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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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.

Original language	English
Title of host publication	2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509028757
DOIs	https://doi.org/10.1109/TDC-LA.2016.7805634
State	Published - 04 Jan 2017
Externally published	Yes
Event	2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016 - Morelia, Mexico Duration: 20 Sep 2016 → 24 Sep 2016

Publication series

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

Conference

Conference	2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016
Country/Territory	Mexico
City	Morelia
Period	20/09/16 → 24/09/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Robust optimization
Scenario reduction
Transmission planning
Uncertainty
Wind generation

Access to Document

10.1109/TDC-LA.2016.7805634

Cite this

Correa-Florez, C. A., Salcedo, A. S., & Marulanda, G. (2017). Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration. In 2016 IEEE PES Transmission and Distribution Conference and Exposition-Latin America, PES T and D-LA 2016 Article 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",

month = jan,

day = "4",

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",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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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. in 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, Mexico, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

ER -

Correa-Florez CA, Salcedo AS, Marulanda G. Reduced scenario methodology for treating uncertainty in transmission expansion with large wind power penetration. In 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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