a real options model for the disinvestment in conventional
TRANSCRIPT
FCN Working Paper No. 9/2015
A Real Options Model for the Disinvestment in
Conventional Power Plants
Barbara Glensk, Christiane Rosen and Reinhard Madlener
August 2015 Revised November 2018
Institute for Future Energy Consumer Needs and Behavior (FCN)
School of Business and Economics / E.ON ERC
FCN Working Paper No. 9/2015
A Real Options Model for the Disinvestment in Conventional Power Plants
October 2015
Revised November 2018
Authors’ addresses:
Barbara Glensk, Christiane Rosen, Reinhard Madlener Institute for Future Energy Consumer Needs and Behavior (FCN) School of Business and Economics / E.ON Energy Research Center RWTH Aachen University Mathieustrasse 10 52074 Aachen, Germany
E-Mail: [email protected], [email protected], [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 10, 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]
A Real Options Model for the Disinvestment
in Conventional Power Plants
Barbara Glensk, Christiane Rosen, and Reinhard Madlener∗
Institute for Future Energy Consumer Needs and Behavior (FCN),
School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University,
Mathieustrasse 10, 52074 Aachen, Germany
August 2015, revised November 2018
Abstract
The liberalization of the energy markets and the merit order effect lead to diffi-
culties in the operation even of modern, highly energy-efficient conventional power
plants (’missing money problem’). Their operation often becomes unprofitable, so
that sometimes the only remaining option is to liquidate the plant altogether. Deci-
sions about further operation or shut-down can be supported by applying real options
analysis. This approach has been used successfully for assessing investment projects
in different sectors of the economy including the energy supply industry. In this paper,
we develop a real options model for the disinvestment in conventional, fossil-fuelled
power plants. Applying the proposed real options approach, we aim to determine the
optimal timing for the shut-down of unprofitable gas-fired power plants as well as
the probability level for all possible decisions. The results show that the decision and
its probability value regarding continued operation, or the optimal timing for shut-
down of the power plant, depend strongly on the volatility level of the capacity factor.
Keywords: real options, renewables, gas-fired power plants, flexibility, energy
market
∗Corresponding author. Tel: +49 241 8049 820, fax: +49 241 8049 829,e-mail: [email protected] (R. Madlener).
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1 Introduction
The transition process in Germany’s energy sector, the so-called Energiewende, has been
evolving for over two decades and brings extraordinary challenges to energy as well as to
climate policy. With this transition process, Germany tries to reduce fossil fuel consump-
tion (decarbonizing the economy), as well as the phasing out of nuclear power. The three
main aims of the Energiewende are: (1) electricity from renewable energy sources (RES)
shall achieve one-third of total power generation by 2020; (2) primary energy consumption
shall fall 20% below 2008 levels by 2020, and 50% by 2050; (3) the emissions of greenhouse
gases shall be 40% lower by 2020, and 80–95% by 2050 (BMWi, 2014); these goals are
integrated into Germany’s Energy Concept, the federal government’s guiding document
for Germany’s energy policy to 2050 (BMUB, 2011).
The high increase in the share of electricity generated by RES in Germany has been
achieved, in large part, because of the German Feed-in-Tariff (FiT) scheme. However, this
“green policy” significantly affects the power generation sector and the profitable operation
of many modern conventional power plants. Their operation is changing as a consequence of
the increasing penetration of variable renewables energy sources (VRES) in the electricity
generation system and merit order effects (see Figure 1).
Figure 1: Merit order curveSource: Adopted from Benhmad and Percebois (2016)
From this perspective, decisions about the further operation or shut-down of conven-
tional power plants become more relevant and virulent. The other option is the possibility
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to operate the conventional power plant more flexibly, which can also improve its profitabil-
ity. On the management side, this leads to two possible remedy pathways apart from taking
no action. One can either decide to divest and liquidate the plant, or to invest in more
flexibility in order to be able to enter other markets, e.g. for reserve energy or capacity.
Both possibilities, from a financial point of view, constitute options. A useful methodology
to define when the power plant should be shut down, or for how long it should still be oper-
ated, in light of uncertainty and irreversibilities, is the real options approach (ROA) (Dixit
and Pindyck, 1994). This valuation technique is based on option pricing methods used in
finance and was developed by Black and Scholes (1973) and Merton (1973), respectively.
