Optimal generation scheduling for a deep-water semi-submersible drilling platform with uncertain renewable power generation and loads

Yuqing Huang, Hai Lan, Ying-Yi Hong, Shuli Wen and He Yin

Energy, 2019, vol. 181, issue C, 897-907

Abstract: Owing to the depletion of oil reserves and the low efficiency of traditional deep-water semi-submersible drilling platforms, the application of photovoltaic generation and energy storage systems in marine power systems has been increasingly attracting attention. However, the intermittent and uncertain nature of solar energy brings crucial challenges in maintaining stable and sustainable operations. Furthermore, unlike land-based power systems, the deep-water semi-submersible drilling platform depends on a dynamic positioning system to be fixed in an area of deep water for producing oil, but irregular and stochastic waves make the outputs of a dynamic positioning system uncertain, increasing the difficulty and complexity of energy management. This paper develops a novel hybrid day-ahead probabilistic scheduling method to reduce the total fuel cost of the whole system and improve the energy efficiency; the method combines Taguchi's orthogonal algorithm with particle swarm optimization and a probabilistic load flow method. A detailed model of the deep-water semi-submersible drilling platform is established to validate the proposed method, which is compared with various classical methods. With the help of the proposed method, the total operation cost and greenhouse gas emissions are reduced from $7562.52 to $7333.68, and from 18.95 tons to 16.81 tons, respectively. Furthermore, compared to the two-point estimation method, the experimental times drops by half, which minimizes the computational burden. The simulation results clearly demonstrate the necessity for day-ahead energy scheduling, and the advantages of the proposed method.

Keywords: Deep-water semi-submersible drilling platform; Photovoltaic generation; Day-ahead scheduling; Taguchi orthogonal algorithm; Particle swarm optimization technique (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:181:y:2019:i:c:p:897-907

DOI: 10.1016/j.energy.2019.05.157

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