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A Long-Term Evaluation on Transmission Line Expansion Planning with Multistage Stochastic Programming

Author

Listed:
  • Sini Han

    (Department of Electrical Engineering, Konkuk University, Seoul 05029, Korea)

  • Hyeon-Jin Kim

    (Department of Electrical Engineering, Konkuk University, Seoul 05029, Korea)

  • Duehee Lee

    (Department of Electrical Engineering, Konkuk University, Seoul 05029, Korea)

Abstract

The purpose of this paper is to apply multistage stochastic programming to the transmission line expansion planning problem, especially when uncertain demand scenarios exist. Since the problem of transmission line expansion planning requires an intensive computational load, dual decomposition is used to decompose the problem into smaller problems. Following this, progressive hedging and proximal bundle methods are used to restore the decomposed solutions to the original problems. Mixed-integer linear programming is involved in the problem to decide where new transmission lines should be constructed or reinforced. However, integer variables in multistage stochastic programming (MSSP) are intractable since integer variables are not restored. Therefore, the branch-and-bound algorithm is applied to multistage stochastic programming methods to force convergence of integer variables.In addition, this paper suggests combining progressive hedging and dual decomposition in stochastic integer programming by sharing penalty parameters. The simulation results tested on the IEEE 30-bus system verify that our combined model sped up the computation and achieved higher accuracy by achieving the minimised cost.

Suggested Citation

  • Sini Han & Hyeon-Jin Kim & Duehee Lee, 2020. "A Long-Term Evaluation on Transmission Line Expansion Planning with Multistage Stochastic Programming," Energies, MDPI, vol. 13(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1899-:d:345035
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    References listed on IDEAS

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    5. 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.
    6. Francisco Munoz & Jean-Paul Watson, 2015. "A scalable solution framework for stochastic transmission and generation planning problems," Computational Management Science, Springer, vol. 12(4), pages 491-518, October.
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    Cited by:

    1. Yilin Xie & Ying Xu, 2022. "Transmission Expansion Planning Considering Wind Power and Load Uncertainties," Energies, MDPI, vol. 15(19), pages 1-18, September.
    2. Gengli Song & Hua Wei, 2022. "Distributionally Robust Multi-Energy Dynamic Optimal Power Flow Considering Water Spillage with Wasserstein Metric," Energies, MDPI, vol. 15(11), pages 1-18, May.
    3. Hamdi Abdi & Mansour Moradi & Sara Lumbreras, 2021. "Metaheuristics and Transmission Expansion Planning: A Comparative Case Study," Energies, MDPI, vol. 14(12), pages 1-23, June.
    4. Alexander Vinogradov & Vadim Bolshev & Alina Vinogradova & Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Radomir Goňo & Elżbieta Jasińska, 2020. "Analysis of the Power Supply Restoration Time after Failures in Power Transmission Lines," Energies, MDPI, vol. 13(11), pages 1-18, May.

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