Bartlett School of Graduate Studies
University College London
SUSTAINABLE, LOW EMISSION, HIGH RENEWABLE
ENERGY SCENARIOS FOR EUROPE
Mark Barrett
[email protected]
Complex Built Environment Systems
University College London
June 2008
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Contents
•
Policy objectives
•
Measures
•
Modelling
•
Scenarios
•
Renewable energy
•
Conclusions
Work sponsored by the Swedish Environmental Protection Agency, but opinions are
those of the author. Report available here: [email protected]
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What’s it all for?
NEEDS
Task
Energy form
Technologies
Food
storage
heat (cold)
refrigerator
cooking
heat
cooker
shelter
materials
buildings
thermal
heat
cool
heater
air conditioner
lighting
light
light bulb
window
personal
heat
shower / bath
clothes
heat & power
washing machine
dishes
heat & power
dishwasher/hands!
house
power
hoover
Health
miscellaneous
miscellaneous
medical services
Culture
travel
power
vehicles
telecommunications
electricity
telephone, internet
electronic media
electricity
TV, hi-hi etc.
Comfort
Hygiene
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The challenge
Develop EU integrated policy that achieves environmental and energy goals at least
overall cost.
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European policy targets
Energy security
• 20 % share of renewable energy in overall EU energy consumption by 2020;
• 10 % minimum target share of biofuels in petrol and diesel consumption by 2020
Climate change
• Kyoto; 8% reduction in greenhouse gases 1990-2010
• Council commitment 20 (maybe 30) % reduction 1990-2020
Air
•
•
•
pollution
Air quality standards, biodiversity
National Emission Ceilings
Emission standards for vehicles, combustion plant etc
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Scenario context: UK Energy flow chart: 1990
SENCO
Trade
Environment
Extraction
Fuel processing
Electricity and heat
GBR : TechBeh : Y1990
Delivered
CO2
Sectors
Useful energy
CO2
Biomass
Food
Res_G_
Gas
Ext_G
Solid
Res_S_
Res_E_
Res_L_
Mot W
Ser_G_
Ser_S_
Ser_E_
Ser_L_
Proc W
H>120C
Ext_S
Solid
H<12-C
Ind_G_
Ind_S_
Ind_E_
Trd_E
Solid
Trd_N
Nuclear
ElOnly
Elec
Ind_L_
Nuclear
Oth_G_
Oth_L_
L_FueOil
Tra(nat) E
Tra(nat) L
Liq
Ext_L
Refinery
Liq
Tra(int) L
Waste energy
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Water H
Space H
Space AC
Cool
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Scenario context: UK Energy flow chart: animation 1990 to 2050
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The system modelled : UK : sample energy flow chart for 2050
SENCO
Trade
Environment
Extraction
Fuel processing
Electricity and heat
GBR : TechBeh : Y2050
Delivered
Sectors
Useful energy
CO2
Biomass
Food
Gas
Trd_G
Res_G_CHP
Res_H_Solar
Res_E_
G_CHP
Mot W
Ext_G
H_Solar
Ext_S
Solid
Ser_G_CHP
Ser_H_Solar
Ser_E_
Solid
Auto_H
Ind_G_
Ind_G_CHP
Trd_E
ElOnly
Wind
Wind
Tide
Wave
Tide
Wave
Solar
Solar
Elec
Auto
Ind_L_
Ind_L_CHP
Waste
CHPDHFuI
Ind_H_Solar
Ind_S_
Ind_E_
El equip
Proc W
Light
H>120C
H<12-C
Oth_G_
CHPDH_H
Cooking
Heat
Tra(nat) E
Tra(nat) L
Water H
Biowaste
Space H
Biomass
Biomass
proc
S_Bio
L_Bio
Space AC
Cool
L_CHP
Trd_L
Refinery
Liq
Liq
Tra(int) L
Waste energy
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Objectives, instruments and measures
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Energy : physical measures
Measures that reduce finite fuel consumption and atmospheric emissions. Mix of measures can be applied to different
degrees at a ‘natural’ rate (years); note the general rapid rate of introduction of behavioural/operational measures
which helps meet near term targets (e.g. 2020).
Class
Examples of options
Effective comfort temperature in buildings
Rate
yrs
10
Decision
variable
BeTi
Behaviour
Passenger transport demand control
20
BeTPass
Aviation transport demand control
15
BeAvi
Passenger mode; from car to bus/rail
20
BePMod
Freight mode; from truck to rail
25
BeFMod
Downsizing cars
15
BeCar
Speed control on motorways, aircraft
5
BeSpeed
Transport load factor
20
DMTLF
Demand management in transport
30
DMTra
Building insulation and ventilation control
40
DMBui
Demand management in non-residential sectors
30
DMInd
Shift to electric vehicles, CHP and renewables
in end use sectors
Shift to CHP and renewables in supply sectors
35
FMDel
40
FMSup
Improved efficiency of boilers, heat pumps, etc
35
EFDel
Demand
management
Fuel mix
Efficiency
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Scenarios
Six scenarios for each EU25 country were constructed to reach these objectives using different
combinations of NEOP measures implemented to different degrees.
Label
Target:
Reduction
date
2020
Nuclear
NEOPs
EU30pc20N
Target:
% CO2
reduction
30
New
Mix
EU40pc20N
40
2020
New
Mix
EU30pc20NN
30
2020
No new
Mix
TecNN
No new
Maximum technology
BehNN
No new
Maximum behavioural
TecBehNN
No new
Maximum technology and
behaviour
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SEEScen: Society, Energy, Environment Scenario model
•
•
•
SEEScen is applicable to any
large country having IEA
energy statistics
Other data
