilience enhancement requires modeling of both the transportation system constraints and the power grid operational constraints. These aspects are discussed, along with a discussion on the cost–benefit analysis of mobile energy resources. The paper concludes by presenting research gaps, associated challenges, and potential future directions to address these challenges.

Design and Simulation of an Autonomous Smart Microgrid for Energy Independence

The project designs a microgrid based on downtown community of El Monte city, California. The system main components include a solar PV system, a battery, a diesel generator, an inverter, a control system, and loads. The microgrid design is simulated using MATLAB Simulink. The results show that the microgrid can supply power to its community adequately and independently without relying on a utility power grid. The microgrid is smart as it can operate autonomously thanks to its automatic control system. For various operational scenarios, the microgrid proves to be resilient where it can supply its load demand successfully using its solar system, battery, and diesel generator. The load voltage is kept at satisfactory values of around 1.0 per unit. The power distribution efficiency is around 99%. The results contribute to development of smart microgrids which, in turn, improve the reliability and resiliency of large power grids, as well as provide energy independence to communities

Data-driven operation of the resilient electric grid: A case of COVID-19.

Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID‐19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi‐fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML‐driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID‐19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID‐19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID‐19 pandemic.

Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications

• Studies on energy flexibility of residential buildings have been reviewed. • Energy flexibility technologies by types, purposes, and scopes are summarized. • Modeling techniques, tools, and characterization of flexible resources are presented. • Quantification methods and metrics of energy flexibility are analyzed. • Research trends, open questions, and future research opportunities are identified. With building electric demand becoming increasingly dynamic, and a growing percentage of intermittent renewable power generation from solar photovoltaics and wind turbines, the power grid is facing increasing challenge to manage the real time balance between the supply and demand. With advancements in smart sensing and metering, smart appliances, electric vehicles, and energy storage technologies, demand side management of residential buildings can help the grid to improve stability by optimizing flexible loads. This paper reviews recent studies on residential building demand side management, with a focus on characterization and quantification of energy flexibility covering various types of flexible loads, metrics, methods, and applications. The reviewed studies showed four levels of applications: building level (45%), district or community level (29%), system level (19%), and building sector level (7%). Shifting loads is the dominant flexibility type in 60% of applications, followed by shedding (19%), generation (16%), and modulating (6%). Depending on the technology and application scope, flexible operations have a wide range of performance, with peak power reductions of 1%~65%, energy savings up to 60%, operational cost reduction of 1%~48%, and greenhouse gas emission reductions of up to29%. More than half (51%) of the studies employed control strategies to achieve flexibility; among those 72% used optimal controls, while 28% used rule-based controls. About 58% of the studies used mathematical formulation to quantify energy flexibility. Most studies were based on simulation, while less than 15% of the studies had measurements from experiments or field tests. The review reveals research opportunities to address significant gaps in the existing literature: (1) establishing a common definition and performance metrics for energy flexibility of buildings that are technology and application agnostic, (2) developing an ontology to standardize representation of flexibility resources for interoperability, (3) integrating occupant impacts into the quantification and optimization of energy flexibility, and (4) developing requirements and credits of energy flexibility in building energy codes and standards. Findings from the review can inform future research and development of energy flexible buildings which are essential to a reliable and resilient power grid.

Strategies for an Adaptive Control System to Improve Power Grid Resilience with Smart Buildings

Low-voltage distribution grids face new challenges through the expansion of decentralized, renewable energy generation and the electrification of the heat and mobility sectors. We present a multi-agent system consisting of the energy management systems of smart buildings, a central grid controller, and the local controller of a transformer. It can coordinate the provision of ancillary services for the local grid in a centralized way, coordinated by the central controller, and in a decentralized way, where each building makes independent control decisions based on locally measurable data. The presented system and the different control strategies provide the foundation for a fully adaptive grid control system we plan to implement in the future, which does not only provide resilience against electricity outages but also against communication failures by appropriate switching of strategies. The decentralized strategy, meant to be used during communication failures, could also be used exclusively if communication infrastructure is generally unavailable. The strategies are evaluated in a simulated scenario designed to represent the most extreme load conditions that might occur in low-voltage grids in the future. In the tested scenario, they can substantially reduce voltage range deviations, transformer temperatures, and line congestions.

Braess paradox and double-loop optimization method to enhance power grid resilience

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.

