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Probabilistic Load Flow Algorithm of Distribution Networks with Distributed Generators and Electric Vehicles Integration

Author

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  • Bowen Zhou

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Xiao Yang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Dongsheng Yang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Zhile Yang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Tim Littler

    (School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT9 5AH, UK)

  • Hua Li

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

Probabilistic Load Flow (PLF) calculations are important tools for analysis of the steady-state operation of electrical energy networks, especially for electrical energy distribution networks with large-scale distributed generators (DGs) and electric vehicle (EV) integration. Traditional PLF has used the Cumulant Method (CM) and Latin Hypercube Sampling (LHS) method. However, traditional CM requires that each input variable be independent of one another, and the Cholesky decomposition adopted by the traditional LHS has limitations in that it is only applicable for positive definite matrices. To solve these problems, taking into account the Q-MCS theory of LHS, this paper proposes a CM PLF algorithm based on improved LHS (ILHS-CM). The cumulants of the input variables are obtained based on sampling results. The probability distribution of the output variables is obtained according to the Gram-Charlier series expansion. Moreover, DGs, such as wind turbines, photovoltaic (PV) arrays, and EVs integrated into the electrical energy distribution networks are comprehensively considered, including correlation analysis and dynamic load flow analysis for EV-coordinated charging. Four scenarios are analyzed based on the IEEE-30 node network, including with/without DGs and EVs, error analysis and performance evaluation of the proposed algorithm, correlation analysis of DGs and EVs, and dynamic load flow analysis with EV integration. The results presented in this paper demonstrate the effectiveness, accuracy, and practicability of the proposed algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4234-:d:284213
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    References listed on IDEAS

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    Cited by:

    1. Joao Soares & Bruno Canizes & Zita Vale, 2021. "Rethinking the Distribution Power Network Planning and Operation for a Sustainable Smart Grid and Smooth Interaction with Electrified Transportation," Energies, MDPI, vol. 14(23), pages 1-4, November.
    2. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.

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