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Applied Sciences
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Probabilistic Methods for Power System Resilience Assessment
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Probabilistic Methods for Power System Resilience Assessment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 14697
Related Special Issue: Methods, Metrics and Tools for Power System Resilience Analysis and Enhancement

Special Issue Editor

Dr. Andrea Pitto

E-Mail Website
Guest Editor
Ricerca sul Sistema Energetico - RSE S.p.A., Milan, Italy
Interests: probabilistic risk based methods for the assessment and enhancement of power system resilience to threats
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The concept of resilience is playing a more and more important role in modern power systems, also influencing recent regulations in the electricity sector in many countries. Severe weather events and cyberattacks affecting power systems are an increasing concern for operators and institutions, due to climate change and growing interdependences between power and ICT infrastructures. Although resilience still lacks an established definition, it is generally agreed that the major characteristics of a resilient system are the ability to limit the extent, severity, and duration of its degradation and to recover fast.

Researchers worldwide have recently proposed indicators to quantify resilience, based on concepts of probability and risk and modeling system threats and component vulnerability. Multidisciplinary approaches are thus needed, which require experts from different fields (power system engineers, meteorologists, climatologists, cyber experts, statisticians and data analysts, etc.) to work together to develop probabilistic approaches.

The Special Issue “Probabilistic Methods for power system resilience assessment” is intended for a wide and interdisciplinary audience and covers topics such as:

  • Probabilistic risk-based approaches to power system resilience assessment to support operators in managing the power system over different time frames (from planning to real time operation);
  • Contingency and risk forecasting based on weather prediction systems to support operators’ situational awareness;
  • Big data analytics and experimental activities for the characterization and the validation of the probabilistic models related to component vulnerability and threats;
  • Probabilistic models of climate evolution in the next decades;
  • Probabilistic modeling of cyberattack scenarios;
  • Effective visualization of resilience analysis results, also exploiting GIS-based tools.

Dr. Andrea Pitto
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Probabilistic risk-based approaches to power system resilience assessment
  • Geographical information system (GIS)
  • Weather forecasting system application
  • Climate change modeling
  • Hazard and vulnerability analysis
  • Big Data analytics to model component vulnerability and threats
  • Probabilistic modeling of cyberattack scenarios

Published Papers (6 papers)

