Benefits of Measuring the Wind Resource Wind ...

Wind Measurement Campaigns Offshore: Benefits ofthey Measuring the Wind Resource and how do Create Value? How Value is Created for the Windfarm

Fergal Darcy DONG Energy DONG Energy

Andrew Henderson Andrew Henderson

Wind Resource Assessment Forum 2014, London European Wind Energy Conference 10th April 2014 Barcelona, Spain 11th March 2014

DONG Energy Wind Power

Outline of Presentation Introduction Measurements in Dong Energy The Ideal Campaign Future Campaigns Summary and Conclusions Valuing the Benefits of Wind Measurements Method: Uncertainty and Value Candidate Wind Measurement Strategies Cost-Benefit Analysis Value of Portfolio Summary and Conclusions

2

Introduction • • •

•

• •

Offshore wind in Northern Europe Oil & Gas Electricity and heat production from Biomass Market leader in constructing and operating offshore wind farms Circa 1200 employees in Offshore wind By the end of 2014 close to 900 WTG’s in operation offshore

Introduction

• Wind Analysis and Layout dept. • 31 employees • Working for all DONG Energy wind\\ power projects • All stages of the project life cycle


5

Measurements in Dong Energy

Measurements in DE

• Carbon Trust OWA • Negotiation • IEA forum • Consultancies and 3rd party engineers

• Consultant Engineer • Data checker • Package Manager • Internal R&D

6

Measurements in Dong Energy

Wind

Waves

Measurements database interface

Loads and monitoring

Power Curve Verification

7

Ground Based Lidar



Large bank of knowledge built up with this technology



Deployments both onshore and offshore



Collaboration on Bankability project



All lidars must be certified before they use

8

Floating Lidars 

Currently 2 Floating lidars in the DONG Energy fleet in the Irish Sea



AEP’s on commercial projects to be used in the FID decision for these farms



Difficult to see Met Masts in DONG Energy’s future



How to ensure an industry standard future for this technology?



What else can we use these for?

9

Nacelle Lidar 

A method of Power Curve testing



Blade change outs



Software upgrades



Localised wind field for a turbine in conjunction with other measurements



Turbulence intensity for the future Site conditions

10

Wave Buoys



Foundation Design Campaign



Safe passage of vessels



Monitoring of met ocean conditions for wind farm operation



Consenting requirements (sediment monitoring)



Site assessment

11

Load Monitoring



Verification of foundation design and monitoring the fatigue life of the foundation



Grout monitoring



Secondary steel monitoring



Results of these campaigns used with load monitoring from turbines to provide a full picture of the structure

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The Ideal Campaign 

Looking for a site set up where we target all measurements on a single area and specific turbine in the wind farm



Combine the nacelle and Load monitoring campaigns on the same turbine where possible



Include wave buoys in this package located near the turbine



We want to understand all the fatigue loading on the turbine as best as possible



Plus monitoring as many environmental conditions as possible in a targeted area of the farm.

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Future Measurements in Dong Energy

Measurements in DE Wind • Scanning Lidar • 3D wind mapping

Met Ocean • COE reduction • Supporting other campaigns

Load Monitoring • Improve internal procedures • Support other campaigns PCV • Multi beam units • Possibility of turbulence • More and more deployments • 16

Data Monitoring • Better accessibility • Improve accuracy


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Conclusion



Reduce the Cost of energy by 35 % – 40% 

More Remote sensing



Floating technology



R3 challenges

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Wind Measurements Offshore Current Status

Overview 



around 50 met masts built in the seas around Northern Europe 



Challenges many of the current projects are in deeper waters 

majority on monopile foundations in shallow waters

larger support structure required  more severe metocean climate, impacts:  ground conditions surveys  design  installation

the "reference standard" for wind resource assessment offshore 

availability of vessels  characterising large areas typical of some modern offshore windfarms / windfarm clusters  costs have risen by x10

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Method: Uncertainty and Value

