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Conference programme 

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Poster session

Lead Session Chair:
Stephan Barth, Managing Director, ForWind - Center for Wind Energy Research, Germany
Matthias Henke SgurrEnergy Ltd, United Kingdom
Co-authors:
Molly Iliffe (1) F P
(1) SgurrEnergy Ltd, Glasgow, United Kingdom

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Poster
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Presenter's biography

Biographies are supplied directly by presenters at OFFSHORE 2015 and are published here unedited

Matthias Henke is in charge of SgurrEnergy’s Hamburg office and working with his team on offshore and onshore wind projects in Europe. After starting working in wind energy in 1999 with the development of wind energy projects he worked as technical advisor to on- and offshore wind projects. His work experience includes a considerable number of independent engineering services for offshore and onshore wind energy projects around the world. Among his clients are international banks, investors, utilities and developers. Matthias Henke studied electrical engineering and got an additional degree in economics as well as an MBA in international management.

Abstract

How to increase project value using lidar

Introduction

Lidar (Light detection and ranging) is a mature remote sensing technology used to measure wind speed and direction. Real world data from Lidar deployments at wind farms demonstrates that a Lidar measurement campaign can lead to significant reductions in energy yield uncertainty, increases to energy yield and reductions in maintenance costs. The associated impact on project valuation and access to financing has been quantified through the construction of a financial model for a generic UK offshore wind farm. Generic inputs and assumptions based on SgurrEnergy’s extensive industry experience were used, which will be presented to delegates.

Approach

Two cases were considered: i) deploying both Lidar and a conventional offshore meteorological (met) mast, and ii) using a met mast only. The key variables differentiating the two cases were as follows:
• Wind farm design
• Increase to energy yield arising from improved operating strategy and control strategy (to reduce yaw misalignment, active wake management etc)
• Energy yield uncertainty
• Maintenance cost reduction arising from reduced loads
• Improved power curve testing techniques and reduced power curve testing costs
• Continued risk mitigation
These key areas are briefly discussed below.


Main body of abstract

Lidar allows direct measurement of wind speed around the site and thereby accurate determination of sweet-spots of highest productivity, no-go areas of high shear and/or turbulence, and optimum hub-height. This results in a better optimised design with higher energy yield, IRR and NPV.

Understanding the character of wind flow, specifically any anomalies, enables improved operating strategy and control strategy to reduce loads and increase yield, for example through a reduction to yaw misalignment and active wake management.

A Lidar measurement campaign reduces energy yield uncertainty due to reduced flow model uncertainty, array loss modelling and power curve uncertainty. This causes an increase to the predicted 10th percentile energy yield (P90) for a given median energy yield (P50). Reduced uncertainty enables developers to raise more finance, retaining more equity. This increases the internal rate of return on the developer’s equity investment.

Maintenance costs will reduce under an improved wind farm operating and control strategy. Production can be curtailed under wind flow conditions causing excessive loads.

Power curve tests using Lidar can be used to quantify the benefit of performance enhancement upgrades and understand performance in a range of real world conditions. The use of a Lidar deployment reduces the cost of these activities.

By measuring (using Lidar), understanding and specifying the fullest possible set of information on the site’s wind characteristics, the turbine supplier can be directly informed about the conditions the turbines must work under. This ensures that the design is appropriate, and the OEM takes full responsibility.


Conclusion

The financial modelling of the above points using real world data demonstrated that the deployment of Lidar resulted in an increase to the project valuation and access to finance.

While the impact of a Lidar measurement campaign will be project specific, in this generic case the modelling demonstrated that the campaign would provide the developer with significant quantifiable financial benefits.

The author will present the full results to delegates during the presentation.



Learning objectives
An understanding of the key ways in which a Lidar measurement campaign will increase project value, reduce project risk and improve access to financing.
An understanding of the real world data available to demonstrate and quantify these benefits.
Quantification of the impact on NPV and IRR of a Lidar measurement campaign.
Generic inputs and assumptions used in the financial modelling of an onshore UK wind farm.