Society Energy Environment SEE University College London UCL ENERGY INSTITUTE International energy...

Society Energy Environment SEE

University College London UCL ENERGY INSTITUTE

1

International energy scenariosa systems approach to modelling energy scenarios at an international level

UCL Energy Institute MRres

Mark Barrett

[email protected]

mailto:[email protected]



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Contents

• What’s it all for?

• What systems are there?

• What is a model?

• Modelling process?

• Scenarios for the EU

• The problem of space and time



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Homo sapiens

• Energy and material demands– tissue formation and maintenance– keeping warm, keeping cool– movement– information processing

• Energy from oxidising carbon in food, renewable biomass

• Refined control systems to minimise energy and water consumption

• Comfort is when energy and water consumption is minimised

Most exosomatic services (buildings, transport) designed to minimise endosomatic energy consumption, to achieve comfort – this is a basic driver of energy demand

e.g. 10% UK energy & emissions to keep warm air next to skin

Carbon dioxide400 litres/day

Energy 130 W

Oxygen 400 litres/day

Water 4 litres/day

40 day energy store

Adaptive envelope- minimise energy losses- minimise water loss



The society, energy environment system

People in society have energy service demands that are met by energy systems which cause primary inputs to the environment. These inputs are modified and transported via media to impact on biota.

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The energy system: demand and supply options

Energy demands and sources can be linked in many ways. The appropriate linkage depends on a complex of their distribution in space and time, and the economics of the technologies used.

5



Energy, space and time problem

What is the best configuration?

What capacities?

Where to locate converters and stores?

Where to place transmission nodes?

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Building store CHPWave

22 Acacia

Local storeCoal

23 Acacia

Wind

SystemDemand store

Gas CCConverter

Store

Node CityWind

FRANCEUnit cost

Time varyingDemand

SupplyStorage



DYNAMICS – WHAT AND WHY

1. Long term dynamics – changes to capital stocks (buildings, power stations, etc.) in scenarios transforming whole energy system

2. Short term dynamics - demand-supply matching over minutes to months

1 and 2 required for optimisation of system design.

7

2 required:• to ensure system is technically feasible e.g. mix of renewables and dispatchable

generators works; • to explore potential of fast measures such as load management and spatiotemporal

controls in buildings• to do accurate costing and estimation of emissions, environment and health impacts,

etc.



The society, energy environment system and models

8



Models and data - UCL Energy Institute

Energy systems are fractal, so a range of models and data are required

9



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What is a model?

• Representation of a bounded physical system (social and/or technological)– Internal relationships based on historical data– Exogenous data inputs:

• Initial system state based on historical data• Future values from system environment

• Domains of model– Physical variables only– Socioeconomic

• Types and methods of modelling– Dynamic and static– Simulation: with differential equations– Statistical: Monte Carlo– Optimisation: linear /non linear programming, genetic algorithms, etc.



11

The modelling process

• What is the question? What is the curiosity?

• Collation of information about the world– Processes – how do things work?– Historical state of the system being modelled and exogenous factors

• Build a model– Structure data– What software and hardware environment will be used?– Write programme to

• input data• emulate processes, simulate, optimise• Output data

• Validate the model– Does the model reflect reality as described by historical data?

• Practicalities– Money?– Who will build model?– Who will run it?



The challenge

Develop EU integrated policy that achieves environmental and energy goals at least overall cost.

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LOW EMISSION ENERGY SCENARIOSFOR THE EUROPEAN UNION

A study forThe Swedish Environmental Protection Agency

Energy security

Climate change

Air pollution

Integrated

policy

Socialgoals



Objectives, instruments and measures

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OBJECTIVES

Energy security

Socioeconomy

Global warming

Toxic pollutants

MEASURES: energyBehaviouralDemand managementEfficiencyRenewables

INSTRUMENTSRegulationMarketInformation

MEASURES: End-of-pipeCatalysts, FGD,etc.



