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Electrical Engineering
Erschienen in: Electrical Engineering 2/2022

15.06.2021 | Original Paper

Stochastic transmission expansion planning in the presence of wind farms considering reliability and N-1 contingency using grey wolf optimization technique

verfasst von: Ali Asghar Ghadimi, Mohammad Amani, Mohammad Bayat, Saeid Ahmadi, Mohammad Reza Miveh, Francisco Jurado

Erschienen in: Electrical Engineering | Ausgabe 2/2022

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Abstract

In this paper, a stochastic transmission expansion planning (TEP) in the presence of wind farms under uncertain load and wind source conditions when reliability and N-1 contingency is taken into consideration is proposed. The main aim of the suggested TEP is to minimize the total planning cost while satisfying techno-economic constraints. In this paper, reliability cost is also incorporated as the loss of load cost (LOLC) into the objective function in addition to the investment cost. In order to provide more accurate and real results, the N-1 contingency is also considered as a constraint in the TEP problem. Uncertainties for the wind source and loads in the TEP problem are modeled using a scenario-based approach and Monte Carlo simulation. Grey wolf optimization (GWO) is applied to solve the TEP problem, and it is compared to other optimization techniques. The simulation results are provided in various scenarios to confirm the efficiency of the suggested stochastic TEP.
Vorheriger Artikel Design and implementation of a high-efficiency low-voltage synchronous reluctance motor
Nächster Artikel Advanced modeling and mapping of severe pollution stress required for outdoor insulation coordination

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Metadaten
Titel
Stochastic transmission expansion planning in the presence of wind farms considering reliability and N-1 contingency using grey wolf optimization technique
verfasst von
Ali Asghar Ghadimi
Mohammad Amani
Mohammad Bayat
Saeid Ahmadi
Mohammad Reza Miveh
Francisco Jurado
Publikationsdatum
15.06.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Electrical Engineering / Ausgabe 2/2022
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-021-01339-w

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