Climate, insulation...
SEEScen calculates energy
flows in the demand and supply
sectors, and the microeconomic
costs of demand management
and energy conversion
technologies and fuels
SEEScen is a national energy
model that does not address
detailed issues in any demand
or supply sector.
Method
•
Simulates system over years,
or hours given assumptions
about the four classes of policy
option
•
HISTORY
Optimisation under
development
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INPUTS /
ASSUMPTIONS
IEA data
Energy
Population, GDP
End use fuel mix
End use efficiency
FUTURE
ENERGY
IMPACTS
Delivered fuel
Delivered fuel by
end use
Useful energy
Socioeconomic
Lifestyle change
Useful energy
Demand
End use efficiency
Conversion
End use fuel mix
Delivered energy
COSTS
Capital
Running
Distribution losses
Supply efficiency
Conversion
Supply mix
Primary energy
Trade
Emissions
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Exogenous assumptions (from PRIMES WCLP scenario): basic drivers
Population peaks and declines
More households
M
500
450
400
350
300
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
SWE
SLV
SLK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRE
HUN
GRE
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZR
CYP
BEL
AUT
M
250
200
150
100
50
0
1995
2000
2005
2030
SWE
SLV
SLK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRE
HUN
GRE
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZR
CYP
BEL
AUT
000
20000
18000
16000
GDP growth
14000
12000
10000
8000
6000
4000
2000
0
1995
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2000
2005
2010
2015
2020
2025
2010
2015
2020
2025
2030
13
SWE
SLV
SLK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRE
HUN
GRE
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZR
CYP
BEL
AUT
Bartlett School of Graduate Studies
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Exogenous assumptions (from PRIMES): transport demand
More passenger travel
But is saturation occurring, e.g. UK?
SWE
SLV
SLK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRE
HUN
GRE
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZR
CYP
BEL
AUT
8000
7000
6000
5000
4000
3000
2000
1000
0
1995
2000
2005
2010
2015
2020
2025
2030
35
1.1
33
1.0
31
29
kph/km per day
9000
0.9
27
25
0.8
23
0.7
21
Speed
19
0.6
km/day
17
15
1970
1980
1990
2000
Gtkm
4500
4000
3500
3000
More freight transport
2500
2000
1500
1000
500
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0
1995
2000
2005
2010
hrs/day
Gpkm
2015
2020
2025
2030
SWE
SLV
SLK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRE
HUN
GRE
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZR
CYP
BEL
AUT
Hrs/day
2010
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Exogenous assumptions: nuclear power
Profile with 35 years life
140000
SVN
120000
SVK
LTU
100000
HUN
CZE
SWE
80000
MW
NLD
ITA
60000
GBR
FRA
FIN
40000
ESP
20000
DEU
BEL
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2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
1960
1955
0
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UK dwellings scenario : space heat demand
•
•
Building space heat demand dominated by existing dwellings because of slow turnover rate, about
1000 years!
Demolish houses faster, but building new houses requires about 10-20 years annual dwelling
energy consumption
800
700
600
PJ
500
400
300
200
100
0
1990
2000
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2010
2020
2030
2040
2050
New 2050
New 2045
New 2040
New 2035
New 2030
New 2025
New 2020
New 2015
New 2010
New 2005
New 2000
New 1995
New 1990
Pre-1990 Refurbished
Pre-1990 Orig
16
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UK dwellings scenario : monthly heat demand
With reduced annual space heating:
How to model temporal and spatial diversity
across building stocks?
160
New 2050
• Space heat demand has lower load factor
(average/maximum) because heating season shortened
• Overall heat demand load factor changes little because
of water heating fraction
New 2045
140
Year: 2005
Note: these are monthly factors
New 2035
120
120
New 2050
New 2030
New 2045
New 2025
100
Year: 2050
100
New 2040
New 2035
New 2020
New 2030
New 2015
80
New 2010
60
New 2005
40
New 2000
New 1995
PJ / month
PJ / month
New 2040
80
New 2025
60
New 2020
New 2015
New 2010
New 2005
40
New 2000
New 1990
20
Pre-1990 Refurbished
New 1995
20
New 1990
Pre-1990 Refurbished
Pre-1990 Orig
0
1 2 3 4 5 6 7 8 9 10 11 12
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Water
Pre-1990 Orig
0
1
2
3
4
5
6
7
8
9 10 11 12
Water
17
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UK dwellings scenario : diurnal space and water heat demand
100
New 2050
2005
90
New 2045
New 2040
80
GW
•
How will heat demand vary
by house type?
•
Again, how to model
temporal and spatial
diversity across building
stocks?
New 2030
60
New 2025
50
New 2020
New 2015
40
New 2010
30
New 2005
New 2000
20
New 1995
10
New 1990
Pre-1990 Refurbished
0
1
5
9
13
17
21
Pre-1990 Orig
Water
Hour
80
New 2050
New 2045
70
New 2040
60
New 2035
New 2030
50
GW
This is an example of heat
demand, net of incidental
solar and internal gains,
without any storage
New 2035
70
2050
•
