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Slide 1 / 10-Sep-13
Decision support
for installation of offshore wind turbines
Prepared by:
Yngve Heggelund
with contributions from
Birgitte Furevik, Sigrid Ringdalen Vatne, Angus Graham,Idar Barstad, John Dalsgaard Srensen, Joachim Reuder,
Rune Yttervik
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Motivating problem
The cost of installing offshore wind turbinesmust be distinctly reduced
Waiting for weather windows is a significantcost contributor
Criteria to commence installation operationsare related to simple parameters
Significant wave height
Average wind velocity at referenceheight
The physical limitation are however relatedto response parameters
Motions
Accelerations Forces
Uncertainties are currently not properlytaken into account in the decision making
Slide 2 / 10-Sep-13
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General project idea
Couple weather forecast models to an advanceddynamical model (SIMO) to obtain responseparameters
Improve local weather forecasts by utilizing localmeasurements
Calibrate forecast models
Provide estimates of uncertainty
Use statistical models to capture uncertainty ofresponse characteristics
Integrate the above into an online risk baseddecision support system
Clear and informed view of the risks andpotential costs of carrying out an operation ina given timeframe
Slide 3 / 10-Sep-13
Local weathermeasurements
Calibratedweather modelswith uncertainty
SIMO
Models of
operationalphases
Decisionsupport system
Costs offailed
operations
Rationallimits forresponses
Statistical
models
EPSStatisticalcalibration
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Research proposal
Title: Decision support for installation of offshore wind turbines Research partners: CMR, met.no, Uni Research, UiB, AAU,
Marintek, UiS, UiA.
Industry partner: Statoil.
Associated partners: Reinertsen Engineering, Fred. OlsenWindcarrier.
Proposal for competence building project was submitted to theMAROFF program in the Research Council of Norway September5th 2012. Total budget: 8.4 MNOK over 3 years (80% by RCN, 20% by Statoil).
Project management by CMR. Consortium agreement signed August 15th2013.
Slide 4 / 10-Sep-13
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Project overview
Slide 5 / 10-Sep-13
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Installation test case 1
Integrated installation of offshorewind turbines of gravity-basetype Reduce installation cost by reducing
offshore heavy-lifting activities
Complete, or partly completestructure transported to site(integrated installation operation)
Operating phases:
Tow out
Mooring and positioning on site
Lowering of foundation to sea-floor Setting foundation down into sea floor
Slide 6 / 10-Sep-13
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Installation test case 2
Installation of wind turbinerotor by floating crane vessel Installation of one piece at a time
on site
Operating phases:
Transportation of rotor to site Mooring and positioning on site
Lifting the rotor from the deck ofthe transportation vessel
Placing the rotor onto the pre-installed nacelle
Slide 7 / 10-Sep-13
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WP 1: Responses and requirementsgiven met-ocean conditions
Couple a simulation tool (SIMO) to weather forecast models Transform forecasted environmental parameters to input parameters
for SIMO
Description of critical responses for selected test cases Describe the operating phases of the test case operations
Establish simulation models
Identify durations, dependenciesand point of no return
Identify critical response
parameters and their value
Slide 8 / 10-Sep-13
Marintek & Statoil
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SIMO: Equipment response simulator
Slide 9 / 10-Sep-13
SIMO (Simulation of Marine Operations) developed andowned by Marintek
Non-linear time domain simulation of motions and stationkeeping of multi-body systems
Used in the oil and gasindustry: Offshore crane operations
Subsea installation
Jacket installation and removal
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WP 2: Improved forecasting frommodels and measurements
Collect and organize observations Existing observations and tailored measurement campaigns
Met-ocean instrumentation from NORCOWE and the NORCOWE-NOWITECH infrastructure projects EFOWI and NOWERI will beavailable
2 buoy-mounted atmosphericturbulence measurementsystems
1 oceanic turbulencemeasurement system
1 scanning wind lidar
1-2 met-ocean buoy systems
2 WindCube lidarwind profilers
2 ZephIR lidar wind profilers
1 scintillometer
Data will be available through the METAWIND portal
Slide 10 / 10-Sep-13
Universitetet i Bergen
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WP 2: Improved forecasting frommodels and measurements (cont.)
Ensemble prediction system (EPS) Uncertainties in forecasts occur because
there are uncertainties in the initial conditions
numerical models approximates the exact lawsof physics
An EPS runs the same model many times
with slightly perturbed initial conditions The EPS of the European Centre for Medium-
Range Weather Forecasts (ECMWF) iscurrently tuned to develop a realistic spreadonly after 48 hours
Part of doctoral study to modify the scheme toyield one fit for purpose (under NORCOWE basefunding)
Slide 11 / 10-Sep-13
Downscaling of weather forecasts tocapture the effect of coastaltopography and bathymetry 2.5 km for wind
0.25 km for waves and currents
Uni Research & met.no
Wave height
Temperature (yr.no)
Precipitation (yr.no)
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WP 2: Improved forecasting frommodels and measurements (cont.)
Calibrated probabilistic forecasting Use measurements and raw ensemble forecasts to develop a statistical
method to calibrate the probabilistic forecasting of wind, waves andcurrent (example from deterministic model shown below)
Quantify forecast skill of using real time measurements for short term
forecasting (6-24 hours lead time) Export weather forecasts with associated uncertainty in a format
suitable for SIMO
Slide 12 / 10-Sep-13
Met.no & Uni Research
Original forecast
Calibrated
forecast with
uncertaintyWavehe
ight
January 2009
Wave
model
prognoses
Observations
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WP 2: Improved forecasting frommodels and measurements (cont.)
Statistical models to capture the uncertainty of leadingresponse characteristics based on the uncertainty of wind,waves and currents
Use the computer simulation tool (SIMO) to make models of theresponse characteristics as a function of the geophysical variables
Develop methods to estimate the probability of exceeding criticallevels as a function of time
Slide 13 / 10-Sep-13
Aalborg universitet
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WP 3: Decision support system foroperation planning
Map visualization Of weather variables (with uncertainty)
Of response characteristics (with uncertainty?)
Plan and optimize the transportation route
Slide 14 / 10-Sep-13
Christian Michelsen Research
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WP 3: Decision support system foroperation planning (cont.)
Compute and visualizebelow critical time intervalsfor operational phases User defined probability of
being below a critical level
User evaluation ofpresentation and interaction Establish a representative user
group of potential end-users
Task the user group withtesting, and collect feedback
Compare existing methods tothe proposed method
Slide 15 / 10-Sep-13
Christian Michelsen Research
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Challenges
Cross discipline project between institutionswith little or no prior project cooperation Do we speak the same language? Do we understand
each other?
Choice of project test case site ECMWF ensembles are not stored in full in the
archives, making it difficult to use a historical testcase like Sheringham Shoal
Dudgeon will probably not be scheduled until afterproject completion (virtual test case?)
FINO3?
Slide 16 / 10-Sep-13
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Summary
Provide an objective foundation for decision support takinginto account The real physical limitations of the equipment being used
The uncertainties in the weather-dependent data
Challenge existing practice of using simple parameters suchas significant wave height and average wind velocity Enable evaluating different installation procedures
Ideas and principles can also be applied to the operational
phase
Main goal: Reduce the cost of installing offshore windturbines
Slide 17 / 10-Sep-13
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Thank you for your attention!
Slide 18 / 10-Sep-13