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Journal articles on the topic 'Power systems resilience'

Author: Grafiati

Published: 23 August 2021

Last updated: 1 February 2022

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1

Schulte, Fiona, Eckhard Kirchner, and Hermann Kloberdanz. "Analysis and Synthesis of Resilient Load-Carrying Systems." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (July 2019): 1403–12. http://dx.doi.org/10.1017/dsi.2019.146.

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AbstractResilient systems have the capability to survive and recover from seriously affecting events. Resilience engineering already is established for socio-economic organisations and extended network-like structures e. g. supply systems like power grids. Transferring the known principles and concepts used in these disciplines enables engineering resilient load-carrying systems and subsystems, too. Unexpected load conditions or component damages are summarised as disruptions caused by nesciense that may cause damages to the system or even system breakdowns. Disruptions caused by nescience can be controlled by analysing the resilience characteristics and synthesising resilient load-carrying systems. This paper contributes to a development methodology for resilient load-carrying systems by presenting a resilience applications model to support engineers analysing system resilience characteristics and behaviour. Further a concept of a systematically structured solution catalogue is provided that can be used for the classification of measures to realise resilience functions depending on system adaptivity and disruption progress. The resilience characteristics are illustrated by 3 examples.

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2

Sarker, Partha, and Henry D. Lester. "Post-Disaster Recovery Associations of Power Systems Dependent Critical Infrastructures." Infrastructures 4, no. 2 (May 29, 2019): 30. http://dx.doi.org/10.3390/infrastructures4020030.

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The complete failure of the power systems infrastructure in Puerto Rico, following Hurricanes Irma and Maria in 2017, severely hampered the recovery efforts of multiple critical infrastructure systems (CIS). Understanding the relationships of infrastructure recovery efforts between power infrastructure systems and the other CIS has the potential to be a key in developing an effective recovery plan leading to resilient infrastructure systems, and thereby a more resilient community. This paper explores the critical interfaces and interdependencies in CIS recovery by examining the disruptions and recovery progress of the CIS, including the power infrastructure systems, in Puerto immediately following the events of Hurricane Maria. This research uncovers that strong CIS recovery interdependency relationships exist between the power infrastructure systems and other CIS in Puerto Rico, and these relationships contribute to the resilience of these CIS. The resultant CIS recovery associations may potentially predict the recovery progress of post-disaster CIS recovery centered on the power infrastructure systems and lay the groundwork for further interdependency analysis of CIS in post-disaster scenarios. The results may also be helpful while designing CIS for resiliency in natural disaster areas.

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3

Li, Jia, Feng Liu, Ying Chen, Chengcheng Shao, Guanqun Wang, Yunhe Hou, and Shengwei Mei. "Resilience Control of DC Shipboard Power Systems." IEEE Transactions on Power Systems 33, no. 6 (November 2018): 6675–85. http://dx.doi.org/10.1109/tpwrs.2018.2844161.

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Shen, Lijuan, Yanlin Tang, and Loon Ching Tang. "Understanding key factors affecting power systems resilience." Reliability Engineering & System Safety 212 (August 2021): 107621. http://dx.doi.org/10.1016/j.ress.2021.107621.

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5

Tapia, Mariela, Pablo Thier, and Stefan Gößling-Reisemann. "Building resilient cyber-physical power systems." TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 29, no. 1 (April 1, 2020): 23–29. http://dx.doi.org/10.14512/tatup.29.1.23.

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Power systems are undergoing a profound transformation towards cyber- physical systems. Disruptive changes due to energy system transition and the complexity of the interconnected systems expose the power system to new, unknown, and unpredictable risks. To identify the critical points, a vulnerability assessment was conducted, involving experts from the power as well as the information and communication technologies (ICT) sectors. Weaknesses were identified, e. g., the lack of policy enforcement, which are worsened by the unreadiness of the actors involved. Due to the complex dynamics of ICT, it is infeasible to keep a complete inventory of potential stressors to define appropriate preparation and prevention mechanisms. Therefore, we suggest applying a resilience management approach to increase the resilience of the system. It aims at better riding through failures rather than building higher walls. We conclude that building resilience in cyber-physical power systems is feasible and helps in preparing for the unexpected.

