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]
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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
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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
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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
εCoal = 342 gCO2/kWhHu
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
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
εCoal = 342 gCO2/kWhHu
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
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 %
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 80%
81.81CCGT plant | Nat. Gas
εGas = 198 gCO2/kWhHu
Ʃ 40.70(Losses)
Primary energy demandPrimary energy demand
In total: 120.69
Fuel utilization ζ = 66%
Primary energy savings:
40.70 - 20
120.69= 17 %
CO2-Emission savings:
81.81 + 38.88 - 100= 17 %
81.81 + 38.88
ηth = 90 %38.88
Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
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 %
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 80%
81.81CCGT plant | Nat. Gas
εGas = 198 gCO2/kWhHu
Ʃ 40.70(Losses)
Primary energy demandPrimary energy demand
In total: 120.69
Fuel utilization ζ = 66%
Primary energy savings:
40.70 - 20
120.69= 17 %
CO2-Emission savings:
81.81 + 38.88 - 100= 17 %
81.81 + 38.88
ηth = 90 %38.88
Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
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
εCoal = 342 gCO2/kWhHu
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
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
εCoal = 342 gCO2/kWhHu
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
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 %
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 80%
81.81CCGT plant | Nat. Gas
εGas = 198 gCO2/kWhHu
Ʃ 40.70(Losses)
Primary energy demandPrimary energy demand
In total: 120.69
Fuel utilization ζ = 66%
Primary energy savings:
40.70 - 20
120.69= 17 %
CO2-Emission savings:
81.81 + 38.88 - 100= 17 %
81.81 + 38.88
ηth = 90 %38.88
Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
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 %
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 80%
81.81CCGT plant | Nat. Gas
εGas = 198 gCO2/kWhHu
Ʃ 40.70(Losses)
Primary energy demandPrimary energy demand
In total: 120.69
Fuel utilization ζ = 66%
Primary energy savings:
40.70 - 20
120.69= 17 %
CO2-Emission savings:
81.81 + 38.88 - 100= 17 %
81.81 + 38.88
ηth = 90 %38.88
Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
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 %
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 85%
Ʃ 73.11(Losses)
Primary energy demand
In total: 158.11
Fuel utilization ζ = 53%
Primary energy savings:
73.11 - 15
158.11= 37 %
CO2-Emission savings:
40 · 575 + 50 · 198 – 100 · 198= 40 %
40 · 575 + 50 · 198
ηth = 90 %50
Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
108.11German power mix
εMix = 575 gCO2/kWhel
Primary energy demand
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 %
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 85%
Ʃ 73.11(Losses)
Primary energy demand
In total: 158.11
Fuel utilization ζ = 53%
Primary energy savings:
73.11 - 15
158.11= 37 %
CO2-Emission savings:
40 · 575 + 50 · 198 – 100 · 198= 40 %
40 · 575 + 50 · 198
ηth = 90 %50
Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
108.11German power mix
εMix = 575 gCO2/kWhel
Primary energy demand
Combined Heat & PowerMicro-turbine CHP
Conventional GenerationGerman power mix + Gas-fired heat boiler
Power
Heat
100Micro Turbine CHP | Nat. Gas
ηel = 30 %
ηth = 52 %
30
52
18(Losses)
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 82%
Ʃ 56.88(Losses)
Primary energy demand
In total: 138.88
Fuel utilization ζ = 59%
Primary energy savings:
56.88 - 18
138.88= 27 %
CO2-Emission savings:
30 · 575 + 57.77 · 198 – 100 · 198= 31 %
30 · 575 + 57.77 · 198
ηth = 90 % 57.77Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
81.11German power mix
εMix = 575 gCO2/kWhel
Primary energy demand
ηel = 37 %
Combined Heat & PowerMicro-turbine CHP
Conventional GenerationGerman power mix + Gas-fired heat boiler
Power
Heat
100Micro Turbine CHP | Nat. Gas
ηel = 30 %
ηth = 52 %
30
52
18(Losses)
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 82%
Ʃ 56.88(Losses)
Primary energy demand
In total: 138.88
Fuel utilization ζ = 59%
Primary energy savings:
56.88 - 18
138.88= 27 %
CO2-Emission savings:
30 · 575 + 57.77 · 198 – 100 · 198= 31 %
30 · 575 + 57.77 · 198
ηth = 90 % 57.77Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
81.11German power mix
εMix = 575 gCO2/kWhel
Primary energy demand
ηel = 37 %
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 %
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 85%
Ʃ 73.11(Losses)
Primary energy demand
In total: 158.11
Fuel utilization ζ = 53%
Primary energy savings:
73.11 - 15
158.11= 37 %
CO2-Emission savings:
40 · 575 + 50 · 198 – 100 · 198= 40 %
40 · 575 + 50 · 198
ηth = 90 %50
Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
108.11German power mix
εMix = 575 gCO2/kWhel
Primary energy demand
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 %
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 85%