Principle 

Method

Reduced Uncertainty is More Valuable  assume project is Project Financed (debt funded)  portfolio effect  savings accounts / bonds vs. more  debt is sized on free-cash flow based volatile stock market on P90 energy production  …  If Greater Certainty of Wind resource, → Higher P90 energy production → Higher P90 income → Greater debt available → Higher Return on Equity



Note that DONG Energy does not use debt funding for offshore windfarm projects currently; however our Investment Partners do, as do many other developers.

this is different from the principal of Greater Reliability being more valuable 21

Candidate Wind Measurement Strategies







fixed  floating 

satellite

on-site LiDAR 

bespoke fixed structure  existing structure i.e. oil & gas  floating

shoreline measurements 

met mast  LiDAR 

on-site met mast 

mesoscale

remote sensing 





numerical desk-top studies

portfolio methods

Outline Measurement Campaign Programme for fixed Met Mast (sMP) on shallow-water Monopile (~25m)

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Sources of Uncertainty Objective is to Minimise in a Cost-Effective Manner

Wind 

Measurements 

Temporal Variation; typical assumptions:

instrument precision  flow distortion



6% annual standard deviation  uncorrelated 

met mast  adjacent structures, i.e. on O&G platform

Spatial Variation 







greatest variation and uncertainty at the coastal zone  hence impracticality of measuring onshore

correlation with suitable long-term record

Vertical Variation

Chris Garrett

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No Measurements Reference for Assessment of Candidate Wind Measurement Strategies 

Wind Atlas / Desk-top analysis of portfolio of sources 

assume gross uncertainty of 7.5%  arguably low, hence valuations of alternatives could in reality be higher

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Existing Met Mast in the Vicinity



cost of measurement campaign encourages sharing of met masts  if available, highly attractive option 

since also can increase length of measurement campaign (subject to construction of adjacent windfarms)

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Fixed Met Mast



conventional approach  around 30 installed across Europe 

Potential Approach: InstallationOptimised Strategy 

majority of cost is installation-related  combine with other windfarm installation  align design with windturbine foundations  inevitably increased procurement costs  aim for significant reduction in mobilisation / demobilisation  i.e. reuse of sea-fastening  mast lift will be bespoke

another 5 installed last couple of years



effectively similar strategy as onshore  costs have risen from ~€1m to ~€10m deeper waters → need for highspecification installation plant  tight market conditions 

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LiDAR on Light-weight Jacket



Jacket on Suction Caisson Foundation 

requires suitable soils  alternative: GBF 

Loads from a LiDAR unit significantly reduced compared with a met mast  Optimised operating life 

i.e. 5 years rather than 25 years



Increased Value if Scanning LiDAR installed  can be redeployed 

suitable ground conditions required 

alternative foundation variants possible

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Floating LIDAR



rapid deployment; reduced requirements for: 

geotechnical survey  consent type 

LiDAR technology itself has gained widespread acceptance 

also now offshore on fixed platforms DONG Energy FLiDAR at Burbo Banks



impact of floater motion on LiDAR unit  reliability to be proven [i.e. as per the generic offshore risk]

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Portfolio Benefits



Greater knowledge of the wind resource will deliver:  Improved understanding of the project  Improved Decision-making across the portfolio

Applies to any typical portfolio where there are: 

different opportunities of broadly similar attractiveness  ability for some redeployment of capital, either between projects or between sectors

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Summary and Conclusions



Wind Measurements deliver concrete value to offshore windfarms  Met masts remain cost-effective at today's installation prices 



however development risk?

several potentially attractive new technologies on the horizon

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Thank you

Contributions from this work's co-authors and contributors is gratefully acknowledged and appreciated: Miriam Marchante Jiménez, Hugh Yendole, Martin Méchali, Kunal Patel, Rémi Gandoin 34

Wind Resource Assessment Forum 2014 Wind Measurements Campaigns Offshore and how do they Create Value?

