Operational planning for offshore Wind Energy projects

1. 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

2. Overview • Objectives • Data Collection • Methodology • Statistical Analysis • Project/Work Package Duration • Weather Criteria • Operational Planning

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

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

5. 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

6. 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

7. 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

8. Project Duration Statistics

9. Statistics: Installation Duration

10. Probability Distribution: Project Durations

11. 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

12. 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

13. Bayesian Updating of Project Schedules • Results: Sheringham Shoals Wind Farm

14. Bayesian Updating of Project Durations

15. 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

16. Data Analysis • The Concept of Weather Windows • The Features of Weather Windows • Seasonality • Duration • Probabilities of Available Work Window • Selection of Work Boats

17. Operating Weather Windows • Threshold Wind Speed (w) and Wave Height (Hs) • Duration

18. Seasonality

19. Probabilities of Weather Windows • Joint Probability of Wind and Waves • Conditional Probability w: wind speed; Hs: wave height; subscript t: threshold TS: Total Season

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

21. Varying Threshold Durations

22. Selection of Work Boats • 3 Categories of Work Boats • Small • Medium • Large

23. Selection of Work Boats

24. Selection of Work Boats

25. 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.

26. 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

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

28. 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

29. Comments and Questions