ROA is an application of traditional option theory to real commodities (such as power
plants) and constitutes a powerful alternative for determining their economic value. Real
options theory focuses on assessing the value of flexibility within projects under uncertainty.
This flexibility arises from the competence of managers to adjust projects in response to
materializing uncertainties associated with the projects and their environments. Moreover,
ROA can be applied to different projects, especially where flexibility is embedded in their
design. By taking the “value of waiting” explicitly into account its application can increase
the value of the projects and also the project’s competitiveness, but also leads to a higher
investment hurdle because of the lost option value in the case of an investment (cost of the
lost opportunity).
For the term real option (RO), several definitions exist, but in general RO is the right,
not the obligation, to adjust a project in response to the evolution of uncertainty. This ad-
justment can cause different actions that affect a project, such as postponing the project,
changes in the manner the project operates, selling the project etc. (Martinez Cesena,
2012). Optimal timing for an investment is a common research subject (option) in this
field, but in our case we investigate the opposite decision problem, thus contributing to
the literature, while observing strong practical relevance. The analysis of disinvestment
decisions considering uncertainty of the market, in recent years, has started to attract some
interest, e.g. in studies applied to the agricultural and diary sector. Moreover, disregard-
ing disinvestment options in decision-making processes can lead to incorrect valuations of
investment strategies at the firm level. The aim of this study is to develop a model for the
optimal dis investment of a power plant, where the optimal timing (when to disinvest) is an
essential parameter for the maximal profit (see also Glensk et al., 2014). Profitable power
generation depends on the capacity factor of the power plant, which in this study is used
as the underlying asset. This novel application of the real options approach to the power
3
generation sector can support power plant owners to make optimal strategic decisions.
The remainder of the paper is structured as follows. Section 2 reviews the literature on
applications of different real options models to the energy sector as well as the other sectors
of the economy. In Section 3, the disinvestment real options model is introduced, and how
it can be applied to power plants. A case study is presented in Section 4, illustrating
its application for a real world case, followed by a discussion of the results in Section 5.
Section 6 concludes.
2 Literature review
Numerous publications nowadays aim at making RO theory accessible to a wide range
of projects, presenting diverse fields of application as well as solution methods regarding
different circumstances. Some of these real options models as well as solution approaches
suitable to the problem in question can be found e.g. in Guthrie (2009) or Mun (2006). A
comprehensive review of the state-of-the-art in the application of ROA to the energy sector
(for non-renewable as well as for renewable energy sources) is provided in Fernandes et al.
(2011). The authors present briefly various applications of ROA in the oil industry, power
generation sector, energy markets, as well as emission mitigation policy, and emphasize
the necessity of further research especially regarding renewable energy projects.
A critical review of existing publications addressing the application of ROA to energy
generation projects is provided in Martinez Cesena (2012). The author describes the
different applications of the real options theory, discussed in the literature, but also existing
gaps and research opportunities in the area. The author refers to publications which
demonstrate that RO methods: (1) are more accurate than the standard discounted cash
flow analysis for estimating the value of projects under energy market uncertainty; (2)
allow to adjust projects in response to different types and sources of uncertainty (e.g.
electricity price or demand); (3) can be used to increase competition in the deregulated
market environment; or (4) are useful valuation methods when policy uncertainty and
environmental concerns are taken into consideration.
As mentioned before, the option to defer (for determining the optimal timing of an
investment) is the most common approach used also in the energy sector. In contrast,
the evaluation of disinvestment projects under uncertainty (decision to divest business
units, decision to abandon a technology, etc.) is a relatively rarely used approach. So far,
real options for disinvestment have been discussed and implemented only in a few applied
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articles especially in the fields of agriculture (e.g. Ihli et al., 2012 or Musshoff et al., 2013),
diary (Feil and Musshoff, 2013a), and production planning (Fontes, 2008). Our study, to
the best of our knowledge, is the first application to the energy sector.
The competitive and uncertain market environment influences the decision about flexi-
ble production (or capacity) planning significantly and gives the decision-maker possibilities
to consider different decision options. Fontes (2008) proposed the use of the real options
approach for the planning of the production capacity. For fixed and flexible production
systems he considers three standard options: the option to postpone an investment, the
option to abandon an investment, and the option to temporarily shut-down production. He
developed stochastic dynamic programming models implemented on a standard binomial
lattice in order to evaluate the investment decision numerically. His results show that an
increase in flexibility always leads to an increase in the predicted project value.
