the impact of modified eu ets allocation principles on the

FCN Working Paper No. 4/2011

The Impact of Modified EU ETS Allocation Principles

on the Economics of CHP-Based District Heating

Networks

Günther Westner and Reinhard Madlener

February 2011

Institute for Future Energy Consumer Needs and Behavior (FCN)

Faculty of Business and Economics / E.ON ERC

FCN Working Paper No. 4/2011

The Impact of Modified EU ETS Allocation Principles on the Economics of CHP-

Based District Heating Networks

February 2011

Authors’ addresses: Günther Westner E.ON Energy Projects GmbH Arnulfstrasse 56

80335 Munich, Germany E-mail: [email protected]

Reinhard Madlener Institute for Future Energy Consumer Needs and Behavior (FCN) Faculty of Business and Economics / E.ON Energy Research Center

RWTH Aachen University Mathieustrasse 6 52074 Aachen, Germany E-mail: [email protected]

Publisher: Prof. Dr. Reinhard Madlener Chair of Energy Economics and Management Director, Institute for Future Energy Consumer Needs and Behavior (FCN) E.ON Energy Research Center (E.ON ERC) RWTH Aachen University Mathieustrasse 6, 52074 Aachen, Germany

Phone: +49 (0) 241-80 49820 Fax: +49 (0) 241-80 49829 Web: www.eonerc.rwth-aachen.de/fcn E-mail: [email protected]

- 1 -

The impact of modified EU ETS allocation principles on the

economics of CHP-Based district heating networks

Günther Westner a and Reinhard Madlener

b,*

a E.ON Energy Projects GmbH

**, Arnulfstrasse 56, 80335 Munich, Germany

b Institute for Future Energy Consumer Needs and Behavior (FCN), Faculty of Business and

Economics / E.ON Energy Research Center, RWTH Aachen University,

Mathieustrasse 6, 52074 Aachen, Germany

February 2011

Abstract

The economics of large-scale combined heat and power (CHP) generation for district heating

(DH) applications are strongly affected by the costs and allocation mechanism of CO2

emission allowances. In the next period of the European emission trading system (EU ETS),

from 2013 onwards, the allocation rules for CHP generation will be modified according to the

principles announced in EU Directive 2009/29/EC. By means of a discounted cash-flow

model we first show that the implementation of the modified allocation mechanism

significantly reduces the expected net present value of large-scale CHP plants for DH. In a

next step, by applying a spread-based real options model we analyze the decision-making

problem of an investor who intends to invest in CHP generation. Our results provide some

evidence that the modified EU ETS principles contribute to reducing the attractiveness of

investments in energy-efficient large-scale CHP plants that feed into DH networks. In

contrast, decentralized small-scale CHP, which is not subject to the EU ETS, may benefit

from this development and could, therefore increasingly replace large-scale CHP assets. In

other words, European legislation is indirectly promoting the further diffusion of

decentralized CHP generation units.

Key words: Combined heat & power; Emission trading system; Investment under uncertainty;

Spread; Real options

JEL Classification Nos.: C61; D81; Q41; Q43

* Corresponding author

* Corresponding author. Tel. +49 241 80 49 820, Fax. +49 241 80 49 829; E-mail: [email protected]

aachen.de (R. Madlener). **

Please note that all statements in this article are made by the authors only and do not necessarily reflect the

views of E.ON Energy Projects GmbH.

- 2 -

1 Introduction

The EU climate and energy package, released in January 2008, sets three ambitious targets for

the year 2020: the cut in greenhouse gas (GHG) emissions by at least 20% relative to 1990

levels (30% if other developed countries commit to comparable cuts), the increase of the share

of renewable energy resources (wind, solar, biomass, etc.) to 20% of total energy production,

and the cut in total energy consumption by 20% of projected 2020 levels through improved

energy efficiency. In order to realize the considerable reduction of GHG emissions by 2020,

the EU Commission revised the general principles of the EU Emissions Trading System (EU

ETS). The new ETS Directive 2009/29/EC of the European Union, released in April 2009,

determines the framework of the allocation mechanism for CO2 emission allowances in the

third ETS period from 2013 to 2020. The directive also modifies the allocation rules for

combined heat and power (CHP) generation and harmonizes them across all EU member

states. In our research we investigate how the economics of CHP installations for district

heating (DH) are affected by the modified principles as described in the EU ETS Directive.

District heating, defined as space and water heating of several buildings or larger areas by

means of centralized generation units, can significantly contribute to achieving emission

reductions. The intended adjustments of the allocation rules for CHP-based heat generation

could dissuade companies from investing in efficient large- and medium-scale CHP

installations for DH networks, as these plants cause a total increase in carbon emissions on-

site, although they contribute to increasing energy efficiency and reducing the CO2 emissions

of the system as a whole. The economic disadvantages caused by the EU ETS could lead to a

development where existing heat boilers will not be replaced by new and efficient CHP units.

In our research we investigate the intended adjustments of the EU ETS in the context of CHP-

based DH in Germany. We chose Germany for our investigation as it represents the largest

European energy market and, according to the national potential study (Eikmeier et al., 2005),

has a large economic potential for CHP-based DH installations of up to 240 TWhel per year.

Only about 20% of this potential is utilized today. The realization of the remaining potential

depends, among other things, crucially on the future design of the EU ETS.

This paper is structured as follows. Section 2 describes the benefits of CHP-based DH.

Section 3 provides a concise overview of the existing European emission trading system and

the modified rules for CHP generation in the next EU ETS period. The input parameters and

results of the financial model are described in section 4. Section 5 applies a spread-based real

- 3 -

options model to evaluate the impact of the modified EU ETS principles on future investment

decisions. Section 6 concludes.

2 CHP-based district heating networks

In this section we introduce a possible classification of CHP-based DH networks and describe

the benefits of CHP generation concerning primary energy savings and reductions in CO2

emissions.

2.1 Classification of district heating networks

DH networks primarily focus on supplying low- and medium-temperature heat demands for

space heating and hot water preparation. A typical characteristic of DH systems is that the

heat is generated centrally and then distributed via a well-insulated network of pipes to the

locations where it is utilized for residential, public, or commercial heating requirements. In

principle, various heat sources, such as heat from CHP generation, heat boilers or different

forms of renewable heat sources (e.g. geothermal energy), can be used to feed a DH system.

The decision about which heat source is chosen depends on the timing and nature of the

thermal load, fuel availability, and the economic utilization of the electrical power that is in

many cases produced in a combined process. Even ―waste‖ heat streams that are difficult to

utilize otherwise can be implemented. Over time, the heat input of DH systems can gradually

be converted into renewable heat sources as new technologies become available and

economical. In this way, DH networks create a bridge towards future low-carbon energy

supply systems. Population density is a key consideration for new DH systems, as these rely

on a concentrated demand for space heating to minimize heat transportation distances and

losses and thus mitigate operational costs. Development and construction of new DH

networks require high investment costs, but help to provide a long-term asset with the

perspective of a transition to a low-carbon energy system.

The huge variety of different applications of DH and vague distinguishing criteria make it

hard to find a proper classification for DH networks. One possible approach is a classification

into local heat systems and large-scale urban DH networks, according to structure, size, and

the kind of heat sources or heat sinks connected (see Grohnheit and Mortensen, 2003). Local

heat systems are DH networks of comparably small extension in villages or insulated urban

- 4 -

districts. In many cases they are fed by few small-scale CHP units. Large urban DH networks

are interconnected grids of a wider extension. They are supplied by a number of different

energy sources, such as coal, oil, natural gas, solar power, geothermal heat, waste, or surplus

heat from industrial production. The heat production units usually apply various generation

technologies to produce the heat demand. A main purpose of urban DH networks is to connect

the different kinds of heat sources. The baseload of the heat demand is in many cases

delivered by large-scale coal- or gas-fired CHP plants. Table 1 provides an overview of the

typical characteristics of local heat systems in comparison with urban DH networks.

