Institute for Energy Systems Matching Renewable …...Page 2 Institute for Energy Systems Matching...
Transcript of Institute for Energy Systems Matching Renewable …...Page 2 Institute for Energy Systems Matching...
Page 1
Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Matching Renewable Electricity Generation
with DemandGareth Harrison
November 2006
Page 2
Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Comments• “One of the most obfuscating
pieces of research.” - Prof. James Lovelock (“Gaia” theory)
• “Misleading and effectively meaningless. Not worth the paper it is written on.” - Prof. David Simpson (“Tilting at windmills”)
• “It’s appalling. They are trying to fudge [the target].” - Bob Graham (Highlands Against Windfarms)
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
The Scottish Challenge
Demand
Time
Supply
Time
Network
MEC Load
41 TWh demand7.3 GW peak
1.5 GW hydro1.5 GW wind (existing, consented)
0.5 GW biomass (estimated)Wave & tidal current?
2020
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Constraints GIS Mapped
Resourcetime series
Long-termresource
Generatorlocations
Converter
Constraints
Macro
Renewablegeneration
GIS
Projectcosts
Selectioncriteria
MacroScenarios
Demand
Contents
Networ
kKeyfiguresResults
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
StudyArea
N = 500 kmE = 0 km
N = 500 kmE = 500 km
500 km
750 km
Source: Ordnance Survey
• Spatial resolution: 1 km2
• Use of British National Grid coordinates
N = 1250 kmE = 500 km
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Political
• International limit (200 nmi, 12 nmi)
• National limit• Fishing limit
(6 nmi)• Planning
authorities• Local
regulations
Sources: OS, UKHO
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Physical
• Water depth• Slope• Lakes• Rivers• etc.
Sources: OS, BGS, BODC, SRTM
Example:Average slope > 15% in a 1 km by 1 km square.
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
• Recreation interests (high sensitivity)
• Biodiversity interests (high sensitivity)
• Medium sensitivity areas
Sources: OS, SNH, SEGIS
National Scenic AreaNational ParkRegional park
AGLV, LNR, ...
Natura 2000, SSSI, ...
Environment
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Aviation Interests
• Civil radars
• Military radars• Met Office radars• Low Flying System
Sources: OS, DTI, CAA, BWEA
15 km exclusion zone30 km consultation zoneNATS high impactNATS lower impact
Tactical Training Area
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
• Urban areas
• Navigational risk
• Seismological measurements
• Ammunition dumping
• Distances, etc.
Sources: OS, DTI, CAA, BWEA
Cities, towns, villages
Eskdalemuir
Further Constraints
very highmediumvery low
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Onshore wind
absoluteconsultation (10% used)
Example:
Constraints
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Long-Term Resource
Resourcetime series
Long-termresource
Generatorlocations
Converter
Constraints
Macro
Renewablegeneration
GIS
Projectcosts
Selectioncriteria
MacroScenarios
Demand
Contents
Networ
kKeyfiguresResults
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Average wind speed at 80 m height agl.
Abbreviations:Au – AultbeaAv – AviemoreD – DunstaffnageK – KinlossSa – SalsburghSk – Skye, LusaSt – StrathallanT – Tulloch Bridge
Kirkwall
Wick
Lerwick
Sella Ness
Dyce
Leuchars
Charterhall
Eskdalemuir
West Freugh
Tiree Airport
Port Ellen
South Uist
Stornoway
Dundrennan
Machrihanish
Altnaharra
AvSk
StD
Sa
Au
K
T
OnshoreWindResource
Meteorological station (21 + 3)
4 10 16 m/s
Sources: OS, Met Office
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Average wind speed at 80 m height asl.
OffshoreWindResource
4 10 16 m/s
Sources: OS, DTI, Met Office
Met Office Simulation Point(11)
Water depth, 5 km offshore
0 ... 30 m30 ... 40 m40 ... 50 m
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Wave power per metre crest length
WaveResource
5 30 55 kW/m
Sources: OS, DTI, Met Office
Met Office Simulation Point(84 + 11)
Water depth, 5 km offshore
50 ... 100 m
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Tidal CurrentResource
Sources: OS, DTI, Robert Gordon Univ.