Especially in agriculture, where uncertainty regarding weather and climate conditions
is so visible, disinvestment decisions become more prominent. Ihli et al. (2012, 2013) com-
bine real options analysis (using the binomial approach) with experimental economics to
answer the question why farmers often decide to postpone the (dis)investment, even when
it appears to be (un)profitable to do so. In their study, they consider a simple optimal
stopping (dis)investment problem to invest or abandon the decision. They find that the
net present value (NPV) approach as well as ROA do not explain exactly the observed
behavior (decisions), but ROA provides better predictions of the (dis)investment behavior.
Musshoff et al. (2013) also applied a real options approach to analyze the (optimal) timing
of disinvestment decisions and to explain the experimentally observed disinvestment be-
havior of agricultural producers. Similar to the results of Ihli et al. (2012, 2013) they find
that real options models can predict actual disinvestment decisions better in comparison
to standard NPV models.
Feil and Musshoff (2013b) developed a flexible real options model which allows the
determination of firms’ optimal investment and disinvestment thresholds simultaneously for
different reversibility levels of the investment costs. They combine genetic algorithms and
stochastic simulation to solve the defined real options model. The authors investigated the
relevance of disinvestment options for the valuation of investment projects at the firm level,
and for the assessment of market interventions at the macroeconomic level. By applying the
proposed model to the German dairy sector they find that ignoring disinvestment options
can lead to incorrect valuations of investment strategies. On the one hand, the authors
explore the explanation potential of the model and, on the other hand, the limitations
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connected with some simplification made in the assumptions.
Regarding the energy sector, Glensk and Madlener (2018) developed a model for lignite-
fired power plants where three possible situations (continue operation, stop operation,
invest in retrofit measure) can be considered simultaneously. In their model, the disin-
vestment option is one of three possibilities which can be considered by the operator of
the power plant. Moreover, their proposed model can be seen as an extension of the RO
model developed here, where the option to reinvest and the unaltered continuation of the
operation are considered jointly with the option to stop the operation. The authors under-
took this analysis by simulating the optimal operation strategy for the power plant. The
proposed simulation of the operation strategy uses the marginal costs of the technology
and applies the electricity price as well as the spark spread as profitability indicators and
source of uncertainty. A similar analysis for gas-fired power plants can be found in Glensk
and Madlener (2015).
3 Real options model for power plant disinvestments
The real options approach is an extension of financial options theory and is used for the
valuation of real assets under uncertainty. The main assumption of this theory is that
projects embody options which, once identified and properly implemented, can increase
the expected value of the projects and decrease their risks. From this perspective, ROA
seems to be very beneficial for the assessment of a project’s value also in the energy sector.
By applying real options theory, the crucial question is always the optimal time, e.g.
to invest, to expand, to contract or to shut down. Therefore, choosing the optimal time to
abandon represents the greatest challenge related to the valuation of a disinvestment op-
tion. As mentioned in the previous section, real options models for disinvestment decisions
are still rare, especially in the energy sector. A theoretical discussion of the “option-to-
abandon” model and its application using the binomial lattice approach1 can be found e.g.
in Mun (2006).
The case of a disinvestment model for a gas-fired power plant is a rather novel applica-
tion of the real options approach in comparison to the more common research topic of the
optimal timing for a new investment. Moreover, by developing this model we contribute
1The binomial-lattice (tree) approach belongs to the main existing techniques used for the assessmentof financial as well as real options. In addition, tree technique simulations and partial differential equations(e.g. the Black & Scholes formula) are also relevant. Their application depends on certain types of optionsand projects. A brief overview of these approaches can be found e.g in Martinez Cesena (2012).
6
to the existing literature with the application of the power plant’s capacity factor as the
underlying asset in comparison to the standard use of the project value. The capacity
factor of the power plant defines the ratio of power plant’s actual output over a period of
time, to its potential output if it were possible for it to operate at full installed capacity
indefinitely. Besides of electricity price or spark spread normally used as underlying assets
also the application of this parameter is justified. The capacity factor vary greatly depend-
ing on (1) the type of fuel used in the power plant, (2) increasing share of VRES which
cause the shift the residual load along the ascending marginal cost curve (merit order) of
the thermal power plants (Brunner and Most, 2015). Still, regarding the energy sector
and increasing uncertainties in the energy market, policies and regulations, the practical
usefulness of such models becomes more relevant.