Wind Measurements Campaigns Offshore and how do they Create Value? Andrew Henderson Fergal Darcy Rémi Gandoin Miriam Marchante Jimémez Hugh Yendole Martin Méchali DONG Energy Windpower; 33 Grosvenor Place, London SW1X 7HY [email protected] ; +44 207 811 5416

0. Executive Summary Currently the cost of the traditional solution for offshore wind measurement, a hub-height lattice met mast on a fixed foundation within the windfarm boundary, has become extortionately expensive, primarily due to a number of factors, which include: • the deeper waters at current project sites, • the more severe wave climates at current project sites, • the vessels able to work under such conditions including • the mobilisation costs for a one-off operation. However an accurate knowledge of the wind resource allows improved decision making and reduces risk, in terms of the windfarm design as well as the investment decision; for the investment decision, risks to project finance also need to be considered. A suitable designed measurement campaign delivers a positive NPV, since the financial return required is lower on a less risky investment. This risk reduction can be quantified via different means, depending upon the project structure. A widely understood approach is the improvements in the project finance conditions that a lender would grant to the project, specifically how much project debt can be offered. Figure 1 presents the normalised NPV for leading candidate wind measurement strategies for an offshore wind farm scenario: • the reference case is to make no measurements and to use a regional wind atlas, preferably based on a multitude of source including satellite data, measurements where available and mesoscale analyses; i.e. methodology such as described in reference [2]; • if a nearby windfarm has a met mast with a number of years of data, this may well deliver significant value;

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•

•

•

•

•

a new conventional met mast on a fixed foundation has been the default wind measurement strategy to date but will be expensive; it will deliver high quality measurement data, subject to appropriate design and operation; a LiDAR unit on a light-weight jacket (termed LiGA within this paper) will be a lower cost strategy; LiDAR has become widely acceptable as the primary source of windspeed measurements, for both onshore and offshore sites; the viability of the structure is subject to suitable ground conditions; the vertical LiDAR measurements could be augment by horizontal measurements with a scanning LiDAR for a relatively low additional cost, thus providing data on variation of windspeeds across the windfarm site; note that a single location of measurement means that only radial rather than full 2D vector wind-fields can be determined, thus reducing the value of the campaign compared with a full dual unit strategy; a number of floating LiDAR concepts are currently under development, including undergoing field testing; the relatively low cost and rapid deployment possible makes such a strategy highly attractive however currently widespread acceptance across the investment community and their advisers is currently lacking, hence deployment as primary source of wind measurement data introduces strategic risk; if the windfarm site is located close to the coast, within the range of scanning LiDAR units located at the shoreline, it would be possible to measure the full wind-field across the windfarm site; the advantages of lower costs and rapid deployment are countered by disadvantages of lack of trials, a prerequisite for commencing route towards widespread acceptance within the investment community.

See Table 10 (page 18) for commentary on Technology Maturity LWJ = Light-weight jacket, or LiGA

Figure 1: Overview of Wind Measurement Strategy Value Clearly the project characteristics as well as uncertainty assumptions will drive the value creating by candidate wind measurement strategies and Figure 1 presents the results for just one example of an imaginary offshore windfarm. Details of the underlying assumptions are

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presented in the main body of the paper. A benchmarking exercise was also undertaken against a bona-fide windfarm project Financial Model, which delivered good agreement. Finally, under typical conditions, increasing the certainty regarding any project in a development portfolio will also deliver benefits to the portfolio as a whole, allowing: • prioritisation of projects during the development phase • selection of projects for realisation at FID An indicative analytical model was developed to undertake a preliminary investigation of potential scale of such benefits which suggested these could be in the order of €10m. Note that this principal will apply to any portfolio, whether solely of offshore wind projects, or of a mix of offshore wind projects together with other investment opportunities.

