OPERATIONAL PLANNING FOR OFFSHORE WIND ENERGY PROJECTS

Installation Scheduling for Offshore Wind Farms in the North Sea and Atlantic Ocean

Team D Johnston Dietz Sam F. Maopeng Gilbert Malinga

Overview

• Objectives • Data Collection• Methodology• Statistical Analysis

• Project/Work Package Duration• Weather Criteria

• Operational Planning

Objectives• Develop a model to estimate operating weather conditions for a given

sea for different seasons within the year

• Accurately estimate project durations for a given size of wind farm

• Show that information attained from the North Sea and Irish sea is applicable for operational planning for proposed wind farms off the US east coast

Mission StatementDevelop a ground breaking model, based on information attained from European projects, to assist in the development of wind farms in the United States.

Data Collection

• Buoy Data• Noaa.gov• Irish Marine Institute• Royal Meteorology Institute

Data Collection

• Work boat criteria• Jumbo Offshore• Volker Wessels Co.• MPI Offshore• Mermaid Maritime

• Criteria of interest• Operational

• Significant wave height• Wind speed• Wave period

• Transit• Significant wave height• Wind speed• Wave period

Data Collection

• Project/Work Package data• Share holder notices• Weekly updates on corresponding project website• 4coffshore.com

• Areas of interest• Foundation Installation• Turbine Installation• Array Cable Installation• Export Cable Installation• Substation Installation

• Data Scaled down to 1 turbine unit/km (exception to the substation)

• Data Scaled up to 90 turbine wind farm

Methodology

• Data Collection & Statistical Analysis• Second Moment Method: Probability distribution (project durations)• Monte-Carlo Simulation: Probability distribution (project durations)• Bayesian Inference: Updating project durations• Joint Probability Model: Distributions of operating weather windows

Project Duration Statistics

Foundation Turbines Export Cable Array Cables Substation TotalMean (Days) 255.3 225.5 75.7 189.7 8.6 579.6

SD (Days) 79.9 87.6 42.3 69.3 6.5 139.8CV 0.31 0.39 0.56 0.37 0.75 0.24

ρij Foundation Turbine Export Cable Array Cable SubstationFoundation 1.00 0.40 0.16 0.34 0.26

Turbine 0.40 1.00 0.36 0.54 0.10Export Cable 0.16 0.36 1.00 0.09 0.12Array Cable 0.34 0.54 0.09 1.00 0.08Substation 0.26 0.10 0.12 0.08 1.00

45%

40%

13%

2%

Installation Durations

FoundationTurbinesExport CableSubstation

Statistics: Installation Duration

Probability Distribution: Project Durations-2

36

-156

-7

6 4 84

164

245

325

405

485

565

645

725

806

886

966

1046

11

26

1206

12

86 0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Probability Distribution of Project Duration

PDF - Second MomentPDF - Monte CarloCDF - Second MomentCDF - Monte Carlo

Duration (Days)

Prob

abili

ty D

ensi

ty

Cum

ulat

ive

Bayesian Updating of Project Schedules

• Systematic method of updating parameters in light of new information….• Bayes Law

Joint Multivariate Normal Probability Density

• P(ϴ)- Prior distribution of parameters• P(D|ϴ)- Conditional probability of pertinent variable (D) given parameters (ϴ)• P(D)- Marginal distribution of pertinent variable (D)• P(ϴ|D)- Posterior distribution of parameters (ϴ) given the pertinent variable (D)• x- N x 1 vector for work package duration (x1, x2, x3,…xN)• µ- N x 1 vector of mean values of work package durations (µ1, µ2, µ3,…µN)• V-N x N Covariance matrix• |V|- Determinant of covariance matrix

( | ) ( )( | )( )

P D PP DP D

1/2

1( ) exp ( ) ( )2(2 ) | |

TX Nf x x V x

V

Bayesian Updating of Project Schedules

• Case Studies• Sheringham Shoals Wind Farm, UK• Capacity: 317 MW (88 Units)

• Horns Rev II Wind Farm, Denmark• Capacity: 209 MW (91 Units)

Source: www.EWEA.org

-236

.275

-1

72.1

65

-108

.055

-4

3.94

5 20

.165

84

.275

14

8.38

5 21

2.49

5 27

6.60

5 34

0.71

5 40

4.82

5 46

8.93

5 53

3.04

5 59

7.15

5 66

1.26

5 72

5.37

5 78

9.48

5 85

3.59

5 91

7.70

5 98

1.81

5 10

45.9

25

1110

.035

11

74.1

45

1238

.255

13

02.3

65

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Bayesian Revision- Sheringham Shoals Wind Farm

Prior

Posterior 1

Posterior 2

Posterior 3

Posterior 4

Duration (Days)

