Multiobjective Multiscenario Framework for RCS Placement in Unbalanced Distribution Systems Considering Uncertainty

Gangwar, Priyanka; Kesherwani, Saurabh; Chakrabarti, Saikat; Singh, Santokh

doi:10.1109/jsyst.2021.3077594

Cited by 7 publications

(1 citation statement)

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“…The problem of optimal placement of sectionalizers has been taken into account considering the cost of electricity for customers, the initial investment cost of sectionalizers, installation cost, and annual maintenance and operation costs of sectionalizers. The mentioned objective function beside total number of reenergized loads and power losses have been considered as a multi objective optimization problem in [11]. In [12], a method for finding the fault zone has been presented.…”

Section: Introductionmentioning

confidence: 99%

Optimal placement of fault indicator and remote‐controlled switches for predetermined reliability of selected buses

Gholami,

Ahmadi,

Pouriani

2023

IET Generation Trans & Dist

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Fault indicators (FI) and remotely controlled switches (RCS) can reduce the power outage time in distribution network (DN) by finding and isolating the faulty area. Therefore, they play an important role in reducing the power outage cost and increasing the reliability of the DN. In DN, there may be high-priority loads. One of the goals of improving and updating the DN is to increase the reliability of all network buses, especially buses feeding such loads. Here, the placement of FI and RCS, as the distribution system automation devices, is designed and modelled to increase the reliability of the buses supplying high-priority loads. For the placement problem, two goals of cost and reliability have been considered. But the problem is not defined as a multi-objective optimization. Rather, it is modelled as a one-objective optimization (i.e. network cost) and constrained to a certain level of reliability in predetermined locations and buses. The IEEE-33bus network is used as the sample network and different scenarios are simulated. The number of FI and RCS and the optimization cost of simulated scenarios are compared to evaluate the efficiency of the proposed model. The obtained results show the adequacy of the modelling.

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Section: Introductionmentioning

confidence: 99%