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

Carlos Adrian Correa Florez 1, * Alejandro Sanchez Salcedo 2 Geovanny Marulanda 2
* Auteur correspondant
1 PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques
2 Department of Electrical Engineering
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.
Keywords : Robust optimization Scenario reduction Transmission planning Uncertainty Wind generation
Type de document :
Communication dans un congrès
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01506667
Contributeur : Magalie Prudon <>
Soumis le : mercredi 12 avril 2017 - 10:53:12
Dernière modification le : lundi 12 novembre 2018 - 11:00:02

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Carlos Adrian Correa Florez, Alejandro Sanchez Salcedo, Geovanny Marulanda. Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration. 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016, Institute of Electrical and Electronics Engineers Inc., Sep 2016, Morelia, Mexico. pp.Article number 7805634 - ISBN 978-150902875-7, ⟨10.1109/TDC-LA.2016.7805634⟩. ⟨hal-01506667⟩

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