Ʃ 73.11(Losses)
Primary energy demand
In total: 158.11
Fuel utilization ζ = 53%
Primary energy savings:
73.11 - 15
158.11= 37 %
CO2-Emission savings:
40 · 575 + 50 · 198 – 100 · 198= 40 %
40 · 575 + 50 · 198
ηth = 90 %50
Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
108.11German power mix
εMix = 575 gCO2/kWhel
Primary energy demand
Combined Heat & PowerMicro-turbine CHP
Conventional GenerationGerman power mix + Gas-fired heat boiler
Power
Heat
100Micro Turbine CHP | Nat. Gas
ηel = 30 %
ηth = 52 %
30
52
18(Losses)
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 82%
Ʃ 56.88(Losses)
Primary energy demand
In total: 138.88
Fuel utilization ζ = 59%
Primary energy savings:
56.88 - 18
138.88= 27 %
CO2-Emission savings:
30 · 575 + 57.77 · 198 – 100 · 198= 31 %
30 · 575 + 57.77 · 198
ηth = 90 % 57.77Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
81.11German power mix
εMix = 575 gCO2/kWhel
Primary energy demand
ηel = 37 %
Combined Heat & PowerMicro-turbine CHP
Conventional GenerationGerman power mix + Gas-fired heat boiler
Power
Heat
100Micro Turbine CHP | Nat. Gas
ηel = 30 %
ηth = 52 %
30
52
18(Losses)
εGas = 198 gCO2/kWhHu
In total: 100
Fuel utilization ζCHP = 82%
Ʃ 56.88(Losses)
Primary energy demand
In total: 138.88
Fuel utilization ζ = 59%
Primary energy savings:
56.88 - 18
138.88= 27 %
CO2-Emission savings:
30 · 575 + 57.77 · 198 – 100 · 198= 31 %
30 · 575 + 57.77 · 198
ηth = 90 % 57.77Heat Boiler | Nat. gas
εGas = 198 gCO2/kWhHu
81.11German power mix
εMix = 575 gCO2/kWhel
Primary energy demand
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
Allocation according
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
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
Allocation according
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)
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
According to EU Directive
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
Net present value [€/kW]
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
Net present value [€/kW]
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
Net present value [€/kW]
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
According to EU Directive
2009/29/EC 7.94 17.67
Gas-fired CCGT-CHP
Double benchmark 9.87 16.06
According to EU Directive
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
Specific spread [ € / MW ]
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
Allocation according to EU Directive
2009|29|EC
Double benchmark
Intrinsic value
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
Specific spread [ € / MW ]
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
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
Allocation according to EU Directive
2009|29|EC
Double benchmark
Intrinsic value
Allocation according to EU Directive
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
to small-scale installations
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
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
according to EU Directive 2009|29|EC)
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
to small-scale installations
(b) Option value of a gas-fired CCGT-CHP plant in comparison
to small-scale installations
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
Coal-fired CHP plant (allocation
according to EU Directive 2009|29|EC)
Engine CHP
Intrinsic value
Micro-turbine CHP
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
according to EU Directive 2009|29|EC)
Engine CHP
Intrinsic value
Micro-turbine CHP
Gas-fired CCGT-CHP plant (allocation
according to EU Directive 2009|29|EC)
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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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
2011 Sorda G., Sunak Y., Madlener R. (2011). A Spatial MAS Simulation to Evaluate the Promotion of Electricity from
Agricultural Biogas Plants in Germany, FCN Working Paper No. 1/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, January.
Madlener R., Hauertmann M. (2011). Rebound Effects in German Residential Heating: Do Ownership and Income
Matter?, FCN Working Paper No. 2/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Garbuzova M., Madlener R. (2011). Towards an Efficient and Low-Carbon Economy Post-2012: Opportunities
and Barriers for Foreign Companies in the Russian Market, FCN Working Paper No. 3/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Westner G., Madlener R. (2011). The Impact of Modified EU ETS Allocation Principles on the Economics of CHP-
Based District Heating Networks. FCN Working Paper No. 4/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
2010 Lang J., Madlener R. (2010). Relevance of Risk Capital and Margining for the Valuation of Power Plants: Cash
Requirements for Credit Risk Mitigation, FCN Working Paper No. 1/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Michelsen C., Madlener R. (2010). Integrated Theoretical Framework for a Homeowner’s Decision in Favor of an
Innovative Residential Heating System, FCN Working Paper No. 2/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Harmsen - van Hout M.J.W., Herings P.J.-J., Dellaert B.G.C. (2010). The Structure of Online Consumer
Communication Networks, FCN Working Paper No. 3/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March.