In our case, the optimal time of project abandonment (disinvestment) can be defined
using the simple binomial-tree approach which specifies how the underlying assets change
over time. The tree approach represents a robust RO solution technique that allows the
modeling of different types of sources of uncertainty and options in discrete scenarios. In
binomial-tree from the current state only two future states are possible, so-called “up” and
“down” movements, which correspond to some good and bad market (situation) develop-
ments. More precisely, we discretize the problem and set up a discrete-valued lattice, to
which the dynamic programming model (solved by backward induction) is applied.
In the proposed real options model the so-called ‘shut-down’ option is considered, which
means that the power plant operator receives an abandonment (or residual) value (i.e. the
selling price obtained for the power plant components minus decomposition costs) in a
situation where the present value of the power plant in operation is lower than the defined
residual value. For the analysis, we use the following assumptions:
• the time horizon for the real options model is equal to the lifetime of the power plant
analyzed;
• the underlying asset is the power plant’s capacity factor (number of full-load hours);
• the capacity factor is normally distributed and approximated by a binomial distri-
bution, resulting in a standard binomial lattice; and
• electricity, CO2, and gas prices are defined as stochastic variables (with underlying
distributions) and are introduced in the calculation of the power plant’s cash-flow.
Then, the shut-down option is taken when the present value of the power plant at the given
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electricity demand is less than the cost resulting from the power plant being in operation
/ or less than the residual value of the power plant.
The real options model where the optimal project value PVi,t(CFi,t) is a function of
the capacity factor, CFi,t, is given by:
PVi,t(CFi,t) = max
{RVt
PCFi,t +α·PVi,t+1(CFi,t+1)+(1−α)·PVi+1,t+1(CFi+1,t+1)
1+rf,
(1)
where RVt denotes the residual value, PCFi,t the project cash flow for the ith “down”
move at time period t, α defines the probability for the “up” movement, CFi,t denotes the
capacity factor for the ith “down” move at time t, rf the risk-free interest rate, and i the
number of “down” movements (i = 1, . . . , T −1). Because the underlying asset represented
here by the capacity factor is not the price of a traded asset, the risk-neutral probability
α is calculated according to the formula
α =K − down
up− down, (2)
where K = E[CF ]−(E[RM ]−rf )β, E[CF ] is the expected proportional change in the state
variable, E[RM ] the expected return on the market portfolio, and β the beta coefficient
(Guthrie, 2009). When calculating the project value for each period and each movement,
the corresponding capacity factor, electricity, fuel and CO2 prices, variable and fixed op-
eration and maintenance costs (O&M), as well as depreciation are taken into account. In
other words, the project value in each period is obtained as the maximum of the sum of
the optimal current period’s profit from operating the plant plus the optimal continuation
value (for the last period the continuation value is equal to zero) and the abandonment
value.
The solution procedure of the proposed model can be divided into three steps:
• Step 1. Definition of the state variable (in our case the capacity factor) and deter-
mination of its “up” and “down” movements used to set up the binomial tree for the
underlying asset. The “up” and “down” movements as well as risk-neutral probabil-
ities (eq. (2)) can be calculated using the probability distribution parameters from
the underlying asset (see e.g. Mun, 2006). Assuming a normal distribution of the
capacity factor (the underlying asset in our case), the “up” and “down” movements
are determined as follows:
up = e(σ√
∆t) and down = e(−σ√
∆t), (3)
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where σ is the associated volatility and ∆t is the time step.
• Step 2. Determination of the future cash-flow values of the existing project (power
plant) in each period using Monte Carlo simulation (in the software package Crystal
Ball R©) to capture the stochastic character of some of the cash-flow parameters.
• Step 3. The optimal project value is then calculated using recursive dynamic pro-
gramming according to eq. (1). If PVi,t(CFi,t) is equal to the residual value (RVt),
then the power plant should be shut down; otherwise, it should be kept in operation.
