Energy and macro-economic effects of decentralisation trends in the European electricity sector

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 2014Linking 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 energyPrimary energy
Domestic
sources
Imports
Demands
Energyprices,Resourceavailability

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
Modell
C80
DEC_EU
C80
DEC_EU
C80
DEC_EU
C80
DEC_EU
2010 2020 2030 2040 2050
Shareofrenewableelectricitygenerationof
totalgrosselectricityconsumption
Netelectricitysupply[TWh]
Electricity storage
(excl. pump storage)
Electricity Imports
Desertec
Net Imports w/o
Desertec
Others / Waste non-ren.
Other Renewables
Biomass / Waste ren.
Solar
Wind offshore
Wind onshore
Hydro (incl. pump
storage)
Nuclear
Gas CCS
Gas w/o CCS
Oil
Lignite CCS
Lignite w/o CCS
Coal CCS
Coal w/o CCS
Share of renewable
energies

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
DEC_EU
C80
DEC_EU
C80
DEC_EU
C80
DEC_EU
2010 2020 2030 2040 2050
Certificateprice[€2000/t]
CO2Emissions[Mt]
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
Modell
C80
DEC_EU
C80
DEC_EU
C80
DEC_EU
C80
DEC_EU
2010 2020 2030 2040 2050
CHPelectricitygeneration[PJ]
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 CO2
CO2 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
TaxesTaxes
Transfers
TaxesTaxes
Households
Utility maximization
Firms
Profit maximization
Foreign trade
Government

CES production functions in NEWAGE (KLEM-structure)
30
Material
KL
Electricity
KLE
Gas CO2
CO2 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
TransportAgriculture
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

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

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)

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

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

Labor L
wmin
w1
LD
1
LD
0
LS
real
wage
L0
=L1
ΔL
w2
Labor L
wmin
w1
LD
1
LD
0
LS
real
wage
L0
=L1
ΔL
w2
Labor L
wmin
w1
LD
1
LD
0
LS
real
wage
L0
=L1
ΔL
w2
wmin
w1
LD
1
LD
0
LS
real
wage
L0
=L1
ΔL
w2
w1
LD
1
LD
0
LS
real
wage
L0
=L1
ΔL
w2
LD
1
LD
0
LS
real
wage
L0
=L1
ΔL
w2
LS
real
wage
L0
=L1
ΔL
w2
real
wage
L0
=L1
ΔL
w2
L0
=L1
ΔL
w2
w2
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
r
r
r
r
rrr demandsupplyUR-1UR
P
w
UR
P
w
demand-supplyUR-1
     0,0,0 











 rrr
T
r
r
r
r
rrr demandsupplyUR-1
P
w
P
w
demand-supplyUR-1
LS
rationed
LS
rationed
LD
LS
wage
curve
L0
=LS0
Labor L
LS1
unemployment
L1
w1
real
wage
w0 LD
LS
wage
curve
L0
=LS0
Labor L
LS1
unemployment
L1
w1
real
wage
w0
LS
wage
curve
L0
=LS0
Labor L
LS1
unemployment
L1
w1
real
wage
w0
L0
=LS0
Labor L
LS1
unemployment
L1
w1
real
wage
w0
L0
=LS0
Labor L
LS1
unemployment
L1
w1
real
wage
w0
L0
=LS0
Labor L
LS1
unemployment
L1
w1
real
wage
w0
Labor LLabor L
LS1
unemployment
L1
w1
real
wage
w0
LS1
unemployment
L1
w1
real
wage
w0
LS1
unemployment
L1
w1
real
wage
w0
w1
real
wage
w0

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
KLE
USKSKLKKLE
i
KLEM
i
ri
USK
riri
SKL
riri
K
ri
E
riri
E
ri
j
M
rijrij
j
M
rij
ri



































)(
;
1
1
1
,,,,,,,,,
,,,,,,
,









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