Linking TIMES-PanEU and NEWAGE...of renewable energy sources, which is supposed to play a key role in reducing GHG emissions Our paper analyses energy and macro-economic effects of

University of Stuttgart

Institute of Energy Economics and the Rational Use of Energy

Linking TIMES-PanEU and NEWAGE: Energy and macroeconomic impacts of decentralization

trends in the European electricity sector

Robert Beestermöller, Markus Blesl, Ulrich Fahl

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

TIMES-CGE Workshop

66th Semi-annual ETSAP meeting

19th November 2014

UN City, Copenhagen, Denmark

19th November 2014 Linking TIMES-PanEU and NEWAGE



R. Beestermöller / M. Blesl

Outline

(1) Introduction

(2) Methodology

(3) Results of the scenario analysis

(4) Conclusions

2





Introduction

● In a world with greenhouse gas emission reduction targets, renewable energy

promotion and public antagonisms to large power plant construction projects,

decentralized electricity generation becomes a considerable phenomenon in the

European electricity sector

● A decentralized system consists of distributed small scale electricity

generation units which are closer to the demand centres and provide electricity

directly to the costumers without feeding-in to the electricity grid

● A more decentralized electricity generation is characterized by an extended use

of renewable energy sources, which is supposed to play a key role in

reducing GHG emissions

● Our paper analyses energy and macro-economic effects of decentralization

trends in the European electricity sector by coupling the energy system model

TIMES-PanEU with the macroeconomic CGE model NEWAGE

● Focus on regionally and sectorally differentiated impacts

3





Outline

(1) Introduction

(2) Methodology


(4) Conclusions

4





Model linking strategy

5

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





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:

Energy supply (electricity, heat, gas)

Energy demand, divided into sectors:

o Residential sector

o Commercial sector

o Agriculture

o Industry

o Transport

● Electricity grid, biofuels and biomass trade

● GHG: CO2, CH4, N2O, SF6

● Other pollutants: SO2, NOx, CO, NMVOC, PM2.5, PM10

6





General structure of TIMES-PanEU

7

Cost and emissions balance

GDP

Process energy

Heating area

Population

Light

Communication

Power

Person

kilometers

Freight

kilometers

Demand services

Coal processing

Refineries

Power plants

and

Transportation

CHP plants

and district

heat networks

Gas network

Industry

Commercial and

tertiary sector

Households

Transportation

Final energy Primary energy

Domestic

sources

Imports

Dem

an

ds

En

erg

y p

rices, R

eso

urc

e a

vailab

ilit

y





The CGE Model NEWAGE

8

16 sectors:

Coal, Gas, Crude oil,

Mineral oil, Electricity

Chemicals, Metals, Iron

& steel, Minerals, Pulp

& paper, Machinery,

Rest of industry

Construction

Transport

Agriculture

Services

10 regions:

Germany

EU-15 (w/o Germany)

NMS-12

Other Europe Annex-B

Rest of Annex-B

Russia

USA

China + India

OPEC

Rest of world

Investments

Foreign

trade

Tax Revenue

Implicit tax

system

Factor

markets

Savings

Labor

Capital

Aggregation

Pool

(Armington)

Sectoral

Production

Internat.

Transport

Households and

Government

Production

Consumption

Representative Agent

Fossil Fuel

Production

Imports

Exports

Carbon

Resources

Electricity

Generation: Technology

based modeling:

portfolio with 18

generation options

Special / hybrid

features:

Imperfect Labor

Market: Rigid wages,

wage curve

Differentiation by

qualification (skilled,

unskilled)

Dynamics: Recursive-dynamic,

2004-2030, 5-year steps

Technological

Change: Autonomous energy

efficiency index (AEEI)

Data: GTAP7, IEA, et al.





Outline

(1) Introduction

(2) Methodology


(4) Conclusions

9





Scenario description

TIMES-PanEU ETS75 / REF C80 DEC_EU

ETS 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

10

NEWAGE ETS75 / REF C80 DEC_EU

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

CO2 emissions 75% 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

-





TIMES-PanEU results

● Net electricity generation in the EU-28

11

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Mo

dell

C80

DE

C_

EU

C80

DE

C_

EU

C80

DE

C_

EU

C80

DE

C_

EU

2010 2020 2030 2040 2050

Sh

are

of

ren

ew

ab

le e

lectr

icit

y g

en

era

tio

n o

f to

tal

gro

ss e

lectr

icit

y c

on

su

mp

tio

n

Net

ele

ctr

icit

y s

up

ply

[T

Wh

]

Electricity storage(excl. pump storage)Electricity ImportsDesertecNet Imports w/oDesertecOthers / Waste non-ren.