New 2025
New 2020
40
New 2015
New 2010
30
New 2005
20
New 2000
New 1995
10
New 1990
Pre-1990 Refurbished
0
1
5
9
13
Hour
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17
21
Pre-1990 Orig
Water
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Scenario context: domestic sector: electricity use
Electricity demand is reduced because of more efficient appliances, including heat pumps for space heating.
GBR: TechBeh: Residential : Electricity
500
AirCon_EH
450
HeatOff_EH
400
Heater_EH
Heater_EH
350
Cooker_EH
PJ
300
CWash_EW
250
Freezer_EH
200
150
Refrig_EH
100
DishW_EW
50
CWash_EW
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2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
Light_EL
1990
0
19
Refrig_EH
Equip_E
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Transport: measures
•
Demand management, especially in aviation sector
•
•
Reduction in car power and top speed
Increase in vehicle efficiency
– light, low drag body
– improved motor efficiency
•
•
Speed reduction for all transport
Shift to modes that use less energy per passenger or freight carried:
– passengers from car to bus and train
– freight from truck to train and ship
Increased load factor, especially in the aviation sector
•
•
Some penetration of vehicles using alternative fuels:
– electricity for car and vans
– biofuels principally for longer haul trucks and aircraft
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Passenger transport: carbon emission by purpose
Commuting and travel in work account for
40-50% of emissions
Holiday
4%
Other
Day trip
0%
4%
Escort
6%
Sport (do)
2%
Entertain
4%
Social
2%
To work
30%
Carbon emission
by purpose
In work
13%
To friends
15%
Eat/drink
2%
Other personal
5%M edical (pers)
1%
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Shopping
10%
Education
2%
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Passenger transport use by mode trip length
Short distance car trips account for bulk of emissions.
25
Carbon Emission (Mt)
20
15
10
Car/van
T axi
Mot orcycle
Bus
Coach
Underground
T rain
Ot her public
5
0
0
50
100
150
200
250
St age Lengt h (km)
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300
350
400
450
500
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SEEScen sample: Transport: passenger demand by mode and vehicle type
Demand depends on complex of factors: demographics, wealth, land use patterns, employment,
leisure travel. National surface demand is limited by time and space, but aviation is not so
limited by these factors.
GBR: TecNN: Passenger : Load distance
3000
Int:Pas:Plane
2500
Int:Pas:Ship
Nat:Pas:Ship
2000
Gpkm
Nat:Pas:Plane
1500
Nat:Pas:Rail
1400
GBR: TecBehNN: Passenger : Load distance
Int:Pas:Plane
1000
Nat:Pas:Bus
1200
Int:Pas:Ship
1000
Nat:Pas:Car
Nat:Pas:Ship
Gpkm
500
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
Nat:Pas:Plane
800
Nat:Pas:MCycle
Nat:Pas:Rail
600
Nat:Pas:Bike
Nat:Pas:Bus
400
Nat:Pas:Car
200
Nat:Pas:MCycle
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2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
Nat:Pas:Bike
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SEEScen sample: Transport: passenger vehicle distance
GBR: EU30pc20N: Passenger : Vehicle distance
800
Int:Pas:Plane_LB
Int:Pas:Plane_K
Int:Pas:Ship_D
700
Nat:Pas:Ship_D
Nat:Pas:Plane_K
600
Nat:Pas:Rail_E
Demand management and modal shift
can produce a large reduction in road
traffic reduces congestion which
gives benefits of less energy,
pollution and travel time.
Nat:Pas:Rail_LB
Nat:Pas:Rail_D
500
Gv.km
Nat:Pas:Bus_E
Nat:Pas:Bus_H2
400
Nat:Pas:Bus_CNG
Nat:Pas:Bus_LB
300
Nat:Pas:Bus_D
Nat:Pas:Car_E
200
Nat:Pas:Car_H2
GBR: TecBehNN: Passenger : Vehicle distance
Nat:Pas:Car_LB
450
Int:Pas:Plane_LB
Int:Pas:Plane_K
Nat:Pas:Car_LPG
100
Int:Pas:Ship_D
400
Nat:Pas:Car_D
Nat:Pas:Ship_D
Nat:Pas:Car_G
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
Nat:Pas:Plane_K
350
Nat:Pas:MCyc_G
Nat:Pas:Rail_E
Nat:Pas:Bike_S
Nat:Pas:Rail_LB
300
Assumed introduction of electric vehicles
to replace liquid fuels, and reduce
urban air pollution.
Gv.km
Nat:Pas:Rail_D
Nat:Pas:Bus_E
250
Nat:Pas:Bus_H2
Nat:Pas:Bus_CNG
200
Nat:Pas:Bus_LB
Nat:Pas:Bus_D
150
Nat:Pas:Car_E
Nat:Pas:Car_H2
100
Nat:Pas:Car_LB
Nat:Pas:Car_LPG
50
Nat:Pas:Car_D
Nat:Pas:Car_G
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2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
Nat:Pas:MCyc_G
Nat:Pas:Bike_S
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Cars: carbon emission by performance
Car carbon emissions are strongly related to top speed, acceleration and weight. Most cars sold can exceed the maximum
legal speed limit by a large margin. Switching to small cars would reduce car carbon emissions by some 50% in 15 years in
the UK (about 7% of total UK emission). Switching to micro cars and the best liquid fuelled cars would reduce emissions by
80% and more in the longer term. In general, for a given technology, the emissions of pollutants are roughly related to fuel
use, so the emission of these would decrease by a similar fraction to CO2.
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Passenger transport: Risk of injury to car drivers involved in