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Research

15 pages, 415 KiB  
Article
Reliability of Supply and the Impact of Weather Exposure and Protection System Failures
by Erlend Sandø Kiel and Gerd Hovin Kjølle
Appl. Sci. 2021, 11(1), 182; https://doi.org/10.3390/app11010182 - 27 Dec 2020
Cited by 2 | Viewed by 1823
Abstract
Extreme weather is known to cause failure bunching in electrical transmission systems. However, protection systems can also contribute to the worsening of the system state through various failure modes—spontaneous, missing or unwanted operation. The latter two types of failures only occur when an [...] Read more.
Extreme weather is known to cause failure bunching in electrical transmission systems. However, protection systems can also contribute to the worsening of the system state through various failure modes—spontaneous, missing or unwanted operation. The latter two types of failures only occur when an initial failure has happened, and thus are more likely to happen when the probability of failure of transmission lines is high, such as in an extreme weather scenario. This causes an exacerbation of failure bunching effects, increasing the risk of blackouts, or High Impact Low Probability (HILP) events. This paper describes a method to model transmission line failure rates, considering both protection system reliability and extreme weather exposure. A case study is presented using the IEEE 24 bus Reliability Test System (RTS) test system. The case study, using both an approximate method as well as a time-series approach to calculate reliability indices, demonstrates both a compact generalization of including protection system failures in reliability analysis, as well as the interaction between weather exposure and protection system failures and its impact on power system reliability indices. The results show that the inclusion of protection system failures can have a large impact on the estimated occurrence of higher order contingencies for adjacent lines, especially for lines with correlated weather exposure. Full article
(This article belongs to the Special Issue Probabilistic Methods for Power System Resilience Assessment)
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31 pages, 3722 KiB  
Article
Quantification of the Benefits for Power System of Resilience Boosting Measures
by Emanuele Ciapessoni, Diego Cirio, Andrea Pitto and Marino Sforna
Appl. Sci. 2020, 10(16), 5402; https://doi.org/10.3390/app10165402 - 05 Aug 2020
Cited by 5 | Viewed by 2273
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Probabilistic Methods for Power System Resilience Assessment)
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11 pages, 589 KiB  
Article
Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather
by Efthymios Karangelos, Samuel Perkin and Louis Wehenkel
Appl. Sci. 2020, 10(15), 5089; https://doi.org/10.3390/app10155089 - 24 Jul 2020
Cited by 9 | Viewed by 2574
Abstract
This paper presents a probabilistic methodology for assessing power system resilience, motivated by the extreme weather storm experienced in Iceland in December 2019. The methodology is built on the basis of models and data available to the Icelandic transmission system operator in anticipation [...] Read more.
This paper presents a probabilistic methodology for assessing power system resilience, motivated by the extreme weather storm experienced in Iceland in December 2019. The methodology is built on the basis of models and data available to the Icelandic transmission system operator in anticipation of the said storm. We study resilience in terms of the ability of the system to contain further service disruption, while potentially operating with reduced component availability due to the storm impact. To do so, we develop a Monte Carlo assessment framework combining weather-dependent component failure probabilities, enumerated through historical failure rate data and forecasted wind-speed data, with a bi-level attacker-defender optimization model for vulnerability identification. Our findings suggest that the ability of the Icelandic power system to contain service disruption moderately reduces with the storm-induced potential reduction of its available components. In other words, and as also validated in practice, the system is indeed resilient. Full article
(This article belongs to the Special Issue Probabilistic Methods for Power System Resilience Assessment)
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19 pages, 1468 KiB  
Article
A Risk-Based Approach to Assess the Operational Resilience of Transmission Grids
by Milorad Papic, Svetlana Ekisheva and Eduardo Cotilla-Sanchez
Appl. Sci. 2020, 10(14), 4761; https://doi.org/10.3390/app10144761 - 10 Jul 2020
Cited by 11 | Viewed by 2627
Abstract
Modern risk analysis studies of the power system increasingly rely on big datasets, either synthesized, simulated, or real utility data. Particularly in the transmission system, outage events have a strong influence on the reliability, resilience, and security of the overall energy delivery infrastructure. [...] Read more.
Modern risk analysis studies of the power system increasingly rely on big datasets, either synthesized, simulated, or real utility data. Particularly in the transmission system, outage events have a strong influence on the reliability, resilience, and security of the overall energy delivery infrastructure. In this paper we analyze historical outage data for transmission system components and discuss the implications of nearby overlapping outages with respect to resilience of the power system. We carry out a risk-based assessment using North American Electric Reliability Corporation (NERC) Transmission Availability Data System (TADS) for the North American bulk power system (BPS). We found that the quantification of nearby unscheduled outage clusters would improve the response times for operators to readjust the system and provide better resilience still under the standard definition of N-1 security. Finally, we propose future steps to investigate the relationship between clusters of outages and their electrical proximity, in order to improve operator actions in the operation horizon. Full article
(This article belongs to the Special Issue Probabilistic Methods for Power System Resilience Assessment)
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24 pages, 760 KiB  
Article
Evidence-Based Analysis of Cyber Attacks to Security Monitored Distributed Energy Resources
by Davide Cerotti, Daniele Codetta-Raiteri, Giovanna Dondossola, Lavinia Egidi, Giuliana Franceschinis, Luigi Portinale and Roberta Terruggia
Appl. Sci. 2020, 10(14), 4725; https://doi.org/10.3390/app10144725 - 09 Jul 2020
Cited by 4 | Viewed by 2614
Abstract
This work proposes an approach based on dynamic Bayesian networks to support the cybersecurity analysis of network-based controllers in distributed energy plants. We built a system model that exploits real world context information from both information and operational technology environments in the energy [...] Read more.
This work proposes an approach based on dynamic Bayesian networks to support the cybersecurity analysis of network-based controllers in distributed energy plants. We built a system model that exploits real world context information from both information and operational technology environments in the energy infrastructure, and we use it to demonstrate the value of security evidence for time-driven predictive and diagnostic analyses. The innovative contribution of this work is in the methodology capability of capturing the causal and temporal dependencies involved in the assessment of security threats, and in the introduction of security analytics supporting the configuration of anomaly detection platforms for digital energy infrastructures. Full article
(This article belongs to the Special Issue Probabilistic Methods for Power System Resilience Assessment)
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13 pages, 1628 KiB  
Article
Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
by Seongmun Oh, Junhyuk Kong, Minhee Choi and Jaesung Jung
Appl. Sci. 2020, 10(14), 4693; https://doi.org/10.3390/app10144693 - 08 Jul 2020
Cited by 5 | Viewed by 2151
Abstract
This study presents a machine learning-based method for predicting the power grid state subjected to heavy-rain hazards. Machine learning models can recognize key knowledge from a dataset without any preliminary knowledge about the dataset. Hence, machine learning methods have been utilized for solving [...] Read more.
This study presents a machine learning-based method for predicting the power grid state subjected to heavy-rain hazards. Machine learning models can recognize key knowledge from a dataset without any preliminary knowledge about the dataset. Hence, machine learning methods have been utilized for solving power grid-related problems. Two sets of historical data were used herein: Local weather data and power grid outage data. First, we investigated the heavy-rain-related outage distribution and analyzed the correlated characteristics between weather and outages to characterize the heavy rain events. The analysis results show that multiple weather effects are significant in causing power outages, even under heavy-rain conditions. Furthermore, this study proposes a cost-sensitive prediction method using a support vector machine (SVM) model. The accuracy of the model was improved by applying a cost-sensitive learning algorithm to the SVM model, which was subsequently used to predict the state of the grid. The developed model was evaluated using G-mean values. The proposed method was verified via actual data of a heavy rain event that occurred in South Korea. Full article
(This article belongs to the Special Issue Probabilistic Methods for Power System Resilience Assessment)
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