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Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

Author

Listed:
  • Julio César Cuenca Tinitana

    (Department of Electromechanical Engineering, Universidad Nacional de Loja, Loja 110150, Ecuador
    These authors contributed equally to this work.)

  • Carlos Adrian Correa-Florez

    (Department of Electronic Engineering, Pontificia Universidad Javeriana, Bogotá 10231, Colombia
    These authors contributed equally to this work.)

  • Diego Patino

    (Department of Electronic Engineering, Pontificia Universidad Javeriana, Bogotá 10231, Colombia
    These authors contributed equally to this work.)

  • José Vuelvas

    (Department of Electronic Engineering, Pontificia Universidad Javeriana, Bogotá 10231, Colombia
    These authors contributed equally to this work.)

Abstract

The integration of renewable generation adds complexity to the operation of the power system due to its unpredictable characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal kriging and analog approaches are used to forecast wind power generation and used as the input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal kriging captures the spatial and temporal information available in the database to improve wind forecasts. We evaluate the performance of using the spatio-temporal kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results demonstrate that the use of kriging based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

Suggested Citation

  • Julio César Cuenca Tinitana & Carlos Adrian Correa-Florez & Diego Patino & José Vuelvas, 2020. "Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty," Energies, MDPI, vol. 13(23), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6419-:d:457002
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    References listed on IDEAS

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    2. Mohammed Alzubaidi & Kazi N. Hasan & Lasantha Meegahapola & Mir Toufikur Rahman, 2021. "Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.

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