Table 1: Classification of CHP-based DH networks

Local heat systems Urban DH networks

Extension Length of pipelines ≤ 10 km Length of pipelines ≥ 10 km

Heat load [GWh] < 100 GWhth/a > 100 GWhth/a

Losses Lower losses through local generation and

short distribution distances

Losses on the heat- and power-side

through long transmission distances

CHP-based

heat sources

Type Small-scale CHP plants (e.g. engine CHP,

micro-turbine, Stirling engine, fuel cell)

Large-scale CHP plants (e.g. coal-

fired CHP, CCGT-CHP)

Size 1 MWel – max. 100 MWel 100 MWel – 400 MWel

Number 1 to max. 10 small-scale plants Several plants of different

size and generation technology

Fuel In many cases natural gas Several fuel types are possible

(hard-coal, lignite, natural gas)

Source: Own illustration, with input from Fischedick et al. (2006).

2.2 Primary energy savings and emission reduction through CHP-based district heating

The heat production of CHP plants can be utilized as thermal input for DH networks. The

selection of the CHP plant depends on the specific requirements of the DH network. In

general there are various CHP installations available, which differ in the applied technology,

the size of the plant, the fuel type, or the mode of operation (see Westner and Madlener,

2009). CHP technologies bear a substantial potential to increase energy efficiency and reduce

CO2 emissions compared to separate power and heat production. The IEA highlights the key

- 5 -

role of CHP generation when it comes to CO2 emissions reductions, and states that CHP

provides a meaningful contribution to achieving significant greenhouse gas emissions

reductions that are necessary to avoid major climate change and resulting disruptions (IEA,

2008a). The evaluation of primary energy savings and CO2 emission reductions through CHP

generation depends significantly on the chosen reference technologies (Verbruggen et al.,

1992). In this section we illustrate primary energy savings and CO2 emission reductions of

selected CHP technologies that we later use for the model-based evaluation of the new EU

ETS principles. We consider large-scale technologies that are applied in urban DH networks

as well as small-scale technologies for local heat systems.

2.2.1 Large-scale CHP technologies

In our investigation, we focus on two commercially available and approved kinds of large-

scale CHP technologies: coal-fired steam plants and combined-cycle gas turbine (CCGT)

plants. We take conventional heat boilers and condensing plants of the same technology as

reference technologies for the evaluation of possible savings. In contrast to CHP plants,

condensation plants do not utilize the heat occurring as a by-product of power generation. The

average annual fuel utilization of coal-fired CHP plants lies in a range between 50% and 80%

depending on the degree of heat utilization at the respective site. In the evaluation carried out

here, we assume an average annual fuel utilization of 65%, which is a typical value for coal-

fired CHP plants that feed into DH networks. As shown in figure 1, the primary energy

savings amount to 16% and the emission reduction to 6%, in comparison to the separate

generation of power and heat. A gas-fired CCGT-CHP plant reaches a higher average fuel

utilization of about 80%. This is due to the fact that a larger part of the employed fuel is used

for power generation. Therefore, the relation between power and heat output, the so-called

power-to-heat ratio, is significantly higher, which increases total fuel utilization. Based on our

assumptions, a CCGT-CHP plant can reach primary energy savings of up to 17% and CO2

emission reductions of 17%. The gas-fired CCGT-CHP, in comparison to the carbon-intensive

coal technology, reaches significantly higher emission reductions.

- 6 -

Combined Heat & PowerCoal-fired CHP plant

Conventional GenerationCoal-fired plant + Gas-fired heat boiler

100CHP Plant | Hard Coal

ηel = 37 %

ηth = 28 %

35(Losses)

ηel = 42 %

εCoal = 342 gCO2/kWhHu

In total: 100

Fuel utilization ζCHP = 65%

88.10Condensing plant | Hard Coal


31.11Heat Boiler | Natural gas

εGas = 198 gCO2/kWhHu

ηth = 90 %

Ʃ 54.21(Losses)

Primary energy demandPrimary energy demand

In total: 119.21

Fuel utilization ζ = 55%

Primary energy savings:

54.21 - 35

119.21= 16 %

CO2-Emission savings:

88.10 · 342 + 31.11 · 198 – 100 · 342= 6 %

88.10 · 342 + 31.11 · 198

Power

Heat

37

28




ηel = 37 %

ηth = 28 %

35(Losses)

ηel = 42 %


In total: 100






ηth = 90 %

Ʃ 54.21(Losses)


In total: 119.21



54.21 - 35

119.21= 16 %


88.10 · 342 + 31.11 · 198 – 100 · 342= 6 %

88.10 · 342 + 31.11 · 198

Power

Heat

37

28

Combined Heat & PowerGas-fired CCGT-CHP plant

Conventional GenerationGas-fired CCGT plant + Gas-fired heat boiler

Power

Heat

100CCGT-CHP Plant | Nat. Gas

ηel = 45 %

ηth = 35 %

45

35

20(Losses)

ηel = 55 %


In total: 100


81.81CCGT plant | Nat. Gas


Ʃ 40.70(Losses)


In total: 120.69



40.70 - 20

120.69= 17 %


81.81 + 38.88 - 100= 17 %

81.81 + 38.88

ηth = 90 %38.88

Heat Boiler | Nat. gas




Power

Heat


ηel = 45 %

ηth = 35 %

45

35

20(Losses)

ηel = 55 %


In total: 100




Ʃ 40.70(Losses)


In total: 120.69



40.70 - 20

120.69= 17 %


81.81 + 38.88 - 100= 17 %

81.81 + 38.88

ηth = 90 %38.88






ηel = 37 %

ηth = 28 %

35(Losses)

ηel = 42 %


In total: 100






ηth = 90 %

Ʃ 54.21(Losses)


In total: 119.21



54.21 - 35

119.21= 16 %


88.10 · 342 + 31.11 · 198 – 100 · 342= 6 %

88.10 · 342 + 31.11 · 198

Power

Heat

37

28




ηel = 37 %

ηth = 28 %

35(Losses)

ηel = 42 %


In total: 100






ηth = 90 %

Ʃ 54.21(Losses)


In total: 119.21



54.21 - 35

119.21= 16 %


88.10 · 342 + 31.11 · 198 – 100 · 342= 6 %

88.10 · 342 + 31.11 · 198

Power

Heat

37

28



Power

Heat


ηel = 45 %

ηth = 35 %

45

35

20(Losses)

ηel = 55 %


In total: 100




Ʃ 40.70(Losses)


In total: 120.69



40.70 - 20

120.69= 17 %


81.81 + 38.88 - 100= 17 %

81.81 + 38.88

ηth = 90 %38.88





Power

Heat


ηel = 45 %

ηth = 35 %

45

35

20(Losses)

ηel = 55 %


In total: 100




Ʃ 40.70(Losses)


In total: 120.69



40.70 - 20

120.69= 17 %


81.81 + 38.88 - 100= 17 %

81.81 + 38.88

ηth = 90 %38.88



Figure 1: Primary energy savings and CO2 emission savings of large-scale CHP plants in

comparison to condensing plants and heat production in gas-fired boilers.

Source: Own illustration.

- 7 -

2.2.2 Small-scale CHP technologies

In a next step, we evaluate primary energy savings and CO2 emission reductions of small-

scale CHP applications. Exemplarily, we consider engine CHP and micro-turbine CHP.