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Faraji, Jamal, Masoud Babaei, Navid Bayati, and Maryam A.Hejazi. "A Comparative Study between Traditional Backup Generator Systems and Renewable Energy Based Microgrids for Power Resilience Enhancement of a Local Clinic." Electronics 8, no. 12 (December 5, 2019): 1485. http://dx.doi.org/10.3390/electronics8121485.

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Extreme weather events lead to electrical network failures, damages, and long-lasting blackouts. Therefore, enhancement of the resiliency of electrical systems during emergency situations is essential. By using the concept of standby redundancy, this paper proposes two different energy systems for increasing load resiliency during a random blackout. The main contribution of this paper is the techno-economic and environmental comparison of two different resilient energy systems. The first energy system utilizes a typical traditional generator (TG) as a standby component for providing electricity during the blackouts and the second energy system is a grid-connected microgrid consisting of photovoltaic (PV) and battery energy storage (BES) as a standby component. Sensitivity analyses are conducted to investigate the survivability of both energy systems during the blackouts. The objective function minimizes total net present cost (NPC) and cost of energy (COE) by considering the defined constraints of the system for increasing the resiliency. Simulations are performed by HOMER, and results show that for having almost the same resilience enhancement in both systems, the second system, which is a grid-connected microgrid, indicates lower NPC and COE compared to the first system. More comparison details are shown in this paper to highlight the effectiveness and weakness of each resilient energy system.

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Shen, Lijuan, Beatrice Cassottana, and Loon Ching Tang. "Statistical trend tests for resilience of power systems." Reliability Engineering & System Safety 177 (September 2018): 138–47. http://dx.doi.org/10.1016/j.ress.2018.05.006.

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8

Jamaluddin, Khairulnadzmi, Sharifah Rafidah Wan Alwi, Zainuddin Abdul Manan, Khaidzir Hamzah, and Jiří Jaromír Klemeš. "Hybrid power systems design considering safety and resilience." Process Safety and Environmental Protection 120 (November 2018): 256–67. http://dx.doi.org/10.1016/j.psep.2018.09.016.

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9

Jordaan, Sarah M. "Resilience for power systems amid a changing climate." Bulletin of the Atomic Scientists 74, no. 2 (February 19, 2018): 95–101. http://dx.doi.org/10.1080/00963402.2018.1436810.

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10

Fanucchi, Rodrigo Z., Michel Bessani, Marcos H. M. Camillo, Anderson da S. Soares, João B. A. London Jr, Luiz Desuó, and Carlos D. Maciel. "Stochastic indexes for power distribution systems resilience analysis." IET Generation, Transmission & Distribution 13, no. 12 (June 18, 2019): 2507–16. http://dx.doi.org/10.1049/iet-gtd.2018.6667.

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11

Ibrahim, Mariam, and Asma Alkhraibat. "Resiliency Assessment of Microgrid Systems." Applied Sciences 10, no. 5 (March 6, 2020): 1824. http://dx.doi.org/10.3390/app10051824.

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Measuring resiliency of smart grid systems is one of the vital topics towards maintaining a reliable and efficient operation under attacks. This paper introduces a set of factors that are utilized for resiliency quantification of microgrid (MG) systems. The level of resilience (LoR) measure is determined by examining the voltage sag percentage, the level of performance reduction (LoPR) as measured by percentage of reduction of load served, recovery time (RT), which is the time system takes to detect and recover from an attack/fault, and the time to reach Power Balance state (Tb) during the islanded mode. As an illustrative example, a comparison based on the resiliency level is presented for two topologies of MGs under an attack scenario.

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Salehizadeh, Mohammad Reza, Mahdi Amidi Koohbijari, Hassan Nouri, Akın Taşcıkaraoğlu, Ozan Erdinç, and João P. S. Catalão. "Bi-Objective Optimization Model for Optimal Placement of Thyristor-Controlled Series Compensator Devices." Energies 12, no. 13 (July 6, 2019): 2601. http://dx.doi.org/10.3390/en12132601.

