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Multi-Time Scale Coordinated Scheduling Strategy with Distributed Power Flow Controllers for Minimizing Wind Power Spillage

Author

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  • Yi Tang

    (Jiangsu Provincial Key Laboratory of Smart Grid Technology & Equipment, Southeast University, Nanjing 210096, Jiangsu, China)

  • Yuqian Liu

    (Jiangsu Provincial Key Laboratory of Smart Grid Technology & Equipment, Southeast University, Nanjing 210096, Jiangsu, China)

  • Jia Ning

    (Jiangsu Provincial Key Laboratory of Smart Grid Technology & Equipment, Southeast University, Nanjing 210096, Jiangsu, China)

  • Jingbo Zhao

    (Jiangsu Electric Power Company Research Institute, Nanjing 211103, Jiangsu, China)

Abstract

The inherent variability and randomness of large-scale wind power integration have brought great challenges to power flow control and dispatch. The distributed power flow controller (DPFC) has the higher flexibility and capacity in power flow control in the system with wind generation. This paper proposes a multi-time scale coordinated scheduling model with DPFC to minimize wind power spillage. Configuration of DPFCs is initially determined by stochastic method. Afterward, two sequential procedures containing day-head and real-time scales are applied for determining maximum schedulable wind sources, optimal outputs of generating units and operation setting of DPFCs. The generating plan is obtained initially in day-ahead scheduling stage and modified in real-time scheduling model, while considering the uncertainty of wind power and fast operation of DPFC. Numerical simulation results in IEEE-RTS79 system illustrate that wind power is maximum scheduled with the optimal deployment and operation of DPFC, which confirms the applicability and effectiveness of the proposed method.

Suggested Citation

  • Yi Tang & Yuqian Liu & Jia Ning & Jingbo Zhao, 2017. "Multi-Time Scale Coordinated Scheduling Strategy with Distributed Power Flow Controllers for Minimizing Wind Power Spillage," Energies, MDPI, vol. 10(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1804-:d:118133
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    References listed on IDEAS

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    1. Yuchong Huo & Ping Jiang & Yuan Zhu & Shuang Feng & Xi Wu, 2015. "Optimal Real-Time Scheduling of Wind Integrated Power System Presented with Storage and Wind Forecast Uncertainties," Energies, MDPI, vol. 8(2), pages 1-21, February.
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    3. Shuang Rong & Zhimin Li & Weixing Li, 2015. "Investigation of the Promotion of Wind Power Consumption Using the Thermal-Electric Decoupling Techniques," Energies, MDPI, vol. 8(8), pages 1-17, August.
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    5. Shengchun Yang & Dan Zeng & Hongfa Ding & Jianguo Yao & Ke Wang & Yaping Li, 2016. "Multi-Objective Demand Response Model Considering the Probabilistic Characteristic of Price Elastic Load," Energies, MDPI, vol. 9(2), pages 1-14, January.
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    Cited by:

    1. Panos Kotsampopoulos & Pavlos Georgilakis & Dimitris T. Lagos & Vasilis Kleftakis & Nikos Hatziargyriou, 2019. "FACTS Providing Grid Services: Applications and Testing," Energies, MDPI, vol. 12(13), pages 1-23, July.
    2. Yi Lin & Wei Lin & Wei Wu & Zhenshan Zhu, 2023. "Optimal Scheduling of Power Systems with High Proportions of Renewable Energy Accounting for Operational Flexibility," Energies, MDPI, vol. 16(14), pages 1-18, July.
    3. Mengmeng Xiao & Shaorong Wang, 2018. "Coordination Control Method Suitable for Practical Engineering Applications for Distributed Power Flow Controller (DPFC)," Energies, MDPI, vol. 11(12), pages 1-15, December.

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