Toward Operational Resilience of Smart Energy Networks in Complex Infrastructures

Taheri, Babak; Jalilian, Ali; Safdarian, Amir; Moeini‐Aghtaie, Moein; Lehtonen, Matti

doi:10.1007/978-3-030-34094-0_9

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…The DSN optimization problems formulated in the MILP approach, were then solved using a CPLEX optimizer in GAMS. The DSN optimization solution proposed in this paper drew it inspiration from the pre-hurricane event DSN optimization problem formulated as the crew dispatch and transportation problem in [25], the pre-and post-event system optimization under hurricane event in [50], [51], and the prospective twostages system optimization under hurricane event in [52] respectively.…”

Section: Pre-hurricane Event Dsn Reconfiguration and Resources Planni...mentioning

confidence: 99%

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

2023

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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".

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Section: Pre-hurricane Event Dsn Reconfiguration and Resources Planni...mentioning

confidence: 99%

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

2023

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“…Resilience enhancement strategies are broadly classified as either planning‐based or operation‐based. Planning‐based methods aim to develop grid plans to harden transmission and distribution systems against future extreme events [12]. In contrast, operational‐based methods are concerned with developing optimization‐based strategies to normalize system operation during and after failures and extreme weather events [13].…”

Section: Introductionmentioning

confidence: 99%

Distribution system resilience improvement against hurricanes: optimal line hardening according to reconfiguration potentials

Pashiri,

Azad,

Ameli

2024

The Journal of Engineering

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In recent years, there has been a notable increase in the frequency and intensity of natural disasters related to global warming. Large‐scale blackouts caused by natural disasters demonstrate the extreme vulnerability of power systems to these devastations. Resilience planning is the key to preparing power systems for natural disasters by enhancing the vital infrastructure's robustness. This paper proposes a new resilience improvement framework based on resiliency assessment and optimal hardening to improve the distribution system's resiliency against hurricanes. The main goal is to find the optimal distribution system line hardening solution according to reconfiguration potentials after the hurricane to minimize the distribution system cost of energy not supplied. Fragility curves and Monte Carlo simulations are used for the distribution system resilience assessment. Poles and conductors are vulnerable components of distribution system lines; therefore, two hardening strategies have been outlined using measures like replacing old poles with new poles, upgrading pole classes, and vegetation management. This method is modelled as an optimization program considering budget limitations and load priorities and implemented by a genetic algorithm on the IEEE 33‐bus standard network. The results show that optimal line hardening according to network reconfiguration potentials significantly increased the distribution system's resilience.

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“…Also, proactively tripping out the likely vulnerable lines in advance of natural disasters has been proposed by previous studies. Therefore, the system operator can reconfigure the system and supply the customers through more reliable paths [20,21]. In [22], the authors have proposed a resilience-oriented DS reconfiguration and repair sequence optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Tie‐line planning for resilience enhancement in unbalanced distribution networks

Taheri

Safdarian

2021

IET Generation Trans & Dist

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Over the past decades, there has been a dramatic increase in the frequency of natural disasters, which are the leading causes of large-scale power outages. This paper, therefore, assesses the significance and role of optimal tie-line construction in improving the service restoration performance of unbalanced power distribution systems in the aftermath of high-impact low-probability incidents. In doing so, a restoration process aware stochastic mixed-integer linear programming model is developed to find the optimal locations for new tie-line construction in unbalanced three-phase distribution systems. In particular, the restoration process of distribution systems, including the fault isolation and system reconfiguration, is contemplated to place tie-lines in the most proper locations to enhance the manoeuvring capability of distribution systems and reducing the customer interruption time. Furthermore, the model is a stochastic one wherein uncertainty associated with potential damages alongside demand uncertainty is captured via a set of likely scenarios. To validate the effectiveness of the proposed stochastic mixed-integer linear programming model, it is tested and verified on the IEEE 13-and 123-bus test systems.

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Toward Operational Resilience of Smart Energy Networks in Complex Infrastructures

Cited by 5 publications

References 28 publications

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

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

Distribution system resilience improvement against hurricanes: optimal line hardening according to reconfiguration potentials

Tie‐line planning for resilience enhancement in unbalanced distribution networks

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