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

Carlos Adrian Correa-Florez; Alejandro Sanchez Salcedo; Geovanny Marulanda

doi:10.1109/TDC-LA.2016.7805634

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

Carlos Adrian Correa-Florez
, Alejandro Sanchez Salcedo
, Geovanny Marulanda

École des mines Paris
Universidad de La Salle, Bogota

Producción: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781509028757
DOI	https://doi.org/10.1109/TDC-LA.2016.7805634
Estado	Publicada - 04 ene 2017
Publicado de forma externa	Sí
Evento	2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016 - Morelia, México Duración: 20 sept 2016 → 24 sept 2016

Serie de la publicación

Nombre	2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016

Conferencia

Conferencia	2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016
País/Territorio	México
Ciudad	Morelia
Período	20/09/16 → 24/09/16

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

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10.1109/TDC-LA.2016.7805634

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Correa-Florez, C. A., Salcedo, A. S., & Marulanda, G. (2017). Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration. En 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016 Artículo 7805634 (2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TDC-LA.2016.7805634

Correa-Florez, Carlos Adrian ; Salcedo, Alejandro Sanchez ; Marulanda, Geovanny. / 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., 2017. (2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016).

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title = "Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration",

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",

author = "Correa-Florez, \{Carlos Adrian\} and Salcedo, \{Alejandro Sanchez\} and Geovanny Marulanda",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016 ; Conference date: 20-09-2016 Through 24-09-2016",

year = "2017",

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doi = "10.1109/TDC-LA.2016.7805634",

language = "English",

series = "2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016",

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Correa-Florez, CA, Salcedo, AS & Marulanda, G 2017, Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration. En 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016., 7805634, 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016, Morelia, México, 20/09/16. https://doi.org/10.1109/TDC-LA.2016.7805634

Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration. / Correa-Florez, Carlos Adrian; Salcedo, Alejandro Sanchez; Marulanda, Geovanny.
2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7805634 (2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016).

Producción: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration

AU - Correa-Florez, Carlos Adrian

AU - Salcedo, Alejandro Sanchez

AU - Marulanda, Geovanny

PY - 2017/1/4

Y1 - 2017/1/4

N2 - 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.

AB - 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.

KW - Robust optimization

KW - Scenario reduction

KW - Transmission planning

KW - Uncertainty

KW - Wind generation

UR - https://www.scopus.com/pages/publications/85016225312

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DO - 10.1109/TDC-LA.2016.7805634

M3 - Conference contribution

AN - SCOPUS:85016225312

T3 - 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016

BT - 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016

Y2 - 20 September 2016 through 24 September 2016

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Correa-Florez CA, Salcedo AS, Marulanda G. Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration. En 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7805634. (2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016). 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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