OBJECTIVES OF STRATEGY

SOCIAL, ECONOMIC, POLITICAL• Meet objectives at least cost with social equity• Avoid irreversible, risky technologies

ENERGY SECURITY• Reduce dependence on finite fossil and nuclear fuels• UK 20% of energy from renewables by 2020 => ~35% renewable electricity?• renewable transport fuels: 5% of by 2010, 10% by 2020

ENVIRONMENT

UK• Government targets for GHG reduction from 1990: 12-20% by 2010, ~30% by 2020,

60-80% 1990-2050, including international transport.• Require >95% GHG reduction for climate control and global equity

Europe• 20/30% GHG reduction 1990-2020

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Ethics: equal CO2 emission per person?

Humans have equal rights to emissions, therefore convergence of emission per person in the EU and elsewhere? What about different resources and climate of countries? Note that for global equity, EU per capita emissions will have to fall by over 95% to reach 60% reduction globally.

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0

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20

25

30

1990 2000 2010 2020 2030 2040 2050

CO

2 t

on

nes

AUTBELDNKFINFRADEUGRE

IREITALUXNLDPRTESPSWEGBRHUNCZRESTLTU

LVAPOLSLKSLVCYPMLT



Policy measures: physical measures and rate of change

Size of effect, rate of effect and cost

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Technical basis: SEEScen: Society, Energy, Environment Scenario model

SEEScen is applicable to any large country having IEA energy statistics

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

• Optimisation under development

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HISTORY

FUTURE

COSTS

INPUTS / ASSUMPTIONS

IMPACTSENERGY

IEA dataEnergyPopulation, GDP

Other dataClimate, insulation...

Delivered fuel

End use fuel mix

End use efficiency

Delivered fuel by end use

Useful energy

Socioeconomic

Useful energy

Delivered energy

Lifestyle change

Demand

End use fuel mix

End use efficiency

Conversion

Primary energy

Supply efficiency

Emissions

Capital

Running Distribution losses

Supply mix

Trade

Conversion



UK Energy flow chart: 1990

SENCO GBR : TechBeh : Y1990Trade Extraction Fuel processing Electricity and heat Delivered Sectors Useful energyEnvironment

Waste energy

Trd_E

Trd_N

Ext_G

Ext_S

Ext_L

Solid

Nuclear

Refinery Liq

Solid

Nuclear

L_FueOil

ElOnly

Gas

Solid

Elec

Liq

Biomass Food

Res_G_

Res_S_Res_E_Res_L_

Ser_G_Ser_S_Ser_E_Ser_L_

Ind_G_

Ind_S_Ind_E_

Ind_L_

Oth_G_Oth_L_

Tra(nat) E

Tra(nat) L

Tra(int) L

Mot W

Proc W

H>120C

H<12-C

Water H

Space H

Space ACCool

CO2 CO2



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Scenario context: UK Energy flow chart: animation 1990 to 2050



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Scenario context: UK Energy flow chart: 2050

SENCO GBR : TechBeh : Y2050Trade Extraction Fuel processing Electricity and heat Delivered Sectors Useful energyEnvironment

Waste energy

Trd_G

Trd_E

Trd_L

Ext_G

Ext_S

Biomass

Solid

Wind

TideWave

Solar

Biowaste

BiomassBiomass

proc

Refinery

S_BioL_Bio

Liq

Wind

TideWaveSolar

Waste

CHPDHFuI

ElOnly

Auto

CHPDH_H

Auto_H

Gas

G_CHP

H_Solar

Solid

Elec

Heat

L_CHP

Liq

Biomass Food

Res_G_CHPRes_H_Solar

Res_E_

Ser_G_CHPSer_H_SolarSer_E_

Ind_G_Ind_G_CHP

Ind_H_SolarInd_S_Ind_E_

Ind_L_Ind_L_CHP

Oth_G_

Tra(nat) ETra(nat) L

Tra(int) L

Mot W

El equip

Proc W

Light

H>120C

H<12-C

Cooking

Water H

Space H

Space AC

Cool

CO2



Scenarios

Six scenarios for each EU25 country were constructed to reach these objectives using different combinations of NEOP measures implemented to different degrees.

