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A New Modeling Approach for the Probability Density Distribution Function of Wind power Fluctuation

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  • Lingzhi Wang

    (School of Automation and Information, Xi’an University of Technology, Xi’an 710048, China
    School of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
    Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an 710048, China)

  • Jun Liu

    (School of Automation and Information, Xi’an University of Technology, Xi’an 710048, China)

  • Fucai Qian

    (School of Automation and Information, Xi’an University of Technology, Xi’an 710048, China
    Autonomous Systems and Intelligent Control International Joint Research Center, Xi’an Technological University, Xi’an 710021, China)

Abstract

With the rapid development of grid-connected wind power, analysing and describing the probability density distribution characteristics of wind power fluctuation has always been a hot and difficult problem in the wind power field. In traditional methods, a single distribution function model is used to fit the probability density distribution of wind power output fluctuation; however, the results are unsatisfying. Therefore, a new distribution function model is proposed in this work for fitting the probability density distribution to replace a single distribution function model. In form, the new model includes only four parameters which make it easier to implement. Four statistical index models are used to evaluate the distribution function fits with the measured probability data. Simulations are designed to compare the new model with the Gaussian mixture model, and results illustrate the effectiveness and advantages of the newly developed model in fitting the wind power fluctuation probability density distribution. Besides, the fireworks algorithm is adopted for determining the optimal parameters in the distribution function model. The comparison experiments of the fireworks algorithm with the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA) are carried out, which shows that the fireworks algorithm has faster convergence speed and higher accuracy than the two common intelligent algorithms, so it is useful for optimizing parameters in power systems.

Suggested Citation

  • Lingzhi Wang & Jun Liu & Fucai Qian, 2019. "A New Modeling Approach for the Probability Density Distribution Function of Wind power Fluctuation," Sustainability, MDPI, vol. 11(19), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5512-:d:273753
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    References listed on IDEAS

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    Cited by:

    1. Yuxing Liu & Linjun Zeng & Jie Zeng & Zhenyi Yang & Na Li & Yuxin Li, 2023. "Scheduling Optimization of IEHS with Uncertainty of Wind Power and Operation Mode of CCP," Energies, MDPI, vol. 16(5), pages 1-17, February.

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