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Exposure to extreme weather conditions increases power systems’ vulnerability in front of high impact, low probability contingency occurrence. In the post-restructuring years, due to the increasing demand for energy, competition between electricity market players and increasing penetration of renewable resources, the provision of effective resiliency-based approaches has received more attention. In this paper, as the major contribution to current literature, a novel approach is proposed for resiliency improvement in a way that enables power system planners to manage several resilience metrics efficiently in a bi-objective optimization planning model simultaneously. For demonstration purposes, the proposed method is applied for optimal placement of the thyristor controlled series compensator (TCSC). Improvement of all considered resilience metrics regardless of their amount in a multi-criteria decision-making framework is novel in comparison to the other previous TCSC placement approaches. Without loss of generality, the developed resiliency improvement approach is applicable in any power system planning and operation problem. The simulation results on IEEE 30-bus and 118-bus test systems confirm the practicality and effectiveness of the developed approach. Simulation results show that by considering resilience metrics, the performance index, importance of curtailed consumers, congestion management cost, number of curtailed consumers, and amount of load loss are improved by 0.63%, 43.52%, 65.19%, 85.93%, and 85.94%, respectively.

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Mar, Adriana, Pedro Pereira, and João F. Martins. "A Survey on Power Grid Faults and Their Origins: A Contribution to Improving Power Grid Resilience." Energies 12, no. 24 (December 9, 2019): 4667. http://dx.doi.org/10.3390/en12244667.

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One of the most critical infrastructures in the world is electrical power grids (EPGs). New threats affecting EPGs, and their different consequences, are analyzed in this survey along with different approaches that can be taken to prevent or minimize those consequences, thus improving EPG resilience. The necessity for electrical power systems to become resilient to such events is becoming compelling; indeed, it is important to understand the origins and consequences of faults. This survey provides an analysis of different types of faults and their respective causes, showing which ones are more reported in the literature. As a result of the analysis performed, it was possible to identify four clusters concerning mitigation approaches, as well as to correlate them with the four different states of the electrical power system resilience curve.

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14

Shen, Lijuan, and Loon Ching Tang. "Enhancing resilience analysis of power systems using robust estimation." Reliability Engineering & System Safety 186 (June 2019): 134–42. http://dx.doi.org/10.1016/j.ress.2019.02.022.

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15

Ouyang, Min, and Leonardo Dueñas-Osorio. "Multi-dimensional hurricane resilience assessment of electric power systems." Structural Safety 48 (May 2014): 15–24. http://dx.doi.org/10.1016/j.strusafe.2014.01.001.

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16

Hosseini, Mohammad Mehdi, and Masood Parvania. "Artificial intelligence for resilience enhancement of power distribution systems." Electricity Journal 34, no. 1 (January 2021): 106880. http://dx.doi.org/10.1016/j.tej.2020.106880.

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17

OBOUDI, Mohammad Hossein, Mohammad MOHAMMADI, and Mohammad RASTEGAR. "Resilience-oriented intentional islanding of reconfigurable distribution power systems." Journal of Modern Power Systems and Clean Energy 7, no. 4 (July 2019): 741–52. http://dx.doi.org/10.1007/s40565-019-0567-9.

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18

Raoufi, Habibollah, Vahid Vahidinasab, and Kamyar Mehran. "Power Systems Resilience Metrics: A Comprehensive Review of Challenges and Outlook." Sustainability 12, no. 22 (November 20, 2020): 9698. http://dx.doi.org/10.3390/su12229698.

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Recently, there has been a focus on natural and man-made disasters with a high-impact low-frequency (HILF) property in electric power systems. A power system must be built with “resilience” or the ability to withstand, adapt and recover from disasters. The resilience metrics (RMs) are tools to measure the resilience level of a power system, normally employed for resilience cost–benefit in planning and operation. While numerous RMs have been presented in the power system literature; there is still a lack of comprehensive framework regarding the different types of the RMs in the electric power system, and existing frameworks have essential shortcomings. In this paper, after an extensive overview of the literature, a conceptual framework is suggested to identify the key variables, factors and ideas of RMs in power systems and define their relationships. The proposed framework is compared with the existing ones, and existing power system RMs are also allocated to the framework’s groups to validate the inclusivity and usefulness of the proposed framework, as a tool for academic and industrial researchers to choose the most appropriate RM in different power system problems and pinpoint the potential need for the future metrics.

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19

Lu, Jiazheng, Jun Guo, Zhou Jian, Yihao Yang, and Wenhu Tang. "Resilience Assessment and Its Enhancement in Tackling Adverse Impact of Ice Disasters for Power Transmission Systems." Energies 11, no. 9 (August 29, 2018): 2272. http://dx.doi.org/10.3390/en11092272.

