Distribution systems resilience enhancement via pre‐ and post‐event actions

Abstract

Recently, resilience studies have become an indispensable tool for sustainable operation of energy infrastructure. In line with the need, this study presents a mathematical model to enhance resilience level of power distribution systems against natural disasters. The model is designed as a three-stage algorithm according to system operators' actions. The first stage schedules pre-event actions. At this stage, forecasts about the approaching disaster as well as fragility curves of system components are used to identify failure probability of system components. The failure probabilities are used to trip out the lines as much as possible to defensively operate the distribution network, and advantages of alternatives such as distributed energy resources and normally-open switches are taken to serve critical loads. The second stage is to monitor system operating conditions during the event and identify the status of system components. The third stage mainly focuses on scheduling postevent actions. At this stage, based on real data about different elements of the network, available alternatives are taken to restore as much critical load as possible. To evaluate performance of the model, it is applied to a distribution test system and the results are discussed in detail.

A review of power system predictive failure model for resilience enhancement against hurricane events

“…Hence, this strategy is not appropriate for a realtime outage forecast as the huge time cost limits its application [23,32]. In addition, the MCS has been performed in several studies such as [7,21,22,33,34] with no consensus on the proper stopping criteria that can guarantee precise accurate prediction results. However, one of the ways to proactively enhance the distribution power system resilience against hurricane events is to develop a pre-disaster weather-related artificial intelligence statistical probabilistic system line outage predictive model.…”

A machine learning approach for real‐time selection of preventive actions improving power network resilience

“…The decision whether to apply a preventive action, and, if several alternative actions are available, which one to apply, is a non-trivial classification problem, and must consider the stochastic nature of extreme events. Established resilience enhancement frameworks usually include an optimisation problem for a resilience metric, for example, the energy not supplied [8,10,11]. However, calculating these metrics and solving the optimisation problem is time-consuming, and often involves Monte Carlo (MC) simulations.…”

“…Preventive actions, such as topology switching, islanding, generator re-dispatching, and deployment of storage systems or mobile substations have been shown to efficiently prevent the uncontrolled propagation of cascading failures and to increase power network resilience to extreme events [2,[6][7][8]. Compared to physical network reinforcements, preventive actions are entirely operational, build on the intrinsic capabilities of a modern smart grid, make use of the availability of decentralised energy systems and distributed generation, and do not require investments in bulk infrastructure [9].…”

Tie‐line planning for resilience enhancement in unbalanced distribution networks

“…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.…”