1. Approach and Method A bespoke wind-finance model has been developed to estimate the benefit of undertaking wind measurement campaigns through in the improved conditions . The principles behind the Provision of Non-recourse Project Debt are: 1. Windfarm must be able to cover any debt repayments from within the windfarm’s own cashflow and with a suitable margin, called the Debt Service Coverage Ratio (DSCR) a. This is the ratio between the anticipated free cash-flow once all operating costs are covered and the debt repayment b. This ratio depends on the uncertainty scenario, hence a lower DSCR is required for the P90 than for the P50 energy estimate case 2. The difference between the P90 and P50 energy production estimates will depend on the uncertainties, including that of the long-term average windspeed at the site a. The P90 is generally the driving criterion when determining debt levels for preoperational projects; this is because the uncertainty will invariably be relatively high b. A higher P90 figure means more debt can be provided, hence improving the project economics from the equity investors perspective 3. Hence to estimate the benefit of onsite wind measurements, modelling of the following is needed: a. Impact of wind measurements undertaken on project uncertainty, i.e. how has the uncertainty reduced and hence the P90 figure increased, once the wind measurement device (i.e. met mast) has been installed and operating for a period (i.e. one, two or three years etc.) b. The resulting increase in provision of non-recourse debt for the project and hence the improvement in the project’s financial metrics: i.e. IRR or NPV For this analysis, a number of assumptions for a generic offshore windfarm have been made, the most important of which are presented in Table 1 below. In general, appropriately conservative assumptions have been taken, to ensure that the valuation of wind measurement campaigns presented in this paper tends to be an underestimate rather than an overestimated.

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Table 1: Approach and Method - Key Assumptions for Evaluation Assumption

Value

Comment

700MW

Capacity driven by two 220kV export cables, including windturbine generation capacity optimisation

CapEx

€3.00m/MW

Assumes progress in reducing costs

Tariff

€125/MWh

Applicable for first 15 years of operations; market price of €50/MWh for final years of operation

Windspeed

9.5 m/s

Mean value; Weibull distribution assumed

Wake Loss

10%

Central value; uncertainty assumed to be 50% of this value

30%/15%/12%

During development / construction / operation respectively; for modelling purposes only

Cost of Debt

5.5%

Variable, in particular depending on project characteristics, financing structure and lender’s appetite; for modelling purposes only; source [1]

Debt Ratio

Service

1.35

against P90 revenues, as anticipated at FID

1.1

Benchmarking of Modelling Approach

Capacity

Cost of Equity

The approach has been benchmarked against the financial model used in a recent transaction. The discrepancy between the two models in terms of NPV uplift at FID was less than 25%, which must be considered to be acceptable. No review of the cause of this discrepancy has been undertaken however potential reasons include: • the necessary simplifications in the financial modelling aspects within the analysis tool used for this paper and • the differences between the estimated assumptions used for this paper, i.e. Table 1, and the confidential values used in the real project financial model.

2. Valuation of Candidate Wind Measurement Strategies 2.1

Wind Atlas (Desk-top Study)

An immediate estimate of the wind resource at any site can be made using a wind atlas; for greatest accuracy this should be derived from a composite of measurements and models, such as satellite measurements, offshore met masts in the vicinity, mesoscale modelling, reanalysis (long-term) etc.

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Table 2: Wind Atlas (Desk-top Study) - Key Assumptions for Evaluation Assumption

Wind speed Uncertainty §

Cost

Value

Comment Actual value will depend on level of data and quality and proximity of calibration points; this value is arguably low (unconservative); consequentially benefits of measurement campaign would be higher than presented in Figure 1

7.5%

~ Zero

Timing

Immediate § = combined

This scenario is used as the reference for analysis. Figure 2 shows that for this scenario, obviously improved knowledge of the wind resource is only gained once the windfarm commences production.

10y P90-EY DCR FID = 10 year, P90 Energy yield estimate, Debt Cover Ratio set at FID LTR = Long-term reference (wind measurement)

Figure 2: Evolution of Wind Resource Uncertainty - Wind Atlas (Desk-top Study) If an offshore wind mast is located in the vicinity and the topography of any nearby shorelines is flat, the uncertainty would be lower, i.e. effectively evolving into the following "Neighbouring Offshore Met Mast" strategy.

