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A bi-level planning approach for hybrid AC-DC distribution system considering N-1 security criterion

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  • Wu, Zhi
  • Liu, Pengxiang
  • Gu, Wei
  • Huang, He
  • Han, Jun

Abstract

Renewable energy is widely distributed in remote or coastal areas. In order to improve the economic efficiency and the consumption rate of renewable energy in distribution system, such distributed generators can be connected through a DC network, which provides power supply for AC system through collection lines. This paper presents a bi-level planning model for AC-DC distribution system with consideration of N-1 contingency. The upper-level model optimizes the total investment costs and operating costs in both AC and DC system over the planning horizon. The lower-level model aims to improve the reliability of the DC system by minimizing curtailment cost of wind farm and photovoltaic under the worst N-1 contingency. Dual technology and Big-M method are applied to reformulate lower-level model as a robust optimization. A novel solving strategy is proposed by combining modified genetic algorithm and numerical method in a nested way. Case studies illustrate that the proposed planning approach is more reliable in dealing with N-1 contingency and more effective in solving large-scale optimization compared with the existing planning approach.

Suggested Citation

  • Wu, Zhi & Liu, Pengxiang & Gu, Wei & Huang, He & Han, Jun, 2018. "A bi-level planning approach for hybrid AC-DC distribution system considering N-1 security criterion," Applied Energy, Elsevier, vol. 230(C), pages 417-428.
    • Handle: RePEc:eee:appene:v:230:y:2018:i:c:p:417-428
    DOI: 10.1016/j.apenergy.2018.08.110
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    References listed on IDEAS

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    1. Frank, Stephen M. & Rebennack, Steffen, 2015. "Optimal design of mixed AC–DC distribution systems for commercial buildings: A Nonconvex Generalized Benders Decomposition approach," European Journal of Operational Research, Elsevier, vol. 242(3), pages 710-729.
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    Citations

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    Cited by:

    1. Huang, Manyun & Wei, Zhinong & Lin, Yuzhang, 2022. "Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems," Applied Energy, Elsevier, vol. 306(PB).
    2. Pinto, Rafael S. & Unsihuay-Vila, Clodomiro & Tabarro, Fabricio H., 2021. "Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties," Applied Energy, Elsevier, vol. 297(C).
    3. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    4. Yi Luo & Yin Zhang & Muyi Tang & Youbin Zhou & Ying Wang & Defu Cai & Haiguang Liu, 2021. "A Novel Receiving End Grid Planning Method with Mutually Exclusive Constraints in Alternating Current/Direct Current Lines," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    5. Zhou, Bo & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2019. "Data-adaptive robust unit commitment in the hybrid AC/DC power system," Applied Energy, Elsevier, vol. 254(C).
    6. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.

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