Pro

babi

lity

Bayesian Updating of Project Schedules

• Results: Sheringham Shoals Wind Farm

ActivityNumber

ExpectedDuration

Actual DurationReported

Revised / PredictedDuration of Units

Predicted Duration

at Completion

PredictedStdev

Probability ofOverrunning

565.10 160.28 5.00%

1 255.3 266 266.0 581.6 102.3 0.78%2 75.7 32 32.0 498.7 82.4 0.00%

3 8.6 4 4.0 521.3 87.2 0.02%4 225.5 219.3

Bayesian Updating of Project Durations

ActivityNumber

ExpectedDuration

Actual DurationReported

Revised / PredictedDuration of Units

Predicted Duration

at Completion

PredictedStdev

Probability ofOverrunning

565.10 160.28 5.00%

1 255.3 150 150.0 402.5 102.3 0.00%2 75.7 111 111.0 522.1 82.4 0.01%

3 8.6 26 26.0 535.9 87.2 0.04%4 225.5 248.9

-236

.275

-1

56.1

38

-76.

000

4.13

7 84

.275

16

4.41

2 24

4.55

0 32

4.68

7 40

4.82

5 48

4.96

2 56

5.10

0 64

5.23

8 72

5.37

5 80

5.51

3 88

5.65

0 96

5.78

8 10

45.9

25

1126

.063

12

06.2

00

1286

.338

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Bayesian Revision- Horns Rev II Wind Farm

Prior

Posterior 1

Posterior 2

Posterior 3

Posterior 4

Duration (Days)

Pro

babi

lity

Operating Weather Windows• Crucial for operational planning: Scheduling & Vessel Selection• Work packages may have different total durations• Installation vessels (work boats) have threshold operating conditions….• Weather windows are seasonal in nature

Data Analysis

• The Concept of Weather Windows• The Features of Weather Windows

• Seasonality• Duration

• Probabilities of Available Work Window• Selection of Work Boats

Operating Weather Windows

• Threshold Wind Speed (w) and Wave Height (Hs)• Duration

Seasonality

Probabilities of Weather Windows

• Joint Probability of Wind and Waves

• Conditional Probability

w: wind speed;

Hs: wave height;

subscript t: threshold

TS: Total Season

Seasonality Effects

• Spring, Summer-Autumn (Su-Au), Winter• Threshold: w = 6 m/s, Hs = 2 m

Varying Threshold Durations

Selection of Work Boats

• 3 Categories of Work Boats• Small• Medium• Large

Selection of Work Boats

Work Boats Threshold Wind Speed (m/s)

Threshold Wave Height (m)

Sea Installer 6 2

Oleg 12.5 4

Selection of Work Boats

Oleg Sea Installer

Spring Su-Au Winter Spring Su-Au WinterIrish 87% 97% 85% 36% 51% 22%Total Available

333 days 143 days

Delaware 88% 96% 85% 35% 54% 30%Total Available

327 days 155 days

Conclusions

• Average project durations about 18 months for wind farms with 90 units• Foundations and turbine installations take up 85 % of total project durations• Estimates of predicted total project duration at completion were similar to

project manager’s projections for both case studies• The model we developed is able to capture the influence of seasonality and

threshold duration• Trends between seasons are similar between Irish Sea and Delaware coast

except for the fact that the Irish sea has a harsher winter• Small work boats are more vulnerable to sea conditions, but they have a

longer working windows in US job for the milder sea state. Therefore wind farms in the states have the potential to cost less than wind farms in Europe.

Future Work

• Develop a cost model for the different work boats available• As projects over seas wrap up data will be taken from those projects

and put into our model for the sake of developing more accurate results

• Incorporate wave period into model

Acknowledgments

• Irish Marine Institute• Royal Meteorology Institute, Netherlands• National Oceanographic & Atmospheric Association (NOAA)• Vattenfall Limited (UK)• DONG Energy• Jumbo Offshore• 4C Offshore• Mermaid Maritime

References

• Graham et al., 1982. The Parameterization and Prediction of Wave Height and Wind Speed Persistence Statistics for Oil Industry Operational Planning Purposes. Coastal Engineering, Vol. 6: 303-329.

• Fouques. S, D. Myrhaug and F. G. Nielsen. 2004. Seasonal Modeling of Multivariate Distribution of Metocean Parameters with Application to Marine Operations. Transactions of ASME. Vol. 26: 202-212.

• www.noaa.gov. Accessed on 10.31.2011• Reinschmidt. K. 2011. Project Risk Management Course Notes. Civil Engineering Department, Texas A&M

University.• Boutkan, Brian; Jumbo Offshore• Brooks, Robert; 4C Offshore• Parratt, Ann; Vattenfall

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