analysis of pre and post construction wind farm …...analysis of pre – and post construction wind...

© Lahmeyer International GmbH 2012

Analysis of pre – and post construction wind farm energy yields with focus on uncertainties

Abdelbari Redouane REMENA Master Program 2014

Contact: [email protected] 1

Author: Abdelbari Redouane

Supervisor: Prof. Adel Khalil

Supervisor: Prof. Siegfried Heier

Co-supervisor: Dr. Kurt Rohrig

Supervisor on-site: Dipl. Ing. Nicolás Veneranda

Defense of Master Thesis

Analysis of pre – and post construction wind farm energy yields with focus on uncertainties

Improvement of Lahmeyer’s spreadsheet application software for estimating wind farm energy yield uncertainties

2

• Introduction

• Motivation

• Uncertainty concept

• Model

• Input data for the Model

• Results

• Conclusion

Agenda

3

Data Source: GLOBAL WIND STATISTICS 2013, GLOBAL WIND ENERGY COUNCIL, 05.02.2014

Introduction

Motivation

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Predict Gross Yield

•Wind speed measurements

•Long Term Prediction Process

•Terrain and surface roughness model

•Wind flow model

•Turbine layout

•Turbine power curve

Calculate Losses and Predict NET Yield

•Wake Effects Availability

•Turbine Performance

•Electrical Environmental

•Curtailment Estimate

Uncertainties

Financial Model

•Site developer

•Independent

•Consultants

•Turbine Manufacturers

•Financiers

Error sources that can contribute in the uncertainty of the final result.

• Incomplete definition of the measurand ;

• Imperfect realization of the definition of the measurand ;

• Non-representative sampling if a sampling is used;

• Inadequate knowledge of the effects of environmental conditions on the measurement or imperfect measurement of environmental conditions;

• Personal bias in handling the instruments;

• Finite instrument resolution or discrimination threshold;

• …

JCGM 100:2008: Evaluation of measurement data – Guide to the expression of uncertainty in measurement (ISO/IEC Guide 98-3)

Uncertainty concept

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• In most cases, the best available estimate of the expectation or expected value 𝑞 of a quantity q that varies randomly [a random variable], and for which n independent observations qk have been obtained under the same conditions of measurement, is the arithmetic mean or average of the n observations

Type A evaluation: Method of evaluation of

uncertainty by the statistical analysis of series

of observations

• Previous measurement data;

• Experience with or general knowledge of the behavior and properties of relevant materials and instruments;

• Manufacturer’s specifications;

• Data provided in calibration and other certificates;

• Uncertainties assigned to reference data taken from handbooks.

Type B evaluation: Method of evaluation of

uncertainty by means other than the statistical

analysis of series of observations

Uncertainty concept

JCGM 100:2008: Evaluation of measurement data – Guide to the expression of uncertainty in measurement (ISO/IEC Guide 98-3) 7

Model

Win

d S

tud

y U

nce

rtai

nty

Prediction horizon [years]

1-year wind Total Total Uncertainty on Gross

Production (1-year)deviation

N-year wind deviation Total Uncertainty on Gross

Production (N-year)

Total Uncertainty

Measurement and Data Processing wind to Energy

converted

Measurement and Data Processing - wind speed

related

Data Integrity

Data Analysis

Long-term correlation

Measurement - wind speed related

Calibration

Type of Anemometer

Mounting

Flow modeling

Wake modeling

Power curve

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Production data Variability

Standard deviation of the monthly energy production of a set of turbines

Calculate the different standard deviation and average them and scale the average to the AEP level

Sum the root square of the individual standard deviations

Use of the correlation and the covariance matrices

Wind Study uncertainties

Combining uncertainties

Wind to Energy uncertainties

Sensitivity analysis

Combining uncertainties

Wind related uncertainties

Energy related uncertainties

Model

Image source: http://www.vectortemplates.com

Input data for the Model

Farm Location Designation

Site1 Italy Confidential

Site 2 Italy Confidential


Site 4 France Confidential



Site 7 Portugal Confidential

Site 8 Pakistan Confidential


Farm Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9

number WTG 19 15 48 5 6 5 6 33 40

Nominal Power 2 2,5 2 2 2 2,3 2 1,5 0,85

Hubhight m 78 100 67 78,3 80 85 80 80 55

mast with measurement height

50 m 30 m.50 m 30 m 40 m

53 m 49,5

85.0 m, 66.0 m and

42.0 m 49,5 81, 40 m 31, 50 m N/A

number of met masts

3 2 4 1 1 1 2 2 1

distance from mast to WTGs On site On site On site On site On site On site On site

1 On site , 1 22km

On site

Extension climate data input

4 years 8 months

4 years 6 years 1 year 1 year (16 months)