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Ice disasters have frequently occurred worldwide in recent years, which seriously affected power transmission system operations. To improve the resilience of power grids and minimize economic losses, this paper proposes a framework for assessing the influence of ice disasters on the resilience of power transmission systems. This method considers the spatial–temporal impact of ice disasters on the resilience of power transmission systems, and the contingence set for risk assessment is established according to contingency probabilities. Based on meteorological data, the outage models of power transmission components are developed in the form of generic fragility curves, and the ice load is given by a simplified freezing rain ice model. A cell partition method is adopted to analyze the way ice disasters affect the operation of power transmission systems. The sequential Monte Carlo simulation method is used to assess resilience for capturing the stochastic impact of ice disasters and deriving the contingency set. Finally, the IEEE RTS-79 system is employed to investigate the impact of ice disasters by two case studies, which demonstrate the viability and effectiveness of the proposed framework. In turn, the results help recognize the resilience of the system under such disasters and the effects of different resilience enhancement measures.

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20

Lewis, Ted G., Thomas J. Mackin, and Rudy Darken. "Critical Infrastructure as Complex Emergent Systems." International Journal of Cyber Warfare and Terrorism 1, no. 1 (January 2011): 1–12. http://dx.doi.org/10.4018/ijcwt.2011010101.

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The United States Department of Homeland Security (DHS) charge is to, “Build a safer, more secure, and more resilient America by preventing, deterring, neutralizing, or mitigating the effects of deliberate efforts by terrorists to destroy, incapacitate, or exploit elements of our Nation’s CIKR …” using an all-hazards approach. The effective implementation of this strategy hinges on understanding catastrophes and their potential effect on the functioning of infrastructure. Unfortunately, there has been no unifying theory of catastrophe to guide decision-making, preparedness, or response. In this paper, the authors present a framework based on network science and normal accident theory that can be used to guide policy decisions for homeland security. They show that exceedance probability encompasses operational definitions of risk and resilience and provides a unifying policy framework for homeland security investments. Such an approach allows one to classify hazards as ‘high’ or ‘low’ risk, according to the resiliency exponent, and guide investments toward prevention or response. This framework is applied to cyber exploits and electric power grid systems to illustrate its generality.

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21

Mottahedi, Adel, Farhang Sereshki, Mohammad Ataei, Ali Nouri Qarahasanlou, and Abbas Barabadi. "The Resilience of Critical Infrastructure Systems: A Systematic Literature Review." Energies 14, no. 6 (March 12, 2021): 1571. http://dx.doi.org/10.3390/en14061571.

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Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases.

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22

Kubacki, Krzysztof, Dariusz Siemieniako, and Linda Brennan. "Building positive resilience through vulnerability analysis." Journal of Social Marketing 10, no. 4 (June 27, 2020): 471–88. http://dx.doi.org/10.1108/jsocm-09-2019-0142.

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Purpose The purpose of this paper is to propose an integrative framework for vulnerability analysis in social marketing systems by identifying, investigating and problematising the relationships among several interrelated concepts, including power, power asymmetry, vulnerability and resilience, in the context of social marketing systems. Design/methodology/approach This is a conceptual paper synthesising literature from social marketing, sociology and marketing management. Findings The main outcome of the discussion is a proposed integrative framework for vulnerability analysis. The framework identifies the main groups of stakeholders within a social marketing system and the bases for their power and consequential power asymmetries. It focusses on the types and states of vulnerability to identify the distinct characteristics of the social conditions of vulnerability for micro-level system actors. It leads to building positive resilience through efforts aiming to change the power asymmetries at the downstream, midstream and upstream levels. Originality/value The integrative framework for vulnerability analysis answers the call from Wood (2019) for the development of practical approaches to better understand resilience-building approaches in social marketing programmes. The framework provides reconciliation for diverse dimensions of vulnerability as a natural characteristic of all social marketing systems and as a universal, constant and inherent social condition.

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23

Romero, Natalia, Linda K. Nozick, Ian Dobson, Ningxiong Xu, and Dean A. Jones. "Seismic Retrofit for Electric Power Systems." Earthquake Spectra 31, no. 2 (May 2015): 1157–76. http://dx.doi.org/10.1193/052112eqs193m.