Average spring tide velocity (surface)
0.5 1.5 2.5 m/s
Water depth30 ... 50 m
South-West
OrkneyIslands
Pentland Firth
Shetland
Mull of Galloway
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Future Generator Locations
Resourcetime series
Long-termresource
Generatorlocations
Converter
Constraints
Macro
Renewablegeneration
GIS
Projectcosts
Selectioncriteria
MacroScenarios
Demand
Contents
Networ
kKeyfiguresResults
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Estimated onshorewind project costsexcluding connection to existing network
low costmedium costhigh cost
Example:
“no go” zone
• 3 x 2.5 MW per km2
• 80 m hub height• 20 years, 8 % discount rate
Assumptions:
including
Project Costs
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Creating Power Time Series
Resourcetime series
Long-termresource
Generatorlocations
Converter
Constraints
Macro
Renewablegeneration
GIS
Projectcosts
Selectioncriteria
MacroScenarios
Demand
Contents
Networ
kKeyfiguresResults
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Energy ConvertersOn/Offshore Wind Waves Tidal Currents
Source: Nordex, OPD, MCT
• 3-bladed horizontal axis turbine with pitchableblades
• 80 / 120 m diameter• 2.5 / 5 MW• 80 m hub height• Offshore: < 40 m water
depth
• Semi-submerged articulated structure
• 180 m long• 1.5 MW• 50 ... 150 m water depth
• Twin-rotor horizontal axis turbine with pitchable blades
• 20 m rotor diameter• 2 x 500 kW• 30 ... 50 m water depth
Poweroutput
Wind speedcut-outcut-in
nominal
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Generation vs. Demand
Resourcetime series
Long-termresource
Generatorlocations
Converter
Constraints
Macro
Renewablegeneration
GIS
Projectcosts
Selectioncriteria
MacroScenarios
Demand
Contents
Networ
kKeyfiguresResults
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
0
1,000
2,000
3,000
4,000
5,000
0 h 4 h 8 h 12 h 16 h 20 h 0 h
Demand - DailyS
yste
m D
eman
d (M
W)
Time
ScottishPower
ScottishandSouthernEnergy
max.
min.
avg.
Source: SP and SSE 2003 Seven Year Statements
24 h
Example:2002/03
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 20060
1,000
2,000
3,000
4,000
5,000
Demand - AnnualS
yste
m D
eman
d (M
W)
Time
ScottishPower
weekly max.
weekly min.
weekly avg.
Source: SP 2002 and 2003 Seven Year Statements
J F M A M J J A S O N D
Example:2002
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
0
25
50
75
100
21 Jul 22 Jul 23 Jul 24 Jul 25 Jul 26 Jul 27 Jul
0
25
50
75
100
20 Jan 21 Jan 22 Jan 23 Jan 24 Jan 25 Jan 26 Jan
Orkney with existing & new generationDemand Matching January, July 2003
(MW)
(MW)Onshore wind(a) new(b) existing
Tidal current
Wave
40%
, 100
% d
eman
d
27 Jan
28 Jul
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Key Figures and Results
Resourcetime series
Long-termresource
Generatorlocations
Converter
Constraints
Macro
Renewablegeneration
GIS
Projectcosts
Selectioncriteria
MacroScenarios
Demand
Contents
Networ
kKeyfiguresResults
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Simulation Scenarios
• Resource: 2001-2003 hourly data• Generation: Renewable expansion
by 2020• Demand: 2001-2003 hourly
demand scaled for 2020• Network: ideal
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Findings
• After application of constraints it could be possible to develop at least – 6 GW of onshore wind,– 3 GW of offshore wind,– 3 GW of wave, and – 1 GW for tidal current,
• or any combination of these technologies.
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Plant Capacity Factors
60%
30%
0 6 GW
Winter
Summer
• Exceeding 30%for wind, wave and tidal current
• Seasonal values for wind and wave are significantly higher in winter than in summer.
Incl. 1.5 GW optimallydispatched hydro
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
60%
40%
0 6 GW
Winter
Summer20%
Long-Term Matching
• 3 GW of onshore wind, offshore wind or wave would on average meet 20% of Scottish demand.
• A renewable mixof about 6 GWcould meet, on average, 40% of Scottish demand.
Incl. 1.5 GW optimallydispatched hydro
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Conclusions
• Scotland could, in 2020, meet on average 40% of its demand for electricity from renewable resources with a total renewable capacity of around 6 GW.