Madlener R., Neustadt I. (2010). Renewable Energy Policy in the Presence of Innovation: Does Government Pre-
Commitment Matter?, FCN Working Paper No. 4/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April (revised June 2010).
Harmsen-van Hout M.J.W., Dellaert B.G.C., Herings, P.J.-J. (2010). Behavioral Effects in Individual Decisions of
Network Formation: Complexity Reduces Payoff Orientation and Social Preferences, FCN Working Paper No. 5/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Lohwasser R., Madlener R. (2010). Relating R&D and Investment Policies to CCS Market Diffusion Through Two-
Factor Learning, FCN Working Paper No. 6/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, June.
Rohlfs W., Madlener R. (2010). Valuation of CCS-Ready Coal-Fired Power Plants: A Multi-Dimensional Real
Options Approach, FCN Working Paper No. 7/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July.
Rohlfs W., Madlener R. (2010). Cost Effectiveness of Carbon Capture-Ready Coal Power Plants with Delayed
Retrofit, FCN Working Paper No. 8/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Gampert M., Madlener R. (2010). Pan-European Management of Electricity Portfolios: Risks and Opportunities of
Contract Bundling, FCN Working Paper No. 9/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Glensk B., Madlener R. (2010). Fuzzy Portfolio Optimization for Power Generation Assets, FCN Working Paper No. 10/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Lang J., Madlener R. (2010). Portfolio Optimization for Power Plants: The Impact of Credit Risk Mitigation and
Margining, FCN Working Paper No. 11/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.
Westner G., Madlener R. (2010). Investment in New Power Generation Under Uncertainty: Benefits of CHP vs.
Condensing Plants in a Copula-Based Analysis, FCN Working Paper No. 12/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.
Bellmann E., Lang J., Madlener R. (2010). Cost Evaluation of Credit Risk Securitization in the Electricity Industry:
Credit Default Acceptance vs. Margining Costs, FCN Working Paper No. 13/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.
Ernst C.-S., Lunz B., Hackbarth A., Madlener R., Sauer D.-U., Eckstein L. (2010). Optimal Battery Size for Serial
Plug-in Hybrid Vehicles: A Model-Based Economic Analysis for Germany, FCN Working Paper No. 14/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Harmsen - van Hout M.J.W., Herings P.J.-J., Dellaert B.G.C. (2010). Communication Network Formation with Link
Specificity and Value Transferability, FCN Working Paper No. 15/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Paulun T., Feess E., Madlener R. (2010). Why Higher Price Sensitivity of Consumers May Increase Average
Prices: An Analysis of the European Electricity Market, FCN Working Paper No. 16/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Madlener R., Glensk B. (2010). Portfolio Impact of New Power Generation Investments of E.ON in Germany,
Sweden and the UK, FCN Working Paper No. 17/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Ghosh G., Kwasnica A., Shortle J. (2010). A Laboratory Experiment to Compare Two Market Institutions for
Emissions Trading, FCN Working Paper No. 18/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Bernstein R., Madlener R. (2010). Short- and Long-Run Electricity Demand Elasticities at the Subsectoral Level:
A Cointegration Analysis for German Manufacturing Industries, FCN Working Paper No. 19/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Mazur C., Madlener R. (2010). Impact of Plug-in Hybrid Electric Vehicles and Charging Regimes on Power
Generation Costs and Emissions in Germany, FCN Working Paper No. 20/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Madlener R., Stoverink S. (2010). Power Plant Investments in the Turkish Electricity Sector: A Real Options
Approach Taking into Account Market Liberalization, FCN Working Paper No. 21/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Melchior T., Madlener R. (2010). Economic Evaluation of IGCC Plants with Hot Gas Cleaning, FCN Working
Paper No. 22/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Lüschen A., Madlener R. (2010). Economics of Biomass Co-Firing in New Hard Coal Power Plants in Germany,
FCN Working Paper No. 23/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Madlener R., Tomm V. (2010). Electricity Consumption of an Ageing Society: Empirical Evidence from a Swiss
Household Survey, FCN Working Paper No. 24/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Tomm V., Madlener R. (2010). Appliance Endowment and User Behaviour by Age Group: Insights from a Swiss
Micro-Survey on Residential Electricity Demand, FCN Working Paper No. 25/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Hinrichs H., Madlener R., Pearson P. (2010). Liberalisation of Germany’s Electricity System and the Ways
Forward of the Unbundling Process: A Historical Perspective and an Outlook, FCN Working Paper No. 26/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
FCN Working Papers are free of charge. They can mostly be downloaded in pdf format from the FCN / E.ON ERC Website (www.eonerc.rwth-aachen.de/fcn) and the SSRN Website (www.ssrn.com), respectively. Alternatively, they may also be ordered as hardcopies from Ms Sabine Schill (Phone: +49 (0) 241-80 49820, E-mail: [email protected]), RWTH Aachen University, Institute for Future Energy Consumer Needs and Behavior (FCN), Chair of Energy Economics and Management (Prof. Dr. Reinhard Madlener), Mathieustrasse 6, 52074 Aachen, Germany.