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This paper develops a two-stage stochastic program and solution procedure to optimize the selection of seismic retrofit strategies to increase the resilience of electric power systems against earthquake hazards. The model explicitly considers the range of earthquake events that are possible and, for each, an approximation of the distribution of damage experienced. This is important because electric power systems are spatially distributed and so their performance is driven by the distribution of component damage. We test this solution procedure against the nonlinear integer solver in LINGO 13 and apply the formulation and solution strategy to the Eastern Interconnection, where seismic hazard stems from the New Madrid seismic zone.

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Arghandeh, Reza, Alexandra von Meier, Laura Mehrmanesh, and Lamine Mili. "On the definition of cyber-physical resilience in power systems." Renewable and Sustainable Energy Reviews 58 (May 2016): 1060–69. http://dx.doi.org/10.1016/j.rser.2015.12.193.

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25

Bessani, Michel, Julio A. D. Massignan, Rodrigo Z. Fanucchi, Marcos H. M. Camillo, Joao B. A. London, Alexandre C. B. Delbem, and Carlos D. Maciel. "Probabilistic Assessment of Power Distribution Systems Resilience Under Extreme Weather." IEEE Systems Journal 13, no. 2 (June 2019): 1747–56. http://dx.doi.org/10.1109/jsyst.2018.2853554.

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Mahzarnia, Maedeh, Mohsen Parsa Moghaddam, Payam Teimourzadeh Baboli, and Pierluigi Siano. "A Review of the Measures to Enhance Power Systems Resilience." IEEE Systems Journal 14, no. 3 (September 2020): 4059–70. http://dx.doi.org/10.1109/jsyst.2020.2965993.

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27

Parise, Giuseppe, Luigi Parise, Marco Allegri, Amedeo De Marco, and Michael A. Anthony. "Operational Resilience of Hospital Power Systems in the Digital Age." IEEE Transactions on Industry Applications 57, no. 1 (January 2021): 94–100. http://dx.doi.org/10.1109/tia.2020.3032941.

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Panteli, Mathaios, Dimitris N. Trakas, Pierluigi Mancarella, and Nikos D. Hatziargyriou. "Power Systems Resilience Assessment: Hardening and Smart Operational Enhancement Strategies." Proceedings of the IEEE 105, no. 7 (July 2017): 1202–13. http://dx.doi.org/10.1109/jproc.2017.2691357.

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Yang, Yihao, Wenhu Tang, Yang Liu, Yanli Xin, and Qinghua Wu. "Quantitative Resilience Assessment for Power Transmission Systems Under Typhoon Weather." IEEE Access 6 (2018): 40747–56. http://dx.doi.org/10.1109/access.2018.2858860.

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Trakas, Dimitris N., Mathaios Panteli, Nikos D. Hatziargyriou, and Pierluigi Mancarella. "Spatial Risk Analysis of Power Systems Resilience During Extreme Events." Risk Analysis 39, no. 1 (October 23, 2018): 195–211. http://dx.doi.org/10.1111/risa.13220.

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31

Shahzad, Umair. "The concept of vulnerability and resilience in electric power systems." Australian Journal of Electrical and Electronics Engineering 18, no. 3 (June 28, 2021): 138–45. http://dx.doi.org/10.1080/1448837x.2021.1943861.

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Umunnakwe, A., H. Huang, K. Oikonomou, and K. R. Davis. "Quantitative analysis of power systems resilience: Standardization, categorizations, and challenges." Renewable and Sustainable Energy Reviews 149 (October 2021): 111252. http://dx.doi.org/10.1016/j.rser.2021.111252.

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Ma, Shanshan, Shiyang Li, Zhaoyu Wang, and Feng Qiu. "Resilience-Oriented Design of Distribution Systems." IEEE Transactions on Power Systems 34, no. 4 (July 2019): 2880–91. http://dx.doi.org/10.1109/tpwrs.2019.2894103.

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Kong, Simonovic, and Zhang. "Resilience Assessment of Interdependent Infrastructure Systems: A Case Study Based on Different Response Strategies." Sustainability 11, no. 23 (November 20, 2019): 6552. http://dx.doi.org/10.3390/su11236552.