Usually, it is not meaningful to install these technologies without heat utilization and,

therefore, we take the German power mix as a reference value to evaluate the electrical output

of the units. In 2009 the average efficiency of power production in Germany was 37%, with

an average CO2 emission factor of 575 gCO2/kWhel (Umweltbundesamt, 2010). According to

figure 2, the primary energy savings of engine CHP compared to power supply from the

German grid and heat production in gas-fired heat boilers amounts to 37%. The primary

energy savings of micro-turbine CHP is 27% and thus considerably lower due to the poor

power-to-heat ratio. This is also the reason why the emission reductions for engine CHP

(40%) are higher compared to micro-turbines (31%).

- 8 -

Combined Heat & PowerEngine CHP plant

Conventional GenerationGerman power mix + Gas-fired heat boiler

Power

Heat

100Engine CHP | Nat. Gas

ηel = 40 %

ηth = 45 %

40

45

15(Losses)

ηel = 37 %


In total: 100


Ʃ 73.11(Losses)

Primary energy demand

In total: 158.11



73.11 - 15

158.11= 37 %


40 · 575 + 50 · 198 – 100 · 198= 40 %

40 · 575 + 50 · 198

ηth = 90 %50



108.11German power mix

εMix = 575 gCO2/kWhel




Power

Heat


ηel = 40 %

ηth = 45 %

40

45

15(Losses)

ηel = 37 %


In total: 100


Ʃ 73.11(Losses)


In total: 158.11



73.11 - 15

158.11= 37 %


40 · 575 + 50 · 198 – 100 · 198= 40 %

40 · 575 + 50 · 198

ηth = 90 %50






Combined Heat & PowerMicro-turbine CHP


Power

Heat

100Micro Turbine CHP | Nat. Gas

ηel = 30 %

ηth = 52 %

30

52

18(Losses)


In total: 100


Ʃ 56.88(Losses)


In total: 138.88



56.88 - 18

138.88= 27 %


30 · 575 + 57.77 · 198 – 100 · 198= 31 %

30 · 575 + 57.77 · 198

ηth = 90 % 57.77Heat Boiler | Nat. gas





ηel = 37 %



Power

Heat


ηel = 30 %

ηth = 52 %

30

52

18(Losses)


In total: 100


Ʃ 56.88(Losses)


In total: 138.88



56.88 - 18

138.88= 27 %


30 · 575 + 57.77 · 198 – 100 · 198= 31 %

30 · 575 + 57.77 · 198






ηel = 37 %



Power

Heat


ηel = 40 %

ηth = 45 %

40

45

15(Losses)

ηel = 37 %


In total: 100


Ʃ 73.11(Losses)


In total: 158.11



73.11 - 15

158.11= 37 %


40 · 575 + 50 · 198 – 100 · 198= 40 %

40 · 575 + 50 · 198

ηth = 90 %50








Power

Heat


ηel = 40 %

ηth = 45 %

40

45

15(Losses)

ηel = 37 %


In total: 100


Ʃ 73.11(Losses)


In total: 158.11



73.11 - 15

158.11= 37 %


40 · 575 + 50 · 198 – 100 · 198= 40 %

40 · 575 + 50 · 198

ηth = 90 %50








Power

Heat


ηel = 30 %

ηth = 52 %

30

52

18(Losses)


In total: 100


Ʃ 56.88(Losses)


In total: 138.88



56.88 - 18

138.88= 27 %


30 · 575 + 57.77 · 198 – 100 · 198= 31 %

30 · 575 + 57.77 · 198






ηel = 37 %



Power

Heat


ηel = 30 %

ηth = 52 %

30

52

18(Losses)


In total: 100


Ʃ 56.88(Losses)


In total: 138.88



56.88 - 18

138.88= 27 %


30 · 575 + 57.77 · 198 – 100 · 198= 31 %

30 · 575 + 57.77 · 198






ηel = 37 %

Figure 2: Primary energy savings and CO2 emission savings of small-scale CHP plants in

comparison to the German power mix and heat production in gas-fired boilers.

Source: Own illustration

- 9 -

3 The European emission trading system

In this section, we first briefly describe the general principles of the European Union emission

trading system (EU ETS). Second, we introduce the most important changes in the next ETS

trading period from 2013 onwards. Finally, we discuss the treatment of CHP installation for

DH within the modified EU ETS principles.

3.1 General principles of the EU emission trading system

The intention of the EU ETS is to support the member states in reaching the agreed

greenhouse gas (GHG) emissions targets in a cost-efficient manner. The start of the EU ETS

in the year 2005 also marks a shift in environmental policy from command-and-control

regulation towards more market-based instruments. An essential part of the EU ETS is the

common trading ‗currency‘ of emission allowances. One allowance (EU Allowance - EUA)

represents the right to emit one ton of CO2. Another decisive issue within the EU ETS are the

rules for allocating allowances to affected GHG emitters. Free allowances can either be

allocated according to the grandfathering or the benchmarking principle. Grandfathering

applies the historic emissions of an existing site as a proxy for the number of allowances that

the site will receive in the future. The actual allocation according to the grandfathering

principle is in some EU member states slightly below the historic emissions of the respective

plant to provide an incentive to reduce emissions. Benchmarking uses absolute, technology-

based benchmarks to determine how many allowances a site will get. In the case that the

allowances are not allocated for free, the main allocation principle is auctioning, where both

existing and new installations are required to purchase allowances. The principles for the free

allocation of allowances are determined by the national allocation plans (NAP) of the EU

member states. A limitation of the number of freely allocated CO2 allowances creates the

necessity for auctioning. Companies that keep their emissions below the level of free

allocation are able to sell the surplus at a price determined by supply and demand at that time.

Installations that face difficulties in remaining within their emission limit have the choice

between taking measures to reduce emissions or auctioning the additional allowances needed

at the market price. The theoretical idea behind the EU ETS is that the costs of CO2 emissions

that are reflected in the market price for EUA‘s induce the demand for innovative, energy-

efficient and carbon-saving processes, products, and services (Rogge et al., 2011).

- 10 -

Currently, more than 11,500 energy-intensive facilities in 30 countries (the 27 EU Member

States plus Iceland, Liechtenstein, and Norway) are covered by the EU ETS. These facilities

include oil refineries, power plants with more than 20 MW in thermal capacity, coke ovens,

iron and steel plants, as well as cement, glass, lime, brick, ceramics, and pulp and paper

installations (CEC, 2005). The entities covered emit about 40-45% of the EU‘s total GHG

emissions. Up to now, the EU ETS does not cover CO2 emissions of the transportation sector,

which account for about 25% of the EU‘s total GHG emissions, and emissions of non-CO2

greenhouse gases, which account for about 20% of the EU‘s total GHG emissions (EEA,

2009).

The implementation of the EU ETS takes place in phases, with periodic reviews and

opportunities for expansion to further greenhouse gases and sectors. The first trading period

lasted between January 1, 2005, and December 31, 2007. The second trading period began on

January 1, 2008 and covers the first commitment period of the Kyoto Protocol until the end of

2012. The upcoming phase three will begin in 2013 and will last for eight years until the end

of 2020. In the following, we describe the general adjustments of the EU ETS in the

upcoming third period and take a more detailed look at the treatment of CHP installations for

district heating.

3.2 General principles in the third EU ETS period

The ETS Directive 2009/29/EC (CEC, 2009a) of the European Union, released in April 2009,

defines the principles for the allocation of CO2 emission allowances in the next EU ETS

period from 2013 onwards. Since its release, the Directive 2009/29/EC has been

supplemented by two further commission decisions that define the sectors which are deemed

to be exposed to a significant risk of carbon leakage (CEC, 2009b) and determine union-wide

rules for the harmonized free allocation of emission allowances (CEC, 2010). The new

principles promote the harmonization of the existing rules within the community and differ in

some points significantly from the allocation principles of previous EU ETS periods.

