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Reducing wind energy cost through
O&M
• Money!
• From Tractors to Turbines
• What Drives the Cost
• Wind Turbines as Assets
• O&M as a cost lever
• Case studies
The Good News
Levelised cost of Energy =~ (CAPEX + OPEX) / MWh
The Caveats
Future Projects Predicated on:
• Further upscaling to 13MW
• Installation cost savings
• High Reliability
• O&M cost savings
Tractors to Turbines
Many key Turbine OEMS started
life as agri equipment vendorsEarly commercial machines
(late 1970s) – 0.2MW
Wind boom (early
2000s) – 0.5MW
BAU (now) – ~3MW
Offshore: Big is Beautiful
Offshore: is Big Beautiful?
DFIG (onshore)
Direct drive synchronous
Siemens 6-8MW DD
FRC medium speed gen
Vestas-MHI 7-9.5MW FRC
Carroll et al (2015) Reliability Comparison of Wind Turbines With DFIG,
and PMG Drive Trains. IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 30, NO. 2
Challenges in Wind O&M –
A very young operational fleet
Ownership Structure
• Developer builds everything• Then sells the OSP export cable, onshore sub to a 3rd party (OFTO)
https://www.slideshare.net/JustinHayward1/sgcp12-beelofgem
OSP
Challenges in Wind O&M - spatial
http://www.epaw.org/documents.php?lang=en&article=d5
• Wave Radars• Buoy• Anemometers (lots)• Big grid square NWP• Vessel motion
A set of non-homogenous, patchy & flawed spatial
measurements
Challenges in Wind O&M - OPEX
Legacy Equipment Challenge
Heavy lift challenge
Interfaces/ KPIs
OEM
CTV skipper
Offshore technicians
SafetyNumber of turbines restored
Safety
SafetyAvailabilityProduction Target/ YieldOPEX
Marine co-ordinator
Weather forecast (LINK)
Owner/ operator
Production/ OPEXUtility Share Price
Contracted KPIOEM Share Price
Accuracy metric
3RD Party CMS
Fault detection accuracyDowntime
Challenges in Wind O&M
.. And where research can help:
• Spatial/ Logistical
• Weather/ Scheduling
• Uncertainty (and what to do about it)
• Interfaces
• KPI (mis) alignment
• Planning (years) vs Operational
(days) timescales
The Prize – My Targets
aMaples et al (2013) Installation, Operation, and Maintenance Strategies to Reduce the Cost of Offshore Wind Energy, NREL http://www.nrel.gov/docs/fy13osti/57403.pdf bBrowell et al (2016)Forecasting for day-ahead offshore maintenance scheduling under uncertainty. In: Proceedings of the European Safety and Reliability (ESREL) Conference
NREL: Circa £15m OPEX savings per operational wind farma
Strath: 0.3-0.5m Yield uplift per annumb
Decision Support for Offshore Wind
David McMillan• Translation of data & algorithm output into improved decisions• Solving the offshore work scheduling problem• Measuring uncertainty and using it to lower costs
ROMAX
WIND OPS - ONSHORE
Residual life estimation
1.Real options 2.Copulas3.Decision making under uncertainty
WIND OPS - OFFSHORE
Real world constrained decision-making
Visualisation of decision options
KPI impact of decisions fed direct to engineer
Lifting a Blade:
“Business as Usual”
Lifting a Blade:
“Business as Usual”
• Use average weather forecast to make blade lift scheduling decision• What day to schedule the lift?
Average forecast
Blade lift limit
Lifting a Blade:
Dealing with Uncertainty
• Can we use our knowledge of forecast errors to make a better decision?• Link key decision points and present ‘real options’
Blade lift limit
Forecast Uncertainty
The Scheduling Challenge
Inputs
Weather Forecast Weather Scenarios
• Probabilistic forecast (GFS/NOAA) produced as required for scenario generation
• Forecast errors sampled from statistical model, captures forecast uncertainty
Average forecast
Actuals
Blade lift limit
The Decision-Maker
• Technician KPI– Job Completion
– Downtime
Simple decision
• Budget holder KPI– OPEX reduction
– Yield increase
Detailed decision
Outputs – Simple Decision
• KPI: Job Completion
Outputs – Detailed Decision
Similar Expected Cost
Net-costCost Increase Cost Reduction
• KPI: OPEX Reduction
Different Risk
“10% chance net-cost > x”
Innovate: Project outcomes
• Delivered model front end - http://wind.datasmarthub.eu/
• Tool used to support blade lifts @Whitelee summer 2017
• Romax (Now Onyx Insight) sponsored one follow on 3yr KTP, developing a second
• Paper.. WIP
http://onyxinsight.com/
Improved Access Forecasting: ORACLES
The pressure to achieve increased access to turbines implies a greater number of marginal-weather
transfers, which carry a greater safety risk.
https://www.offshorewind.biz/2018/03/06/jfms-to-help-oracles-with-safe-crew-transfer-vision/?utm_source=emark&utm_medium=email&utm_campaign=daily-update-offshore-wind-2018-03-07&uid=57246
Lets talk about Optimisation
“The gap between academic models and application in a business specific context is still the biggest problem encountered in the field of maintenance optimization.” [1]
"Many of the works have been published for mathematical purposes, whereas only very few number of industrial cases (∼6% of the total publications) have been presented.” [2]
"There is, however, a sense that several of the most successful metaheuristics are over-engineered and one should now attempt to produce simple and more flexible algorithms capable of handling a larger variety of constraints, even if this were to translate into a small loss in accuracy." [3]
[1] A. Van Horenbeek, L. Pintelon, and P. Muchiri, “Maintenance optimization models and criteria,” Int. J. Syst. Assur. Eng. Manag., vol. 1, no. 3, pp. 189–200, 2010.
[2] M. Shafiee and J. D. Sørensen, “Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies,” Reliab. Eng. Syst. Saf., vol. 0, pp. 1–19, 2017.[3] G. Laporte, “Fifty Years of Vehicle Routing,” J. Transp. Sci., vol. 43, pp. 408–416, 2009.
Improved Vessel dispatch (1)
Improved Vessel dispatch (2)
• Uncertain diagnosis• Uncertain MTTR• Effect of this on order/ schedule?
Improved Vessel dispatch (3)
• Supergen R4 follow on with M Shafiee Summer 2018
https://www.supergen-wind.org.uk/funding/round-4-winners
Conclusions – Specific & General
• Lots of cost reduction potential for offshore wind (10s of £m)• OPEX savings can be accrued from day 1 of operation• No dependence on OEMs to achieve it• Work across interfaces and boundaries – join things up!
• To achieve it – understand the limits of decision making• Pragmatic approach – always talk to the decision makers before starting (Techs, CEOs)• Take account of reality, including the need to take people with you