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Europaudvalget 2007-08 (2. samling)
KOM (2008) 0019
Offentligt

JEergy

Fraunhofer U)5titute

Systems and

Innovation Research

cQi7QflflcS

rjfVUP

ECOFYS

Economic anaVysis of reaching a 20%

share of renewable energy sources in

2020

Annex I to the final report:

Methodological aspects & database

for the scenarios of

RES

deployment

Mario Ragwitz Fraunhofer 1SF

Gustav Resch, Thomas Faber - EEG

August 2006

by order of the:

European Commission

DG Environment

ENV.C .2/SER/200510080r

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TaNe of Contents

A. 1.General methodology of the model Green-X 2

A. 2. Development of the cost-resource curve for RES-E, RES-

H and RES-T 2

A. 3. The data requirement 2

A. 4. Calculation of electricity, heat and biofuel generation

costs 2

A. 5. Assessment of the potentials for RES 2

A. 6. Assessment of & overview on the economic data for RES 2

A 7. Calculation of the dynamic cost-resource curve 2

A. 8 Data for the dynamic aspects 2

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A. i.Gerieral methodology of the model Green.X

The computer model

Green-X

is an independent sof1iare tool developed under

Microsoft Windows by EEG in the EC-funded project Grcen-X (5th FWP DO

Research, Contract N°: ENG2-CT-2002-00607).' Two major variants of the Gnen-X

model are currently available:

An extended variant with respect to the intra-sectoral coverage was developed,

which includes besides RI3S-E endogenous modelling of

all conventional

power

generation options

of the

electricity sector (mel. interconnections and according

restrictions). Geographically this variant covers solely the EU-IS. It allows a

comparative, quantitative analysis of interactions between RES-E, conventional

electricity and CHP generation, demand-side activities and 01-JO-reduction in the

electricity sector, both within the ELI-I 5 as a whole, as well as for individual member

states.

An extended variant with regard to the geographical and scetoral coverage for

RES. It covers besides the EU-IS all new member states (EU-IO) as well as the

EU candidate countries Bulgaria, Rornania and Croatia. It enables a comparative

and quantitative analysis of the ftmture deployment of RES in all energy sectors (i.e.

electricity, (grid-connected and non-grid) heat and transport) based on applied energy

policy strategies in a dynamic context. In this context, the impact of the conventional

supply portfolio within each sector is described by exogenous forecasts of reference

energy prices and corresponding C02 emission-factors etc., all set on countLy level.

For the purpose of this study, the modelling approach has been extended by the concept

of a cross-sectoral quota: The key approach in the ca[cvlations is that the European

energy market optimizes the additional generation costs for R.ES against the background

of a RES target which can be set on a yearly base up to the

year

2020. This overall

optimization is modelled by comparing the difference between RES generation costs and

conventional reference prices across all sectors (heat, electricity and biofuels), all

technologies and all countries. Results are presented in terms of additional costs, that is,

the total costs of generation per energy output minus the refeience cost of energy

production per unit of energy output. To avoid underestimation of the resulting cost with

regard to an enhanced RES-deploynient, negative additional cost are not counted ic. set

to zero. The optimisation is conducted across all three sectors (RES-E, RES-H arid RES-

T). As biomass may play a role in all sectors, the allocation of hiomass resources is a key

issue. Consequently the overall optimization across sectors includes an integrated

optimization of the distribution of bioniass aniong the

sectors.

For more clet&Is see:

h.p:fJjygreen-y..at

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Within the model

Greeii-X,

the most important

RES-E

(e.g. biogas, biomass, biowaste,

wind on- & offshore, hydropower large- & small-scale, solar thermal electricity,

photovoltaics, tidal & wave energy, geothermal electricity), RES-H technologies (e.g.

biomass - subdivided into log wood, wood chips, pellets, district heating - , geothermal

and so]ar heat) and RES-T options (e.g. traditional biofuels such as biodiesel and

bioethanol, advanced biofuels as well as the impact of biofuel imports) are described foi

each investigated countiy by means of

dynamic cost-resource

curves.

Dynamic cost

curves are chaiacterised by the fact that the costs as well as the potential for electricity

generation / demand reduction can change each year. The magnitude of these changes is

given endogenously in the model, i.e. the difference in the values compared to the

previous year depends on the outcome of this year and the (policy) framework conditions

set for the simulation year.

In most analysis conducted with the model

Greeji-X

an economic assessment takes Place

on the basis of the dynamic cost curves derived and scenario-specific conditions like

selected policy strategies, investor and consumer behaviour as well as primary energy

and demand forecasts. Within this step, a transition takes place from generation and

saving

costs

to bids, offers and switch

prices.

It is worth mentioning that time policy

setting influences the effective supporE e.g. the guaranteed duration and the stability of

the planning horizon or the kind of policy instrument to be applied.

Policies that can be selected are the most important price-driven strategies (feed-in

tariffs, tax incentives, investment subsidies, subsidies on fuel input) and demand-driven

strategies (quota obligations based

tradable green certificates

(including

international

trade),

tendering schemes). All the instruments can be applied to all RES technologies

(and conventional options within the EU-i

separately for the various energy sectors. In

addition, general taxes can be adjusted and the effects simulated. These include energy

taxes

(to

he applied

all primaiy energy carriers as well

to electricity and heat) and

environmental taxes on CO-emission as well

as policies supporting

demand-side

measures. As

Grceii-X

is a dynamic simulation tool, the user has the possibility to

eJiange policy and parameler settings within a simulation run (i.e. by yeal). Furthermore,

each instrument can

set

for each country individually.

Note

that in the least-cost analysis conducted in this study a policy neutral modelling

approach has been chosen. This nicans that no specific

support policies are

assumed,

Modelling results

are

derived on a yearly basis by deterniining the equilibrium level of

e.g. tradable

green

supply

and demand

within

each

considered market segment

certificate market (TOC, both national and international), electricity powem' market and

tradable emissions allowance market. This means that the supply foi' the different

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technologies is summed up within each market and the point of equilibrium varies with

the demand calculated.

broad

set of results with respect to RES can be gained on country and technology-

level:

total energy output by sector (RES-E, RES-H, RBS-T), by country, by technology

total installed capacity by sector (RES-E, RES-H, RES-T), by country, by technology

share on gross domestic electricity / heat / transport fuel production or demand3

average generation costs by sector (RES-E, RES-H, RES-T1, by country, by

technology

import / export balance for the power sector (only for EU-15 countries),

impact of simulated energy policy instruments on supply portfolio, generation costs,

etc.

impact of selected energy policy instruments on total costs and benefits to the society

(consumer) - premium Price clue to RES-E / RES-l-l

RES-T strategy.

The latter option is not used in the RES2O2O study.

Table 1: Main characteristics ol the Green-X model

The most important RES-E included

The most important RES-FI included

The most important RES-T included

Geographical aggregation

Encluded policies:

Price-driven strategies

(not used in the RES2O2O study)

thcluded policies:

Demand-driven strategies:

(not used in the RES2O2O siucly)

l3iogas, bioniass, biowaste, wind on & -

offshore, hydropower large & small-scale,

solar thermal electricity, photovoltaics,

tidal & wave energy, geothermal electricity

Biornass, geothermal, solar thennal, heat

PLL11)S

l3iodiesel, bioetjianol, Advanced

bioethanol, BtL

Country level, EU -25

feed-in tariffs, tax incentives, investment

subsidies, subsidies on fuel input

Quota obligations based on tradable green

certificates, tendering schenies

A. 2. Development of the cost-resource curve for

RES-, RS-H and RES-T

A (static) cost-resource curve shows the correlation bei:wecn electricity (respectively heat

and hiofuels) costs per unit and the cumulative amount of electricity (respectively heat

and biofuels) production froni one specific technology in one country per annum. 1-lence,

the development of a cost-resource curve implies knowledge of the Iwo items explained

above:

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costs for electricity (or heat) per unit;

total quantity of electricity (or heat) that can be generated per annum at certain

cost levels, The cumulated sum of these amounts is equal to the totally available

potential of a certain technology.

