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Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves

Author

Listed:
  • Walter Gil-González

    (Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, km 1 vía Turbaco, Cartagena 131001, Colombia)

  • Oscar Danilo Montoya

    (Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, km 1 vía Turbaco, Cartagena 131001, Colombia
    Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Carrera 7 No. 40B-53, Bogotá D.C. 11021, Colombia)

  • Luis Fernando Grisales-Noreña

    (Grupo GIIEN, Facultad de Ingeniería, Institución Universitaria Pascual Bravo, Campus Robledo, Medellín 050036, Colombia)

  • Alberto-Jesus Perea-Moreno

    (Departamento de Física Aplicada, Universidad de Córdoba, ceiA3, Campus de Rabanales, 14071 Córdoba, Spain)

  • Quetzalcoatl Hernandez-Escobedo

    (Escuela Nacional de Estudios Superiores, Campus Juriquilla, UNAM, Queretaro 3001, Mexico)

Abstract

This paper presents an optimization model for the optimal placement and sizing of wind turbines, considering their reactive power capacity, wind speed, and demand curves. The optimization model is nonlinear and is focused on minimizing power losses in AC distribution networks. Also, paired wind turbine and power conversion systems are treated via chargeability factor η at the peak hour. This factor represents the percentage of usage of the power conversion system in the nominal wind speed conditions, and allows to support reactive power dynamically during all periods of the day as a function of the distribution system requirements. In addition, an artificial neural network is used for short-term forecasting to deal with uncertainties in wind power generation. We assume that the number of wind power distributed generators could be from zero to three generators integrated into the system, considering unit power factors and reactive power injections to follow up the effect of reactive power compensation in the daily operation. The General Algebraic Modeling System (GAMS) is employed to solve the proposed optimization model.

Suggested Citation

  • Walter Gil-González & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Alberto-Jesus Perea-Moreno & Quetzalcoatl Hernandez-Escobedo, 2020. "Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves," Sustainability, MDPI, vol. 12(7), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2983-:d:343006
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    References listed on IDEAS

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    Cited by:

    1. David Steveen Guzmán-Romero & Brandon Cortés-Caicedo & Oscar Danilo Montoya, 2023. "Development of a MATLAB-GAMS Framework for Solving the Problem Regarding the Optimal Location and Sizing of PV Sources in Distribution Networks," Resources, MDPI, vol. 12(3), pages 1-19, March.
    2. Oscar Danilo Montoya & Walter Gil-González & Edwin Rivas-Trujillo, 2020. "Optimal Location-Reallocation of Battery Energy Storage Systems in DC Microgrids," Energies, MDPI, vol. 13(9), pages 1-20, May.
    3. Andrés Felipe Buitrago-Velandia & Oscar Danilo Montoya & Walter Gil-González, 2021. "Dynamic Reactive Power Compensation in Power Systems through the Optimal Siting and Sizing of Photovoltaic Sources," Resources, MDPI, vol. 10(5), pages 1-17, May.
    4. Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Alberto-Jesus Perea-Moreno, 2021. "Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    5. Jianfeng Dai & Cangbi Ding & Xia Zhou & Yi Tang, 2022. "Adaptive Frequency Control Strategy for PMSG-Based Wind Power Plant Considering Releasable Reserve Power," Sustainability, MDPI, vol. 14(3), pages 1-17, January.
    6. Brandon Cortés-Caicedo & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya, 2022. "Optimal Selection of Conductor Sizes in Three-Phase Asymmetric Distribution Networks Considering Optimal Phase-Balancing: An Application of the Salp Swarm Algorithm," Mathematics, MDPI, vol. 10(18), pages 1-34, September.
    7. Adel F. Alrasheedi & Ahmad M. Alshamrani & Khalid A. Alnowibet, 2023. "Investing in Wind Energy Using Bi-Level Linear Fractional Programming," Energies, MDPI, vol. 16(13), pages 1-14, June.

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