A Time Series Comparison of Pre-

Construction Energy Yield Model

and Operational Data

Alex Clerc, Lee Cameron & Hannah Staab

December 9, 2014

EWEA Technology Workshop, Malmö

Introduction and Contents

• We are seeking to answer two questions:

– How accurate is the RES energy yield methodology?

– What can be done to improve it?

• Presentation contents:

– Data set description

– Validation Results

– Validation Methodology Details

– Lessons Learned

0

2

4

6

8

10

12

14

16

-12% -8% -4% 0% 4% 8% 12%Num

ber

of

Win

d F

arm

Years

Energy Prediction Error

Site speed up map using Ventos CFD

The validation database consists of:

• 31 wind farms

• 638 turbines

• 1.2 GW installed capacity (0.4% of entire world capacity)

Validation Database

Wind farm count by region and turbine manufacturer (31 total)

Senvion

Validation Results

• Overall observed error by turbine for 31 wind farms. Average validation

result: -2% after correcting for unforeseen losses using SCADA analysis.

0

50

100

150

200

250

-35% -30% -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25%

Num

ber

of

Turb

ines

Energy Prediction Error

Before SCADA Corrections After SCADA Corrections

Over prediction Under prediction

Validation Results

• Subset of 18 wind farms representative of modern development.

• Average result: 0%

• Latest modelling methodology used (not the original yield prediction)

• Standard deviation lower than predicted by uncertainty model (note IAV

not applicable because windiness corrections have been made)

0

2

4

6

8

10

12

14

16

-12% -8% -4% 0% 4% 8% 12%

Num

ber

of

Win

d F

arm

Years

Energy Prediction Error

Over prediction Under prediction

Problem 1: Original pre-construction models may be outdated

in terms of layout, exposure, curtailment schemes, modelling

methodology

Solution: Create an “as-built yield model” using up to date

information and methodology

Example: Wind farm extended in 2007, original yield does not

consider wake effect of extension

Validation Methodology

Alta I (2003)

Alta II (2007)

Historic and Operational Windiness

Problem 2: Understanding of long-term windiness is based on

specific reference data period.

Solution: Time series comparison of modelled and measured

production, accounting for real windiness at the site. This

requires Time Series MCP (TSMCP).

Validation Methodology

Start of

Operations

(e.g. MERRA, available hourly)

Hourly predictions by turbine

10-minute turbine data

Monthly site meter data

Validation Methodology

• Wind farm climate and yield modelled on an hourly basis

• Model is run for every turbine, not just the WF as a black box

• Model identical to pre-construction yield model, but by turbine by hour

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30

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Sep

-07

Dec

-07

Mar

-08

Jun

-08

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-08

Dec

-08

Mar

-09

Jun

-09

Sep

-09

Dec

-09

Mar

-10

Jun

-10

Sep

-10

Dec

-10

Mar

-11

Jun

-11

Sep

-11

Dec

-11

Mar

-12

Jun

-12

Sep

-12

Dec

-12

Mar

-13

Jun

-13

Sep

-13

Mo

nth

ly E

ne

rgy

Yie

ld [

GW

h]

Mo

nth

ly W

ind

Sp

ee

d [

m/s

]

Month

Reference Wind Speed TSMCP Site Wind Speed Net Yield Model Net Yield

Validation Methodology

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14

Sep

-07

Dec

-07

Mar

-08

Jun

-08

Sep

-08

Dec

-08

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-09

Jun

-09

Sep

-09

Dec

-09

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-10

Jun

-10

Sep

-10

Dec

-10

Mar

-11

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-11

Sep

-11

Dec

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Mar

-12

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-12

Sep

-12

Dec

-12

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-13

Jun

-13

Sep

-13

Mo

nth

ly E

ne

rgy

Yie

ld [

GW

h]

Month

Net Yield Model Net Yield

• Wind farm climate and yield modelled on an hourly basis

• Model is run for every turbine, not just the WF as a black box

• Model identical to pre-construction yield model, but by turbine by hour

Validation Methodology

0

2

4

6

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14

Sep

-07

Dec

-07

Mar

-08

Jun

-08

Sep

-08

Dec

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-10

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-10

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-10

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-10

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-11

Jun

-11

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Dec

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-12

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-12

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-12

Dec

-12

Mar

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Jun

-13

Sep

-13

Mo

nth

ly E

ne

rgy

Yie

ld [

GW

h]

Month

Monthly Average Model Net Yield Model Net Yield

• Wind farm climate and yield modelled on an hourly basis

• Model is run for every turbine, not just the WF as a black box

• Model identical to pre-construction yield model, but by turbine by hour

Problem 3: Measured production can differ from the model not

just due to availability, but also due to other losses (icing,

curtailment, sub-optimal operation, ...)

Solution: Quantify these “running losses” and account for them

in the comparison with the model.

IcingTurbines

de-rated

High wind

hysteresis

Validation Methodology

Automatic

categorisation of

all 10-minute

SCADA data

Lessons Learned

• Total gross to net losses were reasonably accurate for most projects, but

accuracy of loss breakdown can be improved

• Example: default downtime losses can be set to match historic data

Turbine B.O.P. Grid

Measured Downtime (Time-Based)

Measured Downtime Loss (Energy-Based)

RES Standard Downtime Assumption

Lessons Learned

• Using a CFD flow model helps avoid over-predictions at specific turbines

• CFD flow model improves prediction accuracy by:

• 0.7% for the fleet

• Up to 5% for individual sites

• Up to 20% for individual turbines

0

20

40

60

80

100

120

-30% -25% -20% -15% -10% -5% 0% 5% 10% 15% 20%

Num

ber

of

Turb

ines

Energy Prediction Error

Linear CFD

Over prediction Under prediction

Lessons Learned

• TSMCP can have considerable diurnal and seasonal errors which also

show up in the energy validation results

• Downscaling of ReAnalysis data can help to some extent

• Analogue Ensemble (alternative to linear TSMCP) appears promising

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ene

rgy

Pre

dic

tio

n E

rro

r

Month of Year

Energy Prediction Error by Month of Year

UK&I Southern France

• A time series approach to energy yield validation has been developed

and implemented on a fleet of 31 wind farms

• This approach to validation has allowed RES to ensure an unbiased pre-

construction methodology

• Three key challenges to validation have been addressed:

1. Changes to the wind farm and its surroundings since construction

2. Weather during the operational period which differs to long-term

3. Operational losses which add noise to the comparison

• There is ample opportunity for further improvement to the model:

• Flow modelling

• Seasonal bias

Summary

Suspected

icingTurbines

de-rated

High wind

hysteresis

Any Questions?