The procedure for deriving the dynamic cost-resource curves is exemplarily depicted in

Figure 1 for the electricity sector. The starting point is the

input-database supply

for

the

first year under investigation.

The database contains information about already existing power plants (at the end of

2001) as well as possible new plants. The outputs of the database are cost-resow'ce

curves for each category containing information with respect to actual generation costs

and the possible potential for electricity generation for the year under investigation.

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Figure 1 Overview of creatinq dynamic cost-resource curves f-or electricity

geration

At the end of the simulation i-wi for the year n-i , the input database for the following year

will be cicated by adapting the input database for the year a.

This adapted input-database serves as a starting point for the dynamic cost-resource curve

development for the next subsequent year.

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Note that in the RES2O2O study an overafl optimisation is made across afl sectors.

Therewith one overall cost-resource curve is specified that includes all RES-E, -1-I and -T

options.

A 3.The data requirement

Jnfonnation for the development of dynamic cost-resource curves must be available on

different levels. In general, three levels of data are required in the model Grcen-X,

namely: Country-, technology and band-level. The data requirements at each level will be

briefly outlined below.

The interaction of countty-specific, technology-specific and band-specific data is

indicated in Figure 2 below.

Tcy1e

BaMLl

oh1*ç'.

£2

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Figure 2 Overview of different levels of supply-side data

Country-specific

data

is characterised by the fact that these values and l)ara!lleters are

valid for all considered technologies in the specific region. Of course, variations occur in

a dynamic context - i.e. from year to year. Country-specific data is swnmarised in Table

2.Despite the fact that the parameters are given exogenously, dynamic effects can be

expected because values are available

time-series from 2002 to 2020 in the database.

Technology-specific data is valid and equivalent for all investigated regions. Of course,

changes occur over time and data refers only to a certain technology, see Table 2.

Band specific data are introduced as it is assumed that most of the parameters (data) ai'e

not constant within a region and technology, respectively. I.e. they may vary depending

on the sub-technologies (e.g. combined cycle or steam turbines), energy efficiency

standards, the fuel

input,

the location of the plant, oi the full-load hours, Therefore, it is

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necessaty to create several bands within each RES-E & RES-H categoiy, Bands are

characterised by the same economic, technical, social and geographical conditions.2

In the practical implementation, the supply-side database consists of two sub-bases,

namely:

Database: Existing plants

Database: New

plants

Aim of the input-database existing plants' is to provide generation costs for electricity or

heat, respectively, as well as the potential for this generation from bands (plant) which

are already

operation in the investigated year a. Possible new generation options of the

year a are described in the database new plants'. The required band-specific information

is sunitnarised for both categories in Table 3.

Equivalent to the conditions at the other levels, parameters can differ over time.

Table 2: Summary of supply side country-specific data

Pam meter

Country level

Population, land size, GDP (per capita)

Fuel prices for renewable primary energy carriers

Conventional electricity / heat prices (for each

Sector)

Specific (iHO-emission by energy carrier

Grid extension constraints

Mai'ker. transparency

Investor behaviour

interest rate

Willingness to accept new plants

Technology level

Lifespan of technology

________

Payback time

Dynamic cost development by technology

(i.e. global projections with regard to development

and technological leanting)

Growth ale industry

_________________

Grid

CStCnSiOi)

cOflsti'aiflts

Market_transparency

Investor heliaviotir / interest rate

Willingness to accept new plants

Aim

To receive comparative results among the countries

To calculate electricity generation COStS

Reference prices - To calculate additional costs for society

due to the promotion of RlS-E & RlS-l4

To derive additional generation costs due the

C02-

constraints and the coflsideration of cxternali tics

for clyntiiii ic palameter asscssmcnl

for dynamic parameter assessment

For dynamic parameter assessment

Aim

__________

To derive date oidecornmissioning of the plant

To derive generation costs cia new

plant

l'o derive investment costs for the year ,i4-l

For dynamic parameter assessment

For dynamic paruneter_assessment

For dynamic paranicter assessnicnt

For cLymiamnic parameter a.sscssniciil

Ior clymuimniic pcmraiueter assessment

Same fuel inputs, sub-technologies, energy efficiency standards, full-load hours, etc.

Investor behaviour depends on various factoms such as e.g. support scheme, planning horizon, technology.

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Table 3 Summary

bandspecific data

Parameter

Technology parameter

Construction year

Full-load hours elecir.

Full-load hours heat

(in case ofClIP / district heat)

Efficiency electricity generation

___________________________________

Valid for existilig

(Ex) / new (New)

pIats

(iii) i

Aim

output (Out) data

lIlflLJl

Ex and New

__________________

Ex and New

Efficiency heat geneottion

Ex and New

______________________________

Fuel category

Ex and New

To estimate date ofdccommssioning4

To calculate electricity generation costs

To calculate generation costs

(for clectricity and heat)

To calculate generation costs and

emissions; (his is a dynamic parameter

which changes for new plants

To calculate generation costs and

emissions; (his is a dynamic parameter

which changes for new plants

To calculate generation costs and

emissions; link with fuel p11cc (country

database), mark if fuel switch possible

Table 4 provides an overview of the cost and potential parameters used to specify the

cost/potential curves in the

Green-X

model.

Date ofdecoiniaissioning for a specific plant depends on the lifespan of (lie technology. If the year of

decommisaJoning is reached, the plant will be deleted from (he databaso.

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Table 4 Parameters used to specify costs and potentials

Potential parameter

Mid-term potential of electricity New

generation ___________________

In Mid-term potential electricity generation

Dynamic restriction

new pLants

Potential of electricity

generation yearn:

Cost parameter

New

lii

Link with dynamic restriction calculation

tool

Ex and New Out Value represents the maximum

electricity generation of the band in year

To calculate generation costs; this is

dynamic parameter, i.e. inveslmen costs

are adapted year by year

Operation nd maintenance costs Ex and Nw In

To calculate generation costs; this is a

dynamic

parameter,

i.e.

an adaptation of

this parameter

takes

place year by year

_ (link to investment costs)

_____________________________________

_________________ ____________

Fuel category

Ex and New In

To calculate generation costs and

emissions; link with fUel price

(country database)

______________________________

_____________

Payback time

Ex and New

Parameter set at the technology level. hut

information necessary on band level for

various calculations

Interest rate

New in Parameter set at the coirntrv and techn

level but information necessary on band

level for various calculations

Short-term marginal generation Ex Out

Generation costs for existing pLants,

costs

important input for economic assessment

____________________ ______________ ____________ ______

Long-term marginal generation l3x Out

calculate profit of the invOslor

costs

(ycar_orcomistnictiomm)

__________________ _____________________

_____________________________________________

Long-term marginal generation New Out

Generation costs for new plants;

costs

import aol input for economic assessment

(year oiconstruetion)

Investment costs

New5 In

A. 4. Ca!cuatIon of eiectrcity, heat and biofuel

generation costs

For calculating the generation costs a distinction must be made between already installed

capacities and potentially new plants. For existing plants, only the running costs (short-

term marginal costs) are ielevant for the economic decision whether the plant should be

used for electricity (or heat) generation or not, while for new capacities, the long-term

marginal costs are important.

A fuither distinction has

been

applied in the following: Generation costs are explained

separately for pure power

heat generation options, CHP and district heating.

Note: Invest meni costs for

existing

plants must also

available for their date ofconstrmicmioni.

Note: Information must also be available for existing plant for their year of construction.

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Existing plants

Yearly running costs consist of two parts: fuel costs and operation & maintenance

(O&M) costs. Fuel costs depend on the fuel price of the piiinaiy energy carrier and the

efficiency. O&M costs are set as annual expenditures.

Apart from all kinds of biornass (biogas, solid biomass, sewage and landfill gas),

renewables have zero fuel costs, so running costs are determined by operation &

maintenance costs only.

