4.2 Article

Estimating the impact of drone-based inspection on the Levelised Cost of electricity for offshore wind farms

期刊

RESULTS IN ENGINEERING
卷 9, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.rineng.2021.100201

关键词

LCOE; Offshore wind; Unmanned aerial vehicles; Cost benefit analysis; Visual inspection

资金

  1. UK Engineering and Physical Sciences Research Council (EPSRC) [EP/P009743/1]
  2. EPSRC [EP/P009743/1] Funding Source: UKRI

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The use of drones for infrastructure inspection has become routine due to its benefits of reducing risk and costs. Research shows that replacing rope-access inspection with drones can significantly reduce operational costs and decrease revenue lost due to downtime compared to traditional methods.
Using drones for infrastructure inspection is becoming routine, driven by the benefit of reducing risk and costs. In this paper, the business case for drone-based inspection is examined from the perspective of the wind farm operator and the Drone Service Provider (DSP). A physical and financial model of an offshore wind farm is built using techno-economic analysis and activity-based costing, and data from the open literature. Drone operational models are developed based on domain specific knowledge of operation practices and the predicted physical environment. Rope-access inspection is used as a baseline and accounts for 0.7% of the wind farm operational expenditure. Replacing rope-access inspection with drones reduces costs by up to 70% and decreases revenue lost due to down-time by up to 90%. Increasing autonomy of drones increases the speed at which inspections can be performed but increases costs and complexity. For wind farm operator there is marginal economic benefit (2% reduction in inspection costs) in moving towards a fully autonomous drone-based inspection system from the current visual line of sight operation of single drone. However, from the point of view of the DSP, fully autonomous operations allow greater scalability of the business and enables higher utilisation of the fleet.

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