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Label Target: % CO2

reduction

Target: Reduction

date

Nuclear NEOPs

EU30pc20N 30 2020 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



Energy services and demand drivers

Demands for energy services are determined by human needs, these include• food• comfort, hygiene, health• culture

Important drivers of demand include:• Population increases• Households increase faster because of smaller households• Wealth, but energy consumption and impacts depend on choices of expenditure on goods and

services which are somewhat arbitrary

The drivers are assumed to be the same in all scenarios.

The above drivers are simply accounted for in the model, but others are not, for example:• Population ageing, which will result in increases and decreases of different demands• Changes in employment• Environmental awareness• Economic restructuring

More on consumption at:

http://www.sencouk.co.uk/Consumption/Consumption.htm

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http://www.sencouk.co.uk/Consumption/Consumption.htm



Exogenous assumptions (from PRIMES WCLP scenario): basic drivers

Population peaks and declines

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1995 2000 2005 2010 2015 2020 2025 2030

SWESLVSLKPRTPOLNLDMLTLVALUXLTUITAIREHUNGREGBRFRAFINESTESPDNKDEUCZRCYPBELAUT

M

0

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250

1995 2000 2005 2010 2015 2020 2025 2030


M

0

2000

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6000

8000

10000

12000

14000

16000

18000

20000

1995 2000 2005 2010 2015 2020 2025 2030


000

More households

GDP growth



Exogenous assumptions (from PRIMES): transport demand

But is saturation occurring, e.g. UK?

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0

1000

2000

3000

4000

5000

6000

7000

8000

9000

1995 2000 2005 2010 2015 2020 2025 2030


Gpkm

15

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23

25

27

29

31

33

35

1970 1980 1990 2000 2010

kph/

km p

er d

ay

-

0.6

0.7

0.8

0.9

1.0

1.1

hrs/

day

Speed

km/day

Hrs/day

More passenger travel

More travl per capita

0

5000

10000

15000

20000

25000

1995 2000 2005 2010 2015 2020 2025 2030

AUTBELCYPCZRDEUDNKESPESTFINFRAGBRGREHUNIREITALTULUXLVAMLTNLDPOLPRTSLKSLVSWE

km



Exogenous assumptions (from PRIMES): transport demand

More freight transport

25

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1995 2000 2005 2010 2015 2020 2025 2030


Gtkm

Why?

What if travel costs go up?



Exogenous assumptions: nuclear power

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0

20000

40000

60000

80000

100000

120000

140000

160000

1995 2000 2005 2010 2015 2020 2025 2030


MW

0

20000

40000

60000

80000

100000

120000

140000

1955

1960

1965

1970

1975

1980

1985

1990

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2000

2005

2010

2015

2020

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2030

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2045

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MW

SVN

SVK

LTU

HUN

CZE

SWE

NLD

ITA

GBR

FRA

FIN

ESP

DEU

BEL

Profile with 35 years life

PRIMES profile with replacement.

Is this feasible?

0%

1%

2%

3%

4%

5%

6%

7%

8%

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

% E

U2

5 C

O2 (

199

0)

30 yr

35 yr

40 yr



SEEScen sample: Domestic sector: house heat loss factors

Implementation of space heat demand management (insulation, ventilation control) depends on housing needs and stock types, replacement rates, and applicability of technologies. Insulation of the building envelope and ventilation control can reduce house heat losses to minimal levels.

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0

50

100

150

200

250

300

350

1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

W/o

C

Vent loss

Roof

Window

Wall opaque

Floor

GBR: TecNN: W/oC : Elements



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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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.

