ier präsentation e 10-2016 [kompatibilitätsmodus] · ier presentation content 1. institute of...

Hybrid modelling Some practical applications

Ulrich Fahl

Institute of Energy Economics and the Rational Use of Energy (IER), University of Stuttgart

SET-Nav Workshop:

Top-down bottom-up hybrid modellingNovember 24 – 25, 2016

Norwegian University of Science and Technology (NTNU), Trondheim

IER Presentation

Content

1. Institute of Energy Economics and the Rational Use of Energy (IER)

2. Global Analysisi. NEWAGE – Integration of hybrid features in a CGE model

ii. TIAM-MACRO – Energy system model with macroecomomic extension

iii. TIAM-LOPEX – Energy system model and oil market model

3. European Analysis i. Linking TIMES-PanEU and E2M2

ii. Linking TIMES-PanEU and NEWAGE

4. Outlook: The LCE21 project REEEM2

IER Presentation 3

11/2016

Institute of Energy Economicsand the Rational Use of Energy (IER)

Faculty of Energy-,Process- and Bio- University of StuttgartEngineering

IER Presentation 4

Research Emphases

• Analysis and assessment of new technologies and

energy systems

• Technology assessment and environmental analysis

• Development of models and decision support

systems for energy economics and energy policy

• Energy systems analysis

• Rational use of energy / Energy efficiency

IER Presentation 5

Departments

• Energy Efficiency (EE)

• Energy Markets and Intelligent Systems (EI)

• Energy Economics and System Analysis (ESA)

• System Analysis and Renewable Energies (SEE)

• System-analytical methods and Heat Market (SAM)

• Technology Assessment and Environment (TFU)

IER Presentation 6

Energy Economics and Systems Analysis (ESA) Analysis of electricity and heat supply concepts as well as of

new transport technologies and fuels

Roadmaps to a sustainable development of the energy system

Development and application of energy system and energy economic models on international, national, regional and urban level (greenhouse gas control strategies, importance of the different energy technologies, supply guarantee and trade relations, assessment of policy instruments)

IER Presentation 7

IER Energy-Environment-Economy (E³) Models

E-Cost (LLCEC)

Balance (LCA)

Technology TIMES-EU

E2M2S

JMM

LEMI

Electricity System TIMES

• Bavaria, Saxonia,

Hessen, Baden-Württ.

• Germany

• EU

• World (TIAM)

Energy SystemEnergy-Economy

NEWAGE

TIAM-MACRO

ResourcesLOPEXBalance (LCA)

EcoSense (External Costs)

Environmental System

IER Presentation

Categories of Energy Models

8

Simulation Optimization Computational

General EquilibriumEconometric

Characteristics: Sectoral coverage or Entire energy system Single region or Multi regions Short term or Long-term Recursive dynamic or Perfect foresight

Characteristics:i. Single region or Multi regionsii. Recursive dynamic or Perfect foresight

Integrated Assessment

ModelsClimate Models

Energy Models

Bottom-up models Top-down models

Attempt to link

model types

Economic models

Input-Output

IER Presentation

CGE models vs. I-O models● Critical aspects

i. Prices

ii. Closed circle of income (net effects)

iii. Behavioral assumptions

iv. Fixed input ratio and substitution elasticities

v. Constant returns to scale

● Perspectivei. Short-run / ex-post / economic interlinkages

ii. Long-run / ex-ante / policy assessment

● Ability to capture policy framework (e.g. climate policy targets)

9

IER Presentation

Categories of Energy Models

10

Simulation Optimization Computational

General EquilibriumEconometric

Characteristics: Sectoral coverage or Entire energy system Single region or Multi regions Short term or Long-term Recursive dynamic or Perfect foresight

Characteristics:i. Single region or Multi regionsii. Recursive dynamic or Perfect foresight

Integrated Assessment

ModelsClimate Models

Energy Models

Bottom-up models Top-down models

Attempt to link

model types

Economic models

NEWAGE

Hybrid modelling

Input Output

1

NEWAGE for Applied Economic Research

• Objective and rationale: Simulation and quantification of micro- and macroeconomic effects of economic, energy and environmental policy intervention

• Comprehensive total analysis: Simultaneous consideration of all factor and commodity markets and their interdependencies. Accounting for all feedback effects within the economy, i.e. direct and indirect

• Multi regional model: Representation of international trade relations, regarding primary production factors and produced commodities, e.g. energy products

• Multi sectoral model: Representation of various industry and service sectors and their relation in intermediate production, allocation and consumption

• Technology rich model: Technology oriented representation of the electricity generation sector and household energy demand through a hybrid approach

IER University of Stuttgart

2

General Characteristics of the CGE Model NEWAGE• Total-analytic perspective ( macroeconomic efficiency analysis)

• Neoclassical equilibrium conditions: Cleared markets, zero profit, income balance

• Endogenous factor and commodity allocation via Walras price system

• Factor inputs into production are capital, two different specifications of labor, energy and materials. CO2-allowances can be an additional input if fossil fuels are used

• Every production technology is implemented by a nonlinear nested CES production function (Constant Elasticity of Substitution) that relates input to industry output

• Profit maximization through cost minimization by representative firms

• Utility maximization through consumption under budget constraint of representative agent following nonlinear utility function

• Modeling of restrictions: Market organization restrictions e.g. labor market; technical restrictions in the energy system

• Data basis: GTAP9, Input-Output tables, bilateral trade flows, technological and economic power plant data, energy consumption, energy carrier specific emission coefficients, etc.

• Rutherford (2000) GTAP-EG; Böhringer (1996)IER University of Stuttgart

Global Trade Analysis Project (GTAP), Version 9 (base year 2011)

3

140 regions + 57 Sectors

No. Code Description No. Code Description1 PDR Paddy rice 30 LUM Wood products2 WHT Wheat 31 PPP Paper products, publishing3 GRO Cereal grains nec 32 P_C Petroleum, coal products4 V_F Vegetables, fruit, nuts 33 CRP Chemical, rubber, plastic products5 OSD Oil seeds 34 NMM Mineral products nec6 C_B Sugar cane, sugar beet 35 I_S Ferrous metals7 PFB Plant‐based fibers 36 NFM Metals nec8 OCR Crops nec 37 FMP Metal products9 CTL Bovine cattle, sheep and goats, horses 38 MVH Motor vehicles and parts10 OAP Animal products nec 39 OTN Transport equipment nec11 RMK Raw milk 40 ELE Electronic equipment12 WOL Wool, silk‐worm cocoons 41 OME Machinery and equipment nec13 FRS Forestry 42 OMF Manufactures nec14 FSH Fishing 43 ELY Electricity15 COA Coal 44 GDT Gas manufacture, distribution16 OIL Oil 45 WTR Water17 GAS Gas 46 CNS Construction18 OMN Minerals nec 47 TRD Trade19 CMT Bovine meat products 48 OTP Transport nec20 OMT Meat products nec 49 WTP Water transport21 VOL Vegetable oils and fats 50 ATP Air transport22 MIL Dairy products 51 CMN Communication23 PCR Processed rice 52 OFI Financial services nec24 SGR Sugar 53 ISR Insurance25 OFD Food products nec 54 OBS Business services nec26 B_T Beverages and tobacco products 55 ROS Recreational and other services27 TEX Textiles 56 OSG Public Administration, Defense, Education, Health28 WAP Wearing apparel 57 DWE Dwellings29 LEA Leather products


No. Code Description No. Code Description No. Code Description No. Code Description1 AUS Australia 36 ECU Ecuador 71 NLD Netherlands 106 ARE United Arab Emirates2 NZL New Zealand 37 PRY Paraguay 72 POL Poland 107 XWS Rest of Western Asia3 XOC Rest of Oceania 38 PER Peru 73 PRT Portugal 108 EGY Egypt4 CHN China 39 URY Uruguay 74 SVK Slovakia 109 MAR Morocco5 HKG Hong Kong 40 VEN Venezuela 75 SVN Slovenia 110 TUN Tunisia6 JPN Japan 41 XSM Rest of South America 76 ESP Spain 111 XNF Rest of North Africa7 KOR Korea Republic of 42 CRI Costa Rica 77 SWE Sweden 112 BEN Benin8 MNG Mongolia 43 GTM Guatemala 78 GBR United Kingdom 113 BFA Burkina Faso9 TWN Taiwan 44 HND Honduras 79 CHE Switzerland 114 CMR Cameroon

10 XEA Rest of East Asia 45 NIC Nicaragua 80 NOR Norway 115 CIV Cote d'Ivoire11 BRN Brunei Darussalam 46 PAN Panama 81 XEF Rest of EFTA 116 GHA Ghana12 KHM Cambodia 47 SLV El Salvador 82 ALB Albania 117 GIN Guinea13 IDN Indonesia 48 XCA Rest of Central America 83 BGR Bulgaria 118 NGA Nigeria14 LAO Lao People's Democratic Republic 49 DOM Dominican Republic 84 BLR Belarus 119 SEN Senegal15 MYS Malaysia 50 JAM Jamaica 85 HRV Croatia 120 TGO Togo16 PHL Philippines 51 PRI Puerto Rico 86 ROU Romania 121 XWF Rest of Western Africa17 SGP Singapore 52 TTO Trinidad and Tobago 87 RUS Russian Federation 122 XCF Central Africa18 THA Thailand 53 XCB Caribbean 88 UKR Ukraine 123 XAC South Central Africa19 VNM Viet Nam 54 AUT Austria 89 XEE Rest of Eastern Europe 124 ETH Ethiopia20 XSE Rest of Southeast Asia 55 BEL Belgium 90 XER Rest of Europe 125 KEN Kenya21 BGD Bangladesh 56 CYP Cyprus 91 KAZ Kazakhstan 126 MDG Madagascar22 IND India 57 CZE Czech Republic 92 KGZ Kyrgyzstan 127 MWI Malawi23 NPL Nepal 58 DNK Denmark 93 XSU Rest of Former Soviet Union 128 MUS Mauritius24 PAK Pakistan 59 EST Estonia 94 ARM Armenia 129 MOZ Mozambique25 LKA Sri Lanka 60 FIN Finland 95 AZE Azerbaijan 130 RWA Rwanda26 XSA Rest of South Asia 61 FRA France 96 GEO Georgia 131 TZA Tanzania United Republic of27 CAN Canada 62 DEU Germany 97 BHR Bahrain 132 UGA Uganda28 USA United States of America 63 GRC Greece 98 IRN Iran Islamic Republic of 133 ZMB Zambia29 MEX Mexico 64 HUN Hungary 99 ISR Israel 134 ZWE Zimbabwe30 XNA Rest of North America 65 IRL Ireland 100 JOR Jordan 135 XEC Rest of Eastern Africa31 ARG Argentina 66 ITA Italy 101 KWT Kuwait 136 BWA Botswana32 BOL Bolivia 67 LVA Latvia 102 OMN Oman 137 NAM Namibia33 BRA Brazil 68 LTU Lithuania 103 QAT Qatar 138 ZAF South Africa34 CHL Chile 69 LUX Luxembourg 104 SAU Saudi Arabia 139 XSC Rest of South African Customs Union35 COL Colombia 70 MLT Malta 105 TUR Turkey 140 XTW Rest of the World

Global Trade Analysis Project (GTAP), Version 9 (base year 2011)

4IER University of Stuttgart

NEWAGE: Concept and composition

Investments

Tax revenues

Savings

Labor

Capital Domesticintermediates,

investmentgoods and

consumptiongoods

Internat. Transp.

