Decision Support for Wind Farm Installation_2013

8/9/2019 Decision Support for Wind Farm Installation_2013

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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


2/18

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


3/18

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


11/18

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)


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)


12/18


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


Met.no & Uni Research

Original forecast

Calibrated

forecast with

uncertaintyWavehe

ight

January 2009

Wave

model

prognoses

Observations


13/18


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


Aalborg universitet


14/18

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


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


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?



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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



18/18

Thank you for your attention!


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Decision Support for Wind Farm Installation_2013

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