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Open AccessArticle

A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

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Research Group in Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Calle 67 No. 53-108, 050010 Medellín, Colombia
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ALIADO—Analytics and Research for Decision Making, Department of Industrial Engineering, Universidad de Antioquia, Calle 67 No. 53-108, 050010 Medellín, Colombia
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Author to whom correspondence should be addressed.
Energies 2020, 13(15), 3777; https://doi.org/10.3390/en13153777
Received: 21 May 2020 / Revised: 24 June 2020 / Accepted: 16 July 2020 / Published: 23 July 2020
(This article belongs to the Section Electrical Power and Energy System)
The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraints N 1 that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied. View Full-Text
Keywords: power system optimization; Security-Constraint Unit Commitment; progressive hedging algorithm power system optimization; Security-Constraint Unit Commitment; progressive hedging algorithm
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MDPI and ACS Style

Marín-Cano, C.C.; Sierra-Aguilar, J.E.; López-Lezama, J.M.; Jaramillo-Duque, Á.; Villegas, J.G. A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem. Energies 2020, 13, 3777. https://doi.org/10.3390/en13153777

AMA Style

Marín-Cano CC, Sierra-Aguilar JE, López-Lezama JM, Jaramillo-Duque Á, Villegas JG. A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem. Energies. 2020; 13(15):3777. https://doi.org/10.3390/en13153777

Chicago/Turabian Style

Marín-Cano, Cristian C.; Sierra-Aguilar, Juan E.; López-Lezama, Jesús M.; Jaramillo-Duque, Álvaro; Villegas, Juan G. 2020. "A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem" Energies 13, no. 15: 3777. https://doi.org/10.3390/en13153777

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