Power distribution system improvement planning under hurricanes based on a new resilience index

Najafi, Javad; Peiravi, Ali; Guerrero, Josep M.

doi:10.1016/j.scs.2018.03.022

Cited by 93 publications

(66 citation statements)

References 27 publications

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“…Several proactive planning measures can be applied to enhancing the distribution system resilience. From an operational standpoint, a decision-maker can improve resilience by allocating resources to lessen the average impact (L i ), decrease the damage assessment time (t r -t pe ) to quickly enter the restorative state and/or apply advanced restoration to decrease Algorithm 1: Probabilistic loss for a given HILP event 1 Given: Weather data, Distribution system model 2 Step I: Fragility Modeling 3 Obtain PDF p(I) of the wind-speed profile for a given geographical region using weather data 4 for each distribution lines do 5 Generate fragility curves 6 Obtain component failure probabilities P l (ω) 7 Step II: Monte-Carlo Simulation 8 for each event in I do 9 Component level impact→ System level impact 10 Evaluate system loss for given event U i (I) 11 if enough trials, then 12 Evaluate average loss function 13 else 14 Go to step 9 15 Step III: Probabilistic loss 16 Compute risk-based resilience metrics 17 Output: V aR α , CV aR α impact in restorative state (t ir -t r ). Two specific proactive planning measures and approach to model their impact on resilience curve are discussed in this section.…”

Section: Model the Impacts Of Proactive Planningmentioning

confidence: 99%

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

Poudel

Dubey

Bose

2020

IEEE Systems Journal

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It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks but also evaluate the potential improvements offered by different planning measures. In this paper, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to highimpact low-probability (HILP) events. Specifically, we define two risk-based measures: Value-at-Risk (V aRα) and Conditional Value-at-Risk (CV aRα) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively for the events beyond a prespecified risk threshold, α. Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics. Her research focus is on the analysis, operation, and planning of the modern power distribution systems for enhanced service quality and grid resilience. At WSU, her lab focuses on developing new planning and operational tools for the current and future power distribution systems that help in effective integration of distributed energy resources and responsive loads.Anjan Bose (LF'89) received the B. Tech. (Hons.) degree from the

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Section: Model the Impacts Of Proactive Planningmentioning

confidence: 99%

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

Poudel

Dubey

Bose

2020

IEEE Systems Journal

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“…Another limitation of robust optimization is that when different candidate strategies (line hardening and DG placement) are implemented such as in [10], reconfiguration cannot be implemented to restore the network. Due to these limitations of robust optimization, stochastic optimization has been used to solve the problem in recent works such as [1,16]. In stochastic programming, which is implemented in [1,16], the final decision will be obtained by studying a set of scenarios that are produced by uncertain parameters of the problem.…”

Section: Resilience Study Of Pdnmentioning

confidence: 99%

“…Due to these limitations of robust optimization, stochastic optimization has been used to solve the problem in recent works such as [1,16]. In stochastic programming, which is implemented in [1,16], the final decision will be obtained by studying a set of scenarios that are produced by uncertain parameters of the problem.…”

Section: Resilience Study Of Pdnmentioning

confidence: 99%

“…Although, a number of water pumps might be equipped to emergency generators. But, these units are not efficient in long emergency conditions resulting from natural disasters due to fuel limitation [1]. Furthermore, with simultaneous analysis and improvement of the resilience of PDN and WDN, the allocated budget is spent in such a way that the accessibility of all loads to power and water will be improved.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Integrated Power and Water Distribution Networks Seismic Resilience Leveraging Microgrids

2020

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An earthquake, as one of the natural disasters, can damage vital infrastructures including the power distribution network (PDN) and water distribution network (WDN). The dependency of WDN on PDN is the other challenge that can be highlighted after the earthquake. In this paper, the resilience improvement planning of integrated PDN and WDN against earthquakes is solved through stochastic programming. Power lines and substation hardening in PDN and water pipes rehabilitation with better material are the candidate strategies to minimize the expected inaccessibility value of loads to power and water as the resilience index and to minimize the cost of strategies. The proposed model is tested on the modified IEEE 33-bus PDN with a designed WDN and its performance is evaluated under different cases where the impacts of using distributed generations (DG) in PDN, equipping the water pumps to back-up generators, and the value of loads accessibility to water on the system resilience are investigated.

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“…Las interrupciones de energía en largos periodos de tiempo, provocan afectaciones en las actividades económicas de la sociedad [11], por el alto nivel de dependencia energética. Esto se evidencia por varias catástrofes que ocurrieron en la última década.…”

Section: Reseña Históricaunclassified

Modelo resiliente de redes de distribución considerando flujos de potencia basado en teoría de grafos

López

Pavón

2019

I+D Tecnológico

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El concepto de resiliencia en redes de distribución es un tema emergente como una solución factible para afrontar condiciones climáticas extremas, debido a su naturaleza estocástica y su alto grado de afectación a la estructura topológica de la red, como factores claves dentro de la estrategia de desarrollo de energía sostenible. Por lo tanto, este documento ofrece un enrutamiento y despliegue resiliente de la red de distribución, basado en un modelo matemático meta-heurístico, para el óptimo emplazamiento de los trasformadores y, mínimum spanning tree (MST) basado en la variante del algoritmo PRIM para encontrar el óptimo enrutamiento del sistema de distribución antes, durante y después de un evento catastrófico extremo, minimizando la distancia asociada a cada vano como función objetivo. Cabe destacar que el modelo se probó en un escenario real georreferenciado con cargas estáticas usando el sistema algebraico MATLAB. Para solventar la factibilidad eléctrica el flujo de potencia se simuló con el software Cymdist, tomando en cuenta las distancias de las cargas.

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Power distribution system improvement planning under hurricanes based on a new resilience index

Cited by 93 publications

References 27 publications

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

Enhancing Integrated Power and Water Distribution Networks Seismic Resilience Leveraging Microgrids

Modelo resiliente de redes de distribución considerando flujos de potencia basado en teoría de grafos

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