Operational planning for offshore Wind Energy projects
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Transcript of Operational planning for offshore Wind Energy projects
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
Comments and Questions