Methods for Analysis and Quantification of Power System Resilience

Stanković, A.M.; Tomsovic, Kevin; De, Fabrizio; Braun, Martin; Chow, Joe H.; Aukalevski, N.; Dobson, Ian; Eto, J.; Fink, B.; Hachmann, Christian; Hill, D.; Ji, Chuanyi; Kavicky, James A.; Leví, Víctor; Cc, Liu; Mili, L.; Moreno, Rodrigo A.; Panteli, Mathaios; Petit, Frédéric; Fang, Yi-Ping; Singh, Charanjit Kaur Swaran; Srivastava, Aman; Strunz, Kai; Sun, Hong‐Bo; Xu, Yunhua; Zhao, Shijia

doi:10.1109/tpwrs.2022.3212688

Cited by 44 publications

(23 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present paper, we refer to the power system resilience definition proposed by IEEE PES task force [1], most in line with CIGRE, FERC, and other government and industry bodies, which states as follows: "Power system resilience is the ability to limit the extent, system impact, and duration of degradation in order to sustain critical services following an extraordinary event. Key enablers for a resilient response include the capacity to anticipate, absorb, rapidly recover from, adapt to, and learn from such an event.…”

Section: A General Remarksmentioning

confidence: 99%

“…Power systems have traditionally been designed and operated to ensure reliable supply of electricity to customers, with a focus on minimizing the frequency and duration of outages. However, in recent years, there has been a growing 1 ReXplan is hosted in the GitHub repository [9] recognition that power systems are also vulnerable to disruptions, such as climate driven extreme weather events, physical-attacks, and cyber-attacks. Therefore, the concept of resilience has become increasingly important in the design and operation of power systems, referring to the ability of a system to withstand and recover from disturbances.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

reXplan: A Novel Tool for the Analysis of Climate Resilience in Power Systems

Pizzimbone¹,

Jrad²

2023

Preprint

View full text Add to dashboard Cite

<p> The increase in extreme weather events induced by climate change, and their impact on power systems, has created a need for tools that can assess and improve system resilience. To meet this need, the R&D team of the Energy System Consulting segment of Tractebel Engineering GmbH has developed a novel tool called reXplan. ReXplan is a Python library for resilient electrical system planning under extreme hazard events, such as windstorms, earthquakes, floods, wildfire, etc. It is designed to help power system operators and planners make better-informed decisions to create more resilient and secure power grids. This paper provides an overview of reXplan's main features, architecture, methodology of analysis and metrics. ReXplan is capable of modeling both spatiotemporal extreme events and electrical power systems. It leverages technologies and techniques such as Julia/JuMP package and sequential Monte Carlo analysis with multivariate stratified sampling to achieve high accuracy in the results while reducing computational load. By quantifying resiliency metrics, comparing different planning strategies, and validating technical solutions, reXplan can help reduce the risks of severe outages in the grid. The software can be easily integrated into common data science environments and is available as a Python library. For computationally intensive tasks, such as optimal power flow, reXplan is exploiting the fast speed of Julia programming language, using PowerModels.jl as backend. </p>

show abstract

Section: A General Remarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

reXplan: A Novel Tool for the Analysis of Climate Resilience in Power Systems

Pizzimbone¹,

Jrad²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…BN ⟺ P r (S, HS) (13) where S = {𝑆 𝐿 | 𝑙 ∈ [1, 𝐿]}, 𝑆 𝐿 represents the vector quantity with binary values of 0 and 1 showing the whole system line status after the hurricane event. S represents the whole scenarios of the 48-bus DSN line status.…”

Section: Representation Of Bn-dsn's Pole Failure Probabilitiesmentioning

confidence: 99%

“…In this view, this article is motivated to focus on improving the operational resilience of the DSN against hurricane events. The goal is to lessen the expected load loss, and the impact of the protracted widespread power outages proactively, leveraging the proposed DSN predictive algorithms in this paper for the DSN proactive operational planning decision-making A comprehensive literature review of the steps and strategies utilized to achieve power system resilience improvement against HILL events such as hurricanes was presented in [12], [13]. The papers suggested the need to change the reactive operational resilience improvement techniques proposed by many researchers in assessing the resilience improvement of the DSN against hurricane events, to a more defensive proactive operational resilience improvement approach [14].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive Power System Network Resilience Enhancement Operational Planning