4 Case study
As mentioned before, modern and efficient conventional power plants nowadays have dif-
ficulties with the profitable operation in energy-only markets when there are high shares
of VRES. Moreover, the increasing share of renewable energy sources has pushed power
plants with larger marginal costs, such as gas-fired power plants, to the right-hand side in
the merit order of dispatch. Even though the energy-efficient gas-fired power plants nowa-
days offer very fast reaction times to cater fluctuating energy demands, their operation
hours decrease significantly and often the only remaining option is to liquidate the plant
altogether.
In this case study, we apply the proposed disinvestment real options model to a highly
energy-efficient (net thermal efficiency factor 59.7%) gas-fired power plant built in Germany
(commissioned in 2010), with a net installed capacity of 845 MW. Despite of its high
energy efficiency and flexible operation, this power plant has not been economically viable
(Stromklar, 2014). On the one hand, the gas price has developed unfavorably (see Figure
?? and especially the development between 2010 and 2014) and, on the other hand, the
electricity price at the wholesale market has decreased significantly (see Figure ??). The
crucial question to be answered with our model is when (i.e. at what point in time)
the operation of the power plant should be given up. Table 1 depicts the assumptions
considering technical characteristics as well as the economic parameter values used to
compute the optimal project values based on the methodology presented in the previous
section.
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Table 1: Technical and economic parameters for the power plant analyzed
Parameter Value
Total installed power(2,7) 845 MW (net)
Year of commissioning(2,7) 2010
Total lifetime 25 a
Net thermal efficiency for max load level(2,7) 59.7%
Net thermal efficiency for min load level(1) 49%
Specific CO2 emissions(3) 0.351 t CO2/MWh
Investment costs(7) 456.62 e/kW
Fixed O&M costs 10 e/kW(6); 20 e/kW(4)
Variable O&M costs(9) 0.33 e/MWh
WACC(8) 7.5%
Corporate tax(5) 29.58%
Electricity price (hourly – see Figure 2 (a)) Time series Jan 1, 2004 – Dec 31, 2017
Natural gas price (daily – see Figure 2 (b)) Time series Jan 1, 2004 – Dec 31, 2017
CO2 price (daily – see Figure 2 (c)) Time series Mar 3, 2005 – Dec 31, 2017
1 According Bine Informationsdenst (2015)2 E.ON (2018)3 Erdmann et al. (2017)4 Freund et al. (2012)5 http://www.tradingeconomics.com/germany/corporate-tax-rate6 Konstantin (2009)7 Mainova Press Releases (2010)8 Weighted average cost of capital, Pretax cost of capital of E.ON 20139 Roques et al. (2007)
Additionally, Figure 2 illustrates the development of the electricity, gas and CO2 prices.
The mean electricity price in the period 1/2004 – 12/2017 is 40.82 e/MWh with a standard
deviation of 17.13 e/MWh. Since the commissioning of the power plant in 2010, the
mean electricity price decreased to 37.94 e/MWh during the period 1/2010 – 12/2017.
Regarding the gas price, it can be noticed that its mean value is 20.21 e/MWh with a
standard deviation of 5.71 e/MWh in period 1/2004 – 12/2017, and increased slightly to
20.63 e/MWh over the period 1/2010 – 12/2017. The interesting development of the CO2
price with the significant peak in period 2007 – 2009 is illustrated in Figure 2 (c). The
average value of CO2 price in the period 2005 – 2017 is 8.68 e/EUA (European Union
allowance), with a standard deviation of 6.37 e/EUA, and decreases to 8.00 e/EUA in
the period 2010 – 2017.
10
(a) Electricity price
(b) Natural gas price
(c) CO2 price
Figure 2: Development of the electricity, gas and CO2 prices in Germany, 1/2014–12/2017Source: Own illustration, based on data from EEX
11
Based on the existing literature, and available online data regarding full-load hours of
gas-fired power plants, the impact of different values (more precisely value distributions)
of the capacity factor on the optimal project value and the final decision are analyzed.
Therefore, the capacity factor of the power plant and its stochastic values play a crucial
role in the analysis. Other stochastic variables, such as the electricity, gas, and CO2 prices
with their corresponding probability distributions are also included in the calculations.
5 Results
To analyze the influence of the capacity factor (the underlying asset and stochastic variable
in our study) on the power plant’s output, and thus also its current value, different values
of the probability distribution parameters for the capacity factor were considered.