In the case of simultaneous electricity and heat generation, electricity generation costs are

calculated by considering the revenues gained from the selling of the heat.

New plants

Generation costs pure power (or heat) generation

The calculation of the generation costs of electricity (respectively heat of new plants

consists of two parts, variable costs and fixed costs. In more detail, the generation costs

are given by:

Fixed costs occur independently whcther the plant generates electricity (respectively

heat) or not. These costs are determined by investment costs (1) and the capital recovery

factor (CRF).

Investment Costs and technological improvements

The invesfment costs differ by technology and energy source. As most RES.-E

technologies (with the exception of(targescale) hydropower) are still not mature,

investment costs decrease over time. This evolvement is taken into consideration in the

toolbox GriX, i.e. investment costs are adapted yearly.7

in principle, the model is prepared to include two different approaches on technology

level: (I) standard cost forecasts or (ii) endogenous tcchnologica.l learning (local vs.

global). I-lance, default settings for RES-E & RES-l-l technologies are applied as

indicated in Table 5.

The 'yearly' dctcrL motion 01 he mvcstmncnt

costs represents an

important inpm to the data-tables

described in the previous section, In more detail,

the

tbllowing paramaclel' must

derived

lhr each con n(m

and technology

accorclimig

to the

given

situation br the year n-I arid the year

ii:

quantitative values

for

investment

costs

over

tinie.

o quantilative values

for the development

of the efficiency over time.

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Table 5: Overview of the methodology to dynamicaJy derive investment costs

by technology8

Dynamic cost development

______________________________________

___

________________________________

Biomass electricity (heat, CUP)

l3iofuels for transport

__________________________________

Geothermal electricity (hst, CHP)

Geothermal heat non-gtid

Small scale hydropower (<10 MW)

Large scale hydropower (>10 MW)

Landfilgas

Sewage g

Photovoltaics

Solar thermal e1ectrt'

Solar thermal heat

Tidal energy

Wave energy

Wind on-shore

Wind off-shore

___________

_______________

Methodology to derive investment costs year

(default settings)

learning curve approach

learning curve approach & forecast based on expert

judgpent (depending on technology)

forecast based on expert judgement & learning cureve

approach

learning curve approach

learning curve pproach

forecast based on expert judgent

forecast based on expert judgement

learg curve approach

learning curve pproach

learning curve approach

forecast based on expert udgenient

foi'ecast based on expjudgement

learning curve approach

leartng curve roach

_____________

________________

__________________

_________________

______

_________

Capital recovery factor CRE

The CRF allows investment costs incurred in the construction phase of a J)lallt to be

discowfled. The amount. depends on the interest late and the payback time of the plant.

hi general,

expeuielice

CurVeS

(ICSCLIbC hOw

costs declinc with cumulative

procliictwn.

In many cases

empirical analysis

have proven

!liat costs decline by a constant

percentage

with each doubting of the units

produced or installed, respectively. Iii general, an experience curve is expressed as fohiows:

C10,

where:

Costs per wilt as a function of output

Co Costs of the tirst unit produced or installed

GUM Cumulative production over time

b Experience hides

Thereby, the

cxperieiice index

(h,) is used to describe the icttitive

Cost

reduction i.e.

(i-2°.J

-- for cacti

doubling of

the cumulative

production. The value

called the

progress ratio tPIQ

of cost reduction.

Progress ratioS or their pendant, the

learning rifles

(i/l)

- Ic.

1.R'l.Pl?

arc used

to express the progress of

cost

reduction

for

di1Thrcn technologies. lhcncc_ a progress

ratio

0185%

means

that costs per unit

are

reduced by

for each time

cum

1st ive production is doubled

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For the standard calculation of generation costs these factoi's are set for all technologies

as follows:

• payback time (PT) of all plants: 15 years

• interest rate (z equals

6.5%

In the toolbox

Greeii-X

different interest rates are used. The interest rate depends on

stakeholder behaviour and is a function of

guaranteed political planning hoi'izon

• promotion scheme

(not

used in the RES2O2O study)

• technology

iuaiket sector (i.e. private, residential, tertiary sector)

kind of investor.

Note, as the generation costs are catculated per energy output, the fixed Costs must also

be related to the generation of energy. Hence) the fixed costs pci- unit output are lower if

the operation time of the plant - characterised by the thU load-hours - is high. In general,

no taxes are included in the various cost-components.

Generation costs

CHP

Deriving the generation costs for Ci-IP plants is similar to the calculation for plants only

producing electricity. Beside the short-terni marginal costs, Le. the variable costs, fixed

costs must be considered for new plants.

course, equivalent to the case for existing

plants, variable Costs differ between CE-TP and conventional electricity plants, as the

revenue from purchasing the heat power must be considered in the first case.

Generation costs

biofue/s

Biofuel costs calculations take into account the current entire biofliel production chain

until the distribution at the fuelling station. The production chains for biofuels include the

cultivation and hai-vesting ofbiomass feedstock, transportation to the conversion plant,

biofu els conveis ion and distribution.

5..

Assessment of the potentia!s for RES

The Gra'n-X model differentiates between different types of potentials. Following types

are of ni ai n inipo rtance:

Realisable potential:

The realisable potential represents the maximal acliic-vable

potential assuming that

all

existing baniers

can

be overcome and all driving forces

are active. Thereby, general parameters as e.g. market growlh rates, planning

constraints al-c taken into account. rt is iml)ortant to mention that this potential teim

must be seen in a dynamic context •--

i.e.

the realisable potential has to refer to a

certain year;

Mid-term potential:

The mid-tei-m potential as indicated in Figure 3 is equal to the

i-eat/sable potent/a! in the yeai- 2020.

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Theoretical potential

Mid-term

potential

upt02020

•0

carriers

Dynamic

potential

techno!ogy,

society)

Drivers

(policy,

society)

(market,

industry,

additional

realisable

potential

up to 2020

1995 2000 2005 2010 2015 2020

Figure 3 Methodology for the definition of potentials

Below, values are presented for the achieved potential for 2004 and the future potentials for

2020 for renewable electilcity, heat and transport fuels.

RES such as hydropower or wit-id energy are energy sources characterised by a natural

volatility. Therefore, in order to provide accurate forecasts of the future development of

RES-E, historical data for RES is translated into generation potentials - the

achieved

potential

the end of 2004. This data was derived in a comprehensive data-collection -

based on (Eui-ostat, 2006), (TEA, 2006) and statistical information gained on national level.

in addition,fiuiure potentials - the

additional reaii'able mid-term potentials

up to 2020 -

were assessed taking into account the country-specific situation as well as overall

real isation constraints.

We show in the following the sector specific generation potentials of the different RES

technologies in the sectors electricity, heat and transport. As the biomass potential is

endogenously allocated to the sector by the model, it can not be allocated to the sectors at

this stage. At the end of this

section

we give an overview of the lrimary hiotnass

Poteiltials used in this analysis.

RES-E potentials

Table 6 provides an overview of tue already achieved potential

(at the

end of 2004) and the

additional i-ca] isable mid-teini potential (up to 2020) for differeal RES-E options available

in EU countries separated in EU-IS and EU-] 0. In total EU-I 5 the already achieved

potential for RES-E equals 441 TW]i, wheteas the additional mid-term potential (excluding

biomass options / biogas solid biomass and biowastci amounts

696 TWh. Corresponding

figures for the EU-JO are I 89 TWh for the achieved potential and 37.3 TWIt for the

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additional

mid-term potential (excluding biomass options

biogas solid biomass

and

biowaste).