29

0

200

400

600

800

1000

1200

1400

1990

1995

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2005

2010

2015

2020

2025

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2035

2040

2045

2050

Gpk

m

Int:Pas:Plane

Int:Pas:Ship

Nat:Pas:Ship

Nat:Pas:Plane

Nat:Pas:Rail

Nat:Pas:Bus

Nat:Pas:Car

Nat:Pas:MCycle

Nat:Pas:Bike

GBR: TecBehNN: Passenger : Load distance

0

500

1000

1500

2000

2500

3000

1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Gpk

m

Int:Pas:Plane

Int:Pas:Ship

Nat:Pas:Ship

Nat:Pas:Plane

Nat:Pas:Rail

Nat:Pas:Bus

Nat:Pas:Car

Nat:Pas:MCycle

Nat:Pas:Bike

GBR: TecNN: Passenger : Load distance



SEEScen sample: Transport: passenger vehicle distanceDemand management and modal shift can produce a large reduction in road traffic reduces

congestion which gives benefits of less energy, pollution and travel time.

30

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150

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300

350

400

450

1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Gv.

km

Int:Pas:Plane_LB

Int:Pas:Plane_K

Int:Pas:Ship_D

Nat:Pas:Ship_D

Nat:Pas:Plane_K

Nat:Pas:Rail_E

Nat:Pas:Rail_LB

Nat:Pas:Rail_D

Nat:Pas:Bus_E

Nat:Pas:Bus_H2

Nat:Pas:Bus_CNG

Nat:Pas:Bus_LB

Nat:Pas:Bus_D

Nat:Pas:Car_E

Nat:Pas:Car_H2

Nat:Pas:Car_LB

Nat:Pas:Car_LPG

Nat:Pas:Car_D

Nat:Pas:Car_G

Nat:Pas:MCyc_G

Nat:Pas:Bike_S

GBR: TecBehNN: Passenger : Vehicle distance

0

100

200

300

400

500

600

700

800

1990

1995

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2005

2010

2015

2020

2025

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2035

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2050

Gv.

km

Int:Pas:Plane_LB

Int:Pas:Plane_K

Int:Pas:Ship_D

Nat:Pas:Ship_D

Nat:Pas:Plane_K

Nat:Pas:Rail_E

Nat:Pas:Rail_LB

Nat:Pas:Rail_D

Nat:Pas:Bus_E

Nat:Pas:Bus_H2

Nat:Pas:Bus_CNG

Nat:Pas:Bus_LB

Nat:Pas:Bus_D

Nat:Pas:Car_E

Nat:Pas:Car_H2

Nat:Pas:Car_LB

Nat:Pas:Car_LPG

Nat:Pas:Car_D

Nat:Pas:Car_G

Nat:Pas:MCyc_G

Nat:Pas:Bike_S

GBR: EU30pc20N: Passenger : Vehicle distance

Assumed introduction of electric vehicles to replace liquid fuels, and reduce urban air pollution.



Cars: carbon emission by performance

31

grammes Carbon per km

0

5

10

15

20

25

25 35 45 55 65 75 85

0

50

100

150

200

250

300

Acceleration

Fuel

Speed

UKspeedlimit

Petrol

DieselMicrocars

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.



Transport: road speed and CO2 emission

32

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 10-20%. 0%

100%

200%

300%

400%

500%

600%

5 25 45 65 85 105 125 145

kph

Car (D,> 2.0 l, EURO IV) Car (P,< 1.4 l, EURO IV)Car (P,1.4 - 2.0 l, EURO IV) Car (P,> 2.0 l, EURO IV)HGV (D,Rigid, EURO IV) HGV (D,Artic, EURO IV)Bus (D,0, EURO IV) Van (D,medium, EURO IV)Van (D,large, EURO IV) Mcycle (P,250-750cc 4-s, pre)Mcycle (P,>750cc 4-s, pre)

Motorway

Fraction of minimum CO2 g/km

Low speed emission

Average conceals start/ stop congestion

And car design dependent



SEEScen sample: Transport: passenger: delivered energy

International air travel will become a large fraction of future passenger energy use