UtilityFossil Fuels

ImportsExportsCO2

Resources

Factormarkets Tax system

Represen-tativeAgent

ProductionArmington-Aggregation

Sectoral Production

Consumption

Factor supply≙ Factor income

Foreign trade

18 regions:

Germany, France, Italy, Poland, Unit Kingdom, Benelux, Spain + Portugal, EU-North, EU-Southeast

USA, Rest of OECD

Brazil, Russia, India, China, South Africa

Rest of OPEC, Rest of the World

Current 18x18x4-Mapping:

18 sectors:

Coal, Natural gas, Crude oil, Petroleum, Electricity

Iron & Steel, Non-ferrous metals, Non-metallic minerals, Paper, pulp & print, Chemicals, Food & Tobacco,

Motor vehicles, Machinery, Rest of industry,

Buildings, Transport, Agriculture, Services

4 factors:

Capital, Skilled Labor, Unskilled Labor, Resources, (Carbon)

Closedcircle ofincome

Hyb

rid

feat

ures

:

Imperfect Labor Markets: Wage curve differentiation by qualification (skilled, unskilled) + rigid wages

Electricity Generation: Technology portfolio with 18 electricity generation options

Household Energy Demand: Technology portfolio with 11 vehicle + 16 buildings technologies

Recursive-dynamics from 2011 to 2050 (5-year milestones)

Main sources: GTAP9 database (Narayanan et al., 2012), MPSGE (Rutherford, 1999), GTAPinGAMS (Rutherford, 2010), GTAP-EG (Rutherford & Paltsev, 2000), EconMap (Fouré et al., 2012), IEA, Böhringer (1996), Küster (2009), Zürn (2010), Abrell (2010), Beestermöller (2016)

Household heterogenity: Regionally differentiated by income groups (work in progress)

Exogenous growth drivers: Labor Force (population, education, participation), total factor productivity, energy productivity (AEEI)

Gro

wth

+ d

ynam

ics:

Householdsand

government

Householdsand

government

IER University of Stuttgart 6

18 countries and regionsNEWAGE regional mapping:

BRICS12. Brazil13. Russia14. India15. China16. South Africa

OECD (non-EU) 10. USA11. Rest of OECD

Other17. OPEC + Arabian World18. Rest of the World

EU-281. Germany2. France3. Italy4. Poland5. UK

6. Spain + Portugal7. Benelux8. Baltic EU9. South-Eastern EU

Coal Natural gasCrude oil

PetroleumElectricity

Iron & SteelNon-ferrous metals

Non-metallic mineralsPaper, pulp & print

Chemicals

Food & Tobacco

Motor vehicles

Machinery

Rest of industry

Buildings

TransportAgriculture

Services

Share of world output (GTAP8 data base, base year 2007, in %)


18 production sectorsNEWAGE sectoral mapping:

• -No. Description Group1 Coal

Energyproduction

(5)

2 Natural gas3 Crude oil4 Petroleum5 Electricity6 Iron & Steel

Energy intensive industries

(6)

7 Non-ferrous metals8 Non-metallic minerals9 Paper, pulp & print10 Chemicals11 Food & Tobacco12 Motor vehicles Other

manufacturing(3)

13 Machinery14 Rest of industry15 Buildings

Rest of the economy

(4)

16 Transport17 Agriculture18 Services

Circular flow of income

8

Supply

Demand

Demand

Import/Export

Demand

Supply

DemandIncome

Purchases

Costs

Purchases

Revenues

Purchases

Costs

Demand

= Goods flows = Monetary flows

= Markets = Agents

Goods markets(Machinery, Electricity,

Services, ...)

Factor markets(Capital, Labor,

Resources)

Subsidies

TaxesTaxes

Transfers

Taxe

sTa

xes

HouseholdsUtility maximization

FirmsProfit maximization

Foreign trade

Government



NEWAGE: production output (CES-nesting)

Material

UnskilledLabor

Capital SkilledLabor

Gas CO2CO2 Oil CoalCO2

Electricity

σE

σFE

σLIQ

σGAS σOIL σCOL

σKL

σKLEMNon-energy goods

σKLE

Primary EnergySources

EnergySkilledLabor

UnskilledLabor

σRESExhaustible energy

σKLEM

Capital Material

10

Functional Form of a Nested CES Production

• Example for non-energy sectors i ≠ e(i)

• Capital K, highly skilled labor SKL and less skilled labor USK form value added nest via Cobb-Douglas-Function with value share parameters θK, θSKL, θUSK

• Parameter ρ reflects elasticity of substitution σ where σ = 1/(1 - ρ) with (-∞<ρ<1).• Value added of primary factors is combined with energy aggregate on the next level

• Energy aggregate composes of electricity, coal, gas, oil and if so CO2-allowances

• Final KLEM-Aggregate is formed on the highest level through Leontief function with non-energetic intermediate inputs.

iejiei

USKSKLKE

M

Y

KLEM

KLE

KLEM

KLEUSKSKLKKLE

i

KLEMi

riUSK

ririSKLriri

Kri

Eriri

Eri

j

Mrijrij

j

Mrij

ri

)(

;

1

1

1

,,,,,,,,,

,,,,,,

,



NEWAGE: Armington aggregation (CES-Nesting)

Production ofDomestic Goodsi,r

ImportedGoodsi,s

σAArmington-Aggregationi,r

Transportation Servicei,s

Transportation Servicesi,s

ImportedGoodsi,s

Region (s=1) Region (s=n)…

σIM

σTR 0 σTR 0


NEWAGE hybrid feature: Electricity generation (CES-Nesting)

NuclearEnergy

Geo-thermal

Biomass Lignite Hard Coal

Gas Hard Coal

Gas Oil Gas OilPump Storages

HydroPower

Oil Wind Solar

σELE

σBM

σB

σM σP

σGO

Output (Electricity)

Base Load

Medium Load

Peak Load

Material

UnskilledLabor

Capital SkilledLabor


Electricity

σE

σFE

σLIQ

σGAS σOIL σCOL

σKL

σKLEMNon-energy goods

σKLE

13

Electricity Generation Technology in NEWAGE-W (I)

● Detailed implementation of the electricity generation sector for all represented model regions

● Electricity is produced with 16 generation technologies, i.e. hard coal and lignite, nuclear, natural gas, oil and renewables

● Every generation technology is implemented with a CES production function with inputs of capital, skilled labor, unskilled labor, energy, and materials. CO2 allowances are an additional input if fossil fuels are used

● GTAP data is complemented by information from IEA energy balances and IEA generation cost data. Regionally differentiated generation costs are considered

● Output of all generation technologies is aggregated in a CES production function representing the national power plant system and satisfying the demand of electricity. Elasticities represent the feasibility of technology substitution within and between the load segments

Production


14

Electricity Generation Technology in NEWAGE-W (II)

● Investments in power plants imply that capital is fixed for 30 to 40 years ● Therefore separate capital endowments for every generation technology are

implemented (Putty Clay)● For existing capacities, decommissioning curves are implemented which substitute the

continuous through a discrete depreciation rate● This accounts for the individual age structure of the power plants for all generation

technologies● Investment decisions in the electricity generation sector is a technology oriented

decision

Capital Accumulation

● Efficiency improvements for conventional and nuclear generation● RES-E-Quota● Nuclear phase outs

Additional Aspects and Constraints


NEWAGE: Modelling electricity generation

15

• CES nesting of electricity generation technologies

• Each technology is represented as a CES production function demanding KLEM inputs (interdependency with the rest of the economy)

• Electricity generation takes place in extant and new power plants

Material

KL

Electricity

KLE

Gas CO2CO2 CO2Oil Coal

Fossils

E

UnskilledSkilled Capital

Liquids

Y = f (K, L, E, M)

KLEM

Y

σKL

σKLEM

σE

σKLE

σFE

σCOL

σLIQ

σGAS σOIL

Electricity

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Chem

icals

Labor

Capital

NUCLEA

R

K

Manufacturing

Intermediates (M

)L

Energy(E)

CapitalLaborServicesAgricultureTransportDwellingsConstructionRest of ind.Food&Tob.Motor veh.MachineryPaper&pulpNM‐mineralsNF‐metalsIron&steelChemicalsElectricityPetroleumGasCrude oilCoal

Y = f (K, L, E, M)


NEWAGE: Input cost shares of electricity generation technologies

16

Coal

Gas

Petroleu

m

Chem

icals

Agriculture

Labor Labor

Labor

Labor

Labor

Capital

Capital

Capital

Capital

Capital

Capital

Capital

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%BIOMAS

S

COAL

NUCLEA

R

GAS

SOLAR

WIND

PEAK

OIL

CapitalLaborServicesAgricultureTransportDwellingsConstructionRest of ind.Food&Tob.Motor veh.MachineryPaper&pulpNM‐mineralsNF‐metalsIron&steelChemicalsElectricityPetroleumGasCrude oilCoal

Manufacturing

Intermediates (M

)K

LE

nergy(E

)


DEU FRA AUT EUN EUS EUE SWZ USA OEC BRZ RUS IND CHI RSA ARB OPE ROW

Base load

Lignite 35 37 37 37 37 41 41 37 41 43 37 37 41 38 43 43 41Biomass 81 77 77 77 77 76 76 76 76 76 76 76 76 76 76 76 76Natural gas 58 77 77 78 77 80 - 79 85 84 75 75 85 88 85 85 76Geothermal 49 37 37 33 37 - 37 37 37 37 - - 37 37 15 - 37Hard coal 44 45 45 45 45 44 - 42 44 45 38 38 44 52 45 45 40Hydro 36 43 43 43 43 41 42 42 42 42 41 41 42 42 42 42 42Nuclear 37 39 39 38 39 28 36 37 36 37 37 37 36 43 15 - 36Oil - - - 12 - 119 - - 119 - - - 119 24 119 119 107

Middle load

Natural gas 62 61 61 61 61 61 61 58 59 58 61 61 59 75 63 66 59Hard coal 57 58 58 58 58 57 56 54 57 58 46 46 57 70 58 58 57Oil - 126 126 126 126 126 126 - 126 - - - 126 126 126 126 126Solar 120 120 120 120 120 120 120 289 289 289 289 289 289 289 116 116 289Wind 65 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63

Peak loadNatural gas 119 122 122 120 122 106 107 117 116 116 116 116 116 108 121 125 115

Pump storage 215 254 254 253 254 245 249 249 253 253 253 253 253 251 253 253 252

Oil 203 228 228 228 228 227 227 227 228 228 227 227 228 227 228 228 228

NEWAGE: LCOE of electricity generation technologies



NEWAGE hybrid feature: Imperfect Labor Markets

• 2 degrees of labor qualification: skilled and unskilled labor

• Corresponding wage functions:

Unskilled labor: Real wage remains constant (minimum wage)

Unemployment through wage rigidities:

Skilled labor: Wage curve following Blanchflower & Oswald (1995)

Unemployment related to a wage curve:

19

Labor L

wmin

w1

LD1

LD0

LSreal

wage

L0=L1

∆L

w2

Labor L

wmin

w1

LD1

LD0

LSreal

wage

L0=L1

∆L

w2

Labor L

wmin

w1

LD1

LD0

LSreal

wage

L0=L1

∆L

w2

wmin

w1

LD1

LD0

LSreal

wage

L0=L1

∆L

w2

w1

LD1

LD0

LSreal

wage

L0=L1

∆L

w2

LD1

LD0

LSreal

wage

L0=L1

∆L

w2

LSreal

wage

L0=L1

∆L

w2

real wage

L0=L1

∆L

w2

L0=L1

∆L

w2w2

Modeling Imperfections of the Labor Market

• MC-Problem for the non-clearing of the dual labor market Rigid lower wage

Wage curve (Blanchflower and Oswald)

0,0,0

rrr

T

rr

rr

r

rrrr demandsupplyUR-1UR

PwUR

Pwdemand-supplyUR-1

0,0,0

rrr

T

r

r

r

rrrr demandsupplyUR-1

Pw

Pwdemand-supplyUR-1

LSrationedLSrationed

LD

LS

wage curve

L0=LS0Labor L

LS1

unemployment

L1

w1

real wage

w0 LD

LS

wage curve

L0=LS0Labor L

LS1

unemployment

L1

w1

real wage

w0

LS

wage curve

L0=LS0Labor L

LS1

unemployment

L1

w1

real wage

w0

L0=LS0Labor L

LS1

unemployment

L1

w1

real wage

w0

L0=LS0Labor L

LS1

unemployment

L1

w1

real wage

w0

L0=LS0Labor L

LS1

unemployment

L1

w1

real wage

w0

Labor LLabor LLS1

unemployment

L1

w1

real wage

w0

LS1

unemployment

L1

w1

real wage

w0

LS1

unemployment

L1

w1

real wage

w0

w1

real wage

w0



NEWAGE: Consumption (CES-Nesting)

σSConsumption

Non-energy goodsσC


Electricity

σCE

σGAS σOIL σCOL

21

NEWAGE hybrid feature: Household energy demand technologies



NEWAGE hybrid feature: Household energy demand technologies

11 Vehicle technologies 16 Buildings technologies

Demand category Small Middle Large Old buildingsRenovated

old buildings

New buildings

(Standard)