Table 6 Overview on electricity generation potentials for RES-E in the EU

AT BE DX Fl FR DE CR IE IT LU NL PT ES SE UK EUI5

Electricitygeneration

potentials(EU15)

ileedotentialk2004

l3iogas OWli 218 121 238 6067735115 2 115 1244 7 0

12247

_JQ Jfl

(Solid)

13ioniss

OWl: 2440 314 1228 9134 1764 2972 0 0 522 0 1036 1234 '1442 3578 1613 30748

Biowaslo OWl: 43

316 732

225 2442 2027

23 t351

0 0

518

651

1660

468 003

11464

rLjlelcclrit OWh 7 0 0 0 00 0 55490 0

0 0 0 5682

lIvdro1arscaIe OWh 33587 137 0 p803 60220 576 3280 703 35565 0 W697 29681 68856 4562 213374

Ilydro small-scale OWl: 4138 192 31 1178 6219 7361 163 06 8467 100 1 697 '1710 3224

515

37369

Pliotovollaics GWh

1 I 0 8 621 1 0 17 8 32 34 0

758

Solar thermal electricity OWl 1) 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0

Tidc& Wave _________ OWl: 0

0 0 0 0 0 0 0 0 o 0 0 0 0 6

Wind onshore GWh 1273 95 5943 1711 973 29516 1023 793 2649 73 2383 1171 12592 980 2027 67695

Wind offshore OWh 0 0 14.12 0 0 0 11 80 0 0

53 0 0 21

290 1870

RES-E TOTAL GWI 41921 275

9631

24278 12924 53370 4630 1808 55685 231 5287 14422 58786 77273 14637 441205

Additional resIisable !tial (up to 2020)

Geo1llcrlt1aIrici1 OWl U 00 0 152 0 2210 22 00 159 95 0 0 2399

Ihydro large-scale OWl: 59 4 0 Hl0 5 2974135695 10527

0 0 3353

15110 W63 L70

39312

Hydro small-scale (iWli 5302 98

0494

4723 2228 208 109 7I 0 81076 2630 07 193

20953

Photovoltaics OWI: 972 531 497 000 5902 4840 1043 310 3691 5 1173 955 5101 1287 4321 31220

Solar thernial electricity OWL: 0 0

-_Q __9 .2

0 0 29085

__Q no j2Q_

Tide & Wave OWl: 0 ISO 25112 545 13152 7725 4007 3930 3220 0 1026 7404 13229 3006 58295 119377

Wind nshorc OWl: 70% 4123 2756 7679 55436 23803 7814 959 25977 147 3169 5636 20707 8932 26439 198479

Wind offshore

OWli 0 048 9181 4105 39970

76342 2635 3502 2396 0 19789 6599 144.14 3544

66308

253611

OtES-E TOTAL (cxci. flM) OWli 11447 8603 15216 15534 011111 110412 19917 9906 57428 152 25164 27835 82531 29743 156785 695790

RES-E

- CV Cl EE [ILl LA LI

MT Pt, 5K 51 EUIO lOG 110

Jtleetrtcitygeneration

potentials (EUIQ)

Achieved potential

(2004)

Biomis

(Sohid hiioinass

lOiivSlC

glicrmal cIecriciIy

Flydro large-scale

Hvdr small-scale

Photovoilaics

Sohrllicrnialclcclricity -

TV/I:

TWh

TW1:

TWI:

TV/I:

L'Wh

Tide & Wave

Wind onhre

Wind olTslioic

________

TWh

TW6

TWI,

RES-ETOTAL

mvi

Geothermal

clccLi_

Additional realisable potential

TV/li

h1>1:jr e-sealc

TWl:

TWa

llvilro_small-scale

TV/I:

PhtovoItnics

________

Solar thermal elccIrieii

Tide & Wave

WilidoilSliorc

1WI:

2084

6312

362

610

211

239

(127 4469

271

1158

8349

(up to 2020)

610

214

739

627

271

158

'1489

5309

I'll

_____________________________

____________________________

401 2213

481

736

999

15670 135 5197

702

7373

3431 U277 3419

1199

32 23310

3303

008

62053 743 15426

117

171

112

1614

254

3113

3706 226

926

1090

672

224

45 Jj

4582

7067 SOt

156

109

500

767

3511

2622 401

673

300

115

1051 158

275

0 (1

1223

528

303

3366 802

1112

510

1246 1190 1232 1257

557

110

8482

299

19491 734 6693

ill

301

211

2451

3693 133

151

1970

2839 2473 2923

390 3358

1562 5271

31292

275 35116

___________

_________

1091)

1190

2173

1176

158

231

110

211

390

5011

356

299

5271

767

500

1223

1216

311

2839

528

1232

301

2923

201

257

148

910

1112

6.162

2-151

13358

557

1562

Wind offshore

EES-E TOTAL{xcI. ISM)

iWh

TV/h

TWI:

7067801

5197

21122 '101

673

1651 15$

375

0 0

3366 202

510

19491 131 0693

3693

'133

151

37292

13596

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RES-H potentials

Table 7 shows the achieved potential in 2004 and the additional RES heat generation

potentials (excluding biornass) for 2020 at member state Level (EU-15 and EU-b &

Bulgaria, Romania. The already achieved potential in 2004 amounts to 40.9 Mtoe for the

EU-I

and 8.4 Mtoe for the EU-l0; whereas the additional potential until 2020 totals 63

Mtoe for the EU-IS and 7 Mtoe for the EU-I 0 (excluding biomass).

Table 7: Overview on heat generation potentials for RES-H in the EU

AT lIE DiC FE FR L)E OR JE IT UI NL PT ES

RES-H

I-Teat

generation

UI( EUI5

lotential(EUI5)

________

-_________________________

Achieved potential

(20041

___________________ _________

2.118 461 915 5319 9442 5142 920 191 2393 1$ 382

2480

3453 5085 703 39339

___________________________

81cc

Go0icrmI hcal (CIW&

dO.) kcc 9

3 0 113 7 13 1

160

0 0 9 8 23 2 376

1lcl pcnps 1008 85 7 46 77 86 0 0 0 0 0 263 1 625

Selorcollcclors kcoc 87 2 10 0 29 221 128 0 18 0 IS 6

54 6

25 600

RES-H TOTAL 81cc 2608 492 940 5306 9660 5469 061 192 2592 6 428 2494 3515 5377 731 40940

Additional

realisable potential

(up

2020) ______________________________________

Gcciltcriccccl hcai (CI1P&d.l.)

_to.o. ...J.

.........j!!T. __......j

_.1

... ...........^!.

_..

s ___j

2189

I4cnI

JiLiIIp

2222

27683

SoIarcolIccIrs 81cc 576 826 673 662 5882 6403 764 310 0033 37 1268 854 3323 803 4799 33214

RES-IL TOTAL 8(00 1327 2005 1486 1152 10165 4246 1270 622 11222 102 2515 1144 4668 1510 9653 63087

CV CZ. EE 8181 L.A Li' MT FE. SX

RES-H-Heatgencration , .

EVID PG RO

potential(EUIO) ,i1I.

576

_AcliievcdyoteiitiQfl

81cc

(ico9icruciI Ocal (CO1P & clii.)

]1ct cincpo

Sc1arcoIlcors

81cc

ktec

8cc

8108

______

______________

____

____________________________

___

793

492

564

055

1Q55

2865

3658

265

430

8033

285

709

3647

3050

755

= = =

RES-IL

TOTAL.

603

.194

578

163

164

367

449

339

0383

376

3283

33.19

7008

711

Gccicnlic1lIC141411.7

Sclcrcolicctoi

51)

118

446

129

521

107

1771

3601

216

507

107.

(26

316

RIO

121

361

599

1861

H192

1185

2376

k1o

81cc

'110

883

RES-FI 'rOTAL

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RES-T potentials

Table

8 provides an overview on the achieved biofuel potentials on countiy-leveL The

achieved RES potentials for 2004 showed 1930 ktoe for the EU15, and 171 ktoe for the

EU-l0.

