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Probabilistic Approach to Optimizing Active and Reactive Power Flow in Wind Farms Considering Wake Effects

Author

Listed:
  • Jae-Kun Lyu

    (Wind Energy Grid-Adaptive Technology Research Center, Chonbuk National University, Jeonju 561-756, Korea)

  • Jae-Haeng Heo

    (Wind Energy Grid-Adaptive Technology Research Center, Chonbuk National University, Jeonju 561-756, Korea)

  • Jong-Keun Park

    (Department of Electrical Engineering & Computer Science, Seoul National University, Gwanak-ro 599, Gwanak-gu, Seoul 151-744, Korea)

  • Yong-Cheol Kang

    (Wind Energy Grid-Adaptive Technology Research Center, Chonbuk National University, Jeonju 561-756, Korea)

Abstract

This paper presents a novel probabilistic optimization algorithm for simultaneous active and reactive power dispatch in power systems with significant wind power integration. Two types of load and wind-speed uncertainties have been assumed that follow normal and Weibull distributions, respectively. A PV bus model for wind turbines and the wake effect for correlated wind speed are used to achieve accurate AC power flow analysis. The power dispatch algorithm for a wind-power integrated system is modeled as a probabilistic optimal power flow (P-OPF) problem, which is operated through fixed power factor control to supply reactive power. The proposed P-OPF framework also considers emission information, which clearly reflects the impact of the energy source on the environment. The P-OPF was tested on a modified IEEE 118-bus system with two wind farms. The results show that the proposed technique provides better system operation performance evaluation, which is helpful in making decisions about power system optimal dispatch under conditions of uncertainty.

Suggested Citation

  • Jae-Kun Lyu & Jae-Haeng Heo & Jong-Keun Park & Yong-Cheol Kang, 2013. "Probabilistic Approach to Optimizing Active and Reactive Power Flow in Wind Farms Considering Wake Effects," Energies, MDPI, vol. 6(11), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:11:p:5717-5737:d:30063
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    References listed on IDEAS

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    1. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
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    Cited by:

    1. Shuli Wen & Hai Lan & Qiang Fu & David C. Yu & Ying-Yi Hong & Peng Cheng, 2017. "Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms," Energies, MDPI, vol. 10(5), pages 1-16, May.
    2. Walter M. Villa-Acevedo & Jesús M. López-Lezama & Jaime A. Valencia-Velásquez, 2018. "A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem," Energies, MDPI, vol. 11(9), pages 1-23, September.
    3. Gracita Batista Rosas & Elizete Maria Lourenço & Djalma Mosqueira Falcão & Thelma Solange Piazza Fernandes, 2019. "An Expeditious Methodology to Assess the Effects of Intermittent Generation on Power Systems," Energies, MDPI, vol. 12(6), pages 1-18, March.
    4. Yue Chen & Zhizhong Guo & Hongbo Li & Yi Yang & Abebe Tilahun Tadie & Guizhong Wang & Yingwei Hou, 2020. "Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    5. Asier González-González & Ismael Etxeberria-Agiriano & Ekaitz Zulueta & Fernando Oterino-Echavarri & Jose Manuel Lopez-Guede, 2014. "Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms," Energies, MDPI, vol. 7(6), pages 1-17, June.

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