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500

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2500

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2020

2025

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PJ

Int:Pas:Plane_LB

Int:Pas:Plane_K

Int:Pas:Ship_D

Nat:Pas:Ship_D

Nat:Pas:Plane_K

Nat:Pas:Rail_E

Nat:Pas:Rail_LB

Nat:Pas:Rail_D

Nat:Pas:Bus_E

Nat:Pas:Bus_H2

Nat:Pas:Bus_CNG

Nat:Pas:Bus_LB

Nat:Pas:Bus_D

Nat:Pas:Car_E

Nat:Pas:Car_H2

Nat:Pas:Car_LB

Nat:Pas:Car_LPG

Nat:Pas:Car_D

Nat:Pas:Car_G

Nat:Pas:MCyc_G

Nat:Pas:Bike_S

DEU: TecBehNN: Passenger : Delivered

0

500

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1500

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1990

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PJ

Int:Pas:Plane_LB

Int:Pas:Plane_K

Int:Pas:Ship_D

Nat:Pas:Ship_D

Nat:Pas:Plane_K

Nat:Pas:Rail_E

Nat:Pas:Rail_LB

Nat:Pas:Rail_D

Nat:Pas:Bus_E

Nat:Pas:Bus_H2

Nat:Pas:Bus_CNG

Nat:Pas:Bus_LB

Nat:Pas:Bus_D

Nat:Pas:Car_E

Nat:Pas:Car_H2

Nat:Pas:Car_LB

Nat:Pas:Car_LPG

Nat:Pas:Car_D

Nat:Pas:Car_G

Nat:Pas:MCyc_G

Nat:Pas:Bike_S

GBR: EU30pc20N: Passenger : Delivered



SEEScen sample: UK : electricity generation (not consumption)

Switch from electricity only fossil generation to:• Fossil CHP for medium term, and biomass CHP• Renewable sources

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600

800

1000

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1400

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180019

90

1995

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PJe

E_S_Fos

E_L_Fos

E_G_Fos

E_N_Nuc

E_Hydro

H_Geothe

H_Solar

E_Wave

E_Tide

E_Wind

Pump_E

CHP_S_Fos

CHP_L_Fos

CHP_G_Fos

CHP_S_MunRef

CHP_S_Bio

CHP_L_Bio

CHP_G_Bio

ACHP_S_Fos

ACHP_L_Fos

ACHP_G_Fos

ACHP_S_Bio

ACHP_L_Bio

ACHP_G_Bio

GBR: EU30pc20N: Electricity : Output



SEEScen sample: UK : CO2 excluding international transport

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600

700

1990

1995

2000

2005

2010

2015

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2030

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Mt

Fue:Ext

Fue:Pro

Ele:Gen

Hea:Pub

Hea:Aut

Tra(nat):Other i

Tra(nat):Air: Do

Tra(nat):Rail

Tra(nat):Road: F

Tra(nat):Road: P

Res:Res

Ser:Ser

Oth:oth

Ind:Agr

Ind:Lig

Ind:Hea

Ind:Che

Ind:Iro

GBR: EU30pc20N: Environment: National: (N) : CO2



SEEScen sample: UK CO2 by scenario

36

0

100

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300

400

500

600

700

1990

1995

2000

2005

2010

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2020

2025

2030

2035

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2045

2050

Mt

EU30pc20N

EU40pc20N

EU30pc20NN

TecNN

BehNN

TecBehNN

GBR: Scenarios: Environment: National: (N) : CO2



SEEScen sample: EU25 CO2 emissions by country : EU30pc20N scenario

. The black squares show the targets for 2010 and a 30% reduction by 2020.