Table 2 shows the two binomial lattices analyzed. The first tree (top) represents the
development of the capacity factor, normally distributed with mean µ = 0.14 and standard
deviation σ = 0.10 over the whole remaining operation time. The yellow part of this tree
illustrates the range of the capacity factor which, from a technical point of view, is not
achievable (i.e. technically infeasible). These states for the capacity factor are represented
as the “unreachable” decision on the second tree, the so-called decision tree (Table 2,
bottom). The decision “continue” or “stop” depends on the calculated project values (see
Appendix A, Table A.1) and the procedure proposed in Section 3 (step 3).
Analogously, Table 3 represents the development of the capacity factor, normally dis-
tributed with µ = 0.11 and σ = 0.30 (upper tree) and correspondent decision tree (bottom
tree). In comparison to Table 2, where the probability distribution parameters of the ca-
pacity factor were µ = 0.14 and σ = 0.10, more “unreachable” states can be observed and
the first “stop” decision appears early. A further increase of “unreachable” states can be
observed in Table 4, with the capacity factor parametrization µ = 0.18 and σ = 0.60. For
these probability distribution parameter values, the first “stop” decision appears already
in time period t = 2.
12
Table
2:
Bin
omia
lla
ttic
efo
ra
capac
ity
fact
orpro
bab
ilit
ydis
trib
uti
onw
ithµ
=0.
14an
dσ
=0.
10(u
pp
ertr
ee)
and
dec
isio
ns
(low
ertr
ee)
13
Table
3:
Bin
omia
lla
ttic
efo
ra
capac
ity
fact
orpro
bab
ilit
ydis
trib
uti
onw
ithµ
=0.
11an
dσ
=0.
30(u
pp
ertr
ee)
and
dec
isio
ns
(low
ertr
ee)
14
Table
4:
Bin
omia
lla
ttic
efo
ra
capac
ity
fact
orpro
bab
ilit
ydis
trib
uti
onw
ithµ
=0.
18an
dσ
=0.
60(u
pp
ertr
ee)
and
dec
isio
ns
(low
ertr
ee)
15
Note that the decision-making process regarding disinvestment is highly dependent on
the initial values of the capacity factor. Moreover, it can be noticed that the increased
values of the standard deviation significantly impact the development of our underlying
asset (capacity factor), and indirectly the development of the project value (see Tables
A.1-A.3). “Down” movements of the capacity factor cause a decrease in the project value
and lead to a faster shut-down decision, in contrast to the “up” movements, which cause
an increase of the project value and further operation of the power plant. However, the ca-
pacity factor’s values can become technically infeasible – in these cases, the tree calculated
from the disinvestment model needs to be truncated.
Considering the decision-maker’s position and the different decision possibilities, which
can appear in almost every period (see bottom trees in Tables 2-4), it seems to be important
to know which decision is the best one (or more probable) for each period. For that reason,
the probability of each possible decision – especially the continuation or interruption of
the power plant’s operation – was calculated; the results are presented in Table 5.
Table 5: Probability values for actions taken for different distribution parameter valuesof the capacity factor
16
One can see that with the increasing volatility of the capacity factor the probability of
the continuation decreases faster and the “stop decision” occurs earlier in time. Moreover,
more unreachable states are generated.
Furthermore, it should be mentioned that the residual value RVt, as defined in eq. (1),
has a significant impact on the final decision regarding how long the power plant’s operation
can be continued. The determination of its value is one of the most important and difficult
issues in this model. Based on literature research (see IEA, 2010, p.43 or Blesl et al.,
2012) we assumed the residual value to be constant at about 5% of the amount of a new
investment in a gas-fired power plant.
6 Discussion and conclusions
Increasing shares of renewable electricity generation supported by feed-in tariffs and pref-
erential dispatch, as well as decreasing electricity wholesale prices observed in recent years,
are consequences of Germany’s sustainable energy system transformation process (En-
ergiewende). These are also the reasons why owners of many modern and efficient conven-
tional power plants have huge problems with maintaining profitable operation. Due to these
circumstances, nowadays power plant operators are forced to take action, i.e. to change
their strategy and decide about either continuing operation and e.g. raise profitability by
flexibility enhancements or to shut-down their power plants.