accidents between two cars
Cars that are big CO2 emitters are most dangerous because of their weight, and because they
are usually driven faster. In a collision between a small and a large car, the occupants of the small
car are much more likely to be injured or killed. The most benign road users (small cars, cyclists,
pedestrians) are penalised by the least benign.
8
260
CO2
%serious
7
CO2 g/km
5
4
180
3
2
140
1
0
100
Small
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Small/medium
Medium
Large
Four Wheel
Drive
Risk injury %
6
220
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Transport: road speed and CO2 emission
Energy use and carbon
emissions increase strongly at
higher speeds. Curves for
other pollutants generally
similar, because emission is
strongly related to fuel
consumption.
These curves are only
applicable to current
vehicles. The characteristics
of future vehicles (e.g. urban
internal combustion and
electric powered) would be
different. Minimum emission
would probably be at a lower
speed, and the fuel
consumption and emissions
at low speeds would not
show the same increase.
Potentially, the lowering of
actual speeds on fast roads
might reduce emissions on
those roads by perhaps 1020%.
Fraction of minimum CO2 g/km
600%
Car (P,< 1.4 l, EURO IV)
Car (P,> 2.0 l, EURO IV)
HGV (D,Artic, EURO IV)
Van (D,medium, EURO IV)
Mcycle (P,250-750cc 4-s, pre)
Car (D,> 2.0 l, EURO IV)
Car (P,1.4 - 2.0 l, EURO IV)
HGV (D,Rigid, EURO IV)
Bus (D,0, EURO IV)
Van (D,large, EURO IV)
Mcycle (P,>750cc 4-s, pre)
500%
Low speed emission
400%
Average conceals
start/ stop congestion
And car design
dependent
300%
M otorway
200%
100%
0%
5
25
45
85
65
105
125
145
kph
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Delivered
Transform
Input
Input
Store
Output
Fossil
Biomass
Renewable
Liquid
Liquid Hydrogen HydrogenElectricityElectricityElectricity
100%
Tank
100%
100%
Tank
100%
Liquid
35%
Liquid
35%
33%
17%
Conversion
Engine to
motive power
Overall
Renewable
50%
Gas
94%
CHP
CHP
30%
30%
Crude oil Biomass
Gas
Electricity Electricity Electricity Electricity
99%
99%
99%
93%
93%
93%
93%
Gas
Electricity
85%
70%
Hydrogen Hydrogen
Transmission
Vehicle
Biomass
Primary
Refine
Transform
Crude oil
Vehicle energy supply pathway efficiencies
Primary to power
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100%
100%
90%
90%
90%
Tank
Tank
Battery
Battery
Battery
100%
100%
90%
90%
90%
Hydrogen Hydrogen
45%
45%
Electricity Electricity Electricity Electricity Electricity
90%
90%
90%
90%
90%
34%
26%
68%
20%
20%
note: excludes CHP heat
28
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SEEScen sample: Transport: passenger: delivered energy
International air travel will become a large fraction of
future passenger energy use
DEU: TecBehNN: Passenger : Delivered
2500
Int:Pas:Plane_LB
Int:Pas:Plane_K
Int:Pas:Ship_D
Nat:Pas:Ship_D
2000
Nat:Pas:Plane_K
Nat:Pas:Rail_E
Nat:Pas:Rail_LB
Nat:Pas:Rail_D
1500
Nat:Pas:Bus_E
PJ
Nat:Pas:Bus_H2
Nat:Pas:Bus_CNG
Nat:Pas:Bus_LB
1000
GBR: EU30pc20N: Passenger : Delivered
2500
Nat:Pas:Bus_D
Int:Pas:Plane_LB
Nat:Pas:Car_E
Int:Pas:Plane_K
Nat:Pas:Car_H2
500
Int:Pas:Ship_D
Nat:Pas:Car_LB
Nat:Pas:Car_LPG
Nat:Pas:Ship_D
Nat:Pas:Car_D
2000
Nat:Pas:Plane_K
Nat:Pas:Car_G
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
Nat:Pas:Rail_E
1995
1990
0
Nat:Pas:MCyc_G
Nat:Pas:Bike_S
Nat:Pas:Rail_LB
Nat:Pas:Rail_D
1500
Nat:Pas:Bus_E
PJ
Nat:Pas:Bus_H2
Nat:Pas:Bus_CNG
Nat:Pas:Bus_LB
1000
Nat:Pas:Bus_D
Nat:Pas:Car_E
Nat:Pas:Car_H2
500
Nat:Pas:Car_LB
Nat:Pas:Car_LPG
Nat:Pas:Car_D
Nat:Pas:Car_G
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2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
Nat:Pas:MCyc_G
Nat:Pas:Bike_S
29
Bartlett School of Graduate Studies
University College London
SEEScen sample: UK : electricity generation (not consumption)
Switch from electricity only fossil and nuclear generation to:
•
Fossil CHP for medium term, and biomass CHP
•
Renewable sources
GBR: EU30pc20NN: Electricity : Output
1800
1600
1400
PJe
1200
1000
800
600
400
200
SEE Society Energy Environment
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
S_Fos
L_Fos
G_Fos
N_Nuc
E_Hydro
H_Geothe
H_Solar
E_Wave
E_Tide
E_Wind
Pump_E
S_Fos
L_Fos
G_Fos
S_MunRef
S_Bio
L_Bio
G_Bio
S_Fos
L_Fos
G_Fos
S_Bio
L_Bio
G_Bio
30
Bartlett School of Graduate Studies
University College London
SEEScen sample: UK : CO2 excluding international transport
GBR: EU30pc20N: Environment: National: (N) : CO2
700
Fue:Ext
Fue:Pro
Ele:Gen
600
Hea:Pub
Hea:Aut
500
Tra(nat):Other i
Tra(nat):Air: Do
400
Mt
Tra(nat):Rail
Tra(nat):Road: F
300
Tra(nat):Road: P
Res:Res
200
Ser:Ser
Oth:oth
100
Ind:Agr
Ind:Lig
0
SEE Society Energy Environment
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
Ind:Hea
Ind:Che
Ind:Iro
31
Bartlett School of Graduate Studies
University College London
SEEScen sample: UK CO2 by scenario
GBR: Scenarios: Environment: National: (N) : CO2
700
EU30pc20N
600
EU40pc20N
500
400
Mt
EU30pc20NN
300
TecNN
200
100
BehNN
SEE Society Energy Environment
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
TecBehNN
32
Bartlett School of Graduate Studies