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Resilient infrastructure systems are essential for continuous and reliable functioning of social and economic systems. Taking advantage of network theory, this paper models street network, water supply network, power grid and information infrastructure network as layers that are integrated into a multilayer network. The infrastructure interdependencies are described using five basic dependence patterns of fundamental network elements. Definitions of dynamic cascading failures and recovery mechanisms of infrastructure systems are also established. The main contribution of the paper is a new infrastructure network resilience measure capable of addressing complex infrastructure system, as well as network component (layer) interdependences. The new measure is based on infrastructure network performance, proactive absorptive capacity and reactive restorative capacity, with three resilience features of network—robustness, resourcefulness, and rapidity. The quantitative resilience measure using dynamic space-time simulation model is illustrated with a multilayer infrastructure network numerical test, including different response strategies to floods of different scale. The results demonstrate that the resilience measure provides an evaluation method of various protection and restoration strategies that will optimize the performance of interdependent infrastructure system. The sector-specific decisions could not always lead to optimal system solutions, and systems approach offers significant benefits for increasing infrastructure system resilience. This study can assist municipal decision makers in (i) better understanding the effects of different response strategies on the resilience of interdependent infrastructure system, and (ii) deciding which strategy should be adopted under different types of disasters.

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Ignatiadis, Ioannis, and Joe Nandhakumar. "The Impact of Enterprise Systems on Organizational Resilience." Journal of Information Technology 22, no. 1 (March 2007): 36–43. http://dx.doi.org/10.1057/palgrave.jit.2000087.

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Enterprise systems are used to facilitate the seamless integration and exchange of data between the various departments within an organization. In order to achieve this, rigidly defined control mechanisms must be in place in the system, which safeguard the company's data and protect the company against unauthorized and unintended uses of the system. This is ideal for total control; however, is only achievable to a certain extent. The configuration of controls in the enterprise system may have unintended organizational implications, due to organizational necessities. The purpose of this paper is to present the findings from a company case study, where an enterprise system is being used. We suggest that the introduction of an enterprise system creates power differentials, which serve to increase control in the organization. This results in increased rigidity, and a possible decrease in organizational flexibility and resilience. On the other hand, enterprise systems can also cause drift, resulting from the unexpected consequences of these power differentials, as well as from the role of perceptions of people in solving a problem within the enterprise system. This reduction in control may serve in some circumstances as an enabler to organizational resilience.

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Püschel-Løvengreen, Sebastián, Mehdi Ghazavi Dozein, Steven Low, and Pierluigi Mancarella. "Separation event-constrained optimal power flow to enhance resilience in low-inertia power systems." Electric Power Systems Research 189 (December 2020): 106678. http://dx.doi.org/10.1016/j.epsr.2020.106678.

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Ciapessoni, Emanuele, Diego Cirio, Andrea Pitto, and Marino Sforna. "Quantification of the Benefits for Power System of Resilience Boosting Measures." Applied Sciences 10, no. 16 (August 5, 2020): 5402. http://dx.doi.org/10.3390/app10165402.

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Severe natural events leading to wide and intense impacts on power systems are becoming more and more frequent due to climate changes. Operators are urged to set up plans to assess the possible consequences of such events, in view of counteracting them. To this aim, the application of the resilience concept can be beneficial. The paper describes a methodology for power system resilience assessment and enhancement, aimed at quantifying both system resilience indicators evaluated for severe threats, and the benefits to resilience brought by operational and grid hardening measures. The capabilities of the methodology are demonstrated on real study cases.

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SERVICE, TRAVIS, and DANIEL TAURITZ. "INCREASING INFRASTRUCTURE RESILIENCE THROUGH COMPETITIVE COEVOLUTION." New Mathematics and Natural Computation 05, no. 02 (July 2009): 441–57. http://dx.doi.org/10.1142/s1793005709001416.

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The world is increasingly dependent on critical infrastructures such as the electric power grid, water, gas and oil transport systems. Due to this increasing dependence and inadequate infrastructure expansion, these systems are becoming increasingly stressed. These additional stresses leave these systems less resilient to external faults, both accidental and malicious than ever before. As a result of this increased vulnerability, many critical infrastructures are becoming susceptible to cascading failures, where an initial fault caused by an external force may induce a domino-effect of further component failures. An important implication is that traditional infrastructure risk analysis methods, often relying on Monte Carlo sampling of fault scenarios, are no longer sufficient. Instead, systematic analysis based on worst-case attacks by intelligent adversaries is essential. This paper describes a coevolutionary methodology to simultaneously discover low-effort high-impact faults and corresponding means of hardening infrastructures against them. We empirically validate our methodology through an electric power transmission system case study.

