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Chance-Constrained Real-Time Dispatch with Renewable Uncertainty Based on Dynamic Load Flow

Author

Listed:
  • Pei Bie

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Buhan Zhang

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Hang Li

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yong Wang

    (Guangzhou Power Supply Co., Ltd., Guangzhou 510000, China)

  • Le Luan

    (Guangzhou Power Supply Co., Ltd., Guangzhou 510000, China)

  • Guoyan Chen

    (Guangzhou Power Supply Co., Ltd., Guangzhou 510000, China)

  • Guojun Lu

    (Guangzhou Power Supply Co., Ltd., Guangzhou 510000, China)

Abstract

In this paper, a comprehensive real-time dispatch model considering renewable uncertainty based on dynamic load flow (DLF) is proposed. Through DLF, the primary and secondary frequency regulation amount caused by the variation of renewable energy as well as the line flow when primary and secondary regulation are deployed can be obtained easily. Not only the frequency constraints, but also the regular constraints like generator production limits and line flow limits are respected under both primary and secondary frequency regulation. To solve the dispatch problem with renewable uncertainty, chance-constrained programming based on cumulants and Cornish-fisher expansions (CCP-CMCF) is adopted to get the probability of holding the chance constraints and then the real-time dispatch model can be transformed into a quadratic programming. The simulation results show that the dispatch model proposed in this paper can deal with both primary and secondary regulation well and has a fast computation speed.

Suggested Citation

  • Pei Bie & Buhan Zhang & Hang Li & Yong Wang & Le Luan & Guoyan Chen & Guojun Lu, 2017. "Chance-Constrained Real-Time Dispatch with Renewable Uncertainty Based on Dynamic Load Flow," Energies, MDPI, vol. 10(12), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2111-:d:122682
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    References listed on IDEAS

    as
    1. Yanzhe Hu & Yang Li & Mengjie Xu & Li Zhou & Mingjian Cui, 2017. "A Chance-Constrained Economic Dispatch Model in Wind-Thermal-Energy Storage System," Energies, MDPI, vol. 10(3), pages 1-21, March.
    2. Jun Liu & Xudong Hao & Peifen Cheng & Wanliang Fang & Shuanbao Niu, 2016. "A Parallel Probabilistic Load Flow Method Considering Nodal Correlations," Energies, MDPI, vol. 9(12), pages 1-16, December.
    3. Xiaoyang Deng & Jinghan He & Pei Zhang, 2017. "A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables," Energies, MDPI, vol. 10(10), pages 1-21, October.
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    Cited by:

    1. Yue Cao & Tao Li & Tianyu He & Yuwei Wei & Ming Li & Fengqi Si, 2022. "Multiobjective Load Dispatch for Coal-Fired Power Plants under Renewable-Energy Accommodation Based on a Nondominated-Sorting Grey Wolf Optimizer Algorithm," Energies, MDPI, vol. 15(8), pages 1-19, April.
    2. Bowen Zhou & Xiao Yang & Dongsheng Yang & Zhile Yang & Tim Littler & Hua Li, 2019. "Probabilistic Load Flow Algorithm of Distribution Networks with Distributed Generators and Electric Vehicles Integration," Energies, MDPI, vol. 12(22), pages 1-24, November.
    3. Emanuele Ciapessoni & Diego Cirio & Francesco Conte & Andrea Pitto & Stefano Massucco & Matteo Saviozzi, 2023. "Probabilistic Security-Constrained Preventive Control under Forecast Uncertainties Including Volt/Var Constraints," Energies, MDPI, vol. 16(4), pages 1-26, February.

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