University College London
Future greenhouse gas emissions
•
Total emissions over coming decades important, especially for gases like
CO2 that stay in the atmosphere for centuries.
•
A 20% CO2 reduction by 2020 is as important as an 80% reduction in 2050.
SEE Society Energy Environment
33
Bartlett School of Graduate Studies
University College London
SEEScen sample: EU25 CO2 emissions by country : EU30pc20N scenario
. The black squares show the targets for 2010 and a 30% reduction by 2020.
COUNTRIES: EU30pc20N : Environment: National: (N) Total : CO2
4500
4000
3500
3000
Mt
2500
2000
1500
1000
500
SEE Society Energy Environment
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
SWE
SVN
SVK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRL
HUN
GRC
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZE
CYP
BEL
AUT
Targets
34
Bartlett School of Graduate Studies
University College London
SEEScen sample: EU25 CO2 : variant scenarios
COUNTRIES: EU40pc20N : Environment: National: (N) Total : CO2
500
COUNTRIES: TecNN : Environment: National: (N) Total : CO2
500
2050
2045
2040
2035
2030
SEE Society Energy Environment
2025
2020
2015
2010
2005
2000
1995
1990
0
2050
2045
2040
2035
2030
2025
2020
2015
1500
1000
500
0
2050
1000
2000
2045
1500
2500
2040
2000
3000
2035
Mt
2500
3500
2030
3000
4000
2025
3500
Maximum technology
and behaviour
No new nuclear
2020
4000
SWE
SVN
SVK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRL
HUN
GRC
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZE
CYP
BEL
AUT
Targets
COUNTRIES: TecBehNN : Environment: National: (N) Total : CO2
4500
2015
Maximum technology
No new nuclear
SWE
SVN
SVK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRL
HUN
GRC
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZE
CYP
BEL
AUT
Targets
Mt
4500
2010
0
2010
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
1000
2005
500
1500
2005
1000
2000
2000
1500
2500
1995
2000
3000
2000
Mt
2500
3500
1995
3000
Maximum behaviour
No new nuclear
4000
1990
3500
COUNTRIES: BehNN : Environment: National: (N) Total : CO2
4500
1990
40% reduction
New nuclear
4000
SWE
SVN
SVK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRL
HUN
GRC
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZE
CYP
BEL
AUT
Targets
Mt
4500
SWE
SVN
SVK
PRT
POL
NLD
MLT
LVA
LUX
LTU
ITA
IRL
HUN
GRC
GBR
FRA
FIN
EST
ESP
DNK
DEU
CZE
CYP
BEL
AUT
Targets
35
Bartlett School of Graduate Studies
University College London
EU25 renewable fractions, EU30pc20N scenario
- primary energy
Primary energy renewable fraction increases from 9% in 1990 to 26% in 2020.
(Official sources puts current fraction at 6-7%, so accounting convention here gives larger fraction)
Primary energy equivalent
100%
PE Supp Fossil
90%
PE Del Foss
80%
70%
60%
PE Nuc
50%
40%
LEGEND KEY
PE Del Ren
PE Sup Ren
PE Nuc
PE Del Foss
PE Supp Fossil
Delivered renewable
Supply renewable
Nuclear
Delivered fossil
Supply fossil
PE Sup Ren
30%
20%
PE Del Ren
10%
0%
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
SEE Society Energy Environment
36
Bartlett School of Graduate Studies
University College London
SEEScen sample: Energy security
EU25 energy trade : including fuels for international transport: EU30pc20N scenario
50000
Electricity
Gas
Oil
Nuclear
Total
Total effective
40000
PJ
30000
20000
10000
0
-10000
1990
1995
SEE Society Energy Environment
2000 2005
2010
2015
2020
2025
2030
2035 2040
2045
2050
37
Bartlett School of Graduate Studies
University College London
SEEScen sample: Total cost by scenario: illustrative
It is possible that some low carbon scenarios will cost less than high carbon scenarios.
It is certain that reducing imports will enhance economic stability because of a lower trade imbalance,
and less dependence on fluctuating fossil fuel prices.
ITA: Scenarios: Economics : Cost : Total
60000
EU30pc20N
50000
EU40pc20N
M€/a
40000
EU30pc20NN
30000
TecNN
20000
10000
BehNN
SEE Society Energy Environment
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
0
TecBehNN
38
Bartlett School of Graduate Studies
University College London
Air pollution : emissions and reduction costs
The EU30N energy scenario results in lower emissions and control costs for all pollutants than in the
EUV scenario.
For 2020
Emission
NOx
SO2
VOC
PM
Control cost
NOx
SO2
VOC
PM
EUV
6643
3831
5942
3123
Total
SEE Society Energy Environment
43990
16298
3072
9758
73118
EU30N Reduction
kt
5321
20%
3203
16%
5725
4%
2917
7%
MEuro/year
41345
6%
12531
23%
2954
4%
8135
17%
64965
11%
39
Bartlett School of Graduate Studies
University College London
Further issues: aviation
International aviation and shipping should be included in GHG inventories because their GHG
emissions will become very large fractions of total.
Tra(int):Air
500
400
300
200
100
National
2050
2045
2040
2035
2030
2025
2020
2015
0
2010
Of all the fossil fuels, kerosene is the most
difficult to replace.
600
2005
Tropopause/low stratosphere emission.
The high altitude emission of NOx and
water vapour cause 2-3 times the global
warming due to aviation CO2. Aviation may
well become the dominant energy related
greenhouse gas emitter for the UK over the
coming decades.
700
2000