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Ali, Asfand Yar, Akhtar Hussain, Ju-Won Baek, and Hak-Man Kim. "Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles." Energies 14, no. 1 (December 29, 2020): 142. http://dx.doi.org/10.3390/en14010142.

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The increased intensity and frequency of natural disasters have attracted the attention of researchers in the power sector to enhance the resilience of power systems. Microgrids are considered as a potential solution to enhance the resilience of power systems using local resources, such as renewable energy sources, electric vehicles (EV), and energy storage systems. However, the deployment of an additional storage system for resilience can increase the investment cost. Therefore, in this study, the usage of existing EVs in microgrids is proposed as a solution to increase the resilience of microgrids with outages without the need for additional investment. In the case of contingencies, the proposed algorithm supplies energy to islanded microgrids from grid-connected microgrids by using mobile EVs. The process for the selection of EVs for supplying energy to islanded microgrids is carried out in three steps. Firstly, islanded and networked microgrids inform the central energy management system (CEMS) about the required and available energy stored in EVs, respectively. Secondly, CEMS determines the microgrids among networked microgrids to supply energy to the islanded microgrid. Finally, the selected microgrids determine the EVs for supplying energy to the islanded microgrid. Simulations have shown the effectiveness of the proposed algorithm in enhancing the resilience of microgrids even in the absence of power connection among microgrids.

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Yanhua Hong and K. A. Shore. "Power Loss Resilience in Laser Diode-Based Optical Chaos Communications Systems." Journal of Lightwave Technology 28, no. 3 (February 2010): 270–76. http://dx.doi.org/10.1109/jlt.2009.2038350.

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Zare-Bahramabadi, Majid, Ali Abbaspour, Mahmud Fotuhi-Firuzabad, and Moein Moeini-Aghtaie. "Resilience-based framework for switch placement problem in power distribution systems." IET Generation, Transmission & Distribution 12, no. 5 (March 13, 2018): 1223–30. http://dx.doi.org/10.1049/iet-gtd.2017.0970.

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Mohamed, Mohamed A., Tao Chen, Wencong Su, and Tao Jin. "Proactive Resilience of Power Systems Against Natural Disasters: A Literature Review." IEEE Access 7 (2019): 163778–95. http://dx.doi.org/10.1109/access.2019.2952362.

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Zhang, Han, Hanjie Yuan, Gengfeng Li, and Yanling Lin. "Quantitative Resilience Assessment under a Tri-Stage Framework for Power Systems." Energies 11, no. 6 (June 3, 2018): 1427. http://dx.doi.org/10.3390/en11061427.

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Wang, Yezhou, Chen Chen, Jianhui Wang, and Ross Baldick. "Research on Resilience of Power Systems Under Natural Disasters—A Review." IEEE Transactions on Power Systems 31, no. 2 (March 2016): 1604–13. http://dx.doi.org/10.1109/tpwrs.2015.2429656.

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Panteli, Mathaios, Pierluigi Mancarella, Dimitris N. Trakas, Elias Kyriakides, and Nikos D. Hatziargyriou. "Metrics and Quantification of Operational and Infrastructure Resilience in Power Systems." IEEE Transactions on Power Systems 32, no. 6 (November 2017): 4732–42. http://dx.doi.org/10.1109/tpwrs.2017.2664141.

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Taheri, Babak, Amir Safdarian, Moein Moeini-Aghtaie, and Matti Lehtonen. "Enhancing Resilience Level of Power Distribution Systems Using Proactive Operational Actions." IEEE Access 7 (2019): 137378–89. http://dx.doi.org/10.1109/access.2019.2941593.

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Plotnek, Jordan J., and Jill Slay. "Power systems resilience: Definition and taxonomy with a view towards metrics." International Journal of Critical Infrastructure Protection 33 (June 2021): 100411. http://dx.doi.org/10.1016/j.ijcip.2021.100411.

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Sharafi, Dean. "Australia’s Power Systems: Enabling Renewable Energy Integration & Resilience [Guest Editorial]." IEEE Power and Energy Magazine 19, no. 5 (September 2021): 14–17. http://dx.doi.org/10.1109/mpe.2021.3088708.

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Ma, Xiangyu, Huijie Zhou, and Zhiyi Li. "On the resilience of modern power systems: A complex network perspective." Renewable and Sustainable Energy Reviews 152 (December 2021): 111646. http://dx.doi.org/10.1016/j.rser.2021.111646.