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Mt

SWESVNSVKPRTPOLNLDMLTLVALUXLTUITAIRLHUNGRCGBRFRAFINESTESPDNKDEUCZECYPBELAUTTargets

COUNTRIES: EU30pc20N : Environment: National: (N) Total : CO2



SEEScen sample: EU25 CO2 : variant scenarios40% reduction

New nuclear

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Mt


COUNTRIES: EU40pc20N : Environment: National: (N) Total : CO2

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Mt


COUNTRIES: BehNN : Environment: National: (N) Total : CO2

0

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Mt


COUNTRIES: TecBehNN : Environment: National: (N) Total : CO2

0

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Mt


COUNTRIES: TecNN : Environment: National: (N) Total : CO2

Maximum behaviourNo new nuclear

Maximum technologyNo new nuclear

Maximum technology and behaviour

No new nuclear



SEEScen sample: Energy security

EU25 energy trade : including fuels for international transport: EU30pc20N scenario

39

-10000

0

10000

20000

30000

40000

50000

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

PJ

Electricity

Gas

Oil

Nuclear

Total

Total effective



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.

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M€/

a

EU30pc20N

EU40pc20N

EU30pc20NN

TecNN

BehNN

TecBehNN

ITA: Scenarios: Economics : Cost : Total



Further issues: aviation

· Low level. Airports are emission hot spots because of aircraft taxiing, and landing and take-off, and because of road traffic.

· Tropospheric emission. Aircraft emit a substantial quantities of NOx whilst climbing to tropopause cruising altitude (about 12 km). This will contribute to surface pollution.

· 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.

· Of all the fossil fuels, kerosene is the most difficult to replace.

Further information on this is given in the references.

41

0

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1990

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2045

2050

MtC

O2eq

Tra(int):Air

National

GBR: EU30pc20N: Environment: : Global warming

International aviation and shipping should be included in GHG inventories because their GHG emissions will become very large fractions of total.



Conclusions: 1

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

42



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

43

LOW EMISSION ENERGY SCENARIOSFOR THE EUROPEAN UNION

A study forThe Swedish Environmental Protection Agency

Energy security

Climate change

Air pollution

Integrated

policy

Socialgoals



Detailed spatio-temporal modelling

44

Energy scenarios have annual energy flows.

Will the energy systems work:

Temporally: hour by hour, day by day and month by month?

Spatially: what are the requirements for distributing energy spatially?



45

Building dynamics

Weather andOccupancy, over hours and months, drive:

• ventilation

• energy flows

• pollution

• personal exposure

Wait for animation to run



UK energy, space and time : animated

46



47

Electricity : diurnal operation without load management



48

Electricity : animated load management optimisation



49

Electricity : diurnal operation after load managementEleServe Scenario: Efficiency + CHP + renewables 2025 Winter day : Summer day SENCO

System

-5

0

5

10

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30

1 25

hrs

GW

System demand

Essentialgeneration

Dem Net E

Trade

Dem Net ET

Store

Dem Net ETS

Optionalgeneration

Reserverequirement

Reservestore+hydro

Res req. Net Store

Demand (LM)

0

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hrs

GW

I:Fue:Gen I:Ind:Lig

I:Ind:Mot 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

R:Coo:Spa

Marginal costs

0.00

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h

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Startup energy

Generation energy

Generation

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GW

1 RTid 2 RAer3 RWav 4 RSolPV5 RHydhh 8 Gchp6 Gchp 7 Ochp9 N 10 N12 C 19 C17 C 11 C18 C 13 C16 C 15 C14 C 53 Gcc42 Gcc 39 Gcc28 Gcc 43 Gcc54 Gcc 58 Gcc32 Gcc 30 Gcc60 Gcc 38 Gcc48 Gcc 33 Gcc40 Gcc 41 Gcc59 Gcc 50 Gcc45 Gcc 61 Gcc46 Gcc 52 Gcc31 Gcc 29 Gcc36 Gcc 56 Gcc51 Gcc 27 Gcc44 Gcc 57 Gcc49 Gcc 47 Gcc35 Gcc 37 Gcc55 Gcc 24 G23 G 25 O26 O 22 G21 G 20 G62 Ogt 63 Ogt65 Ogt