Achtnicht M. (2010). Do Environmental Benefits Matter? A Choice Experiment Among House Owners in Germany, FCN Working Paper No. 27/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
2009 Madlener R., Mathar T. (2009). Development Trends and Economics of Concentrating Solar Power Generation
Technologies: A Comparative Analysis, FCN Working Paper No. 1/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Madlener R., Latz J. (2009). Centralized and Integrated Decentralized Compressed Air Energy Storage for
Enhanced Grid Integration of Wind Power, FCN Working Paper No. 2/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November (revised September 2010).
Kraemer C., Madlener R. (2009). Using Fuzzy Real Options Valuation for Assessing Investments in NGCC and
CCS Energy Conversion Technology, FCN Working Paper No. 3/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Westner G., Madlener R. (2009). Development of Cogeneration in Germany: A Dynamic Portfolio Analysis Based
on the New Regulatory Framework, FCN Working Paper No. 4/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November (revised March 2010).
Westner G., Madlener R. (2009). The Benefit of Regional Diversification of Cogeneration Investments in Europe:
A Mean-Variance Portfolio Analysis, FCN Working Paper No. 5/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November (revised March 2010).
Lohwasser R., Madlener R. (2009). Simulation of the European Electricity Market and CCS Development with the
HECTOR Model, FCN Working Paper No. 6/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Lohwasser R., Madlener R. (2009). Impact of CCS on the Economics of Coal-Fired Power Plants – Why
Investment Costs Do and Efficiency Doesn’t Matter, FCN Working Paper No. 7/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Holtermann T., Madlener R. (2009). Assessment of the Technological Development and Economic Potential of
Photobioreactors, FCN Working Paper No. 8/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Ghosh G., Carriazo F. (2009). A Comparison of Three Methods of Estimation in the Context of Spatial Modeling,
FCN Working Paper No. 9/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Ghosh G., Shortle J. (2009). Water Quality Trading when Nonpoint Pollution Loads are Stochastic, FCN Working
Paper No. 10/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Ghosh G., Ribaudo M., Shortle J. (2009). Do Baseline Requirements hinder Trades in Water Quality Trading
Programs?, FCN Working Paper No. 11/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Madlener R., Glensk B., Raymond P. (2009). Investigation of E.ON’s Power Generation Assets by Using Mean-
Variance Portfolio Analysis, FCN Working Paper No. 12/2009, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
2008 Madlener R., Gao W., Neustadt I., Zweifel P. (2008). Promoting Renewable Electricity Generation in Imperfect
Markets: Price vs. Quantity Policies, FCN Working Paper No. 1/2008, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July (revised May 2009).
Madlener R., Wenk C. (2008). Efficient Investment Portfolios for the Swiss Electricity Supply Sector, FCN Working Paper No. 2/2008, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Omann I., Kowalski K., Bohunovsky L., Madlener R., Stagl S. (2008). The Influence of Social Preferences on
Multi-Criteria Evaluation of Energy Scenarios, FCN Working Paper No. 3/2008, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Bernstein R., Madlener R. (2008). The Impact of Disaggregated ICT Capital on Electricity Intensity of Production:
Econometric Analysis of Major European Industries, FCN Working Paper No. 4/2008, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.
Erber G., Madlener R. (2008). Impact of ICT and Human Skills on the European Financial Intermediation Sector,
FCN Working Paper No. 5/2008, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.