1 year 8.5, 5.5 years

4.5 , 5 years

2 years

Farm Nominal Power

38 37,5 96 10 12 11,5

12 (limited to 10 - grid requireme

nt)

49,5 34

terrain complexity

Flat , hilly

Flat (modestly

hilly agricultura

l)

Complex Flat

, hilly Flat

, hilly Complex Flat Flat Complex

Neighboring Farms

2 existing 2

authorized 1 existing 0 0 1 0

0(repowering)

2 authorized

2

data for the LT adjustment 4

NCEP/NCAR ts

1 Weather Station and 1

NCEP/NCAR ts

NCEP / NCAR wind

data

2 Weather Station +NCEP

1 Weather Station +NCEP

Weather Station

NCEP/NCAR ts

NCEP/NCAR

NCEP/NCAR

flow modelling WAsP WAsP WAsP WAsP WAsP WAsP WAsP WAsP WAsP

production data Monthly turbines

Data

Monthly turbines

Data

Monthly turbines

Data

Monthly Farm Data

Monthly Farm Data

Monthly Farm Data

Monthly Farm Data

Monthly turbines

Data

Monthly turbines

Data

Graphs illustrating the monthly production and the variability related to it

Input data for the Model (Example, site 1)

Site 1

6 8

30 31

32 35 37 38

39 40 41

42

43 44

48

54 55

56 57

1-2013 / 8-2014

Farm Site 1

Number of WTG 19

Nominal Power of WTG MW 2

Hub height m 78

Image source: Lahmeyer International

11

Results (Example, site 1)

Site 1 Site 1

Correlation Turbines Correlation Months

AEP PD [kWh] 87 722 129 87 722 129

Average Monthly PD [kWh] 4 616 954 7 310 177

StdDev Monthly PD [kWh] 3 546 014 1 228 586

Average StdDevPD [kWh] 544 615 1 633 887

SQRT(Sum(StdDev²)) PD [kWh] 699 796 2 493 177

SQRT(Sum(Sum COVi)) PD [kWh] 748 245 5 614 522

Average R 75% 48%

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Site 1 Site 1

PD WS

AEP NET [kWh/a] 87 722 129 110 208 538

StdDev 6 640 563 16 095 082

Variability [%] 8%

Uncertainty WS [%] 15%

Site 1 Site 1

PD WS

PoE AEP NET [kWh/a] AEP NET[kWh/a]

50% 87 722 129 110 208 538

75% 83 243 137 99 352 570

90% 79 211 905 89 581 860

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Results

Farm Terrain FLH WS [h] FLH PD [h]

Site 1 Flat 2 900 2 308

Site 2 Flat 2 574 1 859

Site 3 Complex 2 336 1 955

Site 4 Flat 2 551 2 374

Site 5 Flat 2 608 1 776


Site 7 Flat 2 890 3 014

Site 8 Flat 2 872 2 564


Farm Terrain WS Uncertainty [%] PD Variability [%]

Site 1 Flat 15% 8%

Site 2 Flat 13% 4%

Site 3 Complex 12% 8%

Site 4 Flat 12% 4%

Site 5 Flat 13% 5%


Site 7 Flat 12% 7%

Site 8 Flat 16% 8%


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Results

16

Results

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Results

PoE(90%) WS AEP PoE(90%) PD AEP -> Average estimation relative deviation of

+15%

PoE(50%) WS AEP PoE(50%) PD AEP -> Average estimation elative deviation of

+15%

PoE(90%) WS AEP PoE(50%) PD AEP -> Avrage estimation relative deviation of

-2%

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Conclusion

• Review of the uncertainty analysis tool • New approach for uncertainty learning curve was implemented providing a powerful tool for

comparing wind assessment uncertainties and the production data variabilities. • Two components of the variability were calculated as correlated uncertainties using the

correlation and the covariance matrices • Two main aspects of variability of production data are presented here, temporal and spatial

variability. • A method is presented for combining the variabilities as a combined uncertainty that arises in

wind production as correlated and uncorrelated variabilities. • The variability at the wind farm level and annual energy production is scaled accordingly to the

production data available . • A comparison between the results of many farms with different characteristics is presented.

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Recommendations

• In the list below some extension areas are given: – Correct the NET estimated PD AEP with the Long Term of Wind Resources correction – Include more sites and deep analysis of the different aspects of every site, and even better if

some aspects could be quantified and statistically processed. – Resolution of the PD and the time stamp for shorter than monthly production data. – Uncertainties details of the wake modeling, curtailment, losses estimation and availability of

the turbines vs. spatial variability. – Including nacelle wind data in the analysis for directional behavior investigation

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Contact: [email protected]

analysis of pre and post construction wind farm …...analysis of pre – and post construction wind...

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