Formation of ad hoc microgrids for prompt critical load pickup during blackouts by leveraging stochastic distributed energy resources

Extreme weather events that are increasing in frequency and impact can cause extended disruptions to power grids. To increase the resilience of power grids, focus is shifting from infrastructure hardening to quickening the restoration of distribution systems after a disaster. One method to achieve this is through the use of microgrids leveraging distributed generation in islanded mode. While much work has been done in using microgrids for system restoration, the stochastic nature of distributed energy resources (DERs) have so far been neglected. Here, a data-driven approach to leverage stochastic DERs to form ad hoc microgrids to supply local critical loads prior to wide-scale system restoration is presented. The proposed method is tested on a modified IEEE European LV Test Feeder. Results show that considering the stochastic nature of the resources during microgrid formation greatly affects their supply adequacy confidence levels.

How modular structure determines operational resilience of power grids

The synchronization stability has been analyzed as one of the important dynamical characteristics of power grids. In this study, we bring the operational perspective to the synchronization stability analysis by counting not only full but also partial synchronization between nodes. To do so, we introduce two distinct measures that estimate the operational resilience of power-grid nodes: functional secureness centrality and functional robustness centrality. We demonstrate the practical applicability of the measures in a model network motif and an IEEE test power grid. As a case study of German power grid, we reveal that the modular structure of a power grid and particular unidirectional current flow determine the distribution of the operational resilience of power-grid nodes. Reproducing our finding on clustered benchmark networks, we validate the modular effect on power grid stability and confirm that our measures can be the insightful tools to understand the power grids’ synchronization dynamics.

The Key technologies for the construction of resilient distribution networks under the background of smart grids

When the power grid faces sudden extreme events, large-scale blackouts have caused huge economic losses. At the same time, under the development of smart grid, the research of resilient distribution network has received more extensive attention. This article introduces the connotations and concepts of resilient distribution networks in the context of smart grids, and expands the connotations of resilient grids. In addition to the resilience of physical strength under extreme disasters, it also proposes market resilience under the laws of market economy and normal operation mode. This article also introduces several key technologies for improving the resilience of power grids, including intelligent self-sensing power reserve system, adjustable flexible load, source-grid-load-storage coordinated control system, online intelligent diagnosis of power cables, and Operation and maintenance, describes the principles and functions of these technologies in building resilient power grids. The article describes from the perspectives of prevention before disasters and recovery when disasters occur.

Assessment method for wind-resistance resilience of power grid based on extension analytic hierarchy process

In view of the problems of long time, low accuracy and high cost in traditional assessment method for wind-resistance resilience of power grid, an assessment method for wind-resistance resilience of power grid based on extension analytic hierarchy process is proposed. Based on the traditional extension analytic hierarchy process, this method adds the fuzziness of artificial judgment to realise the consistency requirement of the judgment matrix of wind-resistance resilience in power grid. A wind speed model of typhoon is built to obtain the failure rate of power grid equipment under different wind speeds, and evaluate the load power supply capability guaranteed by power grid during the whole process of grid reconstruction-power restoration after typhoon disasters. The test results show that the proposed method has shorter completion time, higher accuracy and lower cost.

Power Grids as Complex Networks: Resilience and Reliability Analysis

Power grids are cyber-physical systems and can be modelled as network systems where individual units (generators, busbars and loads) are interconnected through physical and cyber links. Network components (nodes/edges) may undergo intentional and/or random failures. In catastrophic cases, a failure initiating from a small set of these components can quickly propagate through the whole network, leading to a cascade of failures that might force a deep whole-grid blackout. Often network components have different vitality and protecting some is more critical than others. This manuscript aims to provide a focused overview of modelling power grids as complex networks and their resilience and reliability analysis. We also perform a critical review of vitality metrics and their precision in power grid resilience analysis. The review is accompanied by some simulations on benchmark and real power grids to show the applicability of these concepts in studying resilience.

Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network weights are updated based on real-time data from Phasor measurement units. Resilience assessment based on failure probability, financial impact, and time-series data in grid failure management determine the norm H2. To demonstrate the effectiveness of the proposed framework, a time-domain simulation case study based on the IEEE-39 bus system was performed. For a one-channel attack on the test system, the resiliency index increased to 0.962, and inter-area damping ξ was reduced to 0.005. The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well. Results also offer robust management of power system resilience and timely control of the operating conditions.