Table 8

Overview on biofuel potentials (RES-T) in the EU

AT BE

GR IE IT LU

SE UK

EU 15

RES-T-Biofuelpotential

(EUI5)

Achievcl potential (20041

I3iofLjeI

kwo 51 0

-J

0 41

EULO

0) '

362

975

0 0

285

37 5

L930

LA tT MT PL

5K SI

PG RO

RES-T- Biofuel potential

(EUIO)

Athivepotentialjul04i ____________________________________________

mofuel koc 0 0 I 5 6 0 24 23 0 171 0 0

Primary biomass potentials

A crucial

input to

the model is given

the primary potentials olsolid biomass. These

were determined in this project based on the analysis of the following sectors:

Agricultural l)rod1cts

Agricultural residues

Forestry products

Forestry residues

Biodegradable waste

Forestry impOrtS

In the following 1'ab1e 9 gives an overview on the potentials used in this Project.

Thereby for agricultural products it was assumed that I

of the arabic land will he used

for energy crops. For the total area attributed to energy crops a pre-allocation to the

individual crops was done as indicated in the table. This implies already a cerlain

predetermination of the future conversion lechnologies (e.g. lOt versus generation

hiofuels).

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Table 9 Overview on primary biomass potentials in the EU

Potentials (in terms of

orimarv enerov)

2010

205

Mto

ItU

API

rape & sunflower

4.6

4.7

3.4

AP.2 maize

wheat

(Oom)

143 145 02

AP3 -maize wheat (whole plant)

0.0

11.1

22.2

AP4 SRC willpw

AP5-miscanthus

1.3

3.3

5.2

AP6 swi(ch grass

65 11 2

AP7

sweet sorghum

1.4

4.0

2.7

ARI straw

21 6

227

239

AR2

- other

agricultural residues

2.3

2.4

2.6

FP1 forestry products (cfirreit use (wood chips log Wood))

27 9

279 279

FP2 -forestry products (complementary fellings (moderate))

8.2

8.7

9.1

12,4 .. .13.0

(exphiv))..'

137

FRI

black liquor

14.5

13.6

15.2

FR2 forestry residues (current use)

143

14 3

FR3 -forestry residues (additional)

2.3

2.5

Fl4 demolition Wood industrial residue&

FR5 -addilionat wood processing residues (sawmill, bark)

4.8

5.0

5.3

:25

.-39

8W1

biodegradable fraction of municipal vaste

11.8

13.2

14.8

Agricultural products

51.0

46.3

Agricultural residues

23.9

25.2

26.5

46 6

42.0

Forestry residues

43.4

44.9

Biodegradablewaste

132

118

148

rorestry imports 2 5 3 1 39

Solid biomass

TOTAL

153.5

180.7

201.8

Solid biornass Primary potentials &

corresponding fuel cost

2005

Mth

2020

2.2

33.3

7.1

15 9

5.3

252

2.7

279

9.6

16.0

143

2.7

5.6

16.7

75.8

27.9

52 0

46.4

167

4.8

223.8

A. 6.Assessment of & overview on the economic data

for RES

Assessment of economic data for RES'-E & RES-H

(electricity and grid-connected heat sector)

The assessment of the economic parameter and accompanying technical specifications of

for the various RES-E technologies comprises a COmprCbCUSiVC lilot-ature survey and an

expert consultation. With respect to existing plant, representing the already achieved

poteutia] at the end of 2001, also project specific inforniation is taken into account.

References of major relevance are discussed below.

A set of studies is listed which provide a comprehensive survey on RES-E technologies,

t]iereby including detailed economic and Eechtiical data with respect to most common

technologies. Namely these are, listed in chronological order: (DTI/ETSU, 1999)

(DLR/WI/ZSW/IWR/Forum, 1999), (Neubarth et al., 2002), (Haas et al., 2001), (Resch

et al., 2001), (Nowak et at., 2002), (Kaltschmitt et at., 2003), (BMU, 2004).

References with a focus on selected technologies are listed in the following by

RBS-E category:

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• l3iogas and Biomass: (Fischer et aL, 2002), (Enquete, 2002), (EUBIONET, 2003)

• Geothermal energy: (EMU, 2002)

• Hydropower: (Lorenzoni, 2001)

• Photovoltaics: (Aiserna, 2003), (Schètffer et aL, 2004)

Solar thermal electricity: (Quaschning, Ortrnann , 2003)

• Wind energy: (Greenpeace, 2001), (Neii et aL, 2003), (BTM, 1999-2003),

(Beurskens, Noord, 2003)

• Tidal and wave energy: (Thorpe, 1999), (DTJ/ETSU, 2001), (Michael, 2003)

Assessment of economic data for RES-l-I (non grid)

The assessment of the economic parameter and accompanying technical specifications of

for the various RES-H technologies comprises a comprehensive literature survey and an

expert consultation. in particular the following sources were consulted for the techno-

economic assessment:

Invert (2005)

Ja]rbuch Erneuerbare Energien (2004)

I3STIF (2003)

Kaltschmitt et al. (2003)

DLRIWJ/ZSW/IWR/Forunr (1999)

BMU(2004)

Assessment of economic data for

RES-T

(Iio fuels)

The assessment for potential and cost figures foi' biofliels was based on a comprehensive

literature review and experts conversations among the biofüels industry members in

Europe. For the agricultural and biofuels techno-economic assessment following sources

were used and consulted:

CONCAWE (2003), Well-to-wheeF analysis of future automotive fuels and powertrains

in the European Context, Well Tank Report, Brussels, 2003. Available at:

http://ies.j rc.cec.eu. mt/Down load/eh

Energy Rcsearch Centre of the Netherlands ECN (2003). An overview of hiofuel

technologies, markets arid policies in Europe 2003, Available at:

hftp://www.ecn.rul/docs/library/reportf2003/c03008.pdf

ESTO, IPTS, (2003). Trends in vehicle and fuel technologies: Scenarios for Future

Trends" Ed. Luc Pelknians (VITO), Panayotis Christidis, Jgnacio Hidalgo, Antonio

Soria. Report EUR 20748 EN, 2004. Available at;

bttp:/Iwww.jic.cs

EST JPTS, (2003). Biofuel lroduction potential of EU-candidate countries - Final

Report, EUR 20835, 2003, Available at:

littp://www.jrc.es/home/publications/publication.cfm?pub= 11 20;

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European Commission, DO Energy and Transport (2003), European Energy and

Transport Trends to 2030, Januay 2003 Available at

http://europa.eu. int/comin/dgs/energy_ti'ansportffigurestti'ends2O3 0/index_en.htin

European Commission (2003), Directive 2003/30/EC of the European Parliament and the

Council of 8 May 2003 on the promotion of the use of biofuels and other renewable

fuels for transport (OJ

123, 17.5.2003, p.42)

European Commission (2003) Directive 2003/96/BC of the Council oi27 October 2003

restructuring the Community framework for the taxation of energy products and

electricity (OJ L 283, 31.10.2003, p.51)

European Commission (2001), Green Paper towards a European strategy for the security

of energy supply. Available at http:/(europa.eu. mt/comm/energy transport/doe-

principa!/pubfinalen. pdf

European Commission (2001). White Paper: European Transport Policy 2010: Time to

Decide. http://europa.cu.int/comm/energytransport/library/!btextecom,leten.pdf

European Commission, DG Energy and Transport (2004). Promoting Biofuels in Europe:

Securing a cleaner future for transport. Available at

Energy scientific and technological indicators and references (2005), DO for Research

and Sustainable Energy Systems, EUR 21 611. ISBN: 92-894-91 69-8

Friedrich S. (2004), A World wide review of the conimercial production of Biodiesel - A

technological, economic and ecological investigation based on case studies, Band 41,

Institut fuer Technologie und Nachhaltiges Produktmanagement, Vienna 2004.

I-Iamelinck, C. (2004), Outlook for advanced biofuels, PhD 1)issertation Juiie 2004,

Utrecht University, Department of Science, Technology and Society. Netherlands.

ISBN: 90-393-3691-1

Henke, J., Kiepper, G., Schmitz, N. (2004): Tax Exemption for Biofuels in Germany: Is

B io-Ethanol Really an Option for Climate Policy? Kid Institute of Workl Economics

2004.

