2. Why maintainability matters now
• Device design will determine reliability and maintainability
– But how reliable do devices need to be?
• Cost / benefit analysis of design options
– Base decisions on real wave data
• What is the most appropriate maintenance strategy?
– How can the value of test programmes be maximised?
• Consider maintenance systems as well as devices
• How can project risks be minimised?
• What kind of performance guarantee could be offered?
• Present costs are too high
– How will they change in future?
This presentation introduces methods and evidence
addressing all of these issues
3. Economic Model Structure
ReliabilityCriteria:
Breakdowns by season
Energy Output
loss per
breakdown
Energy Output
potential
per season
Capitalcost
OperatingFixed Costs
Cost per Breakdown
LevellisedCost
Calculation
Levellised
Cost
Energy Weighted
Availability
Wave time series data
MaintainabilityCriteria:
Hs limit for access,
Requiredwindow duration
Device
Power
Matrix
The model uses real wave data from two locations, and a real device
power matrix, together with appropriate cost estimates to determine
how the cost of energy is sensitive to all of these input factors
4. Annual Energy Output Variation
Resource data from M3 Wavebuoy
0
50
100
150
200
250
300
350
400
Oct Nov Dec Jan Feb Mar
EnergyOutput(MWh) 2006/7 2007/8 2008/9
Peak Season 2006/7
had 36% lower output
than following year
• Major variation in output between years
– Increases project risk
• Uncertainty in output predictions
• Long-term resource data is essential
– Challenge for device performance assessment and guarantees
• Any device performance claims must be related to the actual resource
5. Relative Contribution by Month
Data from M3 Wavebuoy
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12
Cumulative%ofyear'soutput
Months, ranked in ascending output order
2007 2008
Lowest 3 months:
10% of output
Top 3 months:
43% of output
• Highly seasonal resource
– Despite output smoothing characteristics of device
• Availability measures based on time are meaningless
– Downtime has almost no revenue impact in some months
– But could quickly lead to major revenue loss if swift repair cannot be
accomplished in peak generating conditions
6. Output losses while waiting for Access
0
100
200
300
400
500
600
700
800
900
1,000
0 6 12 18 24 30 36 42 48 54 60
Averagelosswaitingforwindow
(MWh)
Required Window Duration (h)
2 2.5 3 3.5 4 4.5Hs Limit (m)
Data from M6 Wavebuoy, Peak Seasons 06-7 & 07-8
• Each curve shows the output losses occurring while waiting for an
access window of different Hs limit and duration
• Hs limit has much greater effect than duration
• This should guide design decisions
• For devices and their maintenance systems
• Measure output losses, not just waiting time
• Time isn’t money – output is!
7. Wave Resource and Access Limits
Data from M3, M6 Wavebuoys, 12 months from Oct ‘06, 15 year life, 15% Discount Rate
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5
Levellisedcost(p/kWh)
Number of breakdowns in peak season
M3 2.5m limit M3 4m limit M6 2.5m limit M6 4m limit
Change
+40%
+32%
+81%
+50%
• Levellised cost increases with higher device failure rates (obvious!)
• BUT rate of increase depends on how well maintenance capability
matches wave environment
• Affects project risk profile
• Must consider device and maintenance system together
• Note that lowest cost curves are from more energetic location (M6 site)
• Despite high production losses from limited access
8. Discount Rate and Project Lifespan
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5
LevellisedCost(p/kWh)
Number of breakdowns in Peak season
15% DR, 15 years 8% DR, 25 years
Data from M3 Wavebuoy, 12 months from Oct ’06, 4m Hs access limit
• Altering Discount Rate and Project Life to reflect
maturing technology reduced Levellised Cost by 40%
• No other performance or cost changes made
• Any lifetime cost claims must state Discount Rate and Life
• Otherwise comparisons are meaningless!
9. Evidence for future viability
• Experience will enable improvements:
– Reduced breakdowns + enhanced maintainability
• Enable arrays to be located in higher energy areas
– Improved energy capture / conversion efficiency
• Economies of scale
– Device manufacture
– Array infrastructure
• Increased investor confidence
– Lower discount rate
– Longer project life
THESE COMBINE TO GIVE MAJOR COST REDUCTION
• So while early arrays will require significant capital and
revenue support, there is a clear path towards
economically-viable wave energy
Increased
Energy
Output
Reduced
Capital
Costs
Reduced
Financial
Costs
10. Further Information
• This presentation was only an introduction
– Also available:
• Copy of conference paper
• Full in-company presentation
• Project-specific analysis
• Email me if interested:
• info@davidrmalcolm.co.uk
Hinweis der Redaktion
Cost of energy – hot topic. Usually one of first questions about any renewable technology.
“What’s it going to cost?”
No simple answer. Presentation will show how reliability and maintainability can have very significant effects on energy output and levellised cost.
Study is on offshore wave; similar issues apply to tidal and offshore wind
To understand LC, set up a model
Using real device performance data, real wave data, estimated costs and varying reliability and maintainability (access) criteria
2 outputs: LC and EWA
Will now review these stages, and their outputs
Was 2006/7 an outlier, or just an indication of normal variability
Challenge for resource assessment!
Challenge for device assessment – 2 years sea trials yet to be achieved by any offshore device
Challenge for revenue and maintenance!
Highly seasonal resource, despite Pelamis smoothing
Affects approach to maintenance:
downtime has almost no revenue impact in some months
but if failures occur in best months, and if unable to repair swiftly due to sea state, major revenue loss
Simple % time based availability meaningless; the value of time varies through the year
Output losses while waiting to carry out an intervention.
Each curve is for a different limiting significant wave height for intervention.
The x-axis shows different intervention durations.
The curves show how much output would be lost for a randomly-occurring breakdown
Significant wave height capability has major effect on losses while waiting – far more than window duration – guides design decisions now
Note units – MWh losses, not just time, recognising higher output in conditions that inhibit access.
Clearly, device failures increase levellised cost.
Rate of increase depends on how access capability matches the wave environment – if poorly matched, very severe impact
Note lowest cost curve comes from most energetic location – difference in resource is outweighing increased downtime.
This shows the effect of pricing the risk of new technology: without changing anything else, confidence in a technology reduced the levellised cost by up to 40%.
Discount rate has a significant effect on levellised cost data – vital for comparisons between data.
Clearly still need to achieve successful first generation!
Not underestimating challenge
This study has produced evidence that a heavily-supported first generation is not a blind alley, but leads to attractive possibilities in future.
Cost of energy – hot topic. Usually one of first questions about any renewable technology.
“What’s it going to cost?”
No simple answer. Presentation will show how reliability and maintainability can have very significant effects on energy output and levellised cost.
Study is on offshore wave; similar issues apply to tidal and offshore wind