0

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VarInt : Sample day : winter’s day of variable supply excessSENCO Energy, space, time model Demand and supply day sampling Month 1 Dummy data Days sampled 1

Resources

0.0

2.0

4.0

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8.0

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14.0

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Tem

p (

C)

/ win

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eed

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) T

ide

(m)

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Tamb_D Wind_D Wind_g1 Wind_g2

Tide_g1 Solar Wave_g1

Inso

lati

on W

/m2

Wav

e kW

/m

Demands and supply

0

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GW

StHe_In

StEl_In

TradeOut

TrLoss

Air con

Light

Space heat

Heat

EV charge

Ele spec

SVar

SupTot

Supplies and demand

0

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50

4 8 12 16 20

GW

Optional

StEl_Out

TradeIn

StHe_Out

Sol_g1

Aer_g2

Aer_g1

Hyd_g1

Wave_g1

Tide_g1

CHP

SupReq

SVar

Cumulative demand and supply

0

100

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GW

h SupReq

SupVar



51

VarInt : Sample day : winter’s day of variable supply deficitSENCO Energy, space, time model Demand and supply day sampling Month 1 Dummy data Days sampled 1

Resources

0.0

2.0

4.0

6.0

8.0

10.0

12.0

1 5 9 13 17 21

Tem

p (

C)

/ win

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eed

(m/s

) T

ide

(m)

0

20

40

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180

Tamb_D Wind_D Wind_g1 Wind_g2

Tide_g1 Solar Wave_g1

Inso

lati

on W

/m2

Wav

e kW

/m

Demands and supply

0

5

10

15

20

25

30

35

40

1 5 9 13 17 21

GW

StHe_In

StEl_In

TradeOut

TrLoss

Air con

Light

Space heat

Heat

EV charge

Ele spec

SVar

SupTot

Supplies and demand

0

5

10

15

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30

35

40

4 8 12 16 20

GW

Optional

StEl_Out

TradeIn

StHe_Out

Sol_g1

Aer_g2

Aer_g1

Hyd_g1

Wave_g1

Tide_g1

CHP

SupReq

SVar

Cumulative demand and supply

0

100

200

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400

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700

4 8 12 16 20

GW

h SupReq

SupVar



52

VarInt : Optimised system : sample yearThese charts show the sampled year performance of the optimised system for one set of weather.

SENCO Energy, space, time model Demand and supply sample day year Months 1,4,7,10 5 Days/month

Resources

0.0

5.0

10.0

15.0

20.0

25.0

30.0

1 1 1 1

Tem

p/W

ind/

Tid

e0

50

100150

200

250

300350

400

450

Tamb_DWind_DWind_g1Wind_g2Tide_g1HydroSolarWave_g1

Demands

0

5

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20

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30

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40

1 1 1 1

GW

Air con

Light

Space heat

Heat

EV charge

Ele spec

Supplies

-10

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30

40

50

60

1 1 1 1

GW

TradeIn

Optional

Aer_g2

Aer_g1

Wave_g1

Sol_g1

Tide_g1

Hyd_g1

CHP

TradeOut

Stores

0

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140

1 1 1 1

GW

h

-15

-10

-5

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GW

StEl_Sto

StHe_Sto

StEl_In

StEl_Out

StHe_In

StHe_Out



53

VarInt : Day sampling : animation



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

54



World

There are global patterns in demands and renewable supplies:• Regular diurnal and seasonal variations in demands, some climate dependent• Regular diurnal and seasonal incomes of solar energy• Predictable tidal energy income

55



World: a global electricity transmission grid?