International Energy Agency lEA (2004). Biofuels for Transport. An international

perspective. Paris, France, April 2004. ISBN 92-64-0151 2-4.

lEA, CADETT, (1998), Mini-review of Energy

from

Crops and Crops Residues. UK,

January, 1998.

IPTS, (2002). Techno-economic analysis of Bio-diesel production in the EU: a short

sunimary for decision-makers, EUR 20279, 2002. Available at

hap://www.jrc.cs/honie/publications/publication.cfin?pub=990

IP'l'S, (2002). Techno-economnic analysis of I3io-alcohol

produetioii iii

the EU: a short

summary for decision-makers, EUR 20280, 2002

http:/fwww.rc.es/homc/pubJications/publication.cfm?pub=99 I

jirs, (2003). Biofuel production potemtial of EU-candidate counti'ies -• Addendum

the

Final Report, EUR 20836, 2003. Available at

http://www.ji'c.es/liome/publ ications/pubi icatiou.cfirm? Pu b 11 21

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IPTS, (2004).The introduction of

alternative

fuels in the European transport sector:

Techno-economic barriers and perspectives - Extended summary for policy makers,

JPTS, Ed. Soria Antonio, et al.

Kaltschmitt, M., I-Iartmann, H. (2001). Energie aus Biomasse, Grundlagen, Techniken

mid Verfahren (In German). Springer Verlag 2001, ISBN:

3-54-64853-4.

Ryan, L.; Convery, F.;

Ferreira,

S.: Stimulating the use

of biofuels in the

European

Union: Implications for climate change policy. Working Paper, University College

Dublin, 2004.

Sustainable Energy fretand (SEt) (2004), Liquid Biofuel Strategy for Ireland study

prepared by Hameliuck Carlo; Van den Broek, Richard; Toro, Felipe; Ragwitz,

Mario; Rice, Bernard. Available at:

http://europa.eu. int/comm/energy/res/legislation/doc/biofuels/rneniber_statesl2004li

quid strategy study ireland.pdf

Toro, F. (2004). Techno-Economic Assessment of l3iofueJ Production in the European

Union. Master Thesis, Kailsruhe, TU Preiberg, 2004,

Wyman, Charles E., Handbook on Bioethanol: Production and Utilization. Applied

Energy Technology Series, Taylor & Francis 1998, ISBN: 1-56032-553-4.

Economic data for RES-E

Table 10 gives an overview economic parameter and accompanying technical

specifications on technological level by RES-E sub-category, referring to

new plant

the database in accordance with the

additional realisable mid-term

polenlial.

In case of

(large- arid small-scale) hydropower and wind onshore non-harmortised cost settings are

applied, i.e. a country-specific9 differentiation of investment- and where suitable also

O&M-costs is trnclei.taken, whilst for all other RES options harmonised cost settings are

applied. In the latter ease expressed ranges of the economic and technical parameter

result low different plant sizes (small- to large-scale) and / or applied conversion

technologies. Please note that nfl data - i.e. investment-, O&M-costs and efficiencies -

refer to the default start year' of the simulations, i.e. 2005, and are expressed in

' Especially in case of hydropower the range of investment costs differs largely between and

within the countries. These capital costs are site-specific, depending on the plant-size and

geographic conditions as well as on additional (country-specific) efforts (acceptance barrier,

planning process, etc.). The applied country-specific settings are based err (Lorenzoni, 2001).

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Table 10 Overview on econornic-& technical-specifications for new RES-E plant

RES-E sub-

category

Fuel input

Investment

COStS

Efficiency

O&M costs (transport)

Efficiency

(electricity)

Lifetime

(average)

(years]

Typical

plant size

]

5-25

[€I(kWy

(11

________

iodiesel plant rape and sunflower

0.66

210 -860

10 . 5 -45

(FAME)

seed

_________

___________________

_________

energy crops (i.e.

Bioelhanol

57-

sorghum and corn ftorn 640-2200

32-110

plant (EtOH)

0.65

maize, triticale. wL

- ___________

______

__________________

--_______________

Advanced

bicethanol

plant(EtOI-l+)

energy crops (i.e.

sorghum and whole

plantsofmaize,

triticale,_wheat)

__________

1130-

1510 1

.71

0,58

0,65 1

0.05-

0.12 '

5-25

BIL (from

gasifier)

energy crops (i.e.

SRC, miscanthus, red

canary graSs,

switchgrass, giant red),

selected waste

streams (e.g. straw)

___________________

750- 56001

38- 280'

0.36

0.02

0.43 1

0.09

750

Remarks: In case of Advanced biosthanol and BtL cost and performance data refer to 2010 - the

year of possible market entrance with regard to both novel technology options.

CacuIation of the dynamic cost-resource curve

In general, in the model

Greeii-X,

dynamic effects will he considered covering the areas

e costs (and related performance parameters) for new plants

available / realisable potential foi' existing and new plants, respectively.

The dynamic adaptation of the costs (investment costs and opei'ation and maintenance

costs) will take place at the

end

of one simulated year, i.e. the investment costs for the

yearn will be determined at.

I:he

end of the year

n-h

'J] dynamic assessment of the potentLat will lake place at two different stages in the

mode I:

o The evaluation of the

ai'aiiabiepoenuial 0/existing

plants

for the year a

will be

made - similar to the cost adapt:ation - at the end of'tlie simulation run in the previous

year.

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For new

plants,

the assessment of the

maximal realiable potent/al for the year n

takes place after the creation of the static cost-resource curve for the year a. The

reason why this step cannot also be carried out at the end of the year a- 1 (as done for

all other dynamic assessment steps), is that not all required information for deriving

the assessment parameters is available at that time - i.e. as policy settings can be

changed year by year, actual settings for the year a must he used which, of course,

are on]y available after the simulation for the year n is started. In moie detail the

following inputs must be available:

Input database supply

o Input database - existing plants

o Input database new plants

Stakeholder behaviour

o Investor

o Society

Policy instruments

o Supply-side strategies

o Demand-side strategies

the following, the development of the dynamic cost-resource curves will be explained

in more detaii for existing and new plant separately.

Dynamic cost-resource curve existing plants

The following describes how to adapt the already achieved potential of existing

l)laIltS.

As mentioned before, in the actual model implementation this step takes place during the

creation of the 4input database existing plants' for the year

ii,

i.e. at the end of the year

ti-I. The results of the simulation of one year show - among others- which poteiitially

new plants have actually been implemented. Therefore the database of existing plants

must be extended by these plants, i.e. the database for existing plants Consists - after

carrying out this step - of data for all plants already installed before the year

n-I

plus

those plants which

were

built in the year n-I . However, this also means that old plants,

which are at the end oftheir lifespan in the year

ii, are

still inciudcd in the adapted

database. Hence, in a second step, a. lifespan assessirent must be carried out. All plamts

which have to be decommissioned in the year n have to be excluded from the 'input

database existing plants.

the database the lifespan of the plant (share) of each band of the techinoIogv wiH be

compai'ed with the construction year of the plant. If construction year plus technology-

specific defined lifespan is smaller than year a, the plant will be decommissioned. This

means l:his potential will be subtracted from the available potential of existing planls in

the year n.' This l)1ocedure is schematically depicted in Figure 6.

Note: costs for replacing old plants with new ones

cheaper and acceptance is

hgher

compared to the construction of totally new plants at new locations. Therefore, the potential

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• .-Achieved potential

technologix nat

the

of yearn4

EndrII unyrnoftho

Irceyx

•

•flffl4

.•

Achieved potential of technology x available ri

yearn

-..------t cQyrn (6Ml

fn),flji$b

.•

Figure Schematic plot of the development of dynamic cost-resource curves

for existing plant for the year n (mci, extension for new plant of the

yeoi- n-i and lifespan assessment of existing plants) (example for

the electricity sector only)

Note: these steps will be carried out at the end of the simulation for year n -

Dynamic ccstresource curve

new

plants

The methodology to derive a dynamic cost-resource

curve

for the year

for potentially

flew p]ant is mote complex titan it is for existing plants, because - as already tndrcatcd in

previous sections - this dynamic cost-resource curve for a certain year must he developed

from the (static) cost-resource curve related to the additional mid-term potential.

removed must be adequately considered in the dynamic parameter assessment in the following

years.