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2.2

Met Mast in a Neighbouring Windfarm

Access to an existing offshore met mast in the vicinity will allow a more accurate estimate of the windspeed to be made, depending on: [1] distance from shore (windfarm and mast) [2] distance between windfarm and met mast [3] complexity of any nearby land terrain [4] extent of knowledge of the local wind-regime Table 3: Met Mast in a Neighbouring Windfarm - Key Assumptions for Evaluation Assumption

Value

Horizontal Uncertainty

4.0%

Duration

Comment Highly dependent on distances

5 years

Cost

Negotiable

Assumed zero for purposes of analysis; i.e. developer has access due to ownership of project or quid-pro-quo arrangement with owner

Timing

Immediate

Depends on project development maturity of neighbouring project

Figure 3 presents the measurement campaign programme, which delivers significantly reduced uncertainty at FID, Figure 4, compared with the no mast assumption (Figure 2). Typically there will be costs associated with procurement of this data, unless a quid-pro-quo arrangement can be reached, however this is ignored for the purposes of this analysis.

Figure 3: Programme - Met Mast in a Neighbouring Windfarm

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Figure 4: Evolution of Wind Resource Uncertainty - Met Mast in a Neighbouring Windfarm

2.3

New Conventional Met Mast

A conventional lattice tower met mast on a monopile or other suitable foundation has historically been the preferred wind measurement strategy, with around fifty units, including now decommissioned masts, located across Northern Europe. This type of equipment will measure at a single point within the windfarm and hence uncertainty remains regarding the variation across any large windfarm or if the site is close to shore. Table 4: New Conventional Met Mast - Key Assumptions for Evaluation Assumption

Value


2.0%

Cost Timing

Comment Due to size of windfarm

Very High 2 years

Requires geotechnical survey

As water depths at windfarm development sites have increased over recent years, the deployment of large installation plant have become necessary for the construction works, due to the deep waters, more severe wave regimes as well as the heavier foundation structures. These vessels have high day-rates, exacerbated by any tight market conditions, which will be due during construction as well as the several weeks of mobilisation and demobilisation. The consequence has been that deployment costs for offshore met masts have risen by a factor of ten over the past decade and installation may contribute over half the cost of a new met mast, as illustrated in Figure 5.

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Figure 5: Indicative Conventional Met Mast Cost breakdown With installation contributing such a large part of the overall cost, savings should be realisable if attention was focused on minimising installation costs rather than implicitly the weight of steel. An "installation-optimised design" could be based on the following principles: • identify a suitable installation spread working on construction at a windfarm in the region • design the foundation around the installation spread, this could involve designing the following parameters accordingly, for example: o monopile dimensions o sea-fastening design The implied objective is to minimise mobilisation and demobilisation costs. Note that the mast installation will require bespoke mobilisation and foundation installation cranes would normally require relatively low hook heights, however installation methods are available that avoid requirements for crane heights to exceed the met mast height. Hence although attractive and significant cost savings may be achievable, the fundamental disadvantage due to the high cost of this concept compared with the alternatives presented below, will remain. The programme presented in Figure 6 assumes a shorter measurement campaign of three years due to the need for geotechnical campaign and procurement and construction. Figure 7 indicates that uncertainty is reduced compared with previously examined options.

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Figure 6: Programme - New Conventional Met Mast


Figure 7: Evolution of Wind Resource Uncertainty - New Conventional Met Mast

2.4

LiDAR Unit on Lightweight Jacket (LiGA)

LiDAR units impose minimal loading on a foundation structure, allowing lighter-weight foundation designs to be deployed. A particularly attractive option appears to be a jacket on a suction-caisson foundation, Figure 8, effectively a derivative of the foundation successfully deployed at Horns Rev II in 2009.

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Figure 8: LiGA: LiDAR Unit on Lightweight Jacket Table 5 presents the key assumptions used for this scenario. Table 5: LiDAR Unit on Lightweight Jacket (LiGA) - Key Assumptions for Evaluation Assumption

Value


2.0%

Cost

High

anticipated

2 years

Requires geotechnical survey

Timing

Comment

The programme would be identical for a conventional met mast, Figure 6, due to a similar need for geotechnical survey and procurement and assumes three years of measurements at FID. Figure 9 presents the gradual reduction in the uncertainty in the energy yield of the windfarm.


