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Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors

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  • Victor H. Hinojosa

    (Department of Electrical Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile)

  • Joaquín Sepúlveda

    (Department of Electrical Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile)

Abstract

In this study, we successfully develop the transmission planning problem of large-scale power systems based on generalized shift-factors. These distribution factors produce a reduced solution space which does not need the voltage bus angles to model new transmission investments. The introduced formulation copes with the stochastic generation and transmission capacity expansion planning problem modeling the operational problem using a 24-hourly load behaviour. Results show that this formulation achieves an important reduction of decision variables and constraints in comparison with the classical disjunctive transmission planning methodology known as the Big M formulation without sacrificing optimality. We test both the introduced and the Big M formulations to find out convergence and time performance using a commercial solver. Finally, several test power systems and extensive computational experiments are conducted to assess the capacity planning methodology. Solving deterministic and stochastic problems, we demonstrate a prominent reduction in the solver simulation time especially with large-scale power systems.

Suggested Citation

  • Victor H. Hinojosa & Joaquín Sepúlveda, 2020. "Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors," Energies, MDPI, vol. 13(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3327-:d:378608
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    References listed on IDEAS

    as
    1. Zipeng Liang & Haoyong Chen & Xiaojuan Wang & Idris Ibn Idris & Bifei Tan & Cong Zhang, 2018. "An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty," Energies, MDPI, vol. 11(8), pages 1-22, August.
    2. Álvaro García-Cerezo & Luis Baringo & Raquel García-Bertrand, 2020. "Representative Days for Expansion Decisions in Power Systems," Energies, MDPI, vol. 13(2), pages 1-18, January.
    3. Victor H. Hinojosa & Francisco Gonzalez-Longatt, 2018. "Preventive Security-Constrained DCOPF Formulation Using Power Transmission Distribution Factors and Line Outage Distribution Factors," Energies, MDPI, vol. 11(6), pages 1-13, June.
    4. Victor H. Hinojosa, 2020. "Comparing Corrective and Preventive Security-Constrained DCOPF Problems Using Linear Shift-Factors," Energies, MDPI, vol. 13(3), pages 1-16, January.
    5. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
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    Cited by:

    1. Seyed Hamed Jalalzad Mahvizani & Hossein Yektamoghadam & Rouzbeh Haghighi & Majid Dehghani & Amirhossein Nikoofard & Mahdi Khosravy & Tomonobu Senjyu, 2022. "A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy," Energies, MDPI, vol. 15(3), pages 1-16, February.

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