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An N - k Analytic Method of Composite Generation and Transmission with Interval Load

Author

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  • Shaoyun Hong

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Haozhong Cheng

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Pingliang Zeng

    (Electric Power Research Institute of China, Beijing 100192, China)

Abstract

N - k contingency estimation plays a very important role in the operation and expansion planning of power systems, the method of which is traditionally based on heuristic screening. This paper stringently analyzes the best and worst states of power systems given the uncertainties of N - k contingency and interval load. For the sake of simplification and tractable computation, an approximate direct current (DC) power flow model was used. Rigorous optimization models were established for identifying the worst and best scenarios considering the contingencies of generators and transmission lines together with their uncertain loads. It is very useful to identify the worst N - k contingencies with interval loads. If the worst existing scenario meets security standards, all scenarios must satisfy it. The mathematical model established for finding the worst N - k contingency with interval load is a bi-level optimization model. In this paper, strong duality theory and mathematical linearization were applied to the solution of bi-level optimization. The computational results of standard cases validate the effectiveness of the proposed method and illustrate that generator contingency has more impact on minimum load shedding than transmission line contingency.

Suggested Citation

  • Shaoyun Hong & Haozhong Cheng & Pingliang Zeng, 2017. "An N - k Analytic Method of Composite Generation and Transmission with Interval Load," Energies, MDPI, vol. 10(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:168-:d:89096
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    References listed on IDEAS

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    1. Jiang, Ruiwei & Zhang, Muhong & Li, Guang & Guan, Yongpei, 2014. "Two-stage network constrained robust unit commitment problem," European Journal of Operational Research, Elsevier, vol. 234(3), pages 751-762.
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    Cited by:

    1. 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.
    2. Ziqi Wang & Jinghan He & Alexandru Nechifor & Dahai Zhang & Peter Crossley, 2017. "Identification of Critical Transmission Lines in Complex Power Networks," Energies, MDPI, vol. 10(9), pages 1-19, August.
    3. Jaber Valinejad & Mousa Marzband & Mudathir Funsho Akorede & Ian D Elliott & Radu Godina & João Carlos de Oliveira Matias & Edris Pouresmaeil, 2018. "Long-Term Decision on Wind Investment with Considering Different Load Ranges of Power Plant for Sustainable Electricity Energy Market," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    4. Mohammad Bagher Abolhasani Jabali & Mohammad Hosein Kazemi, 2017. "Power System Event Ranking Using a New Linear Parameter-Varying Modeling with a Wide Area Measurement System-Based Approach," Energies, MDPI, vol. 10(8), pages 1-14, July.
    5. Yilin Xie & Ying Xu, 2022. "Transmission Expansion Planning Considering Wind Power and Load Uncertainties," Energies, MDPI, vol. 15(19), pages 1-18, September.
    6. 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.

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