Figure 9: Evolution of Wind Resource Uncertainty - LiDAR Unit on Lightweight Jacket (LiGA)

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The value of the campaign could be further increased by also installing a scanning LiDAR on the structure, thus reducing the horizontal uncertainty across the windfarm, as per Table 6. Table 6: LiDAR Unit on Lightweight Jacket (LiGA) augmented by Scanning LiDAR unit Key Assumptions for Evaluation Assumption

Value

Comment


1.0%

Measures only radial component of wind vector hence some uncertainty remains

Additional Cost

Medium

Costs to be confirmed by commercial negotiations

Timing

2 years

Same as Table 5: requires geotechnical survey


Figure 10: Evolution of Wind Resource Uncertainty - LiDAR Unit on Lightweight Jacket (LiGA) with Scanning LiDAR

2.5

Floating LiDAR

LiDAR units on a floating buoy will offer a number of very important practical and economic advantages once reliable performance has been demonstrated and the wider industry, in particular Technical Advisers to the investors and banks, feel comfortable. Table 7 presents the key assumptions used for this scenario.

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Table 7: Floating LiDAR - Key Assumptions for Evaluation Assumption

Value

Comment


1.5%

Assume buoy is redeployed within the site, thus providing knowledge of variation of windspeeds across the site

Cost

Medium

anticipated

Timing

< 1 year

As presented in Figure 11, deployment within a year is possible in theory and taking balanced view of uncertainties introduced by floater motion, Figure 12 indicates a potentially strong wind resource estimate at FID.

Figure 11: Programme - Floating LiDAR


Figure 12: Evolution of Wind Resource Uncertainty - Floating LiDAR

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2.6

Shoreline Scanning LiDAR

A shoreline scanning LiDAR campaign could be an attractive option, however there are relatively few offshore windfarm sites under development where current LiDAR units have sufficient range. Table 8 presents the key assumptions used for this scenario. Subject to suitable progress, conclusions could also apply to scanning radar. Table 8: Shoreline Scanning LiDAR - Key Assumptions for Evaluation Assumption

Value


0.0%

Cost

Medium

Timing

< 1 year

Comment Assumes twin units and full coverage of the offshore windfarm site

Figure 13: Programme - Shoreline Scanning LiDAR

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Figure 14: Evolution of Wind Resource Uncertainty - Shoreline Scanning LiDAR

3. Sensitivity Studies A full three year wind resource measurement campaign delivers greatest value to the project; if the campaign is delayed, the value to the project reduces; however even one year of measurements is beneficial for this strategy, as illustrated in Figure 15.

sensitivity study centred on scenario for Light-weight Jacket (LWJ) presented in Figure 1 (page 2)

Figure 15: Impact of Delays to Commencement of Measurement Campaign

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The main part of the analysis is based on the assumption that annual mean windspeeds vary from year to year with a standard deviation of 6%. There is some evidence that windspeeds may be more consistent offshore with reference [3] suggested a figure of 4.2%. Figure 16 examines the sensitivity of the value of wind measurements to this assumption, which show that the benefits of undertaking a wind measurement campaign remain substantially unaltered, indeed with a small increase.

sensitivity study with assumption of 4.2% [3]; the equivalent for a reference assumption of 6% is presented in Figure 1

Figure 16: Sensitivity to Annual Average Windspeed Variability The main part of this analysis is based on an arguably conservative assumption of 50% uncertainty in the estimate of the wake loss, as per Table 1 (page 4). This is the largest uncertainty within the calculation of the energy yield (i.e. as opposed to the wind resource). Figure 17 illustrates how the value of the wind measurement campaign increases as uncertainty in wake losses are reduced and confirms that this assumption behind the central scenario presented in Figure 1 (page 2) is appropriately conservative.

