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Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

Author

Listed:
  • Yue Chen

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Zhizhong Guo

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Hongbo Li

    (Electric Power Research Institute of HITZ, Harbin Institute of Technology at Zhangjiakou, Zhangjiakou 075000, China)

  • Yi Yang

    (Shenyang Power Supply Company, State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang 110000, China)

  • Abebe Tilahun Tadie

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Guizhong Wang

    (Electric Power Research Institute of HITZ, Harbin Institute of Technology at Zhangjiakou, Zhangjiakou 075000, China)

  • Yingwei Hou

    (Electric Power Research Institute of HITZ, Harbin Institute of Technology at Zhangjiakou, Zhangjiakou 075000, China)

Abstract

With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

Suggested Citation

  • Yue Chen & Zhizhong Guo & Hongbo Li & Yi Yang & Abebe Tilahun Tadie & Guizhong Wang & Yingwei Hou, 2020. "Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:518-:d:307098
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    References listed on IDEAS

    as
    1. Sui Peng & Huixiang Chen & Yong Lin & Tong Shu & Xingyu Lin & Junjie Tang & Wenyuan Li & Weijie Wu, 2019. "Probabilistic Power Flow for Hybrid AC/DC Grids with Ninth-Order Polynomial Normal Transformation and Inherited Latin Hypercube Sampling," Energies, MDPI, vol. 12(16), pages 1-21, August.
    2. Van Ky Huynh & Van Duong Ngo & Dinh Duong Le & Nhi Thi Ai Nguyen, 2018. "Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources," Energies, MDPI, vol. 11(10), pages 1-12, October.
    3. Jae-Kun Lyu & Jae-Haeng Heo & Jong-Keun Park & Yong-Cheol Kang, 2013. "Probabilistic Approach to Optimizing Active and Reactive Power Flow in Wind Farms Considering Wake Effects," Energies, MDPI, vol. 6(11), pages 1-21, October.
    4. 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.
    5. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
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