15ELP044 – Unit 4
Uncertainty, Risk & Energy
Systems
Paul Rowley & Simon Watson
CREST
Loughborough University
Content
In this unit, we will:
• Briefly explore approaches to modelling uncertainty
• Examine some case studies.
• Due diligence is the process by which the risk
involved in an investment is evaluated.
Definitions
• Investment risk is the deviation of actual return from
expected return.
• Uncertainty refers to the unpredictability of known possible
future outcomes.
• Due diligence is the process by which the risk involved in an
investment is evaluated.
The System Lifecycle
• Systems progress through a lifecycle starting with a concept,
and progresses through a service life to eventual disposal
• Revenue from a generation system appears during the
service life
• Costs are incurred from inception to the completion of the
disposal process, or further if a continuing liability exists.
• ISO/IEC BS15288
The System Lifecycle
Predicting the Future – Bayes Theorem
Predicting the Future – Bayes Theorem
Predicting the Future – Bayes Theorem
Case study 1 – Tidal Stream Technology
• Yell Sound, Shetland
• Fictional precommercial array
• Ten 250kW oscillating hydroplanes
Tidal Stream Generating sub-system
Case study – Tidal Stream
Case study – Tidal Stream
Uncertainty, Risk & Energy Systems
Case study 2: Energy & Buildings
2a: The Building Energy Performance Gap
Problem:
 Widespread & significant under-estimates of
predicted building energy and carbon performance
 In general, existing design & compliance modelling
approaches are not ‘fit-for-purpose’
 Impact of ‘human factors’ and technical risk poorly
understood
 Needs to be addressed – otherwise, forget our GHG
and energy performance targets!
The Building Energy Performance Gap
The Building Performance Gap
The Building Energy Performance Gap
Source - Carbon Buzz
The Building Energy Performance Gap
Source - Carbon Buzz
The Building Energy Performance Gap
Source - Carbon Buzz
Data-driven Modelling
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UK government funded ‘sustainable exemplar’
7,500m2 mixed use (offices, public spaces…)
Timber frame fabric
Gas/EAHP/mech vent
Comprehensive wireless monitoring
Case study – Sub-system analysis
Comparison of modelled and monitored sub-system energy use
Data-driven Modelling
Boiler Efficiency
Distribution
??
Condensing temp
Boiler Return Water
Temperature Distribution
Data-driven Modelling
Gas Boiler – Sub-system analysis
Data-driven Modelling
2b: Social Impact Modelling under Uncertainty
Impact of PV on Household Energy Costs
Impact of PV on Household Energy Costs
Probabilistic Outcome
2c: Solar Thermal Performance: Measured Data
Distribution of daily performances ratios during April 2010 - March 2011
559RDE
568PLE
35
30
Frequency [%]
25
20
15
10
5
0
0-10
10-20
20-30
30-40
40-50
50-60
60-70
Performance Ratio [%]
70-80
80-90
90-100
>100
Solar Thermal Performance - Measured Data
Causes of performance variation
Technical Factors
Non-technical
Factors
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System size
Orientation
Inclination
Shading
Competency of installer
Insulation
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DHW profile
DHW volume
Auxiliary timing
Interplay between DHW profile,
aux. timing and available solar
energy
Uncertainty, Risk & Energy Systems
Case Study 3: Offshore Wind:
London Array
Case Study – Offshore Wind
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The Potential
Targets
The Challenge
Case Study: London Array
The Future
UK Offshore Wind Speed Map (100m)
• Good onshore site
~7.5m/s mean annual
wind speed at hub
height
• For many of the offshore
sites being developed:
>10m/s
UK & EU Targets
• EU: 20% of energy from renewable sources by 2020
• UK: 15% of energy from renewable sources by 2020
• Latest DECC roadmap estimates 13GW wind
onshore and 18GW offshore by 2020
• 2015: 8.5GW onshore, 5.1GW offshore
• Total UK system generating capacity: ~80GW
Crown Estates Development Sites
• 3 Development Rounds
• Water depths up to ~35m
The Challenge
• Installation – vessels, size of
machines
• Sea bed – composition, depth
• Access - >100km from coast for
some sites
• Reliability
• Hostile conditions – wind and wave
• Operations and maintenance
• Grid connection
Onshore Reliability and Downtime
Failure/turbine/year and Downtime from 2 Large Surveys of European Wind Turbines over 13 years
Electrical System
LWK Failure Rate, approx 5800 Turbine Years
Electrical Control
WMEP Failure Rate, approx 15400 Turbine Years
Other
LWK Downtime, approx 5800 Turbine Years
Hydraulic System
WMEP Downtime, approx 15400 Turbine Years
Yaw System
Rotor Hub
Mechanical Brake
Rotor Blades
Gearbox
Generator
Drive Train
1
0.75
0.5
0.25
Failure/turbine/year
0
2
4
6
8
10
12
Downtime per failure (days)
14
The London Array
© Siemens
Facts and Figures
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Offshore area of 100km2
20km from shore
Sea depth <25m
175 x 3.6MW Siemens wind turbines
Two offshore & one onshore substation
Nearly 450km of offshore cabling
630MW total installed capacity
Capital cost ~£1.8billion ~£2.9million/MW
Estimated LCOE~11p/kWh (CFD strike price ~12pkWh)
The Developers and Timescales
50% share
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30% share
20% share
Onshore works started July 2009
Offshore works started March 2011
Final turbine installed December 2012
Fully operational April 2013
Turbines
© London Array Ltd
© London Array Ltd
Installation Vessels
© London Array Ltd
Foundations
© London Array Ltd
© London Array Ltd
Substations
© London Array Ltd
© London Array Ltd
© London Array Ltd
Offshore wind – Managing Uncertainty and Risk
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Better understanding of the offshore environment
Bigger more reliable turbines, health monitoring
New materials, e.g. superconducting generators
Different drive train configurations, e.g. direct drive,
multiple drive trains
• More sophisticated control to reduce loads
• Holistic control – make more like a ‘power station’
• HVDC vs HVAC, North Sea grid