According to the directive, the free allocation in the third EU ETS period is based on uniform

product-based benchmarks that are aligned to historic activity levels. The definition of the

benchmarks is based on the average performance of the EU-wide 10% most efficient

installations of their kind in the years 2007-2008. The historical activity levels that are

multiplied by the product benchmarks are based on the median production during the period

from January 1, 2005 to December 31, 2008, or, where it is higher, on the median production

- 11 -

during the period from January 1, 2009 to December 31, 2010. The activity levels for new

installations that enter the market are based on standard or installation-specific capacity

utilization. Carbon leakage sectors (energy-intensive sectors that are subject to international

competition and face major economic disadvantage through the EU ETS) receive a 100% free

allocation of the value calculated based on product benchmark and historical activity levels.

For the rest of the affected industries (non-carbon leakage industries) the percentage rate for

free allocation decreases from 80% of the calculated value in 2013 to 30% in 2020. In the

case that European Union-wide reduction targets, as defined in Directive 2009/29/EC, are not

realized during the period, the quantity of freely allocated allowances can be additionally

decreased by application of cross-sectoral correction factors. For the whole power sector, full

auctioning will be introduced in the next ETS period and new plants for production of

electrical power will not receive a free allocation. The commercial aviation sector will also be

included in the EU ETS from 2013 onwards and more consistent and harmonized monitoring,

reporting, and verification requirements will be introduced.

Table 2: Comparison of allocation methodology between the ETS trading periods

ETS Periods I & II ETS Period III

Degree of harmonization

Allocation according to

heterogeneous national allocation

plans.

Harmonized allocation rules ensure

consistency of scope and definitions;

Greater EU central responsibility.

Free allocation

By majority grandfathering, some

countries (e.g. Germany, Poland,

Denmark) apply benchmarking.

Limited free allocation according

to product benchmarks and

historical activity levels.

Differentiation between carbon

leakage sector (100% free

allocation) and other industries

(decreasing free allocation from

80% in 2013 to 30% in 2020).

Application of a cross-sectoral

correction factor in case the

European Union-wide emission

targets are not reached.

Auctioning

Limited amount of allowances is

free for auctioning (< 5% in period

I; < 10% in period II).

100% auctioning for the power sector;

Increasing share of auctioning for non

carbon leakage industries.

Source: Own illustration, with input from IEA (2008b).

- 12 -

3.3 Modification of allocation rules for CHP-based DH plants in the third ETS period

CHP installations for DH applications with a firing capacity above 20 MW are implemented

in the EU ETS. In the current second ETS period, the principles for the allocation of free

emission allowances in the power sector in general, and for CHP installations in particular,

are not harmonized and differ significantly between the EU member states as well as between

existing assets and new plants (see Rogge and Linden, 2008). Many countries (e.g. France,

UK, Spain, and many Eastern European countries) allocate allowances for CHP plants

according to their historic emissions (grandfathering). Other member states, e.g. Austria,

Denmark, or Ireland, use a uniform benchmark to allocate allowances. In a third group of

countries, including Germany, Italy, and The Netherlands, the allocation for CHP installations

is based on the double benchmark principle.

In our investigation we take a closer look on the double benchmark principle, which is

implemented in the German NAP of the second ETS period. We use the double benchmark

also as a reference for the later investigation and compare the allocation mechanism of the

upcoming third EU ETS period with this principle. According to the double benchmark

principle, CHP plants receive an allocation based on the produced power and an additional

allocation based on the produced heat. The allocation for the electrical output refers to the

emissions of a conventional fossil-fired power plant, whereas the allocation for the heat

output refers to the emissions of a conventional boiler or steam plant. According to the

German NAP, the free allocation for CHP plants is calculated by applying the following

formula:

QQAADB BMVBMVA . (1)

Equation (1) states that the amount of allocated emission allowances (ADB) for a full year in

the second ETS period depends on the power production (VA) and on the heat production (VQ)

of the CHP plant, weighted by the respective benchmark factors for power (BMA) and heat

(BMQ). The production volumes VA and VQ do not represent the actual production of power

and heat in the respective year. They are calculated based on the installed capacity and on

installation-specific capacity utilization factors. The capacity utilization factor for CHP plants

is, e.g., 8,000 utilization hours per year. Table 3 contains the benchmarks for power and heat

production according to the German NAP (BMU, 2006).

- 13 -

Table 3: CHP benchmarks according to the German NAP 2008–2012

Product Benchmark [gCO2/kWh]

(solid fuel / gaseous fuel)

Power 750 / 365

Hot water 290 / 215

Process steam 345 / 225

In the third ETS period, highly efficient installations that produce heat and power in a

combined process (as defined in Directive 2004/8/EG) only receive an allocation for the heat

output according to a uniform product benchmark. For existing installations, the amount of

free allocation depends further on historical activity levels. The cross-sectoral correction

factor that can be used to cut free allocation in the case that European Union-wide emission

targets are not accomplished, is not applied to CHP installations. Based on the currently

available information, CHP installations for DH will receive from 2013 onwards an annual

allocation according to the following formula:

LFBMHALA QQPeriodrd3 . (2)

Equation (2) shows that the amount of freely allocated emission allowances (A3rd Period) for a

full year in the third allocation period depends on the historical activity level of heat

production (HALQ) and on the product benchmark of heat (BMQ). For new installations, the

historical activity level is replaced by a production capacity, which is calculated based on the

total installed heat capacity and a defined standard utilization factor. The values for the

standard utilization factors of new installations have still not been announced. The benchmark

for heat is defined in the Commission‘s decision (CEC, 2010) with 0.0623 allowances/GJ,

which is equal to 0.224 allowances/MWh. A differentiation between solid and gaseous fuels is

no longer intended. The linear reduction factor (LF) indicates that the free allocation of

emission allowances will decrease linearly from 80% of the calculated value in 2013 to 30%

in 2020.

According to these principles, CHP installations receive significantly less allowances from

2013 onwards compared to the current situation in the second period. The example in figure 3

illustrates the difference between the allocation principles for a coal-fired CHP steam plant

- 14 -

and a gas-fired CHP-CCGT plant. The transition from fuel-specific benchmarks, which are

state-of-the-art in the second ETS period, to product-based benchmarks for heat production

leads to a situation where both technologies receive the same amount of free allowances

independently from the carbon intensity of the applied fuel.

5,115,000

280,000110,000

Allocation

in 2013

Allocation

in 2020

2nd EU ETS period

Allocation according

to double BM

3rd EU ETS period

Annual allocation of a coal-fired CHP plant for DH*

* Technical assumptions according to table 4

- 98 %

1,628,000

280,000110,000

Allocation

in 2013

Allocation

in 2020

2nd EU ETS period


to double BM

3rd EU ETS period

Annual allocation of a gas-fired CCGT-CHP plant

for DH*

- 94 %

(Amount of freely allocated emission allowances)

(Amount of freely allocated emission allowances)5,115,000

280,000110,000

Allocation

in 2013

Allocation

in 2020

2nd EU ETS period


to double BM

3rd EU ETS period

Annual allocation of a coal-fired CHP plant for DH*

* Technical assumptions according to table 4

- 98 %

1,628,000

280,000110,000

Allocation

in 2013

Allocation

in 2020

2nd EU ETS period


to double BM

3rd EU ETS period

Annual allocation of a gas-fired CCGT-CHP plant

for DH*

- 94 %



Figure 3: Comparison of the allocation mechanism for CHP plants with a heat capacity of

400 MWth in the second and third ETS allocation periods.

4 Economic evaluation of modified EU ETS allocation rules for CHP-based DH

4.1 Input parameters

In this section, we introduce the main input parameters of our economic evaluation model:

technical and operational parameters of the considered plants and historical commodity

prices. We use these parameters to quantify the economic feasibility of different CHP

applications for heat generation in the context of the modified EU ETS principles.