New buildings(passive house)

Cubic capacity / Heatdemand

< 1,4 l 1,4 – 2,0 l > 2,0 l160

kWh/m²a

65

kWh/m²a

65

kWh/m²a

15

kWh/m²a

Km travelled p.a. (Thd. km)

10,4 15,0 17,6 - - - -

Gasoline X X X - - - -Diesel X X X - - - -PHEV - X X - - - -

Natural Gas X X - X X X XElectricity X - - X X X X

Fuel oil - - - X X X -Biomass - - - X X X X

Coal - - - X - - -

Klein (bis 1.400 ccm) Mittel (1.400 – 2.000 ccm) Groß (ab 2.000 ccm)Benzin Diesel Gas Elektro Benzin Diesel Gas PHEV Benzin Diesel PHEV

Referenz-Fahrzeuge

Renault Twingo,

Opel Corsa

Opel Adam LPG, VW eco-up! CNG

Renault Zoe

VW Golf VW Golf Mercedes B 200 CNG

Toyota Prius

Mercedes E Mercedes E

Mitsubishi Outlander +

Porsche Cayenne

VW Polo BMW 3er BMW 3er Toyota ML350 VW Touran

VW Caravelle

Renault Scenic VW Golf 1.6

BiFuel LPG

Mercedes SLK BMW 7er

Renault Scenic

VW Caravelle BMW 7er Toyota Rav

4

Opel Zafira Mercedes CLK

Ø - Preis 11.766 € 15.111 € 16.208 € 21.700 € 21.410 € 24.543 € 24.882 € 36.200 € 38.422 € 40.159 € 61.039 €

Hubraum (ccm) 1.188 1.248 1.199 - 1.661 1.907 1.795 1.798 2.954 2.595 2.497

Leistung (kW) 47 55 57 65 75 75 88 100 156 132 227,5

CO2-Emissionen (g/km) - Hersteller 137 124 99 - 175 134 131 49 223 183 62

Kraftstoffverbrauch (Liter/100km) -Hersteller 5,8 4,6 - - 7,3 5,4 - 2,1 9,3 6,9 2,7

Kraftstoffverbrauch (Liter/100km) -ADAC/Spritmonitor 6,5 5,1 - - 8,3 6,2 - 3,6 11,0 8,2 5,9

Kraftstoffverbrauch (kWh/100km) -ADAC/Spritmonitor 58 50 48 20 74 60 59 36 98 80 63

Durchschnittliche Fahrleistung pro Jahr (Tsd. km) 10,4 10,4 10,4 10,4 15,0 15,0 15,0 15,0 17,6 17,6 17,6Durchschnittliche CO2-Emissionen (g/km) -

ADAC/Spritmonitor 151 130 125 - 193 158 154 94 257 210 166

PRIV

AT

PRIVAT: Fahrleistung pro Jahr (Mio. km) 121.814 2.580 959 81 236.345 79.526 1.763 694 57.809 43.367 490

PRIVAT: Kraftstoffverbrauch (GWh) 70.072 1.285 458 16 174.678 48.076 1.038 248 56.705 34.717 310

PRIVAT: CO2-Emissionen (Mio. t) -Hersteller 16,7 0,3 0,1 - 41,3 10,7 0,2 0,03 12,9 7,9 0,03

PRIVAT: CO2-Emissionen (Mio. t) -ADAC/Spritmonitor 18,3 0,3 0,1 - 45,7 12,6 0,3 0,07 14,8 9,1 0,08

PRIVAT: Neufahrzeuge 2007 (Tsd.) 405 18 7,6 0,026 518 358 7,1 11,0 145 159 7,4

PRIVAT: Bestand am 01.01.2008 (Tsd.) 11.679 247 92 7,8 15.798 5.316 118 46,4 3.291 2.469 27,9

GES

AM

T

GESAMT: Fahrleistung pro Jahr (Mio. km) 124.432 3.084 955 300 236.023 98.052 1.812 844 56.190 56.146 625

GESAMT: Kraftstoffverbrauch (GWh) 71.578 1.535 456 59 174.440 59.275 1.066 302 55.118 44.946 396

GESAMT: CO2-Emissionen (Mio. t) - Hersteller 17,1 0,4 0,1 - 41,2 13,2 0,2 0,04 12,5 10,3 0,04

GESAMT: CO2-Emissionen (Mio. t) -ADAC/Spritmonitor 18,7 0,4 0,1 - 45,7 16,0 0,2 0,07 14,4 12,1 0,10

GESAMT: Neufahrzeuge 2007 (Tsd.) 739 47 8,4 0,160 726 999 8,2 26,1 158 456 12,3

GESAMT: Bestand am 01.01.2008 (Tsd.) 11.930 296 92 29 15.777 6.554 121,1 56 3.199 3.196 36

Vehicle technologies

23

Faktor 20 Faktor 5



NEWAGE hybrid feature: Household energy demand (CES-Nesting)

Demand category

σSG

Buildings stock

Car stock

(non-durable goods)(durable good) (non-durable goods)

Motor rype

(durable good)

Demand category

Refineries Car industryConstruction Agriculture Power generation

Energyresources

Biomass Gasoline Electr. Coal GasFuel oilDiesel

Purchases of durable and non-durable goods

New / renov. house

New car

Car technologies

σSConsumption

Non-energy goodsσC Energy services

Private transport

Mobility

σED

σMOB

Public transportSpace heating andhot water

Electricity(other)

σSF

σMOIL σNFσNG

Heating type

Buildings technologies

Building Energy carrier Car

σEG σEF


NEWAGE hybrid feature: Household energy demand (private transport)

Private Transport

Small Middle Large

DieselGasoline Natural Gas

Electric Natural Gas

PHEV PHEVDieselGasoline

Gasoline Vehicle

Gasoline Diesel

NewStock

Automobile Industry

CO2

Oil Industry

(…)

(…)

(…)(…) (…)

σSF

σNF

σEF

σOIL

σMOIL

σFK

σFA σFA σFA

(…) (…) (…)(…)(…)

(…)(…)


NEWAGE hybrid feature: Household energy demand (space heating)

Oil Industry

New Buildings(Passive house)

New Buildings(EnEV-Standard)

Renovated Old Buildings

Space Heating (incl. hot water)

Old Buildings

σGKNS

σGK

Heating Oil

Coal Wood Electr. Natural Gas

HeatingOil

Wood Electr.HeatingOil

Wood Electr.Natural Gas

NaturalGas

Wood Electr.

New / Renovated Buildings

Natural Gas

Buildings stock

Heating Oil

Natural Gas

CO2 Building

Stock New(…)

σNG

Construction Industry

(…)σMOIL

CO2

σGHAσGHNS σGHNS σGHNS

σEG

σOIL

σEG

σGAS

σSG

(…)(…) (…) (…)

(…) (…)(…) (…)

(…)(…) (…)

(…)(…) (…)

Circular representation of capital and investment

Household Firms

Capital Stock Kt

Labor

Resources

Exports

Investmentt-1 = Savingst-1

Consumptiongoods

Intermediates

Kt-1 · (1 - δ)δ = depreciation rate

Supply (revenues/income)Demand (costs)


Composition of the aggregated investment good


Schematic diagram of nuclear power life-cycle costs

29

Construction Operation Decommissioning

Cos

ts($

)

Time (years)


Construction and Decommissioning in CGE Models

• In CGE models construction and decommissioning is considered as capital formation, i.e. capital endowment and demand (investment and depreciation)

• Capital demand of firms reflects the demand for fixed assets in production

• In IOT capital is part of value added (depreciation, consumption of fixed capital)

• Stock-and-flow concept: The capital stock of an economy is an aggregation (value) of all fixed assets available in the

economy

The capital flows represent capital demands, which can be interpreted as annuities

• Capital formation and dynamics Capital endowment is distributed to all producers regarding their respective demands

Capital Stockt = Capital Stockt-1 · (1 - δ) + Investmentt (with δ = depreciation or depletion rate)

The investment good is an aggregation of all sectoral production goods (part of final demand), mainly formed by buildings and machinery

The construction of a new power plant is considered as an increase in capital demand

“Decommissioning curves” represent (exogenous) assumptions on depletion and residual capacities of existing plants


Values of the capital stock per region


Depreciation of base year‘s capital stock (exogenous)


Investment related increase of the capital stock


Overall capital stock development


Decommissioning of selected technologies in Germany (exogenous)


Capital demand of electricity technologies in Germany



NEWAGE: Growth and dynamics

, , , ,

Germany 0.6% -1.2% 1.0% 0.4%

France 2.7% -1.2% 1.0% 0.4%

Austria 2.1% -1.1% 1.1% 0.4%

EU-North 2.6% -1.1% 1.1% 0.7%

EU-South 1.7% -1.0% 1.1% 0.5%

EU-East 2.2% -1.0% 2.5% 1.8%

Switzerland 0.6% -0.6% 0.9% 0.1%

USA 1.2% -0.4% 0.8% 0.9%

Rest of OECD 2.0% -0.6% 1.3% 0.5%

Brasil 3.7% 0.6% 1.7% 0.4%

Russia 0.0% -1.4% 3.4% 2.1%

India 4.7% 1.4% 3.5% 1.9%

China 3.2% -0.2% 4.7% 3.2%

South Africa 2.0% 0.9% 1.5% 1.5%

Middle East 4.6% 1.0% 0.9% 0.7%

Rest of OPEC 3.8% 1.7% 1.5% 0.9%

Rest of World 3.6% 1.7% 2.0% 0.7%

, : Labor Force growth (skilled)

, : Labor Force growth(unskilled)

, : Total factor productivity growth

, : Energy productivity growth(AEEI)

Results list

• Electricity output

• Prices

• Electricity demand

• CO2 Emissions

• Competitiveness (RCA, RWTS, …)

• (Net) Exports

• Consumption

• Investments

• GDP

• Employment levels and unemployment rate

• Welfare


Main scenario definition

39

• GHG mitigation scenarios for the EU‐28 with and without technology orientedrepresentation of household energy demand

Nat‐40 / EU‐40 with technology oriented representationNat‐40a / EU‐40a without technology oriented representation

NEWAGE sample application


Scenario Definition of the GHG mitigation regime

Nat‐40

Nat‐40a

EU‐40

EU‐40a

Continuation of the EU‐ETS until 2030Nationally differentiated emission reductions for non‐ETS sectorsaccording to „EU Effort Sahring Decision“Compliance with EU‐wide emission reduction targets until 2030

Trans‐sectoral EU emissions trading system (ETS and non‐ETS sectors)Compliance with EU‐wide emission reduction targets until 2030

10092

8378

74

63

5344

3526

16

0

20

40

60

80

100

1990

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

EU emissions allowances (Index, 1990 = 100)

Main scenario definition

40

• Scenario A (Nat_80)

• Min. 80% reduction in each individual country of the EU28

• Scenario B (EU_80)

• Min. 80% reduction in EU28 as a whole, without binding targets in the individual countries

• The current EU‐ETS‐path (‐43% in 2030 compared to 2005 levels) is extrapolated until 2050 and then used as EU‐wideemissions constraint in both scenarios

achieves more than ‐80% in 2050 compared to 1990:

No climate policies outside the EU

NEWAGE sample application (2)


• Crude oil prices• 2010-2040 (WEO 2014, New Policies

Scenario)• 2040-2050 (ETP 2014, 4DS)

• BASELINE-Database• Regional labor force (skilled and

unskilled labor)• Total factor productivity• Autonomous energy efficiency

improvements (AEEI)

Other exogenous assumptions


CO2 emissions in the Nat-80 scenario

42

10092

8378

74

63

53

44

35

26

16

0

20

40

60

80

10019

90

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

CO2 emissions in the EU(Index, 1990 = 100)


CO2 emissions in the EU-80 scenario

43

‐25%

‐20%

‐15%

‐10%

‐5%

0%

5%

10%

15%

2010

2015

2020

2025

2030

2035

2040

2045

2050

Differences (percentage points) of the CO2 emissions reductions path in the

EU compared to 2010

Germany

France

EU‐East

EU‐North

EU‐South

16%

0%

25%

50%

75%

100%

125%

1990

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

CO2 emissions compared to 1990 levels

Germany

France

EU‐East

EU‐North

EU‐South

EU28


GDP effects in the EU

44

• Net positive effects in the EU28 (+0.8 % in 2050 compared to Nat�80)

