Critical Load Restoration Using Distributed Energy Resources for Resilient Power Distribution System

Poudel, Shiva; Dubey, Anamika

doi:10.1109/tpwrs.2018.2860256

Cited by 206 publications

(116 citation statements)

References 20 publications

Supporting

Mentioning

116

Contrasting

Order By: Relevance

“…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%

“…The effect of a smart network (improved response and automated restoration) is taken into consideration when evaluating the loss function if such measures are available. To do so, the restoration scenarios are embedded using the dedicated algorithms developed in authors prior work [10]. Specifically, the restoration problem is formulated as a mixed-integer linear program (MILP) to maximize the load restored with the help of all available feeder and DGs with intentional island formation [10].…”

Section: A Probabilistic Function For System Performance Lossmentioning

confidence: 99%

See 1 more Smart Citation

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

Poudel

Dubey

Bose

2020

IEEE Systems Journal

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Model the Impacts Of Proactive Planningmentioning

confidence: 99%

Section: A Probabilistic Function For System Performance Lossmentioning

confidence: 99%

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

Poudel

Dubey

Bose

2020

IEEE Systems Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, many extreme natural disasters have caused large-scale power interruption, which led to enormous economic and social losses, and brought unprecedented challenges to power systems. 6 With the development of distributed energy resources, distribution systems have gradually become the centre of energy utilization, and modern distribution systems are becoming into the large and complex cyber-physical systems. 1 With an increase of such threats, power systems are increasingly ill-prepared for extreme disasters.…”

Section: Motivationmentioning

confidence: 99%

“…[2][3][4][5] Statistics indicate that accidents of distribution systems account for about 80% to 90% of power system blackouts. 6 With the development of distributed energy resources, distribution systems have gradually become the centre of energy utilization, and modern distribution systems are becoming into the large and complex cyber-physical systems. Therefore, the resilience of distribution systems becomes more and more urgent and crucial.…”

Section: Motivationmentioning

confidence: 99%

A novel planning‐attack‐reconfiguration method for enhancing resilience of distribution systems considering the whole process of resiliency

Wang

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary To enhance the resilience of distribution systems and fight against extreme disasters, a novel planning‐attack‐reconfiguration optimization method is proposed in this paper. Firstly, according to the processes of prevention, defence, and restoration for a resilient distribution system through disruption, the novel resilience evaluation indicators are presented, which include the node degree of distributed generation (DG) bus, survival rate, and recovery ability. Secondly, a novel planning‐attack‐reconfiguration optimization model is developed to improve the resilience of distribution systems. In DG planning stage, the multi‐objective planning model is formulated, which includes the minimization of the total cost of investment and operation, and the maximization of the node degree of DG buses for critical loads. In the attack stage, a clear worst case of N‐k contingencies on the basis of generalized nodes is presented to reduce the computational complexity. Then, the post‐disaster network reconfiguration model is formulated to maximize the restoration rate of critical loads (RRCL). Finally, the proposed method is illustrated by the case study on PG&E 69‐bus distribution system. The simulation results indicate that all the RRCL can reach about 90% in the four multipoint fault scenarios. Meanwhile, other evaluation indicators are greatly improved. It is shown that the resilience of distribution systems can be dramatically enhanced by the proposed method.

show abstract

“…In Reference , an improved feeder restoration method is presented for the restoration of critical loads using distributed energy resources (DERs). The proposed restoration resilient approach is maximized the recovered critical loads and optimized recovery times by optimizing the allocation of the available DER network resources.…”

Section: Introductionmentioning

confidence: 99%

Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Choopani

Hedayati

Effatnejad

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Self‐healing refers to the specific ability of the smart grid that takes precautionary actions before the fault. It also performs fault location, isolation, and service restoration to minimize network damage after the fault. This process is performed with the help of communications infrastructure and remote control. The main components of this article include the use of graph theory and binary particle swarm optimization algorithm for optimal switching, the use of particle swarm optimization algorithm and forward‐backward sweep load flow for load shedding and distributed generation rescheduling, investigating the effect of self‐healing on improved reliability, power losses reduction, and load shedding reduction and the present a new approach for self‐healing optimization and a new mathematical model. The problem of self‐healing due to nonlinear and unbounded constraints is one of the nonconvex mixed integer nonlinear programming optimization problems. The optimization problem becomes a convex mixed integer linear programming optimization problem using the proposed scheme. Due to the nonlinearity and the nonconvexity of the problem, commercial solvers are not able to solve this problem. Even the Baron solver, which is used to solve nonconvex nonlinear problems, cannot achieve the optimal global solution. However, the proposed method is easily able to achieve the optimal solution by eliminating the nonlinear and nonconvex element. A modified RBTS‐4 distribution system is used as a test system. The IEEE 33‐bus distribution system is used to validate the proposed method. The results of case studies demonstrate the effectiveness of the proposed methodology.

show abstract

Critical Load Restoration Using Distributed Energy Resources for Resilient Power Distribution System

Cited by 206 publications

References 20 publications

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

Risk-Based Probabilistic Quantification of Power Distribution System Operational Resilience

A novel planning‐attack‐reconfiguration method for enhancing resilience of distribution systems considering the whole process of resiliency

Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Contact Info

Product

Resources

About