4.1.1 Technical and operational parameters

In our investigation we consider four different kinds of commercially available CHP

applications. The considered large-scale plants, such as coal-fired CHP and CCGT-CHP, are

typical units to cover the baseload demand of large urban DH networks. Small-scale plants,

such as engine CHP and micro-turbine CHP, are typical applications that feed into local heat

- 15 -

systems or supply isolated heat sinks, such as hospitals or public buildings. The large-scale

units are subject to the EU ETS, while the small-scale applications are not included. Table 4

contains the technical and operational assumptions made in our analysis. Technical

parameters, such as the installed capacity, are determined by the plant design and cannot be

changed easily, while operational parameters, such as utilization or O&M costs, can be

influenced through plant operation.

Table 4: Technical and operational input parameters for modeling CHP technologies

Coal-fired

CHP CCGT-CHP Engine CHP

Micro-turbine

CHP

Electrical capacity [MWel] 800 400 2 0.05

Heat capacity [MWth] 400 400 2.5 0.1

Fuel type Coal Natural gas Natural gas Natural gas

Electrical efficiency [%] 0.42 0.45 0.36 0.30

Total efficiency [%] 0.65 0.80 0.90 0.85

Fixed operation & maintenance cost

[€/kWel*a] 50 36 40 40

Variable operation & maintenance cost

[€/MWh*a] 2 1 0.7 0.5

Specific CO2 emissions [t CO2/MWhel] 0.750 0.360 0.495 0.660

Investment cost [€/kWel] 1,400 1,200 1,300 1,500

Depreciation period [a] 40 30 20 20

Capacity utilization,

power generation [%]

Mean 68 68 45 45

Standard deviation 8.4 12.8 5.7 8.5

Distribution normal normal normal normal

Capacity utilization,

heat generation [%]

Mean 45 45 45 45

Standard deviation 5.7 5.7 5.7 5.7

Distribution normal normal normal normal

Source: Operational data derived from existing E.ON plants

4.1.2 Commodity price assumptions

The commodity prices used in our model are taken from the German commodity markets. As

input parameters we take the historical commodity prices and correlations, reported in table 5.

Means and standard deviations of the price distributions are derived from daily market prices

in the time period from October 1, 2007 to September 30, 2010. All commodity prices are

characterized by a log-normal distribution. As transparent market data of heat prices in

Germany are unavailable, we derive the heat price based on alternative costs for heat

generation. This is a common approach, but in principle there are several options to determine

the price for heat produced in CHP installations (see Phylipsen et al., 1998). In our model we

- 16 -

assume a heat production according to the reference efficiency of stand-alone steam

production. As reference efficiency we take 90%, which corresponds to the reference values

as mentioned in the European Commission‘s Decision 2007/74/EC (CEC, 2007). In our

model we further assume a linear increase in commodity prices with a constant rate of 2% per

year.

Table 5: Commodity price assumptions for the NVP calculation

Price variable Mean Standard deviation

Power a 50.24 €/MWhel 19.43 €/MWhel

Hard coal b 9.22 €/MWhth 2.66 €/MWhth

Natural gas c 18.61 €/MWhth 6.54 €/MWhth

CO2 allowances d 17.61 €/t CO2 5.28 €/t CO2

Heat e 20.68 €/MWhth 7.27 €/MWhth

Price correlation

coefficient Power

a Hard coal

b Natural gas

c CO2 allowances

d

Power a 1.000 0.567 0.692 0.556

Hard-coal b 0.567 1.000 0.766 0.820

Natural gas c 0.692 0.766 1.000 0.727

CO2 allowances d 0.556 0.820 0.727 1.000

Sources:

a EEX Price Index: Phelix Day Base,

b API#2 Index: ARA Quarterly Futures Price,

c EEX Gas Spot

Market EGT, d CO2 Allowance Price Phase 2,

e Calculated on the basis of a gas-fired boiler with an efficiency of

90%.

4.2 Results of the economic evaluation

Our economic evaluation is based on a discounted cash-flow model that generates net present

value (NPV) distributions of various CHP technologies feeding into DH networks. The

discounted cash-flow method values an asset by estimating future cash flows and discounting

them back to the present value. This is a common approach, which has already been used for

several applications in the context of power generation facilities (see e.g. Roques, 2008). Our

model considers all costs and revenues of the CHP plant and generates an annual cash flow

- 17 -

that is subsequently discounted with a discount rate of 8%. For large-scale plants that are

subject to the EU ETS, we consider two different allocation mechanisms for CO2 allowances.

First, we calculate the NPV distribution based on the double benchmark principle, as currently

implemented in the German National Allocation Plan. In a second step, we assume an

allocation according to the principles stipulated in EU Directive 2009/29/EC. Both options

differ concerning the treatment of the CO2 allowances costs (CCO2) that are determined

according to the following formula:

22)( COXCO pAEC , (3)

where E stands for the total emissions of the CHP plant and AX represents the total free

allocation of emission allowances in the considered period as defined previously in (1) and (2)

for various allocation mechanism. pCO2 is the stochastic price of the allowances characterized

by mean and volatility as reported in table 5.

For the previously defined CHP technologies, we compute the probability distributions of the

NPVs by running a Monte Carlo simulation with 50,000 runs. Hereby, electricity, fuel, and

CO2 prices are modeled by random variables on the basis of historical volatilities (see table

5). The capacity utilization is also considered as a random variable (see table 4). The NPV is

very sensitive to commodity prices, plant utilization, investment costs, and discount rate. The

results of the model are shown in table 6.

Table 6: Results from the NPV calculation for the different technologies

Allocation mechanism Expected

mean of NPV

Standard

deviation of

NPV

Internal rate

of return

(IRR)

[€/kW] [€/kW] [%]

Coal-fired CHP

Double benchmark 126 1,205 9.0

According to EU Directive

2009/29/EC -806 1,895 -57.6

Gas-fired CCGT-CHP

Double benchmark 117 1,401 9.8


2009/29/EC -456 1,571 -38.0

Engine CHP n.a. 46 598 5.8

Micro-turbine CHP n.a. 23 413 1.5

- 18 -

We find that the economic attractiveness of large-scale CHP installations for district heating is

dramatically reduced through the modified allocation principles, as established in EU

Directive 2009/29/EC. The internal rate of return (IRR) of new coal-fired CHP steam plants is

reduced from 9.0% to -57.6% and the IRR of gas-fired CCGT-CHP plants from 9.8% to

-38.0% (under the assumption that commodity price levels remain at the same level). Thus,

the intended allocation principles for the third ETS period render investments in large-scale

CHP installations for DH largely uneconomical. In contrast, NPVs and IRRs of small-scale

CHP units for local heat supply are not affected by the modification of allocation principles,

as their thermal-firing capacity is far below 20 MWth and they are therefore not implemented

in the EU ETS.