• Both positive and negative effects across member states• up to +2.6% in France in 2050• down to -3% in the Eastern EU in 2040


Sectoral GVA in the EU28

45

-6,5-1,9-1,8-0,7-0,40,0

1,71,82,93,24,05,6

11,217,7

66,5103,4

-20 0 20 40 60 80 100 120

Transport servicesIron & steel

PetroleumChemicals

Building materialsNF-metals

PaperMotor vehicles

Other ManufacturingMachinery

AgricultureFood & Tobacco

ConstructionElectricity

ServicesTOTAL

2050

EU-2

8

Bn. €

• Differences in EU-80 scenario compared to Nat-80 scenario in 2050 in Bn €


Sectoral GVA in the EU28 (II)

46

• Relative differences in EU-80 scenario compared to Nat-80 scenario in 2050 (in %)

-1,3%-1,9%

-25,4%-0,2%-0,2%

0,0%0,4%0,5%0,3%0,2%

0,9%0,8%0,7%

-22,4%0,5%0,5%

-30% -25% -20% -15% -10% -5% 0% 5%

Transport servicesIron & steel

PetroleumChemicals

Building materialsNF-metals

PaperMotor vehicles

Other ManufacturingMachinery

AgricultureFood & Tobacco

ConstructionElectricity

ServicesTOTAL

2050

EU-2

8

Bn. €


Sectoral competitiveness in the EU28

47

• The relative world trade share (RWTS-indicator) can be used to measurecompetitiveness:

∑∑∑ ,

‐100%

‐80%

‐60%

‐40%

‐20%

0%

EU_80 EU_80 EU_80 EU_80

2020 2030 2040 2050

EU‐East

Relative differences of the RWTS‐indicator in scenario EU_80 (in % to Nat_80)

Mineral oil Iron & steel Chemicals NM‐minerals Transport services


Trade effects

48

• Both positive and negative effects on EU net exports between 2020 and2050

• Both positive and negative effects across member states• up to +50 Bn. € in France in 2050• down to -85 Bn. € in the Eastern EU in 2050


Employment effects in the EU

49

• Net positive effects in the EU28 (+0.1 % in 2050 compared to Nat�80)

• Both positive and negative effects across member states• up to +0.8% in France in 2050• down to -0.6% in the Eastern EU in 2050


Price effects

50

• Exemplary price changes for electricity, mineral oil, transport services andiron & steel

‐10%

‐5%

0%

5%

10%

15%

20%

25%EU

_80

EU_80

EU_80

EU_80

2020 2030 2040 2050

EU‐East

Price differences in the EU_80 scenario compared to Nat_80 (in %)

Electricity Iron & Steel Transport services Mineral oil

‐8%

‐6%

‐4%

‐2%

0%

2%

4%EU

_80

EU_80

EU_80

EU_80

2020 2030 2040 2050

France

Price differences in the EU_80 scenario compared to Nat_80 (in %)

Electricity Iron & Steel Transport services Mineral oil


CO2 prices


Electricity generation technology mix

52

• Electricity generation technology mix of the EU-28 in the EU-80 scenario(in TWh)

‐500

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

2007 2010 2015 2020 2025 2030 2035 2040 2045 2050

EU_80 scenario

EU‐28

[TWh]

NetImp

Geo

Solar

Wind

Biomass

Hydro

oil

gas

ccs

Coal

Nuclear

Lignite


Electricity generation technology mix

53

• Differences in electricity generation technology mix of the EU-28 in the EU-80 scenario compared to the Nat-80 scenario (in TWh)

‐150

‐100

‐50

0

50

100

150EU

_80

EU_80

EU_80

EU_80

EU_80

EU_80

EU_80

EU_80

EU_80

EU_80

2007 2010 2015 2020 2025 2030 2035 2040 2045 2050

EU‐28

[TWh]

NetImp

Geo

Solar

Wind

Biomass

Hydro

oil

gas

ccs

Coal

Nuclear

Lignite


• Exogenous shock: Different EU-ETS emissions caps implied by different carbon leakageprotection measures in the EU (e.g. MSR design) from 2015 to 2030 (in Mio. t CO2eq)


Economic impacts of different carbon leakage protection measures in the EUNEWAGE sample application (3)

Reference: Geres et al. (2016) Geres, R.; Kohn, A.; Nickel, F.; Scholz, D.; Mühlpointner, T.; Sternhardt, M.; Beestermöller, R.; Fahl, U.; Blesl, M.; Haasz, T.; Brunke, J.-C.: „Ausgestaltung des EU-Emissionshandels nach 2020 und seine Auswirkungen – insbesondere auf die industrielle Wettbewerbsfähigkeit und die Energiewirtschaft – unter Berücksichtigung von Optionen zur Vermeidung von Carbon Leakage“, FutureCamp Holding GmbH; FutureCampClimate GmbH; Institut für Energiewirtschaft und Rationelle Energieanwendung (IER) der Universität Stuttgart, Studie im Auftrag des Bundesministeriums für Wirtschaft und Energie (BMWi) , Schlussbericht zum Vorhaben 06/15, (2016).

Scenario 2.1 ("COM-proposal")

Scenario 3.1 ("Industry reserve")

Scenario 4.1 ("Smooth transition")

Scenario 5.1 (Ecofys I)

2015 2498 2622 2484 2593

2020 2371 2271 2394 2223

2025 1846 1592 1847 1777

2030 1294 1414 1285 1342

• Exogenous shock (II): Carbon costs changes (direct + indirect) of the energy intensive industries in Germany induced by different carbon leakage protection measures for the 4th EU-ETS period (2021-2030) compared to the current period‘s regulation (in %)




-10%-3% -2%-4%

-22%

-37%

-12%

-36%-42%

-17%

-53%

-60%

-80%

-60%

-40%

-20%

0%

20%

2021 2025 2030

Scenario 2.1 („COM-proposal")

Scenario 3.1 („Industry reserve“)

Scenario 4.1 („Smooth transition“)

Scenario 5.1 („Ecofys l“)

• Results: Gross value added changes of the energy intensive industries in Germany implied by the (exogenous) carbon costs changes of different carbon leakage protection measures in the EU (in %)



0,0% 0,1% 0,0%0,0%

1,5%

3,9%

0,0%0,6%

3,9%

0,1%

1,8%

9,0%

-2%

0%

2%

4%

6%

8%

10%

2021 2025 2030






• Results: GDP and employment changes in Germany implied by the (exogenous) carbon costs changes of different carbon leakage protection measures in the EU



-36

-24

-12

0

12

24

-6

-4

-2

0

2

4

2021 2025 2030 2021 2025 2030 2021 2025 2030 2021 2025 2030





Em

ploy

men

t[T

hd. e

mpl

oyee

s]

GD

P[re

al, B

n. €

2010

]

GDP Employment


Summary

• NEWAGE calculates direct, indirect and induced economic impacts Ability to assess net employment effects

• NEWAGE distinguishes 2 degrees of labor qualification (skilled, unskilled) Ability to assess employment, unemployment and wage levels for bothqualifications

• NEWAGE distiniguishes 18 different electricity generation technologies includingnuclear power, 16 buildings technologies and 11 vehicle technologies Ability to assess economic impacts of technology‐oriented energy policies (e.g. nuclear power phase‐out)

• NEWAGE considers policy framework (climate and energy policies)

• Usefulness of CGE models depend on the research objective (long‐run policyexperiments)


Summary (2)

• In CGE models the construction of new power plants is reflected in the capital demand of electricity producers (flow, consumption of fixed capital assets)

• Aggregated investment activities increase the capital stock (endogenous)

• The capital stock for electricity generation is made up of existing and new capital (investments)

• Decommissioning of electricity capital is mainly modelled as exogenous assumptions on average lifetimes of power plants

• In the electricity sector labor demand is proportionally connected to capital demand, depending on the respective CES production function and underlying elasticities of substitution (Leontief, Cobb‐Douglas, ….)


Selected IER studies

60

• Küster, R. (2009): „Climate protection, macro‐economy and employment – Analysis of the German and European climate policy strategies using a CGE model“, Dissertation, Mensch und Buch Verlag, Berlin

• IER/ZEW (2010): „Energy market developments until 2030 – The energy forecast 2009“, a Study for the German Federal Ministry for Economic Affairs and Technology (BMWi)

• IER (2011): „Effects of changing operational lives of German nuclear power plants – scenario analysis until 2035“, Institute of Energy Economics and the Rational Use of Energy (IER), University of Stuttgart, Working paper No. 10, June 2011

• Beestermöller, R. (2012), “Net employment effects of renewable energy expansion in Germany”, Presentation to the Symposium “EnergieCampus”, Stiftung Energie & Klimaschutz Baden‐Württemberg, Stuttgart, November 2012

• FutureCamp/IER (2015): “Design of the EU emissions trading system post‐2020 and its effects ‐ in particular on industrial competitiveness and the energy industry ‐ taking into account options to avoid carbon leakage”, a Study for the German Federal Ministry for Economic Affairs and Energy (BMWi)

• Beestermöller, R., “Macroeconomic cost‐effectiveness of climate policy instruments in household energy demand”, ongoing PhD project


IER Presentation 12


E-Cost (LLCEC)

Balance (LCA)

Technology TIMES-EU

E2M2S

JMM

LEMI




• Germany

• EU

• World (TIAM)


NEWAGE

TIAM-MACRO




IER Presentation

MotivationClimate change mitigation as a global concern requires very deep emission reduction in a long‐termperspective. To this end, different options could contribute to reduce energy‐related GHG emissions:

• Analysis of the contribution of these options to the CO2 emission reduction is necessary for anintegrated assessment in the climate change context.

• The technology‐rich global energy‐system model, TIAM (Times Integrated Assessment Model)lacks price‐demand interactions and doesn’t include feedback from and to other sectors of theeconomy.

TechnologicalChanges StructuralShift

Servicedemand

Energyefficiencyimprovement

HighershareofRenewables

HigherpenetrationofNuclear

MoreextensiveuseofCCS

Shiftingtolesscarbonintensivefossilfuels

Reductionofprice‐relatedenergyservicedemands

CO2reduction

13

IER Presentation 14

Global energy system model: TIAM● TIMES Integrated Analysis Model● Based on TIMES model generator:

i. Developed by ETSAPii. Dynamic partial equilibrium model approach with inter-temporal objective function (perfect

foresight) minimizing total discounted system costsiii. Technologically detailed „bottom-up“ model for each regioniv. Covering energy flows from the useful energy demand over end-use sectors and conversion

sector to the primary supply ● Time horizon 2000 – 2100● 15 world regions with

i. Bilateral trade in hard coal, pipeline gas, LNG, crude oil, petroleum products (distillates, gasoline, heavy fuel oil and naphtha) and bioethanol

ii. Global trade in emission permits possible● Emissions: CO2, N2O, CH4

i. Carbon capture and sequestration (power generation and alternative fuel production)ii. Mitigation options for N2O and CH4

● Climate module (3-reservoir model for calculating atmospheric CO2 concentrations)● Multi-stage stochastic programming (uncertainties in emission targets, demands,

bounds)

IER Presentation

TIAM – 15 regions

Africa Eastern Europe Middle-EastAustralia-New Zealand Former Soviet Union Other Developing AsiaCanada India South KoreaCentral and South America Japan United StatesChina Mexico Western Europe

15

IER Presentation

TIAM – Reference Energy System (RES)

TIAM• 15 Regions

(EU-28 + Switzerland + Norway + Iceland)

Oil Reserves

GasReserves

CoalReserves

BiomassResources

Nuclear

Non-bioRenewable(wind,solar,geo,hydro)

Transport Tech.

Agricultural Tech.

CommercialTech.

Residential Tech.Power plants

cogeneration heat plants

hydrogen plants End-Use services

End-Use services

End-Use services

End-Use services

End-Use services

IndustrialTech.