Tropospheric emission. Aircraft emit a
substantial quantities of NOx whilst
climbing to tropopause cruising altitude
(about 12 km). This will contribute to
surface pollution.
1995

GBR: EU30pc20N: Environment: : Global warming
800
1990

Low level. Airports are emission hot spots
because of aircraft taxiing, and landing and
take-off, and because of road traffic.
MtCO2eq

Further information on this is given in the
references.
SEE Society Energy Environment
40
Bartlett School of Graduate Studies
University College London
GREEN LIGHT: AN ELECTRICITY SCENARIO
Objective: to meet minimum fraction of renewable electricity
Measures exercised in the overall energy scenario:
41
•
Phase out of nuclear generation, but with some fossil (coal, oil, gas) capacity
retained for back-up
•
Large scale introduction of renewable electricity only and biomass CHP limited
to waste biomass.
•
Use of heat and electricity storage
•
Increase of transmission capacity with France
SEE Society Energy Environment
Bartlett School of Graduate Studies
University College London
UK energy, space and time illustrated with EST : animated
42
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Bartlett School of Graduate Studies
University College London
Electricity : diurnal operation without load management
43
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Bartlett School of Graduate Studies
University College London
Animated Load management optimisation
– controlled by GOD (Global Optimal Dispatcher) - omniscent, omnipotent
44
SEE Society Energy Environment
Bartlett School of Graduate Studies
University College London
Electricity : diurnal operation after load management
EleServe Scenario: Efficiency + CHP + renewables
System
2025 Winter day : Summer day
Demand (LM)
System demand
Essential
generation
Dem Net E
30
25
SENCO
30
25
Trade
20
20
15
Store
10
GW
GW
Dem Net ET
Dem Net ETS
5
0
1
25
-5
hrs
15
10
Optional
generation
Reserve
requirement
Reserve
store+hydro
Res req. Net Store
5
0
0
0
Generation
30
Distribution
25
8.00
20
6.00
GW
p/kWh
7.00
Startup energy
15
4.00
10
3.00
2.00
Generation energy
1.00
0.00
5
0
0
0
hrs
0
0
hrs
45
Merit order
SEE Society Energy Environment
I:Ind:Pro
O:Far:Gen
O:Pub:Lig
O:Tra:Mot
S:Com:Ref
S:Com:Spa
S:Com:Coo
S:Com:Lig
S:Com:Mis
S:Com:Spa
S:Com:Wat
S:Pub:Ref
S:Pub:Spa
S:Pub:Coo
S:Pub:Lig
S:Pub:Mis
S:Pub:Spa
S:Pub:Wat
R:Fri:Ref
R:Fri:Ref
R:Fre:Ref
R:Coo:Coo
R:Was:Was
R:Clo:Was
R:Dis:Was
R:Tel:App
R:Mis:App
R:Lig:Lig
R:Hot:Wat
R:Unr:Spa
R:Off:Spa
hrs
10.00
5.00
I:Ind:Lig
I:Ind:Mot
R:Coo:Spa
Marginal costs
9.00
I:Fue:Gen
Storage and trade
1 RTid
3 RWav
5 RHydhh
6 Gchp
9N
12 C
17 C
18 C
16 C
14 C
42 Gcc
28 Gcc
54 Gcc
32 Gcc
60 Gcc
48 Gcc
40 Gcc
59 Gcc
45 Gcc
46 Gcc
31 Gcc
36 Gcc
51 Gcc
44 Gcc
49 Gcc
35 Gcc
55 Gcc
23 G
26 O
21 G
62 Ogt
65 Ogt
2 RAer
4 RSolPV
8 Gchp
7 Ochp
10 N
19 C
11 C
13 C
15 C
53 Gcc
39 Gcc
43 Gcc
58 Gcc
30 Gcc
38 Gcc
33 Gcc
41 Gcc
50 Gcc
61 Gcc
52 Gcc
29 Gcc
56 Gcc
27 Gcc
57 Gcc
47 Gcc
37 Gcc
24 G
25 O
22 G
20 G
63 Ogt
Bartlett School of Graduate Studies
University College London
VarInt : Optimisation over a year
To find the best combination of generation, trade and storage options, optimisation is used. The procedure is as
follows:
1. For a fixed run of random weather data, the optimiser tries out different values for the capacities of the
technologies until the cheapest combination is found.
2. This combination may then be tested against random weather to see if the system delivers electricity
services securely in all circumstances.
The optimisation has these objectives and constraints:
Objective: Find the minimum total cost of electricity supply, where costs currently include;
•
Capital and running costs of generation and storage
•
Energy costs of optional generation (biomass, fossil) and trade
Decision variables:
•
Capacities of variable generators, optional generation and stores
Constraints:
•
Demands met
•
Fraction of optional generation less than some specified fraction
•
Renewable capacities less than ‘economic’ maximum
•
Flows and energy storage limited by capacities
The optimisation is run for sample days representing a year of weather.
46
SEE Society Energy Environment
Bartlett School of Graduate Studies
University College London
VarInt : Sample day : winter’s day of variable supply deficit
SENCO
Energy, space, time model
Demand and supply day sampling Month 1
Dummy data
140
8.0
120
100
6.0
80
4.0
60
40
StHe_In
StEl_In
TradeOut
TrLoss
Air con
Light
Space heat
Heat
EV charge
Ele spec
SVar
SupTot
35
30
25
GW
160
10.0
Wave kW/m Insolation W/m2
180
20
15
40
2.0
10
20
0.0
5
0
1
Tamb_D
Tide_g1
5
9
13
Wind_D
Solar
17
21
0
Wind_g1
Wave_g1
Wind_g2
1
5
9
Cumulative demand and supply
13
17
21
Supplies and demand
700
40
Optional
600
35
StEl_Out
TradeIn
30
500
StHe_Out
Sol_g1
25
SupReq
GW
GWh
400
Aer_g2
20
15
Aer_g1
Hyd_g1
200
10
Wave_g1
Tide_g1
100
5
SupVar
300
CHP
SupReq
0
0
4
47
1
Demands and supply
Resources
12.0
Temp (C) / wind speed (m/s) Tide (m)
Days sampled
8
SEE Society Energy Environment
12
16
20
SVar
4
8
12
16
20
Bartlett School of Graduate Studies
University College London
VarInt : Day sampling : animation
48
SEE Society Energy Environment
Bartlett School of Graduate Studies
University College London
VarInt : Optimisation: year graph animated
The animation shows the year sample as the optimiser seeks the least cost mix of supply and storage for fixed
weather and renewable resources.
49
SEE Society Energy Environment
Bartlett School of Graduate Studies
University College London
VarInt : Optimised system : sample year