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

-3750 -2500 -1250 0 1250 2500 3750

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

-3.750 -2.500 -1.250 0 1.250 2.500 3.750

(a) Hard-coal-fired steam CHP plant

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

(b) Gas-fired CCGT-CHP plant

Net present value [€/kW]

Allocation according to double benchmark principle

Probability

distribution [%]

-3,750 -2,500 -1,250 1,250 2,5000

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

Probability

distribution [%]

-3,750 -2,500 -1,250 1,250 2,5000


Allocation according to EU Directive 2009/29/EC

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

-3750 -2500 -1250 0 1250 2500 3750

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

-3.750 -2.500 -1.250 0 1.250 2.500 3.750

(a) Hard-coal-fired steam CHP plant

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

(b) Gas-fired CCGT-CHP plant


Allocation according to double benchmark principle Allocation according to double benchmark principle

Probability

distribution [%]

-3,750 -2,500 -1,250 1,250 2,5000

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

Probability

distribution [%]

-3,750 -2,500 -1,250 1,250 2,5000


Allocation according to EU Directive 2009/29/ECAllocation according to EU Directive 2009/29/EC

Figure 4: Effect of the modified allocation rules on the NPV distribution of large-scale CHP

plants for DH applications (discount rate 8%)

Figure 4 illustrates the effect of an allocation according to EU Directive 2009/29/EC on the

NPV distributions of large-scale CHP plants for DH applications. For both technologies

investigated, the expected mean of the NPV decreases from 126 €/kW (117 €/kW) to -806

€/kW (-456 €/kW) for coal-fired CHP plants (gas-fired CCGT-CHP plants) if the modified

allocation rules are applied. For coal technology, the standard deviation of the NPV increases

from 1,205 €/kW to 1,895 €/kW. Due to the reduction of freely allocated certificates, plant

operators need to purchase significantly more emission allowances at the CO2 market. This

- 19 -

increases the exposure to volatile EUA prices and, subsequently, the standard deviation of the

NPV. For the CCGT-CHP technology, this effect is less significant, as the carbon intensity of

natural gas is lower. Consequently, CCGT technology receives relatively to the output more

free allowances and less EUAs need to be purchased at volatile market prices.

The dependence of the NPV on the market price for European emission allowances is also

significantly increased after the introduction of the new ETS Directive. This effect is

illustrated in figure 5. Under the assumption that free allowances are allocated according to

the double benchmark principle, the NPV at an EUA price level of 30 €/tCO2 is for both

technologies in the range of 0 €/kW. If the modified allocation rules according to EU

Directive 2009/29/EC are introduced, the NPV is highly negative at an EUA price level of 30

€/tCO2, an effect that is more significant for coal-fired CHP (-1,437 €/kW) than for CCGT-

CHP (-1,120 €/kW).

-1500

-1250

-1000

-750

-500

-250

0

250

500

0 5 10 15 20 25 30

500

0

-500

-1,000

-1,500

Coal-fired CHP allocation according to double benchmark

CCGT-CHP allocation according to double benchmark

Coal-fired CHP allocation according to EU Directive 2009/29/EC

CCGT-CHP allocation according to EU Directive 2009/29/EC

EUA price [ € / tCO2 ]

0 5 10 15 20 25 30

Net present value

[ € / kW ]

250

-250

-750

-1,250

-1500

-1250

-1000

-750

-500

-250

0

250

500

0 5 10 15 20 25 30

500

0

-500

-1,000

-1,500

Coal-fired CHP allocation according to double benchmark

CCGT-CHP allocation according to double benchmark

Coal-fired CHP allocation according to EU Directive 2009/29/EC

CCGT-CHP allocation according to EU Directive 2009/29/EC

EUA price [ € / tCO2 ]

0 5 10 15 20 25 30

Net present value

[ € / kW ]

250

-250

-750

-1,250

Figure 5: NPV of large-scale CHP plants for DH as a function of the price for EU emission

allowances

- 20 -

5 Impact of modified allocation rules on investments in new CHP plants for DH

In this section we investigate the impact of the modified EU ETS framework on investment

decisions in new CHP plants for DH applications. The investigation is based on a real options

(RO) model that uses specific spreads to quantify the uncertainty of investment decisions. In

contrast to the static ―now or never‖ proposition of the NPV analysis, the RO approach

includes the possibility of delaying an investment under uncertainty and considers the value of

waiting as part of the decision-making problem.

5.1 Real options approach

The RO approach, as introduced by McDonald and Siegel (1986), Pindyck (1988, 1991,

1993), and Dixit and Pindyck (1994), is an often applied method to evaluate investment

decisions under uncertainty. During the last few years, in a number of studies, RO theory has

been applied to decision-making problems in the energy sector and here especially on

investment decisions in new power generation assets. It would be far beyond the scope of this

article to provide a complete overview of the available approaches proposed in the relevant

literature that differ in model design, modeling of stochastic variables, and choice and

definition of input parameters (for a more detailed overview, see Westner and Madlener,

2010a). We just want to briefly review some recently published research results that apply

real options on CHP generation, consider the impact of emission trading systems on

investment decisions, or use the spread as input parameter for the RO evaluation.

Deng et al. (1999) develop for the first time a spread-based RO model to evaluate electricity

derivatives. In their approach, which is based on future contracts for electricity and fuels, they

apply geometric Brownian motion as well as mean reverting price processes to characterize

spark spread options. The investigation of Deng et al. was done before the introduction of

emission trading systems and, therefore, does not consider the costs of carbon allowances.

Laurikka and Koljonen (2006) investigate the impact of the EU ETS on investments in power

plants. Their research takes specific spreads between commodity prices as input parameter

and focuses on the situation during the first trading period in Finland. Under consideration of

the EU ETS, they investigate two options: the option to postpone the investment and the

option to alter plant operation. They consider a hypothetical condensing power plant with an

electrical capacity of 250 MWel and conclude that the impact of emission trading depends not

only on the expected level of prices for carbon allowances, but also on their volatility and

- 21 -

correlation with electricity and fuel prices. Wickart and Madlener (2007) apply RO theory on

CHP generation and investigate the decision-making problem of an industrial firm,

considering whether to invest either in CHP generation or in heat-only production. According

to their findings, simplistic NPV calculation can be misleading when estimating economic

CHP potentials. Kumbaroğlu et al. (2008) analyze diffusion prospects of new renewable

power generation technologies by applying RO theory. They include learning curve

information in their model and consider price uncertainties concerning the wholesale

electricity price and the input fuel prices through stochastic processes. The results of their

research show that the flexibility to delay irreversible investments can profoundly affect the

diffusion prospects of renewable power generation technologies. Siddiqui and Maribu (2009)

develop and apply an RO model for microgrids that consist of small-scale CHP applications.

In order to reduce the risk exposure, they investigate various investment strategies and come

to the conclusion that a direct investment strategy in microgrids is more beneficial for low

levels of gas price volatility, whereas a sequential strategy is preferable in the case of high

price volatility. Fleten and Näsäkkälä (2010) apply RO theory to new gas-fired power plants

in liberalized energy markets with volatile electricity and natural gas prices. Their model is

based on the spark spread, defined as the difference between the price of electricity and the

cost of gas used for power generation. They derive the value of operating flexibility, and find

thresholds for energy prices that are optimal for entering into investments. Westner and

Madlener (2010a) apply a spread-based RO approach to analyze the decision-making problem

of an investor who has the choice between an irreversible investment in a condensing power

plant without heat utilization and a plant with CHP generation. They find that the specific

characteristics of CHP plants have a significant impact on the option value and, therefore, on

the optimal timing to invest.

5.2 Model description

The RO model that we use here is based on specific spreads. The specific spread per MWh is

the difference between the prices of the produced outputs (power and heat) and the costs of

the input factors (e.g. fuel and emission allowances) and represents the contribution margin

that a plant operator earns for converting fuels into electrical power. Specific spread, plant

utilization, and fixed costs define the profit of a CHP plant. In our simplified model, we

consider plant utilization and fixed costs as constant parameters without volatile deviations to

make the effect of changes in the allocation principles more transparent. With this approach

we assume that there are no impacts of the adjusted ETS rules on the operational

- 22 -

characteristics and that the average utilization is constant over the lifetime of the plant. Our

model considers the costs of CO2 emissions, as we take the so-called clean spreads for our

investigation. The specific spread (S) of a CHP plant in € per MWh is defined as

2CO

el

FCHPHE C

CPRPS , (4)

where PE is the market price of electrical power in €/MWh, RH represents the additional

revenues through heat sales and PCHP represents the governmental promotion for CHP

generation (both in €/MWh), CF denotes the fuel costs in €/MWhth and ηel the electrical

efficiency of the condensing plant. CCO2 denotes the cost of CO2 emissions as defined in (3).