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Son, Changwon, Farzan Sasangohar, and S. Camille Peres. "Redefining and Measuring Resilience in Emergency Management Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1651–52. http://dx.doi.org/10.1177/1541931213601899.

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Inherent limitations of controlling risks in complex socio-technical systems were revealed in several major catastrophic disasters such as nuclear meltdown in Fukushima Daiichi nuclear power plant in 2011, well blowout in Deepwater Horizon drilling rig in 2010, and Hurricane Katrina in 2005. While desired risk management leans toward the prevention of such unwanted events, the mitigation of their impact becomes more important and emergency response operations provide the last line of protection against disasters (Kanno, Makita, & Furuta, 2008). In response to September 11 terrorist attack at World Trade Center in New York, U.S. Government launched the National Incident Management System (NIMS), an integrated national and multi-jurisdictional emergency preparedness and response program (Department of Homeland Security, 2008). The NIMS framework is characterized by a common operating picture, interoperability, reliability, scalability and portability, and resilience and redundancy (Department of Homeland Security, 2008). Among these characteristics, effective emergency response operations require resilience because planned-for actions may not be implementable and therefore the emergency response organizations must adapt to and cope with uncertain and changing environment (Mendonca, Beroggi, & Wallace, 2003). There have been many attempts to define resilience in various disciplines (Hollnagel, Woods, & Leveson, 2007). Nevertheless, such attempts for emergency management systems (EMS) is still scarce in the existing body of resilience literature. By considering traits of EMS, this study proposes the definition of resilience as ‘ a system’s capability to respond to different kinds of disrupting events and to bring the system back to a desired state in a timely manner with efficient use of resources, and with minimum loss of performance capacity.’ In order to model resilience in EMS, the U.S. NIMS is chosen because it allows for investigation of resilient behavior among different components that inevitably involve both human agents and technological artifacts as joint cognitive systems (JCSs) (Hollnagel & Woods, 2005). In the NIMS, the largest JCS comprises five critical functions: Command, Planning, Operations, Logistics and Finance & Administration (F&A) (Department of Homeland Security, 2008). External stimuli or inputs to this JCS are events that occur outside of its boundary such as uncontrolled events. When these events do occur, they are typically perceived by the ‘boots-on-the-ground’ in the Operations function. The perceived data are reported and transported to the Planning function in which such data are transformed into useful and meaningful information. This information provides knowledge base for generating a set of decisions. Subsequently, Command function selects some of those decisions and authorizes them with adequate resources so that Operations actually take actions for such decisions to the uncontrolled events. This compensation process continues until the JCS achieves its systematic goal which is to put the event under control. On the other hand, Logistics feeds required and requested resources such as workforce, equipment and material for the system operations and F&A does the accounting of resources as those resources are actually used to execute its given missions. Such JCS utilizes two types of memory: a collective working memory (CWM) can be manifested in the form of shared displays, document or whiteboards used by teams; similarly, collective long-term memory (CLTM) can take forms of past accident reports, procedures and guidelines. Based on this conceptual framework for resilience of emergency operations, five Resilient Performance Factors (RPFs) are suggested to make resilience operational in EMS. Such RPFs are adaptive response, rapidity of recovery, resource utilization, performance stability and team situation awareness. Adaptation is one of the most obvious patterns of resilient performance (Leveson et al., 2006; Rankin, Lundberg, Woltjer, Rollenhagen, & Hollnagel, 2014). Another factor that typifies resilience of any socio- technical system is how quickly or slowly it bounces back from perturbations (Hosseini, Barker, & Ramirez-Marquez, 2016). In most systems, resources are constrained. Hence, resilience requires the effective and efficient use of resources to varying demands. As such demands persist over time, the system’s performance level tends to diminish. For the EMS to remain resilient, its performance should be maintained in a stable fashion. Finally, EMS is is expected to possess the ability to perceive what is currently taking place, to comprehend what such occurrence actually means, and to anticipate what may happen and decide what to do about it. When this occurs within a team, it is often referred to as team situation awareness (Endsley, 1995; McManus, Seville, Brunsden, & Vargo, 2007). This resilience model for EMS needs validation and many assumptions and simplifications made in this work require further justification. This model will be discussed and validated by using subsequent data collection from Emergency Operations Training Center operated by Texas A&M Engineering Extension Service (TEEX) and will be reported in future publications.

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