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An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty

Author

Listed:
  • Zipeng Liang

    (School of Electric Power, South China University of Technology, Guangzhou 510641, China)

  • Haoyong Chen

    (School of Electric Power, South China University of Technology, Guangzhou 510641, China)

  • Xiaojuan Wang

    (School of Electric Power, South China University of Technology, Guangzhou 510641, China)

  • Idris Ibn Idris

    (School of Electric Power, South China University of Technology, Guangzhou 510641, China)

  • Bifei Tan

    (School of Electric Power, South China University of Technology, Guangzhou 510641, China)

  • Cong Zhang

    (School of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

Abstract

The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that integrates wind power resources, and that seeks to minimize the sum of investment costs and operation costs while accounting for the costs associated with the pollution emissions of generator infrastructure. Auxiliary relaxation variables are introduced to transform the established model into a mixed integer linear programming problem. Furthermore, the novel concept of extreme wind power scenarios is defined, theoretically justified, and then employed to establish a two-stage robust TEP method. The decision-making variables of prospective transmission lines are determined in the first stage, so as to ensure that the operating variables in the second stage can adapt to wind power fluctuations. A Benders’ decomposition algorithm is developed to solve the proposed two-stage model. Finally, extensive numerical studies are conducted with Garver’s 6-bus system, a modified IEEE RTS79 system and IEEE 118-bus system, and the computational results demonstrate the effectiveness and practicability of the proposed method.

Suggested Citation

  • Zipeng Liang & Haoyong Chen & Xiaojuan Wang & Idris Ibn Idris & Bifei Tan & Cong Zhang, 2018. "An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty," Energies, MDPI, vol. 11(8), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2116-:d:163668
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yilin Xie & Ying Xu, 2022. "Transmission Expansion Planning Considering Wind Power and Load Uncertainties," Energies, MDPI, vol. 15(19), pages 1-18, September.
    2. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    3. Victor H. Hinojosa & Joaquín Sepúlveda, 2020. "Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors," Energies, MDPI, vol. 13(13), pages 1-15, June.
    4. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    5. Pakeeza Bano & Kashif Imran & Abdul Kashif Janjua & Abdullah Abusorrah & Kinza Fida & Hesham Alhumade, 2023. "System and Market-Wide Impact Analysis of Coordinated Demand Response and Battery Storage Operation by a Load-Serving Entity," Energies, MDPI, vol. 16(4), pages 1-22, February.
    6. Yixin Huang & Xinyi Liu & Zhi Zhang & Li Yang & Zhenzhi Lin & Yangqing Dan & Ke Sun & Zhou Lan & Keping Zhu, 2020. "Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market," Energies, MDPI, vol. 13(18), pages 1-22, September.
    7. Haokai Xie & Pu Zhao & Xudong Ji & Qun Lin & Lianguang Liu, 2019. "Expansion Planning Method of the Industrial Park Integrated Energy System Considering Regret Aversion," Energies, MDPI, vol. 12(21), pages 1-20, October.

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