Other Renewables

Biomass / Waste ren.

Solar

Wind offshore

Wind onshore

Hydro (incl. pumpstorage)Nuclear

Gas CCS

Gas w/o CCS

Oil

Lignite CCS

Lignite w/o CCS

Coal CCS

Coal w/o CCS

Share of renewableenergies





TIMES-PanEU results (II)

● CO2 emissions and certificate prices

12

0

200

400

600

800

1000

1200

1400

1600

0

500

1000

1500

2000

2500

3000

3500

4000

Modell

C80

DE

C_E

U

C80

DE

C_E

U

C80

DE

C_E

U

C80

DE

C_E

U2010 2020 2030 2040 2050

Ce

rtific

ate

price

[€

2000/t

]

CO

2E

mis

sio

ns [M

t]

Int. Aviation

Transport

Agriculture

Commercial

Residential

Industry

Conversion

GHG price





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 the

reference case) serve as input for NEWAGE

13





NEWAGE results

● Macroeconomic impacts

14





Outline

(1) Introduction

(2) Methodology


(4) Conclusions

15





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. reference

case)

16



Linking TIMES-PanEU and NEWAGE: Energy and macroeconomic impacts of decentralization

trends in the European electricity sector

Robert Beestermöller, Markus Blesl, Ulrich Fahl

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

TIMES-CGE Workshop

66th Semi-annual ETSAP meeting

19th November 2014

UN City, Copenhagen, Denmark

Thank you for your attention!





Back-up

18





Outline

(1) Introduction (1) Rationale

(2) Motivation

(3) Scenario description

(2) Methods

(1) Data harmonisation

(2) Model structure & Linkages

(3) Substitution approach & structure (CES, Leontief)

(4) Revenue recycling (if applicable)

(3) Results

(1) Energy service demand impacts

(2) Economic impacts

(1) Jobs/employment

(2) GDP

(3) Welfare

(3) Additional insights & benefits of model linkages

(4) Conclusions

(1) Critical Messages

19




R. Beestermöller / M. Blesl 20

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-use

technologies

Electricity Grid, Biofuel and biomass trade

Time horizon 2010 - 2050

GHG: CO2, CH4, N2O, SF6 /Others pollutants: SO2, NOx, CO, NMVOC, PM2.5, PM10

TIMES-PanEU





TIMES-PanEU results (II)

● CHP electricity generation in the EU-28

21

0

500

1000

1500

2000

2500

3000

Mo

del

l

C8

0

DEC

_EU

C8

0

DEC

_EU

C8

0

DEC

_EU

C8

0

DEC

_EU

2010 2020 2030 2040 2050

CH

P e

lect

rici

ty g

en

era

tio

n[P

J]

SUP-GAS

COM-OTH

COM-RES

COM-GAS

COM-OIL

IND-OTH

IND-RES

IND-GAS

IND-OIL

IND-COAL

PUB-OTH

PUB-RES

PUB-GAS

PUB-OIL

PUB-COAL





TIMES-PanEU results (IV)

22





TIMES-PanEU results (V)

● Electricity prices (TIMES-PanEU output)

23





NEWAGE results (II)

● Sectoral impacts

24





NEWAGE results (III)

● GDP impacts

25





NEWAGE results (ETS75)

26





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

Modelling electricity generation in NEWAGE ● 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

27





Categories of Energy Models

28

Simulation Optimization Computational

General Equilibrium Econometric

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 regions

ii. Recursive dynamic or Perfect foresight

Integrated Assessment

Models Climate Models

Energy Models

Bottom-up models Top-down models

Attempt to link

model types

Economic models

NEWAGE

Hybrid modelling

Input Output





Circular flow of income

29

Supply

Demand

Demand

Import/Export

Demand

Supply

Demand Income

Purchases

Costs

Purchases

Revenues

Purchases

Costs

Demand

= Goods flows = Monetary flows

= Markets = Agents

Goods markets (Machinery, Electricity,

Services, ...)

