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Comparing Corrective and Preventive Security-Constrained DCOPF Problems Using Linear Shift-Factors

Author

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  • Victor H. Hinojosa

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

Abstract

This study compares two efficient formulations to solve corrective as well as preventive security-constrained (SC) DC-based optimal power flow (OPF) problems using linear sensitivity factors without sacrificing optimality. Both SCOPF problems are modelled using two frameworks based on these distribution factors. The main advantage of the accomplished formulation is the significant reduction of decision variables and—equality and inequality—constraints in comparison with the traditional DC-based SCOPF formulation. Several test power systems and extensive computational experiments are conducted using a commercial solver to clearly demonstrate the feasibility to carry out the corrective and the preventive SCOPF problems with a reduced solution space. Another point worth noting is the lower simulation time achieved by the introduced methodology. Additionally, this study presents advantages and disadvantages for the proposed shift-factor formulation solving both corrective and preventive formulations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:516-:d:311320
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    References listed on IDEAS

    as
    1. Kyungsung An & Kyung-Bin Song & Kyeon Hur, 2017. "Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration," Energies, MDPI, vol. 10(5), pages 1-15, May.
    2. Guillermo Gutierrez-Alcaraz & Victor H. Hinojosa, 2018. "Using Generalized Generation Distribution Factors in a MILP Model to Solve the Transmission-Constrained Unit Commitment Problem," Energies, MDPI, vol. 11(9), pages 1-17, August.
    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. Xi Wu & Zhengyu Zhou & Gang Liu & Wanchun Qi & Zhenjian Xie, 2017. "Preventive Security-Constrained Optimal Power Flow Considering UPFC Control Modes," Energies, MDPI, vol. 10(8), pages 1-15, August.
    5. Wenlei Bai & Duehee Lee & Kwang Y. Lee, 2017. "Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model," Energies, MDPI, vol. 10(12), pages 1-19, December.
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    Cited by:

    1. Diego Larrahondo & Ricardo Moreno & Harold R. Chamorro & Francisco Gonzalez-Longatt, 2021. "Comparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Power," Energies, MDPI, vol. 14(15), pages 1-15, July.
    2. 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.
    3. Maria Dicorato & Michele Trovato & Chiara Vergine & Corrado Gadaleta & Benedetto Aluisio & Giuseppe Forte, 2020. "Extended Flow-Based Security Assessment for Real-Sized Transmission Network Planning," Energies, MDPI, vol. 13(13), pages 1-19, July.

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