These charts show the sampled year performance of the optimised system for one set of weather.
SENCO
Energy, space, time model
Months 1,4,7,10
30.0
Resources
5 Days/month
450
400
350
300
250
200
150
100
50
0
25.0
Temp/Wind/Tide
Tamb_D
Wind_D
Wind_g1
Wind_g2
Tide_g1
Hydro
Solar
Wave_g1
Demand and supply sample day year
20.0
15.0
10.0
5.0
0.0
1
1
1
1
1
1
1
1
1
1
1
1
40
Demands
35
30
Light
25
Space heat
GW
Air con
20
15
Heat
10
EV charge
5
Ele spec
0
60
Supplies
50
TradeIn
Optional
40
Aer_g1
Wave_g1
GW
Aer_g2
30
20
Sol_g1
10
Tide_g1
Hyd_g1
0
CHP
-10
Stores
StHe_Sto
StEl_In
StEl_Out
StHe_In
50
StHe_Out
GWh
StEl_Sto
140
120
100
80
60
40
20
0
SEE Society Energy Environment
15
10
5
0
GW
TradeOut
-5
-10
-15
1
1
1
1
Bartlett School of Graduate Studies
University College London
Electricity demand and supply summary
Demands, variable supplies and stores are summarised in this table. The annuitised costs of capital are
calculated using a 5% discount rate.
•
For this initial development of the scenario, the renewable sources are each represented by one ‘farm’ at
a ‘site’ except for wind which is at two sites. Together they have a potential maximum of 132 GW
generating 266 TWh/a. This simplification means that temporal diversity is not fully exploited, and that no
account is made of different technology types.
•
Currently just two stores are assumed: one for electricity (with the minimum set at current UK pumped
storage) and one for heat.
Capacity
GW GW GW GW GW GW GW GW GW GW GW GW
Current
2.5 4.0 4.0 0.5 3.0 8.0 1.0 55.0 27.0 27.0 12.0 10.0
Maximum
1.0
55 27 27 12 10
Minimum
0.6
0
0
0
0
0
Efficiency
86% 25% 25% 25% 60% 60%
Energy TWh 22 35 35
5 28 70 7.5
68 69 74 41 36
Capacity factor
86% 14% 29% 31% 39% 41%
₤/kW
Unit capital cost
2500 2000 1000 1000 2500 3000
Operating life
Yrs
100
30 20 20 20 25
Discounted life
19.8 15.4 12.5 12.5 12.5 14.1
Capital total
G£
2.5 110.0 27.0 27.0 30.0 30.0
Capital annuitised
G£
0.1 7.2 2.2 2.2 2.4 2.1
O&M cost £/kW/a
25.0 20.0 20.0 20.0 50.0 30.0
G£
0.0 1.1 0.5 0.5 0.6 0.3
Energy cost (O&M, fuel) p/kWh
0.1 0.1 0.1 0.1 0.1 0.1
Energy cost
G£
0.0 0.1 0.1 0.1 0.0 0.0
Total cost
G£
0.2 8.3 2.8 2.8 3.0 2.5
Unit cost p/kWh
2.1 12.2 4.0 3.7 7.4 6.9
SEE Society Energy Environment
StHe_Out
StHe_Sto
StHe_In
Heat
StEl_Out
StEl_Sto
StEl_In
Optional
Trade
CHP
Tide_g1
Wave_g1
Aer_g2
Storage
Electricity
Aer_g1
Sol_g1
Hyd_g1
Ele spec
EV charge
Air con
Supply
Renewables
Space heat
Heat
Light
Demands
GW GW GW GW GWh GW GW GWh GW
6.0 5.5 6.3 5.0 10.0 2.3 16.0 62.0 0.0
6
6 50 100 400 100 100 999
4
2
0
2 10
2
0 10
92%
88% 77% 88% 99% 97% 98%
23 -37
0
44%
0% 0%
0% -5%
500 1500 200 100 400 100 10 50
5
25 50 35 20 20
20 30 30 30
14.1 18.3 16.4 12.5 12.5 12.5 15.4 15.4 15.4
3.0 8.2 1.5 0.5 4.0 0.2 0.2 3.1 0.0
0.2 0.4 0.1 0.0 0.3 0.0 0.0 0.2 0.0
10.0 30.0 4.0 2.0 8.0 2.0 0.1 0.5
0.1 0.2 0.0 0.0 0.1 0.0 0.0 0.0 0.0
4.2 10.4 6.9
1.0 -3.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.2 -3.3 0.1 0.1 0.4 0.0 0.0 0.2 0.0
5.4 -8.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0
51
Bartlett School of Graduate Studies
University College London
VarInt : Optimised system : technical summary
The charts depict the capacities of the generators and trade
link, and of the electricity and heat stores.
Generators
Optional
Trade
The table summarises the energy flows and peak demands.
CHP-bio
Tide_1
Demand
Transmission losses
TWh
Wave_1
282.2
Wind_2
Wind_1
16.9
Solar_1
Supply requirement 299.1
Hydro_1
0.0
Supply
Renewable 292.2
10.0
20.0
98%
30.0
40.0
GW
Spilled
-10.0
-3%
CHP-bio
19.2
6%
Optional
5.2
2%
Storage
2.2
1%
StHe_Sto
Country supply 308.8 103%
StHe_In
S torage
StEl_Out
Country surplus
9.7
Trade
-8.8
Country supply 300.0
StEl_Sto
StEl_In
0.0
20.0
40.0
GW/GWh
52
SEE Society Energy Environment
60.0
80.0
Bartlett School of Graduate Studies
University College London
VarInt : Optimised system: economic summary
The table shows the total cost of the system and the
average unit price of electricity. Both of these
will vary from year to year because of weather
induced changes to demand; and to supply,
particularly of trade and optional generation.
The annual cost of the renewables does not
change significantly, except for expenditure on
maintenance that is related to energy output for
that year.
Annual cost
G£
Capital
16.7
Energy
-0.7
Store
Total
Average
0.3
16.2
5.4 p/kWh
The pie chart shows the distribution of annualised
expenditure.
Annual cost
Optional
Hydro_1
Trade
Solar_1
CHP-bio
Tide_1
Wind_1
Wave_1
Wind_2
53
SEE Society Energy Environment
Bartlett School of Graduate Studies
University College London
Large Point Sources of emissions
The map shows historical emissions (SO2 ) from power stations and other large point sources in Europe and
western Asia.
SEE Society Energy Environment
54
Bartlett School of Graduate Studies
University College London
InterEnergy – trade optimisation animated
This shows
InterEnergy
seeking a least
cost solution.
It illustrates how
patterns of
electricity flow
might change.
An increase in
renewable
electricity will
require a higher
capacity grid with
more
sophisticated
control
55
SEE Society Energy Environment
Bartlett School of Graduate Studies
University College London
Technical and economic conclusions: 1
Need to make fast progress by 2020, 2030 to reduce total CO2 emission over next
decades.
Demand
•
•
Large energy demand reduction feasible with technologies in all sectors, but smaller
reductions in road freight transport, aviation and shipping.
Behavioural change very important, especially in car choice and use, and air travel.
Supply




