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

Anan Zhang, Yangfan Sun, Wei Yang, Huang Huang and Yating Feng
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Anan Zhang: School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China
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

Energies, 2019, vol. 12, issue 17, 1-17

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.

Keywords: offshore microgrids; support vector quantile regression; dragonfly algorithm; probability prediction; optimal operation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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