2023

View full text Add to dashboard Cite

Enhancing the operational resilience of the distribution system network (DSN) proactively in a hurricane-prone region requires a pre-hurricane event DSN optimization model, built on accurate hurricaneinduced DSN line fault prediction scenarios. In the past, the resilience evaluation methods such as statistical sequential and non-sequential Monte Carlo simulation (MCS) contingency-based technique, and Machine learning-based Bayesian Networks (BN) technique, have been proposed to strengthen the operational resilience of the DSN proactively against the forecasted oncoming hurricane events. However, a comparative study is largely unexplored to evaluate which of these two methods is best for proactive operational planning decision-making against the forecasted oncoming hurricane event. In this paper, the Bayesian network (BN) and combined statistical DSN's Fragility-curve(FC)-Monte Carlo simulation (MCS)-Scenario reduction (SCENRED) predictive algorithms were developed. The DSN line fault prediction scenarios simulated leveraging the predicted oncoming hurricane Ewiniar data were utilized to perform pre-hurricane DSN optimization to proactively decrease the DSN expected load loss. The pre-event system optimization problems were formulated in a mixed integer linear programming (MILP) approach and solved using a CPLEX solver in general algebraic modelling system (GAMS) on a redesigned 48-bus DSN. The simulated initial expected load loss of 39% of 35 MWh was decreased to 35.34%, and then to 30.71% with the use of combined statistical DSN's FC-MCS-SCENRED, and the BN-DSN predictive models. These results were validated using the Electrical transient analyzer program (ETAP).This study confirmed that the BN-DSN predictive model is a better operational planning tool compared to combined statistical DSN's line FC-MCS-SCENRED predictive model. RCS "t"Remote-controlled switching time. ω x F Weight factor at load bus "x". 𝐿 𝐷𝑆The binary parameter will be 0, at a damaged scenario (s) of the line′𝑙′. V max, V min Maximum and minimum operational constrained voltage(s). S maxGrid maximum apparent power ,max ,min , DG DG xx PPThe maximum and minimum real power output of the DGs at bus "x".

show abstract

“…1. The dimensions of these performance curves are standard metrics of resilience [1]- [5]. Recent research reveals practical stochastic models of the outages and restores and the resulting performance curves in transmission [4] and distribution systems [5], [6].…”

Section: Modeling Resilience Processes With Poisson Ratesmentioning

confidence: 99%

Models, Metrics, and Their Formulas for Typical Electric Power System Resilience Events

Dobson

2023

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

Poisson process models are defined in terms of their rates for outage and restore processes in power system resilience events. These outage and restore processes easily yield the performance curves that track the evolution of resilience events, and the area, nadir, and duration of the performance curves are standard resilience metrics. This letter analyzes typical resilience events by analyzing the area, nadir, and duration of mean performance curves. Explicit and intuitive formulas for these metrics are derived in terms of the Poisson process model parameters, and these parameters can be estimated from utility data. This clarifies the calculation of metrics of typical resilience events, and shows what they depend on. The metric formulas are derived with lognormal, exponential, or constant rates of restoration. The method is illustrated with a typical North American transmission event. Similarly nice formulas are obtained for the area metric for empirical power system data.

show abstract

Methods for Analysis and Quantification of Power System Resilience

Cited by 44 publications

References 51 publications

reXplan: A Novel Tool for the Analysis of Climate Resilience in Power Systems

reXplan: A Novel Tool for the Analysis of Climate Resilience in Power Systems

A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive Power System Network Resilience Enhancement Operational Planning

Models, Metrics, and Their Formulas for Typical Electric Power System Resilience Events

Contact Info

Product

Resources

About