Multi-objective model for simultaneous distribution networks reconfiguration and allocation of D-STATCOM under uncertainties of RESs

Safari, Amin; Karimi, Masoumeh; Najmi, Parisa Hosseinpour; Farrokhifar, Meisam

doi:10.1080/01430750.2020.1751282

Cited by 7 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to which researches are accelerating towards the development to improve the entrance of renewable resources of energy that can displace fossil fuel. Now renewable energy technologies develop big belief in industries and academics to find the solution for energy that is cheap and eco-friendly, reliable, and self-sustaining [23,24]. DG improves the reliability of the power supply to customers and reduces transmission and distribution losses.…”

Section: 2distributed Generation (Dg)mentioning

confidence: 99%

An efficient allocation of D-STATCOM and DG with network reconfiguration in distribution networks

2022

IJATEE

View full text Add to dashboard Cite

This work solves the optimal allocation problem of distribution static compensator (D-STATCOM) and distributed generation (DG) simultaneously. The network reconfiguration (NR) is also applied in this work for better utilization of the existing distribution network infrastructure. This work proposes a robust and efficient NR algorithm to address the NR problem, including DG and D-STATCOM allocation by considering techno-economic objectives. Power loss minimization is selected as an objective and a gravitational search algorithm (GSA) is used to solve this efficient allocation problem. This approach has been executed on IEEE 33 bus radial distribution system (RDS). The results demonstrate that the application of simultaneous NR, D-STATCOM, and DG allocation has resulted in a reduction in network losses as well as enhancement in the voltage profile of entire RDS. By this simultaneous approach, the power loss has reduced by 77.5% and minimum voltage in the RDShas increased from 0.9131 p.u. to 0.9735 p.u.

show abstract

Section: 2distributed Generation (Dg)mentioning

confidence: 99%