With a real options approach, which takes into account the uncertainty about future
cash-flows, investment irreversibility and time flexibility, we present a new model aimed
at supporting the decision-making process. The proposed real options model can support
decisions concerning the disinvestment of conventional power plants. We have defined the
multi-period optimization problem, which was broken down into a sequence of simpler
problems, where at each point in time the decision on the action to be taken is made.
The capacity factor of the power plant (the underlying asset) was used as the stochastic
variable, assumed to be normally distributed and approximated by a binomial distribution,
which resulted in a binomial lattice. By applying a dynamic programming approach all
relevant information about the past were summarized by the current state of the project
and all relevant information about the future, also taking into account the market values of
the project after up and down movements. The project value was computed by backward
induction for each period, taking into account the level of the capacity factor on the
binomial lattice. Furthermore, the shut-down option was taken when the discounted project
17
value for the given capacity factor level at a particular point in time was less than the
residual value.
We find that the decision-making process is highly dependent on the initial underlying
asset value, in our case the capacity factor of the power plant and its subsequent devel-
opment. For an underlying asset, such as the capacity factor, it should be noted that its
values cannot exceed 100%. Moreover, even at less than 100% (within the range of technical
production possibilities of gas-fired power plants), we identified unreachable states in the
analysis that need to be dealt with. Its volatility level, which explains the changes in the
operation of the power plant, has a significant impact on the decisions and the probability
with which the decision can appear.
Regarding the disinvestment decision for a power generation plant, the capacity factor
is one of many other possible underlyings which can be considered. Because the capacity
factor of the power plant, which is equal to the number of operating hours, depends on
the electricity price offered for the generation, the rational and profit-oriented power plant
operator decides about electricity generation when the electricity price exceeds the costs of
generation (positive yield). Thus, as mentioned before the electricity price or spark spread
can also be considered as an underlying. From this perspective, the proposed model still
leaves some leeway for modifications.
From a decision-maker’s point of view, it should be noticed that for the option to
shut down the asset two possibilities can be distinguished: the temporary suspension of
electricity generation (mothballing) or the permanent decommissioning of the power plant.
The proposed model enables to consider only the second option, but provides the basis for
further research also for the first option.
The probabilities with which the possible decisions can appear in each period are also
important further results from applying the proposed model. The operator of the power
plant cannot directly determine from the model when he/she should make which decision.
As a start, the probability values help to refine the evaluation of the situation. Considering
different parameters for the probability distribution of the underlying asset supports a more
precise assessment with respect to market uncertainties.
Finally, the residual value, computed in a simplistic manner only, also impacts the
final decision, and for how long the power plant’s operation shall be continued. A more
sophisticated calculation of residual values provides plenty of scope for future research.
18
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21
List of Abbreviations
DCF Discounted cash flow
FiT Feed-in-tariff
NPV Net present value
O&M Operation and maintenance cost
RES Renewable energy sources
RO Real option
ROA Real options analysis
VRES Variable renewable energy sources
WACC Weighted average cost of capital
List of Symbols
α probability for the “up” movement
β beta coefficient
µ mean value
σ volatility
CF cash flow
E(CF ) expected value of cash flow
E(RM ) expected return of the market portfolio
PCF project cash flow
PV project value
rf risk-free interest rate
RV residual value
22
Appendix A
Table
A.1:
Bin
omia
lla
ttic
efo
rth
edev
elop
men
tof
pro
ject
valu
es(i
ne
1000
)fo
ra
capac
ity
fact
orw
ithµ
=0.
14an
dσ
=0.
10
23
Table
A.2:
Bin
omia
lla
ttic
efo
rth
edev
elop
men
tof
pro
ject
valu
es(i
ne
1000
)fo
ra
capac
ity
fact
orw
ithµ
=0.
11an
dσ
=0.
30
24
Table
A.3:
Bin
omia
lla
ttic
efo
rth
edev
elop
men
tof
pro
ject
valu
es(i
ne
1000
)fo
ra
capac
ity
fact
orw
ithµ
=0.
18an
dσ
=0.
60
25
List of FCN Working Papers
2015 Michelsen C.C., Madlener R. (2015). Beyond Technology Adoption: Homeowner Satisfaction with Newly Adopted
Residential Heating Systems, FCN Working Paper No. 1/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Garnier E., Madlener R. (2015). The Influence of Policy Regime Risks on Investments in Innovative Energy
Technology, FCN Working Paper No. 2/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March (revised October 2015).