Factor markets (Capital, Labor,

Resources)

Subsidies

Taxes Taxes

Transfers

Ta

xe

s

Ta

xe

s

Households Utility maximization

Firms Profit maximization

Foreign trade

Government





CES production functions in NEWAGE (KLEM-structure)

30

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





BRICS 11. Brazil 12. Russia 13. India 14. China 15. South Africa

NEWAGE mapping

18 countries + regions 1. Baden-Württemberg 2. Germany 3. Austria 4. France

5. Switzerland 6. Northern EU-28 7. Southern EU-28 8. Eastern EU-28

OECD (non-EU) 9. USA 10. Rest of OECD

Europe





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

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

NEWAGE mapping: 18 production sectors

No. Description Group

1 Coal

Energy

production

(5)

2 Natural gas

3 Crude oil

4 Petroleum

5 Electricity

6 Iron & Steel

Energy

intensive

industries

(6)

7 Non-ferrous metals

8 Non-metallic minerals

9 Paper, pulp & print

10 Chemicals

11 Food & Tobacco

12 Motor vehicles Other

manufacturing

(3) 13 Machinery

14 Rest of industry

15 Buildings

Rest of the

economy

(4)

16 Transport

17 Agriculture

18 Services





NEWAGE mapping

World regions Economic sectors

No. Abbr. Description Group No. Abbr. Description Group

1 BAW Baden-Württemberg

EU28

+ 1

1 COL Coal

Energy

production

2 DEU Germany (Rest) 2 GAS Natural gas

3 FRA France 3 CRU Crude oil

4 AUT Austria 4 OIL Petroleum

5 EUE Eastern EU-28 5 ELE Electricity

6 EUN Northern EU-28 6 IRS Iron & Steel

Energy

intensive

industries

7 EUS Southern EU-28 7 NFM Non-ferrous metals

8 SWZ Switzerland 8 NMM Non-metallic minerals

9 USA USA Other

OECD

9 PPP Paper, pulp & print

10 OEC Rest of OECD 10 CHM Chemicals

11 BRZ Brazil

BRICS

11 FOT Food & Tobacco

12 RUS Russia 12 MVH Motor vehicles Other

manu-

facturing

13 IND India 13 MAC Machinery

14 CHI China 14 ROI Rest of industry

15 RSA South Africa 15 BUI Buildings

Rest of the

economy

16 OPE Rest of OPEC

Other

16 TRN Transport

17 ARB Arabian World 17 AGR Agriculture

18 ROW Rest of the World 18 SER Services




R. Beestermöller / M. Blesl 34 /

23

NEWAGE-W 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 through a hybrid approach





23

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: GTAP6, 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)





23

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





23

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





23

Labor L

wmin

w1

LD1

LD0

LS

real

wage

L0=L1

ΔL

w2

Labor L

wmin

w1

LD1

LD0

LS

real

wage

L0=L1

ΔL

w2

Labor L

wmin

w1

LD1

LD0

LS

real

wage

L0=L1

ΔL

w2

wmin

w1

LD1

LD0

LS

real

wage

L0=L1

ΔL

w2

w1

LD1

LD0

LS

real

wage

L0=L1

ΔL

w2

LD1

LD0

LS

real

wage

L0=L1

ΔL

w2

LS

real

wage

L0=L1

ΔL

w2

real

wage

L0=L1

ΔL

w2

L0=L1

ΔL

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

r

r

rr

r

rrrr demandsupplyUR-1UR

P

wUR

P

wdemand-supplyUR-1

0,0,0

rrr

T

r

r

r

rrrr demandsupplyUR-1

P

w

P

wdemand-supplyUR-1

LSrationedLSrationed

LD

LS

wage

curve

L0=LS0

Labor LLS1

unemployment

L1

w1

real

wage

w0 LD

LS

wage

curve

L0=LS0

Labor LLS1

unemployment

L1

w1

real

wage

w0

LS

wage

curve

L0=LS0

Labor LLS1

unemployment

L1

w1

real

wage

w0

L0=LS0

Labor LLS1

unemployment

L1

w1

real

wage

w0

L0=LS0

Labor LLS1

unemployment

L1

w1

real

wage

w0

L0=LS0

Labor LLS1

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





23

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.

iej

iei

USKSKLKE

M

Y

KLEM

KLE

KLEM

KLEUSKSKLKKLE

i

KLEMi

ri

USK

riri

SKL

riri

K

ri

E

riri

E

ri

j

M

rijrijj

M

rij

ri

)(

;

1

1

1

,,,,,,,,,

,,,,,,

,

Documents

Linking TIMES-PanEU and NEWAGE...of renewable energy sources, which is supposed to play a key role in reducing GHG emissions Our paper analyses energy and macro-economic effects of