AgricultureBio burning, rice, enteric ferm, wastewaterManureLandfillsLand-use

Fossil fuels synthetic Fuels

Biofuels-Biomass

SecondaryTransformation

(refinery, gas liquefaction,

biofuel production, synthetic fuel production)

Extraction

CH4 options

CO2 Terrestrial sequestration

CO2 Terrestrial sequestrationCO2 capture

Trade crude

Trade crude

Trade crude

Trade RPP

Trade LNG

N2OCH4CO2 CH4 options CH4 options

N2O options End-Uses

CO2

Non-energy sectors

Direct use

ClimateModule

Atm.Conc

Radiative Forcing

TempratureChanging

16

IER Presentation

Energy‐ServicedemandsoftheTIAM● 42Servicedemandsforfutureyearsareprojectedusingfollowingequation:

ResidentialServicedemands(PJ):

CommercialServicedemands(PJ):

IndustryServicedemands(Mt‐ PJ):

TransportServicedemands(Bv‐km ‐ PJ)

OtherandAgriculture(PJ)

elasticitytt driverdemanddemand 1

Cooling Cooking Heating Hotwater Dishwashing Lighting Refrigeration

Clothesdrying Clotheswashing ElectricAppliances

Cooling Cooking Heating Hotwater Refrigeration Lighting ElectricAppliances

Chemical Nonmetallic Otherindustries Otherconsumptions

Iron& Steel Pulp &Paper Non‐ferrousmetal

LightTruck CommercialTruck HeavyTruck MediumTruck Three‐Two wheels Auto Bus

Rail‐Freight Rail‐Passenger DomesticNavigation

InternationalNavigation

Domestic Aviation InternationalAviation

Mt

PJ

PJ

Bv‐Km

17

IER Presentation

Service‐demandreductionoption

• Since the energy‐service demands in the TIAM model are derived from exogenous drivers, toinclude demand reduction option in such an analysis two alternatives exist:

I. Usingpriceelasticdemandsinthemodel

II. CouplingwithaMacroeconomicmodel

18

ReductionCO2emission

Higherenergyservicecosts

Reductionenergy‐servicedemands

Lowerenergyconsumption

MaincritiqueDemand response is highly dependent toelasticity factors and in literature isobserved as a critical mechanism for CO2reduction.

MaincritiqueDifficult to be implemented

IER Presentation19

TIAM-MACRO

• MACRO model is a non-linear macroeconomic modelwith a long-term economic growth view, based on thework of Alan Manne with ETA-MACRO ().

• It is a single-sector, optimal growth dynamic inter-temporal general equilibrium model.

• The original approach of linking MACRO with TIMESmodels was introduced by Remme and Blesl (2006)which was restricted to small-size models (usually singleregion).

• Recently, Kypreos and Lehtila 2013 developed a newapproach based on decomposition algorithm which makesit possible to link MACRO with large scale multi-regionalTIMES models (e.g. TIAM).

Tech

nolo

gica

l exp

licitn

ess

General equilibrium feedback

Bottom-upmodel

Top-downmodel

Ideal hybrid model

IER Presentation 20

Linkage between TIAM-IER and a MACRO module

TIAM MACROSAEnergySystemCost

ineachregion

Labor

Production

Newcapital

Energy‐Servicedemands

Investment

IronandSteel(Mt)

Chemicals(PJ)

CommercialLighting(PJ)

ResidentialHeating(PJ) Bus (Bv‐Km)

MACRO_SA (MACRO Stand Alone):• Top‐down, Dynamic inter‐temporal general equilibrium• Non‐linear programming (Maximization of utility function for a single representative producer‐consumer agent in a region)

IER Presentation 21

Capital LaborEnergy service

demands

CES production function

Production output

Investment Consumption

TIAM model

Energy system costs

Social welfare

Trade

TIAM-MACRO

IER Presentation

Objective Function: Maximization of Negishi Weighted sum ofregional Consumptions (C):

. , . ln ,

, , , , ,

, , , ,

Production ( , Constant elasticity of substitution function oflabour ( , , capital ( , and service demands ( , , :

, . ,. . ,

. ∑ , . , ,

1 1/

Macroeconomic model (MACRO stand alone)

Quadratic supply-cost function

,

, , , . , ,

Energymodel(TIAM)

, , , , , ,

,

,

,

,

,, ,

,100

For region (r), time period (t) and service-demand type (dm):

: Projected GDP : Trade in the numeraire good : Energy system cost of TIAM : Marginal price : Energy service demand of TIAM

: Energy system cost [MACRO] : Energy service demand [MACRO] : Autonomous energy efficiency improvement

: Discount factor , : Production function constants : Substituion constant (time independent)

: constant term of the QSF : Coefficient of demands in QSF

: Investment : Capital value share : Actual GDP

Source: (Kypreos and Lehtila, 2013)

22

IER Presentation

ImportantequationsofTIAM‐MACRO_SA● Objectivefunction:

. . , . ln ,

:Negishiweight

:period‐wisemutiplierweight

, :Utilitydiscountfactor

, :annualconsumption● Productionfunction:

, , , , ,, : annualinvestmentcost

, :annualenergycosts

, :annualnetexportofnumerairegood

, , , , . , ,

● FlowofenergyservicedemandfromMACRO_SAtoTIAM:

, , :energyservicedemandofTIAM

, , :energyservicedemandofMACRO

, , : autonomousenergyefficiency

improvementfactor● FlowofenergycostsfromTIAMtoMACRO_SA:

, , , , . , , ,

, , 1 , ,2. , ,

, , , , . , ,

, , :undiscountedmarginalprice

, :annualenergysystemcostsoftheTIAMmodel

, :relatedcoststopastinvestments

forregion andperiod :

23

IER Presentation

● The assumption of constant elasticity of substitution, which limits the reaction of economy to energy pricechanges, represents a unique function form of the production function over time.

● However, the constancy of this parameter may contain specification bias in the sense that as time passesfirms and individuals may react differently to price changes.

● To address this issue, Revankar (1966) introduced the concept of Variable Elasticity of Substitution(VES) production function, in which the assumption of constancy is dropped.

● Several studies (e.g., Diwan, 1970; Lovell 1973; Zellner and Ryu, 1998; Karagiannis et al., 2005) analyzedthe validity of VES production function using empirical data and found it a better function compared toCES and Cobb-Douglas.

Variability of elasticity of substitution

● In the MACRO model, elasticity of substitution representsthe ease or difficulty of price-induced substitution betweenenergy-service demands and the value-added pair capitaland labor.

Capital Labor Energy service demands

24

IER Presentation

VES versus CES: scenario description• In order to compare VES production function with CES production function, following two scenarios are

defined:

Scenario Description

Elasticity of substitution 1000 GtCO2 budget

2D (0.25) 0.25 (constant)

2D (0.2-0.3) 0.2 - 0.3 (variable)

0,1

0,15

0,2

0,25

0,3

0,35

2000 2020 2040 2060 2080 2100

Ela

stic

ity o

f sub

stitu

tion

26

IER Presentation

VES versus CES: results World GDP-Loss

• The level of net effect of decarbonisation on GDP is related to the demand reduction possibility. Thus,higher/lower elasticity of substitution leads to lower/higher GDP-loss.

• Due to the assumed function of variable elasticity, service demand reduction of 2D (0.25) case is lower than thatof 2D (0.2-0.3) before 2060 and higher after this year.

• In 2100, service demand reduction and GDP losses of 2D (0.2-0.3) are 23% higher and 12% lower than those ofthe other case, respectively.

• changes of total global energy-service demand:

%

• Total global GDP-Loss:

0

1

2

3

4

5

6

2020 2030 2040 2050 2060 2070 2080 2090 2100

% 2D (0.2-0.3)

2D (0.25)

-30

-25

-20

-15

-10

-5

02020 2030 2040 2050 2060 2070 2080 2090 2100

2D (0.2-0.3)

2D (0.2)

27

IER Presentation

Scenario definition

1. To limit 2 degree temperature increase by the probability of more than 50%.

2. The initial potentials are mainly based on the high estimate of nuclear electrical generation capacity in 2013 report ofInternational Atomic Energy Agency (IAEA).

3. The initial potentials of carbon storages are given based on the “best estimation” of econfys (2004).

4. The initial potentials of renewables are based on the assumed possible expansion pathways of different renewables which areprovided by different studies (e.g. IEA PV roadmap 2014, IEA CSP roadmap 2014, GWEC 2014, …).

Scenario 1000Gt carbon budget 2020-2100 1

25% higher potential of nuclear 2

25% higher potential of carbon storages 3

25% higher potential of biomass 4

25% higher potential of other renewables 4

Elasticity of substitution

Base

2D-DEM ~0

2D 0.2 – 0.3

2D+NUC 0.2 – 0.3

2D+CCS 0.2 – 0.3

2D+REN 0.2 – 0.3

2D+REN+BIO 0.2 – 0.32D+All 0.2 – 0.3

28

IER Presentation

• This two graphs are enough to show the important role that energy-service demands can play in the context ofclimate change mitigation.

• In 2D-DEM scenario which does not have the possibility of reducing energy-services, the global GDP-loss andmarginal CO2 abatement costs will be much higher than those of 2D case.

• In 2100, for example, GDP-loss is almost 93% and marginal abatement costs in 2D-DEM is about 95% higherthat those of 2D.

What if energy-services do not repsond to price changes

0

500

1000

1500

2000

2500

3000

3500

4000

4500

2000 2020 2040 2060 2080 2100

US$

/t-C

O2

Marginal CO2 abatement cost

2D

2D-DEM

0

1

2

3

4

5

6

7

8

9

10

2020 2030 2040 2050 2060 2070 2080 2090 2100

% 2D

2D-DEM

Global GDP-Loss

29

IER Presentation

Theenergysystemunder2Dscenarioin2050

30

IER Presentation

GlobalPrimaryEnergyConsumptionbysource

0

200

400

600

800

1000

1200

14002012

2020

2030

2040

2050

2060

2070

2080

2090

2100

Global‐PrimaryEnergyConsumption(TJ)

Base

0

200

400

600

800

1000

1200

1400

2012

2020

2030

2040

2050

2060

2070

2080

2090

2100

2D

RenewablesNuclearNaturalgasOilBiomassandWasteCoal

Without imposing any CO2 reduction policy, fossil fuels remain the main sources and especiallyafter 2030 coal dominates the other sources.

Under 2DS scenario share of coal is in contrast to Base scenario negligible. However, share ofother fossil fuels is still considerable.

31

IER Presentation

0

200

400

600

800

1000

1200

1400B

ase

2D

2D+N

UC

2D+C

CS

2D+R

EN

2D+R

EN+B

IO

2D+A

ll

Bas

e

2D

2D+N

UC

2D+C

CS

2D+R

EN

2D+R

EN+B

IO

2D+A

ll

Bas

e

2D

2D+N

UC

2D+C

CS

2D+R

EN

2D+R

EN+B

IO

2D+A

ll

2020 2060 2100

EJ

World-Primary Energy Consumption

Coal Oil Natural gas Nuclear Biomass Other renewables

• The total amount of the energy supplyincreases in all scenarios. Therefore:

• The speed of the energy efficiencyimprovement is slower than of demanddrivers (e.g. GDP growth).

• Fossil fuels (especially coal) remain themain sources in the Base scenario.

• Decarbonisation scenarios have lowerprimary energy consumption comparedto the Base case which is mainly aconsequence of reduction in the demandof energy-services.

• In decarbonisation scenarios, the level ofprimary energy consumption varies(mainly) according to the demand ofenergy-services which is set by the priceof energy-services.

6.6%

19.8%

29.7 %

32

IER Presentation

Differences in decarbonisation scenarios• Relative changes compared to the 2D scenario (cumulated

over 2020-2100):

-20%

-10%

0%

10%

20%

30%

Biomass (with CCS)

Biomass (excl. CCS)

Other renewables

Nuclear

Fossil fuels (excl. CCS)

Fossil fuel (with CCS)

2D+NUC 2D+CCS 2D+REN 2D+REN+BIO 2D+All

• Similar fossil fuel consumption inall scenarios is due to theinflexibility of some sectors inbeing completely decarbonized (e.g.some industrial processes).

• Biomass with CCS found to be avital and relatively cost-effectivemeasure. However, its contributionis restricted to the capacity ofcarbon storages and the potential ofBiomass.

33

IER Presentation

Electricification of final consumption and CCS in power generation

• Share of decarbonized electricity in total generation in all mitigation scenarios (especially after 2030) is almostthe same. This is mainly due to the high flexibility of this sector in being decarbonized.

