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Optimal Dispatching of Offshore Microgrid Considering Probability Prediction of Tidal Current Speed

Author

Listed:
  • Anan Zhang

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China
    School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Yangfan Sun

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Wei Yang

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Huang Huang

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Yating Feng

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

Abstract

Oceans contain rich tidal current energy, which can provide sufficient power for offshore microgrids. However, the uncertainty of tidal flow may endanger the operational reliability of an offshore microgrid. In this paper, a probabilistic prediction model of tidal current is established based on support vector quantile regression to reduce the influence of uncertainty. Firstly, the penalty factors and kernel parameters of the proposed prediction model was optimized by the dragonfly algorithm to predict the tidal speed of any time of a day in different quantiles. Secondly, combining the above result with the kernel density to predict the probability density function of the tidal current speed, which is to improve the accuracy of prediction in the absence of information. Thirdly, an optimal generation dispatching strategy with tidal current generators is proposed to minimize the fuel consumption of offshore microgrids. Finally, a case study based on the offshore oil and gas platform in Bohai shows that the mean absolute percent error of the proposed model is 2.8142%, which is better than support vector quantile regression model and support vector regression model optimized by the genetic algorithm.

Suggested Citation

  • Anan Zhang & Yangfan Sun & Wei Yang & Huang Huang & Yating Feng, 2019. "Optimal Dispatching of Offshore Microgrid Considering Probability Prediction of Tidal Current Speed," Energies, MDPI, vol. 12(17), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3384-:d:263361
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    References listed on IDEAS

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    1. Wang, Shuguang & Lam, Wei-Haur & Cui, Yonggang & Zhang, Tianming & Jiang, Jinxin & Sun, Chong & Guo, Jianhua & Ma, Yanbo & Hamill, Gerard, 2018. "Novel energy coefficient used to predict efflux velocity of tidal current turbine," Energy, Elsevier, vol. 158(C), pages 730-745.
    2. Neill, Simon P. & Vögler, Arne & Goward-Brown, Alice J. & Baston, Susana & Lewis, Matthew J. & Gillibrand, Philip A. & Waldman, Simon & Woolf, David K., 2017. "The wave and tidal resource of Scotland," Renewable Energy, Elsevier, vol. 114(PA), pages 3-17.
    3. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
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