• Should transmission be global to achieve an optimum balance between supply, transmission and storage?• Which investments are most cost efficient in reducing GHG emission? Should the UK invest in photovoltaic

systems in Africa, rather than the UK? This could be done through the Clean Development Mechanism

56



Some whole system energy models

Institution Acronym Sectors Link

IEA TIMES/TIAM All http://www.etsap.org/applicationGlobal.asp

IEA MARKAL All http://www.etsap.org/markal/main.html

EPLEPFL GEMINI All http://gemini-e3.epfl.ch/

EEE WITCH All http://www.witchmodel.org/pag/model.html

EnerData POLES All http://www.enerdata.net/enerdatauk/solutions/energy-models/poles-model.php

IEA WEM All http://www.worldenergyoutlook.org/docs/weo2009/World_Energy_Model.pdf

57



References: Barrett

58

GENERAL:http://www.bartlett.ucl.ac.uk/markbarrett/Index.html http://www.bartlett.ucl.ac.uk/markbarrett/Teaching/Educ.html

CONSUMPTION: Report on consumption, energy and carbon dioxide including behavioural measures.http://www.bartlett.ucl.ac.uk/markbarrett/Consumption/EneCarbCons05.zip TRANSPORTConsultancy to DfT on project. Carbon Pathways: Analysis Informing Development of a Carbon Reduction Strategy for the Transport Sector, July 2008 .http://www.dft.gov.uk/pgr/sustainable/analysis.pdf Overview of some aspects of sustainable transport : http://www.bartlett.ucl.ac.uk/markbarrett/Transport/TransportSus_MBarrett_020608.ppt 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 Aviation: Technical scenarios http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Air/Aviation94.zip Effects of charges: http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Air/AvCharge.zip ELECTRICITY: Feasibility of a high renewable electricity systemBarrett, M. 2007, A Renewable Electricity System for the UK. In Renewable Energy and the Grid: The Challenge of Variability, Boyle, G., London: Earthscan. ISBN-13: 978-1-84407-418-1 (hardback).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

SCENARIOSBarrett M, December 2007, Low Emission Energy Scenarios for the European Union, report 5785. ISBN 91-620-5785-5, ISSN 0282-7298. http://www.naturvardsverket.se/Documents/bokhandeln/620-5785-5.htm Naturvårdsverket (Swedish environmental protection agency, SE-106 48 Stockholm www.naturvardsverket.se

Dynamic Physical Energy Model (1981)www.bartlett.ucl.ac.uk/web/ben/ede/BENVGEED/ERG 044.pdfwww.bartlett.ucl.ac.uk/web/ben/ede/BENVGEED/ERG045_complete.pdf

HEALTHBarrett M, Holland M, April 2008, The Costs and Health Benefits of Reducing Emissions from Power Stations in Europe. Published by the Air Pollution and Climate Secretariat and the European Environmental Bureau. ISBN: 978-91-975883-2-4ISSN: 1400-4909. http://www.airclim.org/reports/APC20_final.pdf

http://www.bartlett.ucl.ac.uk/markbarrett/Index.html

http://www.bartlett.ucl.ac.uk/markbarrett/Index.html

http://www.bartlett.ucl.ac.uk/markbarrett/Teaching/Educ.html

http://www.bartlett.ucl.ac.uk/markbarrett/Teaching/Educ.html

http://www.bartlett.ucl.ac.uk/markbarrett/Consumption/EneCarbCons05.zip

http://www.dft.gov.uk/pgr/sustainable/analysis.pdf

http://www.bartlett.ucl.ac.uk/markbarrett/Transport/TransportSus_MBarrett_020608.ppt

http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Land/AutoOil/JCAPWork.ppt

http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Air/Aviation94.zip

http://www.bartlett.ucl.ac.uk/markbarrett/Transport/Air/AvCharge.zip

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

http://www.naturvardsverket.se/Documents/bokhandeln/620-5785-5.htm

http://www.naturvardsverket.se/

http://www.bartlett.ucl.ac.uk/web/ben/ede/BENVGEED/ERG%20044.pdf

http://www.bartlett.ucl.ac.uk/web/ben/ede/BENVGEED/ERG045_complete.pdf

http://www.airclim.org/reports/APC20_final.pdf

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