• Policy recommendation:

0

20

40

60

80

100

120

2000 2020 2040 2060 2080 2100

Share of decarbonized electricity in total generation

Base 2D 2D+NUC2D+CCS 2D+REN 2D+REN+BIO2D+All

%

0

10

20

30

40

50

60

2000 2020 2040 2060 2080 2100 2120

Share of electricity in final energy consumption

Base 2D 2D+NUC2D+CCS 2D+REN 2D+REN+BIO2D+All

%

Decarbonizing power generation and allowing electricity to substitute fossil fuels ininflexible energy uses (e.g. mobility) is a cost-effective decarbonisation strategy.

34

IER Presentation

Decomposition of CO2 emissions

• In all the scenarios, renewables is found to be themain mitigation option.

• Higher contribution of service-demand reductionin the 2D compared to the others, denotes higherenergy prices in this scenario caused by theclimate target.

• The presence of fossil-fuel switching after theyear 2090 (negative emissions) can be tracedback into the inflexibility of some sectors inbeing completely decarbonized.

• Negligible share of efficiency improvement isdue to the fact that the TIAM is an optimizationmodel which selects cost-efficient technologiesin all scenarios (including the base scenario).

• Without Bio-CCS, achieving the 2°C targetseems to be barely possible.

The relative role of different measures in reducing total (cumulated) CO2 emissions over the time horizon:

2D 2D+REN 2D+NUC 2D+REN+Bio 2D+All 2D+CCS

Service-demand 23 22 21 20 18 21

Efficiency 4 5 3 4 3 4

Renewables 29 32 27 34 32 27

Nuclear 18 16 21 15 17 17

CCS 20 19 20 20 24 25Fossil fuel switching 6 6 6 6 6 6

-10

0

10

20

30

40

50

60

70

80

2020 2030 2040 2050 2060 2070 2080 2090 2100

Gt

CO2 reduction in 2D compared to Base case

Efficiency

Service-demand

Renewables

Nuclear

Fossil fuelswitchingFossil fuel CCS

Base

35

IER Presentation

End‐usefuelandelectricityefficiency Demandreduction Renewables Nuclear CCS Afforestation

End‐usefuelswitching Powergenerationefficiencyandfuelswitching

Contributionofmitigationoptions

Shareofoptions(%)2050

2DS 2DS+RES 2DS+CCSdemand 11.89 11.24 11.13Efficiency 12.51 12.94 12.56Renewables 37.41 38.91 35.61Nuclear 3.05 2.8 2.89CCS 18.77 18.44 22.97FuelSwitching 13.36 13.34 13.26Power Generation 3.01 2.87 1.55

Shareofoptions(%)2100

2DS 2DS+RES 2DS+CCSdemand 9.61 9.38 9.42Efficiency 6.64 6.87 6.61Renewables 50.43 51.27 49.77Nuclear 4.74 3.81 4.78CCS 8.54 8.58 9.65FuelSwitching 14.58 14.46 14.44Power Generation 5.46 5.63 5.32

0

10

20

30

40

50

60

70

2020 2030 2040 2050 2060 2070 2080 2090 2100

CO2em

issionreduction(Gt)

2DS+RES

0

10

20

30

40

50

60

70

2020 2030 2040 2050 2060 2070 2080 2090 2100

CO2em

issionreduction(Gt)

2DS+CCS

0

10

20

30

40

50

60

70

2020 2030 2040 2050 2060 2070 2080 2090 2100

CO2em

ission

reduction(Gt)

2DS

36

IER Presentation 37

Macroeconomic impacts of the climate policy

• Total global GDP-loss in 2D+All case is 23% less than that in 2D.• From the global perspective, next to renewables (specially biomass), CCS is found to be relatively cost-

effective mitigation measure. This can be traced back to the vital role of biomass with CCS.

0

0,5

1

1,5

2

2,5

3

3,5

%

GDP-loss differences between 2D and 2D+All

0

1

2

3

4

5

2020 2030 2040 2050 2060 2070 2080 2090 2100

%

Total global GDP-loss over 2020-2100

IER Presentation

0,0%

0,5%

1,0%

1,5%

2,0%

2,5%

3,0%

2040 2060 2080 2100

World‐GDP‐Loss

2DS

2DS+RES

2DS+CCS

0,00 0,50 1,00 1,50 2,00 2,50 3,00

AFR

CAN

MEA

WEU

USA

GDPLossof2020‐2100(%)

RegionalandGlobalGDP‐Loss

0,00 0,50 1,00 1,50 2,00 2,50 3,00

AFR

CAN

MEA

WEU

USA


2.59

0.67

0,00 0,50 1,00 1,50 2,00 2,50 3,00

AFR

CAN

MEA

WEU

USA


2DS 2DS+RES

2DS+CCS

0.83

0.18

0.83

0.14

2.32

0.71

1.40

0.75

0.24

2.07

0.66

1.63

1.72

38

IER Presentation

Conclusions and Outlook

Normalization of production function

- Better economic interpretation

- Necessary for modelling VES production function

VES production function Possibility to consider dynamic reaction of economies to higher prices over the whole century.

A cost effective decarbonisation strategy

Decarbonising power generation and allowing electricity to substitute fossilfuels in inflexible energy sectors (e.g. mobility)

Feasibility of the 2D targetWithout deployment of Bio-CCS technologies it seems to be barelytechnically feasible and comes with huge macroeconomic impacts.

Role of energy-service demand reduction

In all the mitigation scenarios, the cumulative contribution of service demandis more than 18%.

To address the uncertainty in elasticity of substitution

Performing a sensitivity analysis on the elasticity of substitution as aepistemic uncertain factor.

Without the possibility of reducing demands, the GDP-Loss and CO2marginal price can increase to more than 90%.

39

IER Presentation 40


E-Cost (LLCEC)

Balance (LCA)

Technology TIMES-EU

E2M2S

JMM

LEMI




• Germany

• EU

• World (TIAM)


NEWAGE

TIAM-MACRO




IER Presentation

Fundamental factors of long-term oil price formation

41

Demand increase

Supply and

transport costs

Hubbert rent

Hotelling rent

Cartel rent

Amount of oil

Oil

pric

e

Further factors

0

5

10

15

20

25

30

35

40

45

50

55

60

0 2500 5000 7500 10000 12500 15000 17500 20000 22500 25000 27500 30000

Amount of oil [EJ]

Oil

supp

ly c

osts

[$/b

oe]

WEU

USA

SKO

ODA

MEX

MEA

JPN

IND

FSU

EEU

CSA

CHI

CAN

AUS

AFR

0

5

10

15

20

25

30

35

-100 -80 -60 -40 -20 0 20 40 60 80 100

years

[Gb/year]

t0 TTime

PriceBackstop technology costs

Oil price

Unit extraction costs

Demand curve

Supply curve

Supply sideDemand side

Substitution by other energy carriers

Demand decline

GDP growth

Efficiency improvement

Decoupling between GDP and energy use

Alternative fuel production

IER Presentation 42

Fundamental analytic modelling aproach

Under consideration of:● Supply side:

i. Restricted temporal and geographic availability of oil and natural gas,ii. Technological progress in the supply of energy resources,iii. Market power by the OPEC,iv. Detailled description of the interdependencies between the fossil energy markets and alternatives

technologies to produce liquid fuels (e.g. coal-to-liquid, ethanol)● Demand side:

i. Substitution options along the technology chain from primary energy to useful energy service demand,

ii. Technological and/or price induced changes in the global energy demand,iii. Impacts of enery policy measures,

● Interdependencies between supply and demand side of fossil energy carriers

Consistent description and analysis of long-term demand and price trends for fossil energy carriers (especially crude oil)

IER Presentation

Oil market model: LOPEX

43

)()(

)()()()(

tnoptp

tPtdtXt

refrefOPEC

- OPEC covers demand determined by iso-elastic

demand function minus non-OPEC production:

Periods: 10-year periods from 1980 to 2100 (1976-1985,...,2096-2105).

2 Regions: OPEC = perfect cartel, Non-OPEC = competitive fringe (simulation).

Typ: Optimizing overall discounted OPEC-Revenue under perfect foresight

Format: Mixed Complementary Programming (MCP)

t

OPECOPECOPECtXtPtRtXSUPPLYCOSTtXtPtdMax

OPEC

)(),()()()()(),(

)()( tRtX OPECt

OPEC Constraints:

- limited resources:

- Non-OPEC production modeled by Hubbert curves

IER Presentation 44

Hubbert Simulation = Price & Cost dependent Triggering of extractioncycles

p(t)

start criterion hs resource data

bk(=b)

timing of profitability for each Hubbert cycle

t0,k Qoo,k hk(t)

non-OPEC production: k

k th )nop(t) (

p(t)

start criterion hs resource data

bk(=b)

timing of profitability for each Hubbert cycle

t0,k Qoo,k hk(t)

non-OPEC production: k

k th )nop(t) (k

k th )nop(t) k

k th )nop(t) (

IER Presentation 53

Overview: Possible methods for model linking

Oil market

Model Hubbert curves Linking

LOPEX Simulation soft link

or functional

LOPEX

&

TIAM (MIP)

Simulation

R/P or with logistic function

soft link

or funktional

TIAM (MCP) R/P-method or with logisticfunction

hard link (integration)

IER Presentation 54

Modelling approachOil market model LOPEX

Global energy system model

TIAMCost and emissions balance

GDP

Process energy

Heating area

Population

Light

Communication

Power

Person kilometers

Freightkilometers

Demand services

Coal processing

Refineries

Power plantsand

Transportation

CHP plantsand district

heat networks

Gas network

Industry

Commercial and tertiary sector

Households

Transportation

Final energyPrimary energy

Domesticsources

Imports

Dem

ands

Ener

gy p

rices

, Res

ourc

eav

aila

bilit

y

Energy flows

Emissions

Capacities

Costs

Prices

Oil priceNon-OPECproduction

Iterating until convergence

Reference point (crude oil consumption and price) for demand function

Cartel rent for crude oil, Gas price linked to oil price

Non-OPEC modul

Hubbert simulation

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100

[%]

bzw

. [$

/bbl

]

p(t)

10-20 $/bbl

0-10 $/bbl

20-30 $/bbl

30-40 $/bbl

OPEC modul )()())(( tPtnopttPdem

q

p

IER Presentation

Scope of scenario analysis

Socio-economic assumptions2000 -2010

2010 -2020

2020 -2030

2030 -2040

2040 -2050

Global GDP growth 3.1% 2.9% 2.8% 2.6% 2.5%Global population growth 1.1% 0.9% 0.7% 0.7% 0.6%

Maximum liquid supply [million bbl/d]: 2010 2020 2030 2040 2050Unconventional 2 5 8 15 25Alternative fuels 0.6 6 12 25 50

56

● Scenarios analyzed:i. REFERENCE scenario: Long-term equilibrium on oil market incl. OPEC’s

cartel behavior

IER Presentation

0

50

100

150

200

250

300

350

2010 2030 2050 2070 2090

$200

6/bb

l

TIAM_1

Prices and quantities at the beginning of theiteration

0

50000

100000

150000

200000

250000

300000

350000

400000

2010 2030 2050 2070 2090

PJ/y

ear

TIAM_1

57

Oil prices Oil quantities

JahrJahr

IER Presentation

Prices and quantities during the iteration

58


0

50

100

150

200

250

300

350

2010 2030 2050 2070 2090

$200

6/bb

l

TIAM_1LOPEX_1TIAM_2LOPEX_2

0

50000

100000

150000

200000

250000

300000

350000

400000

2010 2030 2050 2070 2090

PJ/y

ear


JahrJahr

IER Presentation

0

50000

100000

150000

200000

250000

300000

350000

400000

2010 2030 2050 2070 2090

PJ/y

ear


Prices and quantities at the end of the iteration

59


0

50

100

150

200

250

300

350

2010 2030 2050 2070 2090

$200

6/bb

l


Jahr Jahr

IER Presentation

Liquid production (at the beginning)

60

TIAM LOPEX

0

50000

100000

150000

200000

250000

300000

350000

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Men

gen

[PJ/

Jahr

]

crude Non-OPECunconv Non-OPECunconv OPECcrude OPEC

50000

100000

150000

200000

250000

300000

350000

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Men

gen

[PJ/

Jahr

]

crude Non-OPECUnconventional Non-OPECUnconventional OPECcrude OPEC

IER Presentation

Liquid production (at the end)

61

TIAM LOPEX

0

50000

100000

150000

200000

250000

300000

350000

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Men

gen

[PJ/

Jahr

]

crude Non-OPECunconv Non-OPECunconv OPECcrude OPEC

50000

100000

150000

200000

250000

300000

350000

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Men

gen

[PJ/

Jahr

]

crude Non-OPECUnconventional Non-OPECUnconventional OPECcrude OPEC

IER Presentation

Scope of scenario analysis (contd.)

