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Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Sep 2016 France Publisher:IEEEJournal:2016 IEEE PES Transmission & Distribution Conference and Exposition-Latin America (PES T&D-LA)

Authors: Correa Florez , Carlos Adrian; Sanchez Salcedo, Alejandro; Marulanda, Geovanny;

doi: 10.1109/tdc-la.2016.7805634

Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration

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Abstract

This paper presents an algorithm for solving the Transmission Expansion Planning (TEP) problem when large scale wind generation is considered. Variability of wind speed and demand uncertainty are taken into account. The formulation includes the DC model of the network, and the obtained expansion plans minimize the investment, the load shedding and the wind generation curtailment. The mathematical model includes uncertainties by means of an extreme scenario methodology that maps the uncertainty set. The Chu-Beasley Genetic Algorithm (CBGA) is used for finding feasible optimal expansion plans that cope with the uncertainties in load forecasting and also to maximize wind power injection. The proposed algorithm is validated on the 6-bus Garver system, IEEE 24-bus RTS test system and the real life South-Brazilian 46-bus system. Comparison with other methods is carried out to demonstrate the performance of the proposed approach.

Country

France

Related Organizations

Mines ParisTech
France
University of La Salle
Colombia
University of La Salle
Colombia
Mines ParisTech
France
French Institute for Research in Computer Science and Automation
France

Keywords

[ SPI.ENERG ] Engineering Sciences [physics]/domain_spi.energ, Uncertainty, Transmission planning, [SPI.ENERG]Engineering Sciences [physics]/domain_spi.energ, Robust optimization, Scenario reduction, Wind generation

2 Research products, page 1 of 1

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