A shift from fossil fuel heating to solar and electric heat pumps
A shift from fossil electricity generation to a mix of renewables
Large renewable electricity potential and Europe might become a net exporter of
electricity, but remain a large importer of oil
Renewable energy fraction difficult to define.

Main problem is replacing fossil liquid transport fuels, especially for aircraft and ships
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56
Bartlett School of Graduate Studies
University College London
Technical and economic conclusions: 2

Large CO2 reductions possible

Date and rate of introduction of
measures critical.

Low carbon scenarios have a
lower total and air pollution
control cost than high carbon
scenarios

Demand reduction and
renewables address all problems
simultaneously
SEE Society Energy Environment
57
Bartlett School of Graduate Studies
University College London
Policies

Apply policies local, national levels, but also at EU and international
levels which is essential to make best use of renewable resources

Identify issues that affect elections– e.g. energy prices and security

Apply known measures already used across Europe and the world

Focus on that act fast to reduce total CO2 emission, and make it
easier to convince China, India etc to avoid our development path

Use instruments like regulation that have certain and rapid effects

Develop policies that address problem of consumption, especially
transport
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Further material covering technical and behavioural aspects that may be of
interest
Low emission scenarios for Europe
http://www.naturvardsverket.se/Documents/bokhandeln/620-5785-5.htm).
UK Energy scenario: presentation
http://www.bartlett.ucl.ac.uk/markbarrett/Energy/UKEnergy/UKEneScenarioAnim140206.zip
Consumption: Report on consumption, energy and carbon dioxide including behavioural measures
http://www.bartlett.ucl.ac.uk/markbarrett/Consumption/EneCarbCons05.zip
Renewable electricity system: Feasibility of a high renewable electricity system
http://www.cbes.ucl.ac.uk/projects/energyreview/Bartlett%20Response%20to%20Energy%20Review%20%20electricity.pdf
http://www.bartlett.ucl.ac.uk/markbarrett/Energy/UKEnergy/UKElectricityGreenLight_100506.ppt
Aviation: http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Air/Aviation.htm
Technical scenarios http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Air/Aviation94.zip
Effects of taxes: http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Air/AvCharge.zip
Transport:
Summary presentation of some Auto-Oil work on transport and air quality, including some non-technical measures
http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Land/AutoOil/JCAPWork.ppt
Large Point Sources: power stations and health effects
http://www.acidrain.org
http://www.bartlett.ucl.ac.uk/markbarrett/Environment/LPS/LPS.htm
General:
http://www.bartlett.ucl.ac.uk/markbarrett/Index.html
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Thank you for your attention
More information available at:
Site : www.bartlett.ucl.ac.uk/markbarrett/Index.html
Email: [email protected]
Tel Mobile: +44 07837 338297
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Renewable energy accounting
How to estimate the renewable energy fraction of total EU energy consumption?
These questions arise:
•
Where in the energy flow system of a country is renewable energy measured?
•
Which renewable energy sources are included?
•
How are the renewable energy flows quantified and accounted?
•
How is the fraction of renewable energy calculated?
The answers to these questions are largely arbitrary.
There is no obvious system for accounting for renewable energy that is applicable to all
countries at all times.
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EU25 renewable fractions, EU30pc20N scenario
- delivered energy
Renewable energy is 5% of delivered energy in 2020
LEGEND KEY
Del: : Foss
Del: Heat: Ren
Del: Liquid: Ren
Del: Heat: Vec
Del: Ele: Vec
Delivered energy
100%
90%
Delivered fossil fuel
End use biomass/solar heat
Delivered liquid
Delivered heat (district heating)
Delivered electricity
Del: Ele: Vec
80%
70%
Del: Heat: Vec
60%
50%
Del: : Foss
40%
30%
Del: Liquid: Ren
20%
10%
Del: Heat: Ren
0%
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
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VarInt : Optimisation :annual summary animated
The animation shows
the annual
summary as the
optimiser seeks
the best mix of
supply and
storage.
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