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sensitivity study centred on scenario for floating LiDAR presented in Figure 1 (page 2)

Figure 17: Sensitivity to Uncertainty in the Estimate of the Wake Losses

4. Portfolio Benefits Wind measurements at a particular project can also deliver value to the broader project development portfolio, in terms of prioritising the projects with the greatest potential, where capital or construction capability is constrained. Consider a scenario where five out of ten 700MW projects can be built and a high-quality wind measurement campaign for Project 1 is deployed, where previously no measurements had been present (i.e. wind assessment would rely on a wind atlas). In brief, the analysis methodology used is: • •

• •

Simulate ten projects, i.e. their actual value; Estimate the value of these projects perceived without a wind measurement campaign at Project 1 (Wind Atlas scenario in Table 9); for the purposes of this analysis, the error of this estimate is determined solely by the uncertainty in the wind measurement; Select the five best projects as predicted by the estimate value and calculate the true value of the portfolio (i.e. based on the actual value); Improve the estimation by implementing a high-quality wind measurement campaign thus reducing the wind measurement uncertainty, (High Quality Wind Knowledge scenario in Table 9); reselect the five best projects with this improved knowledge and determine how much the portfolio increases in value due to this better decision making (note that in many cases there will be no change).

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Table 9: Portfolio Wind Measurement Strategies - Uncertainty Summary

Source of Uncertainty

Wind

Uncertainty impact on Wind Energy production

Wind Knowledge Class Low High Atlas Quality Quality 7.50% 5.48% 4.24% 11.25% 8.22% 6.36%

Energy-production component § Total Energy Uncertainty

7.23% 13.37%

10.95%

9.63%

§ = See Table 10 for commentary on Technology Maturity

Within the example case presented in Figure 18, the resulting additional knowledge of the wind resource at Project 1 brings the estimated energy production at Project 1 closer to the actual value and allows that higher value Project 1 to be selected over Project 8. On average, this improved decision making will increase the value of the portfolio by around €10m.

Projects are either “Selected” for construction, or “Abandoned”

Figure 18: Investment Decision for Offshore Windfarm Project Portfolio

5. Discussion and Conclusions In the current market, the exceedingly high costs for conventional (monopile) offshore met masts make justifying on-site wind measurements challenging. However measurement campaigns can indeed deliver considerable value, in terms of reduced uncertainty; this delivers realisable NPV benefits, for example as improved project finance at FID.

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Much attention is currently being focused on floating LiDAR in particular and this assessment quantifies the attraction of such a Wind Measurement Strategy. LiDARs on Light-weight Jacket Structures also appear promising with the advantage of a lower technical risk. If present, a nearby met mast is also likely to deliver significant value; indeed its more lengthy dataset may well compensate for the higher horizontal uncertainty. In addition to considering the cost-benefit financial analysis of the candidate wind measurement strategies presented above, the technology maturity of the technology must be taken into account. Table 10 presents the classes used in the main conclusions presented in Figure 1. Table 10: Technology Maturity Technology Maturity

Status

Not Suitable

Inherent unsuitable and no prospects of change to status

Low

Suitable for some early stage purposes but not currently anticipated to reach wide-scale acceptance across the offshore wind industry imminently

Potential

Credible evidence that technology may become fully mature in timescale of projects currently under development; technology may already be suitable for early stage assessments and internal decision making

High

Fully mature technology; suitable subject to correct design and successful deployment and operation

References 1. Matt Taylor, Green Giraffe Energy Bankers; What Do Developers Need To Do To Their Risk Allocation & Procurement Arrangements To Secure Project Finance?; Wind Power Forums: Offshore Wind Financial Risk Management Focus Day, London, 14 March 2013 2. Joe Phillips / NORSEWInD; A new wind resource map for the North Sea - Combining the strengths of Earth Observation data, Mesoscale Modelling and Mast Measurements; European Offshore Wind, Stockholm; 14 September 2009 http://proceedings.ewea.org/offshore2009/.../131_EOW2009presentation.ppt

3. Berge Erik et al; NORSEWIND – Mesoscale model derived Wind Atlases for the Irish Sea, the North Sea and the Baltic Sea; http://orbit.dtu.dk/fedora/objects/orbit:119800/datastreams/file_91c79584-33a7-43dd-be61-b277048a274f/content

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