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Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

Author

Listed:
  • Wenlei Bai

    (Energy Management System, ABB Enterprise Software, Sugar Land, TX 77478, USA)

  • Duehee Lee

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

  • Kwang Y. Lee

    (Department of Electrical & Computer Engineering , Baylor University, Waco, TX 76798, USA)

Abstract

The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM)—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC) algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2138-:d:123037
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    References listed on IDEAS

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    Cited by:

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    6. Hui Tang & Yulong Lei & Xingzhong Li, 2019. "An Acoustic Source Model for Applications in Low Mach Number Turbulent Flows, Such as a Large-Scale Wind Turbine Blade," Energies, MDPI, vol. 12(23), pages 1-18, December.
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    10. 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.

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