In our approach the specific spread contains governmental subsidies that are paid for highly

efficient CHP plants in several member states of the EU (see Westner and Madlener, 2010b).

Note that the specific spread S of a generation technology is affected by the development of

prices for electricity, fuel, and CO2 allowances in competitive commodity markets, and can

take positive and negative volumes. Based on historical commodity prices, as described in

table 5, we derive the characteristic parameters of the specific spreads in Germany during the

time period October 1, 2007 until September 30, 2010. The historical development of the

specific spread is best described by a normal distribution.

Table 7: Characteristics of the specific spread for various generation technologies

Allocation mechanism Expected

“clean” spread

Standard deviation of

the “clean” spread

[€/MWh] [€/MWh]

Coal-fired CHP

Double benchmark 15.27 12.75


2009/29/EC 7.94 17.67

Gas-fired CCGT-CHP

Double benchmark 9.87 16.06


2009/29/EC 5.12 18.21

Engine CHP n.a. 8.88 14.07

Micro-turbine CHP n.a. 3.71 14.31

Source: Own calculation, based on Eq. (4), with input parameter reported in table 5

- 23 -

In our investigation we assume that the specific spread of technology i evolves according to

the geometric Brownian motion

dzdtS

dSii SS

i

i , (5)

where αSi and σSi are constants that describe the drift and volatility of the specific spread of

generation technology i, dt is an infinitesimal time increment, and dz is the increment of a

Wiener process. The decisive parameter in our RO model is the volatility of the aggregated

annual spread. Therefore, in the following investigation we ignore the drift and assume αSi =

0.

The decision to invest in a power plant can be interpreted as an optimal stopping problem and

can be solved by using a dynamic programming approach. The value of the option to invest

F(V) in a power plant is given by the Bellman equation

ρF(V)dt = E[dF(V)] . (6)

This equation implies that holding an option with the value F(V) over the period dt yields an

expected gain of E[dF(V)]. The expected gain needs to be equal to the return ρF(V)dt, where ρ

represents the discount rate of the investor. By applying Itô‘s Lemma, we derive the partial

differential equation

))((')²)(("2

1)( dVVFdVVFVdF . (7)

Substituting (5) into (7) and given that E(dz) = 0, we obtain

dtVVFdtVFVVdFEii SS )(')("²²

2

1)]([ . (8)

By substituting (8) into (6), we derive

0)()(')("²²2

1VFVVFVFV

ii SS . (9)

- 24 -

In addition, Vi must satisfy the following boundary conditions:

0)0(F (10)

IVVF **)( (11)

1*)(' VF . (12)

Condition (10) arises from the observation that if the value goes to zero, it will remain zero

(this is an implication of the stochastic process described in (5)). V* represents the critical

plant value at which it is optimal to invest and (11) is the value-matching condition that

defines the net payoff (V* - I) of the investor. Equation (12) is the so-called smooth-pasting

condition that guarantees that the gradient of the first deviation is equal at the exertion point.

To get the value F(V) of the investment option, we need to solve (9) subject to the boundary

conditions (10)-(12). Equation (9) represents a second-order homogeneous differential

equation that is linear in the dependent variable F and its derivatives. The general solution can

be expressed as a linear combination of two independent solutions and written as

21

21)( VAVAVF , (13)

where A1 and A2 are constants and β1 and β2 are the roots of the quadratic function

0)()1(2

1 2

ii SS . (14)

Note that β1 and β2 depend on the parameters αSi and σSi of the differential equation and on the

discount rate of the investor ρ in the following way:

12

2

1

2

12

2

22

ii

i

i

i

SS

S

S

S

i . i = {1,2} (15)

In our case, boundary condition (10) implies that A2=0, so that the solution takes the form

1)( AVVF . (16)

- 25 -

The remaining boundary conditions can be used to define the two remaining unknowns: the

critical value V* at which it is optimal to invest, and the constant A:

IV1

*1

1 , (17)

1

1

1

1

11

1

1 )(

)1(

*)(

*

IV

IVA . (18)

Based on the given equations, it is possible to analytically determine option values of the

above-described CHP technologies under various assumptions concerning the EU ETS

allocation mechanism.

5.3 Discussion and interpretation of the results

In this section, we discuss the results gained with the described spread-based RO model and

derive consequences for utilities that intend to invest in new CHP plants for DH applications.

We further compare large-scale installations with small-scale decentralized CHP units that are

not subject to the EU ETS and draw conclusions on how the diffusion of these technologies

may be influenced by the adjustments of the emission trading system.

5.3.1 Impact of the EU ETS on investments in large-scale CHP applications for DH

The option value of irreversible investments in CHP generation technologies gives an

indication of the uncertainties and risks that need to be covered by an investor. In principle,

investments with high option values are more likely to be postponed or even cancelled in

comparison to investments with low option values. The results of our model show that option

values of large-scale CHP applications for DH, irrespective of whether they are coal-fired or

gas-fired, increase through the intended modifications of the allocation principles, as

described in EU Directive 2009/29/EC. In other words, the implementation of the modified

allocation rules for CO2 emission allowances generally increases uncertainties and risks for

utilities that intend to invest in the CHP units for DH applications considered. For coal-fired

CHP units, as defined in table 4, the difference in the option value between an allocation

according to double benchmark and an allocation according to EU Directive 2009/29/EC

amounts to 6.56 €/MWh at the strike price of 18 €/MWh. For gas-fired CCGT-CHP plants,

the difference in the option value amounts to 2.96 €/MWh at a strike price of 16 €/MWh. This

- 26 -

result, as illustrated in figure 6, clearly shows that the economics of carbon-intensive

technologies, such as hard coal-fired CHP plants, are more negatively affected by the intended

adjustments of the EU ETS rules than gas-fired installations.

0

10

20

30

40

0 10 20 30 40

Specific spread [ € / MW ]

Op

tio

n v

alu

e [

€ /

MW

]

(a) Option value of coal-fired CHP plants

0

10

20

30

40

0 10 20 30 40


Op

tio

n v

alu

e [

€ /

MW

](b) Option value of gas-fired CCGT-CHP plants

Allocation according to EU Directive

2009|29|EC

Double benchmark

Intrinsic value

Specific spread [€/MW]Specific spread [€/MWh]

Op

tion

va

lue

[€/M

Wh]

Option v

alu

e[€

/MW

]

0

10

20

30

40

0 10 20 30 40

Op

tion

va

lue

[€/M

Wh]

0

10

20

30

40

0

Specific spread [€/MWh]

10 20 30 40


2009|29|EC

Double benchmark

Intrinsic value

0

10

20

30

40

0 10 20 30 40


Op

tio

n v

alu

e [

€ /

MW

]

(a) Option value of coal-fired CHP plants

0

10

20

30

40

0 10 20 30 40


Op

tio

n v

alu

e [

€ /

MW

](b) Option value of gas-fired CCGT-CHP plants


2009|29|EC

Double benchmark

Intrinsic value


2009|29|EC

Double benchmark

Intrinsic value


Op

tion

va

lue

[€/M

Wh]

Option v

alu

e[€

/MW

]

0

10

20

30

40

0 10 20 30 40

Op

tion

va

lue

[€/M

Wh]

0

10

20

30

40

0


10 20 30 40


2009|29|EC

Double benchmark

Intrinsic value


2009|29|EC

Double benchmark

Intrinsic value

Figure 6: Option values of large-scale CHP plants for different CO2 allocation mechanisms

5.3.2 Impact of modified EU ETS principles on the diffusion of small-scale CHP units

In a next step, we compare the option values of large-scale CHP plants for DH application

with small-scale units (engine CHP and micro-turbine CHP). These distributed small-scale

CHP units with a firing capacity below 20 MWth are not subject to the EU ETS and, therefore,

not affected by the modified allocation mechanism introduced post-2013. While the option

value of investments in large-scale CHP units increases through the modified allocation rules

for the third ETS period, the option values of the small-scale applications are not affected.

