Norwegian University of Science and Technology
Optimizing Jack-Up Vessel Chartering
Strategies for Offshore Wind Farms
Andreas Jebsen Mikkelsen
Odin Kirkeby
Marielle Christiansen
Magnus Stålhane
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Outline
• Motivation
• Problem description
• Mathematical model
• Preliminary results
• Further research
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Jack-up vessel
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Motivation
*from Dinwoodie et al (2015)
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Motivation
*from Dinwoodie et al (2015)
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Jack-up vessel charter rates
*Based on data from Dalgic et al (2013)
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Current Jack-Up Charter Practices
• Options:
• Annual charter
• Fix-on-fail
• Batch-repair
• Difficult to determine best option
• Obstacles:
• Inflexibility
• Expensive
• Determining optimal batch
• Uncertainty
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Optimal jack-up strategy depends on:
• Size of the wind farm
• Weather conditions at the wind farm site
• Failure rate of the components
• Charter rate for jack-up vessels
• Capabilities of the jack-up vessels
• Goal: To determine when, and for how long, to charter in
a jack-up vessel in order to minimize expected total O&M
cost.
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Mathematical model
• Uncertain parameters:
– When failures that require jack-up vessels occur
– The weather conditions at the wind farm site each day of the
planning horizon
• Two-stage stochastic optimization model
– First stage: Decide when, and for how long, to charter a jack-up
vessel
– Second stage: Given first stage decision, how to deploy the jack-
up vessel in order to minimize the downtime cost
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First stage model
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First stage modelDaily charter rate
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First stage modelMobilisation rate
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First stage modelExpected total downtime cost
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First stage model
Must mobilize vessel to have it available
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First stage model
Must keep vessel for a minimum number of
days, if mobilised
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Second stage model
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Second stage modelDowntime cost of fixinga failure on a given day
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Second stage modelPenalty cost applied if a
failure is not fixed
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Second stage model
Faliures can only be fixed in time periods the vessel is chartered. Repair time is weather dependent
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Second stage model
All failures must be fixed, otherwise a penalty is added
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Solution method
• The two-stage stochastic programming model is solved
using scenario generation and then solving the
deterministic equivalent
• Each scenario represents one realisation of one year
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Scenario
time
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Scenario
time
Wave height
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Scenario
time
Wave height Wind speed
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Scenario
time
Wave height Wind speed
Failure oftype 1
Failure oftype 2
Failure oftype 1
Failure oftype 3
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Scenarios
Scenario 1
Scenario 2
Scenario n
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Scenarios
Scenario 1
Scenario 2
Scenario n
All scenarios represent different possiblerealisations of the weather and failureparameters based on sampling
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Scenarios
Scenario 1
Scenario 2
Scenario n
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First stage decisions – must be the same
in all scenarios
Scenario 1
Scenario 2
Scenario n
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First stage decisions – must be the same
in all scenarios
Scenario 1
Scenario 2
Scenario n
The decision of when to charter a vessel must be the same in all
scenarios
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Second stage decisions – different for
each scenario
Scenario 1
Scenario 2
Scenario n
What a vessel does once it is chartered, depends on the scenario
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Second stage decision – when to fix a
given failure
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Second stage decision – when to fix a
given failure
Why wait?
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Second stage decision – when to fix a
given failure
Why wait?
weather conditions not good enough to perform
maintenance
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Second stage decision – when to fix a
given failure
Why wait?
Or vessel not charteredin these periods
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Downtime costs – depends on wind
speed
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Preliminary Results
• The model is able to solve one-year problems
with 100 scenarios
• Weather conditions at site and vessel
capabilities greatly affect results
• Anything from 50 to 200 days of charter for a
80-100 turbine wind farm
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Future reasearch
• Ensure realistic data
– Huge differences in values used in different research
• Verify model results in a cost of energy simulation model
• Compare strategy with batch-repair strategy
• Add possibility of sub-leasing
Norwegian University of Science and Technology
Optimizing Jack-Up Vessel Chartering
Strategies for Offshore Wind Farms
Andreas Jebsen Mikkelsen
Odin Kirkeby
Marielle Christiansen
Magnus Stålhane
Norwegian University of Science and Technology
Optimizing Jack-Up Vessel Chartering
Strategies for Offshore Wind Farms
Andreas Jebsen Mikkelsen
Odin Kirkeby
Marielle Christiansen
Magnus Stålhane
Norwegian University of Science and Technology
Optimizing Jack-Up Vessel Chartering
Strategies for Offshore Wind Farms
Andreas Jebsen Mikkelsen
Odin Kirkeby
Marielle Christiansen
Magnus Stålhane
Norwegian University of Science and Technology
Optimizing Jack-Up Vessel Chartering
Strategies for Offshore Wind Farms
Andreas Jebsen Mikkelsen
Odin Kirkeby
Marielle Christiansen
Magnus Stålhane
