IER Präsentation e 10-2016 [Kompatibilitätsmodus] · IER Presentation Content 1. Institute of...
Transcript of 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
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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
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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)
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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)
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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
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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
IER University of Stuttgart
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
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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 %)
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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
IER University of Stuttgart
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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
,,,,,,,,,
,,,,,,
,
IER University of Stuttgart
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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
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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
Gas CO2CO2 Oil CoalCO2
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
IER University of Stuttgart
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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
IER University of Stuttgart
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)
IER University of Stuttgart
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
)
IER University of Stuttgart
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
17IER University of Stuttgart
IER University of Stuttgart 18
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
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NEWAGE: Consumption (CES-Nesting)
σSConsumption
Non-energy goodsσC
Gas CO2CO2 Oil CoalCO2
Electricity
σCE
σGAS σOIL σCOL
21
NEWAGE hybrid feature: Household energy demand technologies
IER University of Stuttgart
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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
IER University of Stuttgart
IER University of Stuttgart 24
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
IER University of Stuttgart 25
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
(…) (…) (…)(…)(…)
(…)(…)
IER University of Stuttgart 26
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)
27IER University of Stuttgart
Composition of the aggregated investment good
28IER University of Stuttgart
Schematic diagram of nuclear power life-cycle costs
29
Construction Operation Decommissioning
Cos
ts($
)
Time (years)
IER University of Stuttgart
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
30IER University of Stuttgart
Values of the capital stock per region
31IER University of Stuttgart
Depreciation of base year‘s capital stock (exogenous)
32IER University of Stuttgart
Investment related increase of the capital stock
33IER University of Stuttgart
Overall capital stock development
34IER University of Stuttgart
Decommissioning of selected technologies in Germany (exogenous)
35IER University of Stuttgart
Capital demand of electricity technologies in Germany
36IER University of Stuttgart
IER University of Stuttgart 37
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
38IER University of Stuttgart
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
IER University of Stuttgart
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)
IER University of Stuttgart
• 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
41IER University of Stuttgart
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)
IER University of Stuttgart
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
IER University of Stuttgart
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
IER University of Stuttgart
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 €
IER University of Stuttgart
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. €
IER University of Stuttgart
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
IER University of Stuttgart
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
IER University of Stuttgart
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
IER University of Stuttgart
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
IER University of Stuttgart
CO2 prices
51IER University of Stuttgart
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
IER University of Stuttgart
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
IER University of Stuttgart
• 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)
IER University of Stuttgart 54
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 %)
IER University of Stuttgart 55
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).
-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 %)
IER University of Stuttgart 56
Economic impacts of different carbon leakage protection measures in the EUNEWAGE sample application (3)
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
Scenario 2.1 („COM-proposal")
Scenario 3.1 („Industry reserve“)
Scenario 4.1 („Smooth transition“)
Scenario 5.1 („Ecofys l“)
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).
• Results: GDP and employment changes in Germany implied by the (exogenous) carbon costs changes of different carbon leakage protection measures in the EU
IER University of Stuttgart 57
Economic impacts of different carbon leakage protection measures in the EUNEWAGE sample application (3)
-36
-24
-12
0
12
24
-6
-4
-2
0
2
4
2021 2025 2030 2021 2025 2030 2021 2025 2030 2021 2025 2030
Scenario 2.1 („COM-proposal")
Scenario 3.1 („Industry reserve“)
Scenario 4.1 („Smooth transition“)
Scenario 5.1 („Ecofys l“)
Em
ploy
men
t[T
hd. e
mpl
oyee
s]
GD
P[re
al, B
n. €
2010
]
GDP Employment
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).
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)
58IER University of Stuttgart
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, ….)
59IER University of Stuttgart
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 University of Stuttgart
IER Presentation 12
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
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
GDPLossof2020‐2100(%)
2.59
0.67
0,00 0,50 1,00 1,50 2,00 2,50 3,00
AFR
CAN
MEA
WEU
USA
GDPLossof2020‐2100(%)
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
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
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
Oil prices Oil quantities
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
TIAM_1LOPEX_1TIAM_2LOPEX_2
JahrJahr
IER Presentation
0
50000
100000
150000
200000
250000
300000
350000
400000
2010 2030 2050 2070 2090
PJ/y
ear
TIAM_1LOPEX_1TIAM_10LOPEX_10
Prices and quantities at the end of the iteration
59
Oil prices Oil quantities
0
50
100
150
200
250
300
350
2010 2030 2050 2070 2090
$200
6/bb
l
TIAM_1LOPEX_1TIAM_10LOPEX_10
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
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,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
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tx
tq0Q 1Q 2Q 3Q 4Q 5Q0X
1X
2X3X
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5X
Piece-wise linear approximation
Ensure that only two neighboring points
are selected
IER Presentation 80
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
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
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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
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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
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and
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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
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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
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VDE VDE TUM Bonus 0 TUM Bonus 50 TUM Bonus 50INT
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Wh]
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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“
90
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
92
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
94
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
95
IER Presentation
General structure of TIMES-PanEU
96
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
99
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
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TIMES PanEU Scenario calculations
100
IER Presentation
Significance of Heat storages and Power-to-Heat – profiles
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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
102
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
103
IER Presentation
E2M2s Scenario calculations – investments in generation and storage capacity
104
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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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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
106
IER Presentation 107
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
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
108
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
110
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
111
IER Presentation
TIMES-PanEU results (II) ● CO2 emissions and certificate prices
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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
113
IER Presentation
NEWAGE results● Macroeconomic impacts
114
IER Presentation
NEWAGE results (II)● Sectoral impacts
115
IER Presentation
NEWAGE results (III)● GDP impacts
116
IER Presentation
NEWAGE results (ETS75)
117
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)
118
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]
120
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
128
Thank you for your attention!