Figure 7 illustrates the difference in the option values of CHP installation for DH of different

size. Independently of the specific spread, the option value of small-scale CHP units is

constantly below the option value of the large-scale plants considered. Note that this effect is

independent of the fuel type of the large-scale plant. Consequently, investments in small-scale

CHP units imply lower risks and uncertainties in comparison to large-scale plants. The

preference of investors for small-scale distributed heat production might therefore increase.

- 27 -

0

10

20

30

40

0 10 20 30 40

0

10

20

30

40

0 10 20 30 40

(a) Option value of a coal-fired CHP plants in comparison

to small-scale installations

(b) Option value of a gas-fired CCGT-CHP plant in comparison



Op

tion

va

lue

[€/M

Wh]

Option v

alu

e[€

/MW

]

0

10

20

30

40

0 10 20 30 40

Op

tion

va

lue

[€/M

Wh]

0

10

20

30

40

0


10 20 30 40

Coal-fired CHP plant (allocation

according to EU Directive 2009|29|EC)

Engine CHP

Intrinsic value

Micro-turbine CHP

Gas-fired CCGT-CHP plant (allocation


Engine CHP

Intrinsic value

Micro-turbine CHP

0

10

20

30

40

0 10 20 30 40

0

10

20

30

40

0 10 20 30 40

(a) Option value of a coal-fired CHP plants in comparison


(b) Option value of a gas-fired CCGT-CHP plant in comparison



Op

tion

va

lue

[€/M

Wh]

Option v

alu

e[€

/MW

]

0

10

20

30

40

0 10 20 30 40

Op

tion

va

lue

[€/M

Wh]

0

10

20

30

40

0


10 20 30 40



Engine CHP

Intrinsic value

Micro-turbine CHP



Engine CHP

Intrinsic value

Micro-turbine CHP



Engine CHP

Intrinsic value

Micro-turbine CHP



Engine CHP

Intrinsic value

Micro-turbine CHP

Figure 7: Option value of large-scale units for CHP-based DH in comparison to small-scale

installations

6 Conclusion

In this paper we have investigated the impact of modified EU ETS principles for the third

emission trading period on the economics of CHP plants that feed into DH networks. The

CHP technologies considered bear a substantial potential to increase energy efficiency and

reduce CO2 emissions compared to separate power and heat production. Our investigation

applies a discounted cash-flow model to quantify the economic effects of the adjusted EU

ETS principles as well as a spread-based real options model to analyze their impact on

investment decisions in new CHP plants for DH applications. The result of the discounted

cash-flow model shows that the NPV of large-scale CHP plants is considerably reduced

compared to the situation with the current allocation mechanism for the second ETS period.

Our RO analysis leads to the conclusion that the modification of EU ETS principles increases

the option values of large-scale CHP plants compared to the current situation. This is

equivalent to an increase in risks and uncertainties for potential investors. Summarizing the

results, our research reveals that economic attractiveness of large-scale CHP generation that

feeds into DH systems is negatively affected by the introduction of the modified allocation

principles stipulated in EU Directive 2009/29/EC. This effect is more significant for coal-fired

plants than for gas-fired CCGT units, as the new allocation rules define a uniform benchmark

for heat generation that no longer differentiates between the carbon intensity of the applied

fuel. Economics of small-scale CHP units are not affected by the adjustments of the EU ETS.

They benefit indirectly from the intended modifications and gain competitive advantages in

- 28 -

comparison to large-scale installations. As a consequence, large-scale CHP installations may

gradually be replaced by small-scale distributed units. Such a development could have major

impacts on the future development of the total power supply systems.

Existing literature, such as Lovins et al. (2002) and Tsikalakis and Hatziargyriou (2007),

highlight the environmental benefits of distributed power supply systems, but their impact

needs to be considered more comprehensively. One possible implication of increased

decentralization is that of efficiency losses of the whole power supply system, as small-scale

units are often characterized by lower fuel efficiency in comparison to large-scale plants. This

development also leads to higher GHG emissions, as compared to a system with large-scale

CHP units. The total effect is hard to estimate, as the described efficiency reduction needs to

be weighted against the avoided losses for power and heat transportation, which are less

significant for distributed units due to shorter transmission distances. A further consequence

of an advanced decentralization is a decreasing share of solid fuels (e.g. hard-coal) in the

generation mix, due to a more difficult fuel logistic. This reduces fuel diversification and

increases the dependence of certain primary energy carriers, such as natural gas. Additionally,

higher specific investment costs of small-scale units in comparison to large-scale plants

contribute to increase total system costs and make power generation more expensive.

Furthermore, the exploitation of areas with a high heat demand (like big cities), that can

reasonably be supplied by large-scale applications, could be stopped due to economic

inefficiency. Consequently, a smaller stake of the existing DH potential will be developed

with all the negative impacts involved. Finally, monitoring and regulation of CO2 emissions

from distributed systems is more complex. This makes it more difficult for the responsible

authorities to control emissions and to achieve the aspired emission reduction targets.

In general, it needs to be clarified whether an adjustment of the EU ETS principles that leads

to an increase of distributed generation is actually intended by EU legislation. The political

target that highly efficient CHP installations for district heating should contribute an essential

part to future power and heat generation could be missed, due to the announced allocation

principles for the third ETS period.

- 29 -

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- 33 -

Appendix

A1. Results financial model

Table A1: Descriptive statistics of the NPV for different CHP technologies (in €/kWel)

Coal-fired CHP Coal-fired CHP Engine CHP Micro-turbine

CHP

Allocation

mechanism

Double

benchmark

According to

EU Directive

2009/29/EC

Double

benchmark

According to

EU Directive

2009/29/EC

n.a. n.a.

Runs 50,000 50,000 50,000 50,000 50,000 50,000

Mean 126.3 -806.2 117.1 -456.0 45.7 23.4

Mode --- --- --- --- --- ---

Standard deviation 1,204.7 1,895.1 1,401.4 1,571.2 598.2 413.0

Variance 1,451,213.7 3,591,574.6 1,963,975.6 2,468,135.3 357,801.9 170,569.0

Skewness 1.2 1.1 0.7 0.5 0.6 0.0

Kurtosis 5.5 5.9 5.2 4.9 5.1 4.6

Coeff. of variability 9.5 -2.3 12.0 -3.4 13.1 17.6

Minimum -3,350.6 -4,252.9 -6,708.2 -8,611.6 -3,199.1 -1,320.1

Maximum 9,600.1 9,871.7 11,235.5 10,920.7 5,280.6 1,470.9

Range width 12,950.7 14,124.6 17,943.6 19,532.2 8,479.8 2,791.0

A2. Results real options model

Table A2: Option values in dependence of the specific spread (in €/MW)

Specific Spread 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

Allocation mechanism

Coal-fired CHP

plant

Double Benchmark 0.0 2.3 5.3 8.5 12.0 15.7 19.5 23.5 27.5

According to EU

Directive 2009/29/EC 0.0 4.7 9.5 14.3 19.1 23.9 28.8 33.6 38.4

Gas-fired CCGT-

CHP plant

Double Benchmark 0.0 3.7 7.8 11.9 16.2 20.5 24.9 29.3 33.7

According to EU

Directive 2009/29/EC 0.0 4.9 9.8 14.8 19.7 24.6 29.6 34.5 39.5

Engine CHP n.a. 0.0 2.7 6.0 9.6 13.3 17.1 21.1 25.1 29.2

Micro-turbine

CHP n.a. 0.0 2.5 5.5 8.7 12.1 15.6 19.3 23.0 26.7

List of FCN Working Papers

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