Power outages, climate events and renewable energy: Reviewing energy storage policy and regulatory options for Australia

The incidence of harsh climate change and weather events is having profound impacts on energy networks. In recent times, Australia has suffered several network failures and power outages that have placed human lives and community infrastructure at extreme risk. Subsequently, a diverse array of energy storage systems have been commended as a potential solution for electricity network performance and resilience issues. At this critical juncture, this review suggests that an assemblage of currently lacking targeted energy storage systems and supply development policies will be required to grow and advance future domestic energy storage assets and infrastructure; while also providing a mechanism to address changing climatic conditions, variations in electricity demand and associated demand response, and a reduced dependency on coal-fired power generation. Importantly, the recognition of energy storage systems services value across the energy supply chain will be a critical component of future systems growth. In addition, while some specific regulatory changes to create a new energy capacity market will be required, the majority of electricity market rules and procedures will need to be maintained to allow entry of energy storage systems in scheduled and semi-scheduled modes. The review also identified the requirement for strong governance that supports continued renewable energy and energy storage investment, intergovernmental cooperation, and technical network upgrades. Ideally, the implementation of these policy options and regulations should result in a robust and resilient power grid, reduced emissions and economically efficient energy storage systems rollouts.

State‐of‐the‐art review on power system resilience and assessment techniques

Modern societies these days are more dependent on electrical energy and they expect a continuous supply as per demand. In this regard, the complex power system is designed to supply electrical energy with a certain level of quality and continuity though it is still susceptible to vandalism, natural disasters, and extreme weather. The black sky event where the power grid goes down is more of a possibility nowadays than ever due to more frequent severe weather events. This in turn has increased the need to study resilience in the context of the power system. This study presents a comprehensive review of the literature on power system resilience from various perspectives. First, well-developed power system safety concepts are discussed and critically reviewed in view of large-scale power outages. Then, the various definitions and confounding features of resilience in the power system domain are presented. Subsequently, several frameworks, resilience curves, and quantitative metrics proposed in recent years for power system resilience are investigated, followed by a summary of hardening strategies. Next, a case study is presented to illustrate how the resilience of a 69-bus system is assessed against a hurricane. Finally, the study highlights challenges and proposes several future works to achieve a resilient power grid.

Analysis, simulation and testing of the ZVRT capability of a wind turbine with a doubly fed induction generator

Zero-voltage ride through (ZVRT) is the extreme case of a low-voltage ride through (LVRT), which represents the optimal grid-connection capability of a wind turbine (WT). Enforcing ZVRT will improve the dynamic performance of a WT under the extreme conditions, and therefore significantly enhance the resiliency of a renewable-rich power grid. Compared with LVRT, there are few research on ZVRT with no field validation been conducted. The feasibility of the ZVRT requirement, especially the 400-ms-level fault-time condition, is not clear yet. The first system-level study including the control strategy, transient analysis, hardware enhancement strategy, WT modelling and field test on the ZVRT capability of a Type-3 WT is presented in this study. Particularly, to meet the challenging technical requirements, the authors proposed a hardware enhancement strategy, which accommodates with the widely-used LVRT control approach. The holistic strategy is tested under the extreme grid-connection condition by both simulation and a costly field test. The field test in the 400-ms-level fault-time condition based on an operating WT in a weak power system directly verifies the feasibility and effectiveness of ZVRT for a Type-3 WT. Furthermore, the critical time window and the key parameter ranges during ZVRT, which significantly affect ZVRT performance, are obtained.

Leveraging Network Topology Optimization to Strengthen Power Grid Resilience Against Cyber-Physical Attacks

The contemporary power systems are facing a growing level of risks due to potential deliberate attacks by adversaries. Strengthening the resilience of power grids against malicious cyber-physical attacks is emerging as a critical task considering the importance of power systems to the entire society. In this article, a bilevel optimization model is developed to formulate the coordinated cyber-physical attacks against the power grids. In order to enhance the resilience of the power systems, a networked topology optimization (NTO) based model is proposed to mitigate the coordinated cyber-physical attacks. Resilience metrics are proposed to evaluate the power system resilience against the coordinated cyber-physical attacks. Case studies were conducted on the modified IEEE 57-bus and 118-bus systems to illustrate the impacts of coordinated cyber-physical attacks as well as validate the proposed NTO based approach. The results of the case studies show that the proposed NTO based mitigation strategy can effectively reduce the load loss of the power system under the coordinated cyber-physical attacks. The resilience of the system is improved by the proposed NTO based approach compared with the conventional optimal redispatch (OR) and optimal transmission switching (OTS) based remedial methods.