• It does not mean that the aspirationaldemand target is reached during each hour of a year.
• There will be periods of shortfall and periods of excess.
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
100%
40%
0 6 GW
Time = 1%
25%
50%75%
3 GW
Exceeding 40% of Demand
• Time when 40% of demand is exceeded was estimated.
• 3 GW renewable mix would achieve this for about 15% of the time.
• 6 GW renewable mixwould achieve this for about 45% of the time.
48% incl. 1.5 GW optimallydispatched hydro,
58% with 750 MW pumped storage
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Coincident hours for demand > 90% and production < 10% (h/year)
3 GW 6 GW
Onshore-wind
75-10-10-5% mix
Tidal-current (750 MW)
Wave
Offshore-wind
29
-(22)
-19
-14
29
2018
Demand
Gener
atio
n
Hours of Coincidence
• Coincident hours describe hourly matchbetween generation and demand.
• Hours in a year with shortfall (import, balancing) and excess (export, curtailment) of renewable energy can be estimated.
7 h incl. 1.5 GW optimallydispatched hydro,
19 h with 750 MW pumped storage
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Conclusions
• Sufficient renewable resources exist to meet governmental target (of supplying 40% of electricity from renewable sources by 2020).
• 6 GW of mixed renewables are on average required.
• Diversification and dispersion improve matching.
• Balancing is needed (dispatchableplant, storage, interconnectors).
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Case Study
• Apply renewable time series in AC power flow analysis.
• Examine marine/wind energy deliveryto the demand centres with changes in– weather patterns– demand– network
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Case Study - Area• Orkney and
Northern Highlands
• Wind, wave and tidal current resources.
• Existing wind farms, EMEC test centres.
100
80
60
20
40
60
80
6080
100
100
60
60
Westray
Sanday
Stronsay
Eday
Shapinsay
SouthRonaldsay
Hoy
Wick
Thurso
Halkirk
KirkwallStromness
SuleSkerry
Rousay
H i g h l a n d
OrkneyMainland
ScapaFlow
PentlandFirth
EMECtest site
EMECtest site
20 km0
Burray
Bu Farm
Spurness
Burgar Hill
Gruff
Stroupster
Forss
Scoolary
Burn ofWhilk
Causeymire
Spittal Hill
BroubsterForest
Strathy South
Strathy North
MelvichAckron
Bardnaheigh
South Shebster
Boulfruich,Dunbeath
Wind farmsoperational
consented or planned
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Case Study - Constraints• Development
constraintsinfluence the selection of renewable generation sites.
• Same parameters as for “matching study” used. Wick
Airport 20 km0
KirkwallAirport
15 kmradius
30 kmradius
Navigational risk
notassessed
verylow
low
medium
high
National Scenic Area
High environmental
sensitivity
Medium environmental
sensitivity
Average slope > 15%
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Case Study - Power System• Existing and
new generation mapped.
• Time seriesof power (hourly, one year) created.
• Network modelled.
Limit
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
0
25
50
75
100
20 Jan 21 Jan 22 Jan 23 Jan 24 Jan 25 Jan 26 Jan
0
25
50
75
100
20 Jan 21 Jan 22 Jan 23 Jan 24 Jan 25 Jan 26 Jan
Orkney with existing & new generationDemand Matching January 2003
(MW)
(MW)Onshore wind(a) new(b) existing
Tidal current
Wave
40%
, 100
% d
eman
d
27 Jan
27 Jan
Outage of one undersea cable
Unconstrained
Page 39
Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Case Study - Line LoadingOrkney – Mainland
undersea cableSelected Orkney undersea cables
Selected overhead line
Scenarios: 1 Orkney - Mainland undersea cable, 1E = existing generation, 1N = existing + new generation
Page 40
Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Case Study - Results
• Power flows (in PSS/E) can be run with 26,280 time steps instead of just 2 annual simulations.
• Line loading, voltage levels, reactor usage and required diesel backup can be examined.
• RE generation curtailment due to network issues can be estimated.
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Institute for Energy
Systems
Matching Renewable Electricity Generation
with Demand
UNESPPresentation
Nov 2006
Comments (cntd.)
“Fucking brilliant piece of work!”Dr. Robin Wallace (co-author)