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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
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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 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.
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
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
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
Bayesian Updating of Project Schedules • Results: Sheringham Shoals Wind Farm
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
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
Selection of Work Boats • 3 Categories of Work Boats • Small • Medium • Large
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