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

Author

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  • Cristian Camilo Marín-Cano

    (Research Group in Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Calle 67 No. 53-108, 050010 Medellín, Colombia)

  • Juan Esteban Sierra-Aguilar

    (Research Group in Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Calle 67 No. 53-108, 050010 Medellín, Colombia)

  • Jesús M. López-Lezama

    (Research Group in Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Calle 67 No. 53-108, 050010 Medellín, Colombia)

  • Álvaro Jaramillo-Duque

    (Research Group in Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Calle 67 No. 53-108, 050010 Medellín, Colombia)

  • Juan G. Villegas

    (ALIADO—Analytics and Research for Decision Making, Department of Industrial Engineering, Universidad de Antioquia, Calle 67 No. 53-108, 050010 Medellín, Colombia)

Abstract

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.

Suggested Citation

  • Cristian Camilo Marín-Cano & Juan Esteban Sierra-Aguilar & Jesús M. López-Lezama & Álvaro Jaramillo-Duque & Juan G. Villegas, 2020. "A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem," Energies, MDPI, vol. 13(15), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3777-:d:388479
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    References listed on IDEAS

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    1. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    2. W. Ackooij & I. Danti Lopez & A. Frangioni & F. Lacalandra & M. Tahanan, 2018. "Large-scale unit commitment under uncertainty: an updated literature survey," Annals of Operations Research, Springer, vol. 271(1), pages 11-85, December.
    3. Jean-Paul Watson & David Woodruff, 2011. "Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems," Computational Management Science, Springer, vol. 8(4), pages 355-370, November.
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    2. Leprince, Julien & Schledorn, Amos & Guericke, Daniela & Dominkovic, Dominik Franjo & Madsen, Henrik & Zeiler, Wim, 2023. "Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities," Applied Energy, Elsevier, vol. 348(C).

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