65

● Scenarios analyzed:ii. Sensitivity of oil price on factors on the supply side:

1. EOR+TPROG: Increasing recovery factor from 50% to 60% plus technological progress in oil supply (cost reduction of 0.5%/year)

2. UNCONV: More optimistic assumptions on growth in production of unconventional oil (oil sands, oil shale)

3. FT+BIOFUEL: More optimistic assumptions on growth in production of liquid fuels by Fischer-Tropsch conversion of coal, natural gas or biomass and of methanol/ethanol

iii. COMBI: Combination of all three supply factors plus option to increase electricity use by increased electricity supply from nuclear power

iv. Sensitivity of oil price on oil demand (LOW DEMAND): Lower GDP growth

v. Sensitivity of oil price on OPEC behaviour (OPEC): Disintegration of OPEC

vi. Sensitivity of oil price on climate policy (CO2): Introduction of a CO2 price of up to 350 $/t by 2050

IER Presentation 76

Linkage between the TIAM-IER and the LOPEX model

IER Presentation

Conclusions

77

● Reference scenario: Price peak in 2030 of 150 $/bbl caused byi. decline in conventional non-OPEC production and

ii. at the same time non-sufficient supply from unconventional oil and alternative liquids allowing OPEC to exercise market power;

iii. after 2030 OPEC’s influence decreases by increased production from unconventional (oil sands) and alternative fuels (FT fuels).

● OPEC cartel behavior largest price component. ● Improvements in oil recovery reduce scarcity and lead thus to lower prices.● Rate by which unconventional and alternative fuels can be introduced also critical for

price reductions, since:i. Conventional oil can be saved -> scarcity rent becomes lower (smaller price impact),

ii. OPEC‘s market power shrinks (major price impact).

iii. But, lower prices also imply higher overall liquid fuel demand.

● Factors reducing price and at the same time demand are:i. Substitution options for oil on the demand side,

ii. Lower economic growth,

iii. CO2 mitigation measures (however, overall price for burning oil increases).

IER Presentation

Lessons learned● Soft-linking of TIAM and LOPEX gives some new insights but the

coupling has to be improved furtheri. Improvement of coupling in terms of robustness

ii. Test of implementation of Hubbert curves in TIAM (MIP)

● Additional model changes can support the linkage:i. Increased flexibility of the oil demand in TIAM to reduce the „floor

demand“: Alternatives for the oil use for Non-energy consumption and in industry as well as in air traffic and navigation.

ii. Revision of the substitution possibilities on the supply side (Bioenergy)

iii. Harmonisation of the Cost-supply-curves in both models.

78

IER Presentation

Hubbert curves in MIP

URRqqx t

tt 1

79

5,54,43,32,21,10,0

5,54,43,32,21,10,0

QQQQQQqXXXXXXx

ttttttt

ttttttt

1,5,4,3,2,1,0 tttttt

tt

ttt

ttt

ttt

ttt

tt

yyy

yyyy

yyy

,4,5

,4,3,4

,3,2,3

,2,1,2

,1,0,1

,0,0

1,5,4,3,2,1,0 tttttt yyyyyy

Relationship for Hubbert curve

Piece-wise linear approximation of non-convex function

t

t xq1

curveHubbert eapproximat toused points Selected

periods Timepoints ofSet

chosen are points gneighborinonly ensure toiableBinary var

tperiodin validcurve edapproximat of ipoint offactor Weighting

tuntil production Cumulativein t production Annual

,

,

ii

ti

ti

t

t

,QXti

y

qx

tx

tq0Q 1Q 2Q 3Q 4Q 5Q0X

1X

2X3X

4X

5X

Piece-wise linear approximation

Ensure that only two neighboring points

are selected

IER Presentation 80


E-Cost (LLCEC)

Balance (LCA)

Technology TIMES-EU

E2M2S

JMM

LEMI




• Germany

• EU

• World (TIAM)


NEWAGE

TIAM-MACRO




IER Presentation

Political targets in Germany: “Energiewende”

• The long-term vision – the age of renewables to be achieved in 2050• Expansion of the use of renewable energies:

Power production from renewables: 35% in 2020 50% in 203080% in 2050

Final energy by renewables: 60% in 2050

• Boosting energy efficiency to cut by: Primary energy consumption: 20% in 2020 and 50% in 2050 Electricity consumption: 10% in 2020 and 25% in 2050 Climate neutral buildings in 2050

• Complete nuclear power shut down until 2022• Stick to GHG reduction targets: -40% in 2020; 80 to 95% in 2050• Electric cars – the vehicles of the future: 2020 one million, 2030 six million

81

Role of storage taking into account all the other system developments?

IER Presentation

‐200

‐150

‐100

‐50

0

50

100

150

200

‐10

0

10

20

30

40

50

01.07.2012 08.07.2012 15.07.2012 22.07.2012 29.07.2012

EPEX

‐Spo

t [€/MWh]

Gen

eration RE

S / V

ertical Network Load

[GW]

Generation PV Generation Wind Vertical Network Load EPEX‐Spot

Vertical grid load, PV/wind power generation and power prices

82

Sources: TSOs, EEX

December 2012

IER Presentation 83

Sources: EEX, PointCarbon, ÜNBs, proprietary estimation

Gen

erat

ion

in G

erm

any

in M

Wh

Pow

er p

rice

in E

UR

/MW

h

Impacts on spot market prices today

IER Presentation

Demand load and residual load - 50 % share of RES

• Excess renewable power up to 27 GW• Excess renewable production ~ 2 TWh, about 1 % of the electricity produced

by wind and photovoltaics• Storage capacity requirement ~ 250 GWh

84

-80-60-40-20

020406080

100

0 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000

Dem

and

and

resi

dual

load

[G

W]

Hour [h]

Demand load Residual load

IER Presentation

Dynamics of residual load

• Strong increase of the residual load gradient with increased share of fluctuating production

• Range of residual load change ± 60 GWel (50 % share of renewables)

85

2008 50 % RES

IER Presentation

Demand load and residual load - 80 % share of RES

• Excess renewable power up to 78 GW• Renewable surplus production ~ 43 TWh, about 13 % of the electricity

production by wind and photovoltaics• Storage capacity requirement ~ 6,4 TWh

86

-80-60-40-20

020406080

100

0 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000

Dem

and

and

resi

dual

load

[G

W]

Hour [h]

Demand load Residual load

IER Presentation

Electricity storage requirement – comparison of study results

• Broad range depending on share of renewables and flexibility options considered

• Currently installedi. Charging power:

6,3 GW ii.Storage capacity:

45,5 GWh

87

0

10.000

20.000

30.000

40.000

50.000

60.000

VDE VDE TUM Bonus 0 TUM Bonus 50 TUM Bonus 50INT

VDE UBA

40% 80% 100%

Spei

cher

kapa

zitä

t [G

Wh]

Anteil der EE

Kurz- & Langzeitspeicher

Langzeitspeicher

Kurzzeitspeicher

0

20

40

60

80

100

120

VDE VDE TUM Bonus 0 TUM Bonus 50 TUM Bonus 50INT

VDE UBA

40% 80% 100%

Lad

elei

stun

g [G

W]

Anteil der EE

Kurz- & Langzeitspeicher

Langzeitspeicher

Kurzzeitspeicher

Storage capacity requirement

Charging power requirement

IER Presentation

Regional distribution of wind and PV capacity

88

Wind PhotovoltaicsSource: Fraunhofer IWES

IER Presentation

Required grid extension and reinforcement

Distribution grid

• Grid extension based on wind and photovoltaics:i. Lines: 380,650 km

ii. Transformers: 63,000 MVA

• Investment: up to 27 billion €

89

Source: TSOs, Netzentwicklungsplan , 2013

Transmission grid• Grid extension:

i. AC-lines: 1,500 kmii. Additional AC-circuits: 3,400 kmiii. Upgrading of AC-circuits: 1,200 kmiv. DC-lines: 2,100 km

• Investment: 22 billion €

• Scenario B 2023:i. Wind offshore: 14.1 GWii. Wind onshore: 49.3 GWiii. Photovoltaics: 61.3 GWiv. Share of renewable energies in

electricity generation: 50 %

Source: BDEW, Ausbaubedarf in deutschen Verteilnetzen, 2011

© Colourbox.de

IER Presentation

Research network „Systems Analysis of Energy Storages“

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Systems Analysis

Energy Storages

IER Presentation

IER contribution to „Systems Analysis of Energy Storages“

• System analytics evaluation of energy storage technologies in Germany by an energy economic perspective in the European context

• How can energy storages and load flexibility support the integration of renewable energies in a future European energy supply system at the minimum cost?

• Integrated analysis of the contribution of storage technologies to future requirements in Germany by simultaneous consideration of all important fields of action (electricity, heat, transport – energy efficiency, renewable energies)

To conduct such analysis, further development of the optimisation modelsTIMES-PanEU and E2M2s existing at IER is required. Especially a differentiated representation of storage technologies, flexible loads and grid expansion has to be implemented.

91

IER Presentation

Short model description

Electricity Market Model E2M2s• European Electricity Market Model,

stochastic version• Focus on the electricity system and its

interaction with the heat sector (combined heat and power plants)

• Hourly time resolution• 18 electricity and 27 heat regions in

Germany + 29 European countries• Integral optimisation of investments

(power plants, storages and transmission lines) and unit commitment

• Provision of ancilliary services• Flexibility options (storages, demand

response, power-to-x, curtailment)

Energy System Model TIMES-PanEU• The Integrated MARKAL EFOM System,

Pan-European• Linear optimisation model• 30 regions (EU-28 + Norway, Switzerland)• Time horizon: 2010 – 2050• Representing the whole energy system

and all energy carriers:i. Energy supply (electricity, heat, gas)ii. Energy demand, divided into sectors:

1. Residential sector2. Commercial sector3. Agriculture4. Industry5. Transport

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IER Presentation

TIMES – systems analysis

93

• Global

• TIAM

• Europe

• TIMES-PanEU

• TIMES-EG

• National

• Germany

TIMES-D

• BrazilTiPS-B

• South AmericaTIMES-ESA

• Regional

• Gauteng, South Africa

• Baden-Württemberg

TIMES PanEU

TIMES PanEU• 30 Regions

(EU-28 + Switzerland + Norway)

IER Presentation

The energy system model TIMES-PanEU● Linear optimization model● 30 regions (EU-28 + Norway, Switzerland)● Time horizon: 2010 – 2050● Mapping of the whole energy system:

i. Energy supply (electricity, heat, gas) ii. Energy demand, divided into sectors:

1. Residential sector2. Commercial sector3. Agriculture4. Industry5. Transport

● Electricity grid, biofuels and biomass trade● GHG: CO2, CH4, N2O, SF6 ● Other pollutants: SO2, NOx, CO, NMVOC, PM2.5, PM10

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IER Presentation

TIMES-PanEU 30 region model (EU 28, No, CH, IS) Energy system model

SUPPLY: reserves, resources, exploration and conversion Country specific renewable potential and availability (onshore wind, offshore wind, ocean, geothermal, biomass, biogas, hydro)

Electricity: public electricity plants, CHP plants and heating plants Residential and Commercial: End use technologies (space heating, water heating, space cooling and others)Industry: Energy intensive industry (Iron and steel, aluminium copper ammonia and

chlorine, cement, glass, lime, pulp and paper), food, other industries , autoproducer and boilers

Transport: Different transport modes (cars, buses, motorcycles, trucks, passenger trains, freight trains), aviation and navigation

Country specific differences for characterisation of new conversion and end-usetechnologies

Electricity Grid, Biofuel and biomass trade Time horizon 2010 - 2050 GHG: CO2, CH4, N2O, SF6 /Others pollutants: SO2, NOx, CO, NMVOC, PM2.5, PM10