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Why is it necessary to start with the additional mid-term potential and derive the animal

potential backwards in time from 2020 to year a ('top down') instead of assessing the

additional potential for the next year directly by taking into consideration various

available barriers and obstacles for the next year ('bottom up')? The motivation is given

by practical reasons, namely,

data with respect to the additional mid-term potential are available for various

technoJogies, e.g. from projects like SAFIRE, BlGreen, etc. Therefore, compatibility

with other studies is given and, hence, correction and adaptation are easily feasible,

the potential for the yearn depends on parameters (e.g. policy strategies) which will

be set in the simulation for year n in year n and, hence, are not available as input

parameters for the simulation process before the yearn.

Nevertheless, in many cases, the results of this 'top-down' approach will be accompanied

and compared with the 'bottom up' approach, i.e. deriving the additional potential for

yearn by starting from year n-I. With this 'two-fold' approach it is secured that the

iotential derived directly by the 'bottom up' approach (here the available potential is

given by the minimum barrier for the next year does not exceed the additional mid-term

potential determined by the 'top-down' approach and evaluated in many international

studies. Note, a depiction referring to the 'top down' approach is given in Figure 7.

CO iñQ2O• Dynamic pararheter assessment

Year

c4/

• .. . . . .. . - O1WthCtLiI

d..,

,) .'4

-,Ll

Cost curve additional potential

year n .

(€MWhiJ.

•:

LTc

...

Figure 7 Schematic plot of the cost curve development for the year a and

technology x

Dynamic cost-resource curves for t1e year n

The overall cost-resource curve for the year n can be derived by horizontal addition of

the already achieved potential (existing plants) and the available additional potential

(new plants). This procedure is shown in Figure 8.

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general,

can be said that the generation costs of RES are higher than those of

conventional energy sources. Moreover, costs, as weU as achievable potentials, differ

widely among the specific technologies. The combination of the cost-resource curves for

potentially new and already achieved plants represents the output of the database

'dynamic cost-resource curv&,

Gpst lsting plants.

year

,CoStôUñiêèdditiônl plànts

• :

year

•..•.

-_....

.•

pIsI4

.. ... .

• - ... •:. .

.....

f.srâ •àdd$tiô1al

plantsfyear n

LTMC

ElM

WhJ

eiecyOuu,rIur.(a'Ms

Figure

8 Combirialion of cost-resource curves for already achieved and

additional potential for the year n and technology x (shown for

etectricfty sector only)

Summing up, the future penetration ofa certain technology depends on how ii: prevails

over two categories of obstacles:

Economic barriers - they are reflected by the net generation costs, i.e. inclusive

poi icy strategies (if applicable).

Other (non-economic) barriers as described above they restrict the available

potential of

RES

generation in year n

Penetration of a technology will only take place if both categories of barriers can be

overcome. So, on the one hand, it does not help to support a certain technology via a

quota obligation, a guaranteed feed-in tariff or a tender scheme without preparing the

framework conditions to overcome the other existing barriers, e.g. increasing the social

acceptance by infonnation campaigns. or decreasing administrative burdens for

commissioning new plants, etc.. In other words, low (net) generation costs but high non-

economic barriers still result in less additional penetration. On the other hand, providing

a good environment at acim inisti-ative, social, industria' and technical levels (i.e.

admitting a huge l)ctcflflal) without economic incentives does not increase the future

Ienetration rate of a certain technology. For instance, a high potential of electricity

generation but high generation costs also results in

low market share.

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A 8 Data for the dynamic aspects

A dynamic cost-resource curve

represents a tool to provide the Linkage between the

formal description of costs and potentials by means

of static cost-resource curves

(as

presented in the previous sections of this chapter) and the dynamic cost assessment as

e.g. done by application of

experience curves

as well as the implication of dynamic

restrictions in accordance with

technology dTusion.

Accordingly, data referring to these dynamic aspects will be presented in the following.

First, data with respect to the dynamic Cost (and performance parameter) assessment is

outlined, followed by a description of the specifications for dynamic (non-economic)

barriers.

Data for the dynamic cost assessment

With respect to technological change, the following dynamic developments of the

electricity generation technologies are considered:

Investment costs

Operation & Maintenance costs

Improvement of the conversion efficiency and related performance parameter

For most RES-.E technologies the future development of investment cost is based on

technological

learning.

As learning is taking place on the international level the

deployment of a technology on the global level must be considered. For the model runs

global dcploynient consists of the following components:

Deployment within the EU 25 Member States is endogenously determined, i.e. is

derived within the model.

Expected developments in the 'Rest of the world' are based on forecasts as

presented in the lEA World Energy Outlook 2004

(lEA,

2004).

For the case that only a single Country

investigated, a detault forecast

would

tar<en as

reference for the RES-E deployment on E1J-25 level.

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Table 14 Default settings with respect to the dynamic assessment of

investment costs for

RES-E

& RES-H technologies

RES category

BIOCAS

_________________

Applied approach

EXPERIENCE CURVE

(GLOBAL)

EXPERIENCE CURVE

(GLOBALI

EXPERT FORECAST

___________________

Assumptions

LR (LEARNING RATE)

10-12.5%

___________________________________

10-12.5% AS DEFAULT, COST DECREASE OF

1.5%/YEAR IN CASE OF CO-FIRiNG

COST DECREASE OF 1.5%/YEAR

________________________________________

BIOMASS ELECTRICITY

Clip

BEOMASS DISTRICT

HEATING

BIOMASS NON-GRID

_________________________

EXPERIENCE CURVE

LR =5-10% DEPENDING ON TECHNOLOGY

___________________________________________________

LR= 10%, EXPERT FORECAST UP TO 2012 IN CASE

OF NOVEL TECHNOLOGIES

__________

_________________

__________

(EU25)

EXPERIENCE CURVE

(EU2S)

EXPERIENCE CURVE

(GLOBAL)

EXPERIENCE CURVE

(GLOBAL)

EXPERT FORECAST

EXPERIENCE CURVE

(GLOBAL)

EXPERIENCE CURVE

(GLOBAL)

EXPERIENCE CURVE

(EU2S)

BIOFUEL FOR

TRANSPORT

GEOTHERMAL

ELECTRICITY

GEOTHERMAL

HEAT

_________

LR 5%

_________ ______________________

HYDROPOWER

PHOTOVOLTAICS

_______________

COST DECREASE OF

1.2%/YEAR

20% UP

2010, 12% AFTER 2010

___________________________________

18%

TO 2010, 12%

AFTER

2010

___________________________________

LR =5%

SOLAR THERMAL

ELECTRICITY

SOLAR

THERMAL

_______________________

TIDAL & WAVE

-___________________

WIND ON- & OFFSHORE

________________________________________________

EXPERT FORECAST

COST DECREASE 5%/YEAR

TO 2010, 1%/YEAR

AFTER 2010

__________________

LII

9.5%

EXPERIENCE CURVE

(GLOBAL)

Default assumptions with respect to technological learning or the cost decrease,

respectively, as depicted in Table 1 4 are based on a literature survey and discussions at

eipert level, Major references are discussed below:

Various studies have recently ti-eated the aspects of technologica) learning

with

respect to

energy technologies. Iii a genera] manner, covering a broad set of(RES-E) technologies,

experience curves are discussed in (GrObler eta]., 1998), (Wene C. 0., 2000),

(McDonald, Schrattcnho]zer, 2001) and (BMU, 2004). A focus on photovoltaics is given

in (Alsenia, 2003) and (Schiffer eta]., 2004), whilst in case of wind energy (Neij et at.,

2003) provides the most comprehensive recent survey. With respect to the future cost

development of emerging new technologies like tidal and wave energy a stick to expert

lbrecasts giveil by (OXERA Environmental, 2001) SeemS prcfei'ab]e.''

