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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'Remote sensing: From toys to tools?' taking place on Wednesday, 12 March 2014 at 14:15-15:45. The meet-the-authors will take place in the poster area.

John Medley ZephIR lidar, United Kingdom
Co-authors:

(1) ZephIR lidar, Ledbury, United Kingdom

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Abstract

Finance grade wind measurements with lidar

Introduction

Site wind speed measurements form a major potential source of uncertainty in predicted annual energy production for a prospective wind farm. In particular, significant uncertainty may exist in the vertical extrapolation of measured wind data. Lidar measurements enable reduction of shear extrapolation uncertainties through direct measurement of resource at hub height and reduction of uncertainty in fitted shear models. In this paper the implications of the use of hub height lidar data as the primary data source in wind energy projects is quantified in financial terms in the context of a typical onshore energy yield analysis in non-complex terrain.

Approach

The financing terms agreed between lending institutions and wind farm developers are a critical element in determining the financial success of an operational wind farm. Terms are negotiated based upon a number of project specific details used to model the financial viability of the project. Of these details one of the most important is the projected annual revenue. In this work we aim to demonstrate the equivalence of projected annual revenue based upon hub height data from a lidar and a high quality anemometer mast and the financial benefits of using hub-height measured data compared to using sub-hub height measurements.

Main body of abstract

A finance grade energy yield analysis methodology has been applied using data collected from an IEC compliant 91m anemometer mast and co-located ZephIR 300 lidar spanning a full year. Data from the mast and lidar at 91m above ground level (AGL) have been applied as hub-height wind measurement inputs in separate energy yield analyses. Mast data at 70m AGL and below has been applied with a standard shear extrapolation methodology to calculate AEP from a sub-hub height wind measurement. A bank’s financing model is applied to the AEP results to compare the financial terms of investment determined from the data. The long term wind climates derived from the mast and lidar data at 91m AGL are shown to be equivalent with identical long term predicted mean wind speed for concurrent data and a 0.6% deviation for the overall data sets due to a slight difference in data coverage. Deviation between the mast and ZephIR derived long-term P90 energy yield predictions is found to be 0.2% for the hub height measured data. For the shear extrapolated mast data deviation is significantly larger representing a 6% under-estimation of resource. Financing terms obtained for the hub height mast and lidar measurement scenarios are shown to be equivalent. A £1.7 million saving on equity investment with an increased projected annual P90 revenue of approximately £238,000 is demonstrated for the prediction derived from the hub height measured data in comparison to that derived from the mast data extrapolated from 70m AGL.

Conclusion

The results presented demonstrate the ability of the lidar tested to measure finance grade wind data in non-complex terrain. Current trends are for onshore turbines with hub heights in excess of 80m. Anemometer masts that provide measurements at such heights represent a significant one-off investment in a static resource that cannot be effectively re-used. The portability, re-useability and lack of planning requirements associated with lidar systems make their use in place of hub height mast anemometry an attractive option for wind resource assessment in the context of proven equivalence in the quality and bankability of the data obtained.


Learning objectives
Delegates will gain an understanding of the relative performance of lidars and high quality anemometer masts in determining wind resource in non-complex terrain. They will gain confidence in the use of lidar as a primary hub-height wind data source in these conditions enabling them to incorporate lidar data into their wind energy project methodologies accruing benefits in terms of cost and flexibility.