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IER Presentation

General structure of TIMES-PanEU

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Cost and emissions balance

GDP

Process energy

Heating area

Population

Light

Communication

Power

Person kilometers

Freight kilometers

Demand services

Coal processing

Refineries

Power plantsand

Transportation

CHP plantsand district

heat networks

Gas network

Industry

Commercial and tertiary sector

Households

Transportation

Final energyPrimary energy

Domesticsources

Imports

Dem

andsEn

ergy

pric

es, R

esou

rce

avai

labi

lity

IER Presentation

Model coupling TIMES-PanEU – E2M2s

● Demands

● Prices (CO2-certificates, gas, …)

● Expansion of renewable energies

97

TIMES-PanEU• 12 / 224 types of hours• Integral optimization

2010 – 2050• Germany as point-model

E2M2s

• 8760 hours• 18-regions-model

for transmission network• 37 heat regions• 1-year-optimisation

• Expansion and operation of renewable energies, conventional power plants, storage technologies, power-to-x

• Prices for typical time periods

Output

Output

• Demand (energy services)

• Expansion scenarios for renewable energies

Input

• Detailed electricity prices• Operation of conventional

power plants, electricity storage technologies, demand response, power-to-x, curtailment with high temporal and regional resolution

• Grid expansion and utilisation

TIMES-PanEU

• 224 types of hours Germany• 12 types of hours rest of

Europe• Integral optimization 2010 –

2050

IER Presentation

TIMES PanEU – Modelling of storage processes

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IER Presentation

Significance of Heat storages and Power-to-Heat – profiles

› Investment in Hot water storages is economically attractive from 2015 onwards

› Investment in Power-to-Heat from 2025 onwards as well as 2045

0,00

50,00

100,00

150,00

200,00

250,00

300,00

2010 2015 2020 2025 2030 2035 2040 2045 2050

Storage capacity installedHot water storage

GWh

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

2010 2015 2020 2025 2030 2035 2040 2045 2050

Installed capacity (el.)Power‐to‐Heat

GW

TIMES PanEU Scenario calculations

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IER Presentation

Significance of Heat storages and Power-to-Heat – profiles

‐10

0

10

20

30Frühling 2050

‐5

0

5

10

15

20

Sommer 2050

‐10‐5051015202530

Herbst 2050

‐30

‐20

‐10

0

10

20

30

40Winter 2050

Speicherleistung

Fernwärmelastgang

GW

TIMES PanEU Scenario calculations

IER Presentation

E2M2s Scenario calculations• Analysis of the German electricity system on a development path to a 80%

share of renewable energies of electricity consumption (60% volatile feed-in)

• Renewable feed-in, electricity demand, fuel- and CO2-prices are exogenously provided (by TIMES-PanEU model)

• Investment options: conventional power plants (coal, lignite, natural gas, oil) optionally including or excluding carbon capture and storage/CHP, generic electricity storage options with different capacity volume ratios

• Comparison of four scenarios:i. Base scenario excluding the options of demand response (flexible electricity

demand) or curtailment (limitation of renewable output)

ii. Demand response only

iii. Curtailment only

iv. Demand response and curtailment

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IER Presentation

Significance of the expansion of the interconnectors

› With an increasing share of renewables, the electricity exchange becomes more attractive

› The expansion of the interconnectors

› is a comparatively efficient alternative – even compared to investments in new storage power plants

› has a significant influence on the cost-optimal development of production capacities in Germany

E2M2s Scenario calculations

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IER Presentation

E2M2s Scenario calculations – investments in generation and storage capacity

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0

10

20

30

40

50

60

70

80

no demandresponse orcurtailment

only demandresponse

onlycurtailment

demandresponse and

curtailment

Inve

sted

gen

erat

ion

and

stor

age

capa

city

[GW

el]

Electricity storage

Open-cycle gas turbine

Lignite Carbon Captureand Storage

The application of demand response cuts the investment need in generation capacity by a few percent as peak load is decreased

The curtailment of 2 % of renewable feed-ini. cuts the investment need in storage capacity drastically

as surplus electricity doesn‘t need to be stored entirelyii. increases the investment needs in flexible power plants

of low capital cost as less stored energy can be fed back into the system in times with high residual load

100 %94 %

18 %

7 %

IER Presentation

E2M2s Scenario calculations – Storage capacity

• 50 % share of renewables:i. Present German pump storages, planned new pump storages and purchase rights

from abroad offer sufficient storage capacities• 80 % share of renewables:

i. Cost optimum storage capacity of 4.2 TWh and charging power of 54.8 GWii. Curtailment of fluctuating electricity generation from wind and PV power plants

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0

1000

2000

3000

4000

5000

6000

7000

0 1000 2000 3000 4000 5000 6000 7000 8000

Sto

rage

cap

acity

[GW

h]

Hour [h]

80 % share of renewable energies50 % share of renewable energies

IER Presentation

Summary• The transformation of the energy system towards very high levels of fluctuating

renewable energy sources requires the development of various flexibility options• A management strategy has to be developed to ensure the present high level of

security of supply in the future also.• Electricity storages play an important role to the temporal balance between

production and consumption. At the same time, it provides the option to include larger fractions of supply from renewable sources into the system.

• However, there are powerful alternative options to improve integration of fluctuating regenerative generation: Improved demand side flexibility reduces the requirement for additional controllable

capacity

Acceptance of curtailment of renewable feed achieves a strong reduction of requirement for additional controllable capacity

• Analysing the future role of storage in energy systems requires an integrated assessment. This can lead to new insights taking into account the interactions in the overall energy system and the integration in the European context

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IER Presentation 107


E-Cost (LLCEC)

Balance (LCA)

Technology TIMES-EU

E2M2S

JMM

LEMI




• Germany

• EU

• World (TIAM)


NEWAGE

TIAM-MACRO




IER Presentation

Decentralisation trends in the European electricity sector● Increased use of renewable energies in electricity generation

● Renewable energy systems operate at a lower scale than conventionalplants

● Large power plant projects face acceptance problems in the society There is a decentralization trend in the European electricity sector

● What consequences does this trend trigger?i. Impacts on fossil fuels usage and CO2-emissions

ii. Impacts on renewable energy investments

iii. Macroeconomic impacts

● Coupling an European energy system model (TIMES-PanEU) with a global CGE model (NEWAGE) makes it possible to assess these issues

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IER Presentation

Model linking strategy

109

Model input data

TIMES-PanEU

NEWAGE

Scenario constraints:

Energy and

climate policies

NEWAGE specific data- National accounts (GTAP)- Hybrid technology data

Model interface: CO2-emissions in the EU

(ETS + Non-ETS) Renewable energy shares

in electricity generation of different countries/regions

TIMES-PanEU specific data:- Energy system- Exogenous demands

Model output

Model output

Common inputs: Crude oil price paths

IER Presentation

Scenario descriptionTIMES-PanEU

ETS75 / REF C80 DEC_EUETS target of 75% Climate target of 80% Decentralization in the whole EU

GHG reduction target

75% CO2 reduction in EU-ETS (2005-2050)

80% of overall GHG emissions covering all sectors till 2050 regarding the Kyoto base year 1990.

Large scale power plants projects

No limitation (based on economic decisions)No new large scale power plants beyond 2020 in the whole EU-28

Additional framework

assumptions

National support mechanism for renewable energy sources Use of nuclear energy based on national policies Support of biofuels National E-mobility targets

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NEWAGEETS75 / REF C80 DEC_EU

ETS target of 75% Climate target of 80% Decentralization in the whole EU

CO2 emissions75% CO2 reduction in EU-ETS (2005-2050)

Scenario specific %-changes as in TIMES-PanEU(regionally and sectorally differentiated)

Renewable energy shares in the

electricity sector-

IER Presentation

TIMES-PanEU results● Net electricity generation in the EU-28

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IER Presentation

TIMES-PanEU results (II) ● CO2 emissions and certificate prices

112

0

200

400

600

800

1000

1200

1400

1600

0

500

1000

1500

2000

2500

3000

3500

4000M

odel

l

C80

DE

C_E

U

C80

DE

C_E

U

C80

DE

C_E

U

C80

DE

C_E

U

2010 2020 2030 2040 2050

Cer

tific

ate

pric

e [€

2000

/t]

CO

2E

mis

sion

s [M

t]

Int. Aviation

Transport

Agriculture

Commercial

Residential

Industry

Conversion

GHG price

IER Presentation

Model interface: TIMES-PanEU output = NEWAGE input● Changes of CO2 emissions resulting from TIMES-PanEU (relative to the reference

case) serve as input for NEWAGE

● Changes in renewable energy shares resulting from TIMES-PanEU (relative to thereference case) serve as input for NEWAGE

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IER Presentation

NEWAGE results● Macroeconomic impacts

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IER Presentation

NEWAGE results (II)● Sectoral impacts

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IER Presentation

NEWAGE results (III)● GDP impacts

116

IER Presentation

NEWAGE results (ETS75)

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IER Presentation

Conclusions● Energy sector impacts

The decentralisation constraint blocks emission reduction pathways of CCS and nuclear energy. The decarbonisation of the electricity sector is driven by an intensified use of renewable energies

Electricity plays a key role for the decarbonisation of non-ETS sectors. While there is a lower use of electricity in the medium term (2030, 2040), there is an increased electricity demand in the long run compared to the reference case

Electricity prices increase in the medium term (2030, 2040)● Macroeconomic impacts

Germany and Western EU: As electricity costs rise, price-induced supply and demand adjustments in the rest of the economy overcompensate the increased demand for renewable energy technologies (crowding-out), such that overall macroeconomic performance (jobs, welfare) suffers

Eastern EU: Lower CO2 constraints and higher RES investments (no crowding-out) drive welfare even though employment changes are slightly negative (w.r.t. referencecase)

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IER Presentation

Content

1. Institute of Energy Economics and the Rational Use of Energy (IER)

2. Global Analysisi. NEWAGE – Integration of hybrid features in a CGE model

ii. TIAM-MACRO – Energy system model with macroecomomic extension

iii. TIAM-LOPEX – Energy system model and oil market model

3. European Analysis i. Linking TIMES-PanEU and E2M2

ii. Linking TIMES-PanEU and NEWAGE

4. Outlook: The LCE21 project REEEM119

IER Presentation

Projects funded under the LCE21 callModelling sustainable Energy system Development underEnvironmental And Socioeconomic constraints (EU: 3,735 Mio. €)Contact: Jordi Solé - [email protected]

Role of technologies in an Energy Efficient Economy – Model-basedanalysis of policy measures and transformation pathways to asustainable energy system (EU: 3,997 Mio. €)

Contact: Georgios [email protected]

Analysis of the European energy system under the aspects offlexibility and technological progress (EU: 2,780 Mio. €)Contact: Dominik Möst [email protected]

Navigating the Roadmap for Clean, Secure and Efficient Energy Innovation (EU: 3,999 Mio. €)Contact: Daniel Huppmann - [email protected]

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IER Presentation

REEEM - Overview

Objective

To gain comprehensive understanding of the system‐wide implications of energy strategies.

Focus

Energy strategies focus on transitions to a competitive low‐carbon EU energy society, as described by the Strategic Energy Technology (SET) Plan.

Methodology

A large ensemble of models to study the role of technologies, innovation and consumers in EU decarbonisation pathways. Integrated economic, environmental and social impact assessments produced.

121

IER Presentation

Information flows between assessments in all areas

122

IER Presentation

REEEM - Overview

123

IER Presentation

General model linking approach of TIMES-PanEU and NEWAGE

124

IER Presentation

Disaggregating a representative household of a macroeconomic model into different income groups

125

IER Presentation

Environmental impact toolbox

126

IER Presentation

Impact pathway in ECOSENSE

127

IER Presentation

EMP - Europe

Deliverable of REEEM project. It culminates in annual meeting.

Objective

Analysis and comparison of EU‐wide, regional and national models of SET Plan‐guided transitions to a low carbon EU economy: strengths, weaknesses, developments, integration.

Focus

European and member state development priorities vis‐a‐vis the Energy Union Dimensions and the SET Plan Challenges.

First meeting

3 days event, Spring 2017. INEA and LCE21 collaborating to frame the event, hosted by the JRC in Petten

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