The future development of biomass prices as relevant for electricity and heat

production based on biomass and biowaste is -- as default ' based on the following

' The currently implemented modelling approach ecconts solely learning on the commercial

market place, Efforts with respect to R&D, which do not result in additional deployment

measurab'e in terms of MW instal'ed, would otherwise neglected, but are of crucial relevance for

technologies in the early phase of deployment see (GrUbler et al, 1998).

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settings: On average an increase of

0,5-1.5%

per year is projected, depending on fuel

categoly and country.

Data with respect to dynamic barriers

Within the model

Gi'eej,..X

dynamic barriers describe the impact of non-economic

deficits on the deployment of a certain RES, They represent the key element to derive the

dynamic potential for a certain year from the overall remaining additional realisable mid-

term potential (up to the year 2020) for a specific RES. Thereby, the impact of three

different types of several bai-riers can be investigated, e.g. technical, societal or

market & administrative constraints.

As defauli

technical

and

societal constraints are

considered only for onshore wind

energy. Thereby, the simplified percentage approach has been adopted. More precisely

the yearly realisable potential is restricted to a level of 50% of the remaining additional

mid-term

potential on band-level.

In contrast, the

most

important non-economic constraint, i.e. the combined indicator for

market & administrative barriers, is well applied to all RES-E categories in each

country. The application of this barrier results in a teclmology penetration following an

'S-curve' pattern - of course, only if financial incentives are set appropriate.

The required data in this respect is described below. Thereby, the following parameters

have to be defined:

Econometric factors A, B and C:

They predefine the possible increase of market deployment over time for a certain

technology on countiy-levcl. i.e. a high absolute value of A (e.g. 0.7) would allow a fast

market deployment (of course, if the harrier level bM is set high, too). In this context, the

technology-specific figures are derived from the in-depth investigation of the historical

development of RES-E in Europe imdertaken within the project

"FORRES 2020"

(see

(Ragwitz

aL, 2004)). Hence, the chosen figures refer to best conditions as observed for

several RES technologies in the past in European countries.

Barrier

level

This parameter defines the country-specific conditions - he. how far these conditions

differ from the technology-specific 'idea,l case (i.e. fi'om the as above explained

historical observed best conditions in a certain countly). Thereby, a value of 0 indicates a

Cvely high harrier', whilst a value of 1 refers to a 'vely low barrier', i.e. the ideal case'.

An illustration offlie clefhult setting is given in Figure 9, which depicts the ranges on

technology-level, referring to the electricity sector. These default settings refer to the

cuirCot situation of the various RES-E options in the investigated countries

as assessed

within the project

"FORRES 2020"

(sec (Ragwitz ci al., 2004)).

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1'LOW 4,0

I,irr4r 35

ra1ge

'1.:SI..!

IUGH 05

0.0

.9 E

...

-a

I-

Figure 9 Modelsettings of dynamic parameters: Country-specific ranges of

applied market

barrier level (bM)

by RESE technology

Lower boundary (minimum) for yearly realisable

market potential

constant minimum level of the yearly realisable market potential is considered for each

RES-E

category on country level. Otherwise - if a technology enters a new market - no

market potential would be available at the initial stage.

Similar to above, a depiction is given on countiy as well as on technology Level: Figure

10 indicates ranges on technologylevel, resulting from differing settings by country

(referring to the electricity sector). Again, default settings take into account i:he eulTdnt

conditions for the various RES-E options in each country.

900i

................................

...

0>. 700

........................................................

see

......................................................................................

8004

............................................

o00

300

100

..................................................................................

.......................................... ..

. .,

.rzQr8

...................

...

......

..•.

.............

•

Figur e 10:

Model-settings of dynamic parameters: Country-specific ranges

of applied minimum market potentials (/

mm)

by RlSF

te cli no logy

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Annex Ib: Short characterisation of the model

GreenNet

The toolbox

GreenNet

developed by EEG, represents the core product of the overall

project GreenNet during its duration in the period 01/2003 to 12/2004.

The

GreenNet

model allows to simulate different scenarios, which enable a comparative

and quantitative analysis of strategies for an enhanced least-cost integration of RES-E

within the liberalised electricity sector both for all considered EU countries (I.e. initially

all EU]

countries and the new Member States Czech Republic, Hungary, Poland and

Slovakia) as a whole as well as individual Member States for the period 2005 to 2020, It

is important to mention that the geographical coverage has been recently extended within

ongoing research activities'7 to the EU25 plus Bulgaria and Romania.

Similar to

Green-X,

the general modelling approach to describe both supply-side

electricity generation technologies and electricity demand reduction options is to derive

dynamic co3l-resource curves

for each generation and reduction option in the

investigated region. Dynamic deployment of RES-E is policy-driven where a similar

pathway can he set as for the electricity sector within the

Green-X

model.

Of special interest within this project are the following model features:

Cost of system operation and grid extension in case

of intermittent RES-E

Besides the policy settings, an additional feature is included in the overall simulation

model,

which is worth to mention:

The cost-allocation tool for system operation

and

or grid extension costs

in accrdancc with intermittent

RES-E.

Within the

toolbox GreenNet such costs can be

exemplarily determined for its

most

prominent

representati W rid power.

Besides a variety of settings to determine the overall calculation procecluie the user has

the possibility to determine the allocation of the accordingly calculated cost. In general,

they can either be applied to the consumer (society) ot' to the producer / investor. The

later setting allows getting aware ofa likely impact in terms of reduced wind

installations, etc. In addition, trade-offs between PolicY insti'uments and this cost-

allocation can be clearly expressed and determined.

An overview of the core elements oltlie Green

Wet

model is given in iigwe 11,

Within the follow-up project

GreenNet-E1J27

the extension

of the

geographical

coverage of the model to

all

10 new Member States, the candidate countries Bulgaria, Rornania

was recently undertaken - a luither expansion to include Croatia

well

Switzerland and

Norway is planned for the

near future. For

further information on these follow-up activities please

gi'eennet-europe.og.

Visit

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ackqround dath base GreenNet

country-specific data

for suppiy-&clemand-slde options. for ohc instrumen fo RES-E

cO(baae'aar: 2005) & potentials (base year: 2020)

•i

- dmamlc parameter(cost development

(e.g.

Iaarning rates), -. p° •d ' y

200$t 020

r -

for general parameters:

non.linsncial

realization

barriers)

reference

electricity prices,

demand

lorecait

(2005 to 2020)

_______

______________________

POtICy instrumCnts Gancrat parameters bemnd reduction

(for RES E) (prlce & demand) otrons (DSM)

-------------------------------------------

Sensitivity tool for wind

energy:

Cests

for

jiid

extension

and

svf$tern operation

Selection

of cost-scenario (tow- moricrete- high)

Definition of cost socialization

(coneumer-pmducerj

:DeterflJon of

potentials, costs etch pricCs-.

doma rid srde

per sob secto

cost resource

--.T

curves

or.$'rJi)

eisubsector

demand side

Feedback

year n+1

Feedback

ye5rn+1

Figure JJ-2

Figure ii. Overview on the core elements of the model GreenNef

The model

GrenNet

aims to deliver a broad set of results. All results can be provided

on a yearly basis on country-, EU- and / or tzchnology-levcl.

In more detail, mode] outputs can be categorized as follows:

General

Installed capacity [MW]

- Electricity generation {GWh]

- National electricity consumption [GWh]

- Wholesale market price electricity (yearly average prce) [€/MWh]

Market price Tradable Oreen Ceitificates [E/M Whj

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- Total e[ectdcity savings [GWIiJ

• Impact on producer or society

inciudin. e.g..

- Additiona' costs due to DSM strategy [ME,

€fMWh]

- Additional costs due to system operation [ME, €/MWh}

- Additional costs due to grid extension [ME, E/MWh]