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Economic analysis of Lidar-based proactively controlled wind turbines

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  • Mathur, Rachit R.
  • Rice, Jennifer A.
  • Swift, Andrew
  • Chapman, Jamie

Abstract

This paper analyzes the financial feasibility and the investment attractiveness of a wind farm that uses a Lidar unit for proactive turbine controls. A thorough technical analysis is performed for evaluating the effectiveness of the Lidar for proactive blade pitch control. It is observed that using a Lidar for individual blade pitch control results in a significant reduction of the blade root damage equivalent loads. Furthermore, a pro-forma cashflow based economic tool is developed for modeling, comparing, and analyzing wind energy projects. This tool utilizes capital budgeting tools such as net present value, internal rate of return, equivalent annual annuity and payback period for analyzing investment attractiveness. Moreover, this paper utilizes wind conditions, costs and expenses, financing schemes, incentives, and operational strategies for analyzing wind projects with or without Lidars. The component fatigue load reduction achieved using Lidar based control can either be used to increase the useful life of the farm or to up-rate (repower) the turbine for the same operational life. This work analyzes the financial impacts of each of the aforementioned scenarios for a range of wind conditions. Also, the financial impact of PTC availability on a Lidar-assisted wind project has been analyzed in this research.

Suggested Citation

  • Mathur, Rachit R. & Rice, Jennifer A. & Swift, Andrew & Chapman, Jamie, 2017. "Economic analysis of Lidar-based proactively controlled wind turbines," Renewable Energy, Elsevier, vol. 103(C), pages 156-170.
    • Handle: RePEc:eee:renene:v:103:y:2017:i:c:p:156-170
    DOI: 10.1016/j.renene.2016.10.069
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    References listed on IDEAS

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    1. Wiser, Ryan & Bolinger, Mark & Barbose, Galen, 2007. "Using the Federal Production Tax Credit to Build a Durable Market for Wind Power in the United States," The Electricity Journal, Elsevier, vol. 20(9), pages 77-88, November.
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