Gläsel L., Madlener R. (2015). Optimal Timing of Onshore Repowering in Germany Under Policy Regime
Changes: A Real Options Analysis, FCN Working Paper No. 3/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.
Böhmer M., Madlener R. (2015). Evolution of Market Shares of New Passenger Cars in Germany in Light of CO2
Fleet Regulation, FCN Working Paper No. 4/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.
Schmitz H., Madlener R. (2015). Heterogeneity in Residential Space Heating Expenditures in Germany, FCN
Working Paper No. 5/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Ruhnau O., Hennig P., Madlener R. (2015). Economic Implications of Enhanced Forecast Accuracy: The Case of
Photovoltaic Feed-In Forecasts, FCN Working Paper No. 6/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, June.
Krings H., Madlener R. (2015). Modeling the Economic Viability of Grid Expansion, Energy Storage, and Demand
Side Management Using Real Options and Welfare Analysis, FCN Working Paper No. 7/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July.
Pon S. (2015). Effectiveness of Real Time Information Provision with Time of Use Pricing, FCN Working Paper
No. 8/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August (revised October 2015).
Glensk B., Rosen C., Madlener R. (2015). A Real Options Model for the Disinvestment in Conventional Power
Plants, FCN Working Paper No. 9/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August (revised November 2018).
2014 Sunak Y., Madlener R. (2014). Local Impacts of Wind Farms on Property Values: A Spatial Difference-in-
Differences Analysis, FCN Working Paper No. 1/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February (revised October 2014).
Garnier E., Madlener R. (2014). Leveraging Flexible Loads and Options-based Trading Strategies to Optimize
Intraday Effects on the Market Value of Renewable Energy, FCN Working Paper No. 2/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Kerres B., Fischer K., Madlener R. (2014). Economic Evaluation of Maintenance Strategies for Wind Turbines: A
Stochastic Analysis, FCN Working Paper No. 3/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March.
Loucao S., Madlener R. (2014). External Effects of Hydraulic Fracturing: Risks and Welfare Considerations for
Water Supply in Germany, FCN Working Paper No. 4/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.
Popov M., Madlener R. (2014). Backtesting and Evaluation of Different Trading Schemes for the Portfolio Management of Natural Gas, FCN Working Paper No. 5/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Madlener R., Reismann T. (2014). The Great Pacific Garbage Patch: A Preliminary Economic Analysis of the
‘Sixth Continent’, FCN Working Paper No. 6/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Blum J., Madlener R., Michelsen C.C. (2014). Exploring the Diffusion of Innovative Residential Heating Systems in
Germany: An Agent-Based Modeling Approach, FCN Working Paper No. 7/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July.
Tejada R., Madlener R. (2014). Optimal Renewal and Electrification Strategy for Commercial Car Fleets in
Germany, FCN Working Paper No. 8/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Galvin R., Madlener R. (2014). Determinants of Commuter Trends and Implications for Indirect Rebound Effects:
A Case Study of Germany’s Largest Federal State of NRW, 1994-2013, FCN Working Paper No. 9/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.
Garbuzova-Schlifter M., Madlener R. (2014). Risk Analysis of Energy Performance Contracting Projects in Russia:
An Analytic Hierarchy Process Approach, FCN Working Paper No. 10/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Kumar S., Madlener R., Suri I. (2014). An Energy System Analysis on Restructuring the German Electricity Market
with New Energy and Environmental Policies, FCN Working Paper No. 11/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Rosen C., Madlener R. (2014). Regulatory Options for Local Reserve Energy Markets: Implications for Prosumers, Utilities, and other Stakeholders, FCN Working Paper No. 12/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Rosen C., Madlener R. (2014). Socio-Demographic Influences on Bidding Behavior: An Ex-Post Analysis of an
Energy Prosumer Lab Experiment, FCN Working Paper No. 13/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Kumar S., Madlener R. (2014). A Least-Cost Assessment of the CO2 Mitigation Potential Using Renewable Energies in the Indian Electricity Supply Sector, FCN Working Paper No. 14/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Kammeyer F., Madlener R: (2014). Income Distribution Effects of the German Energiewende: The Role of Citizen
Participation in Renewable Energy Investments, FCN Working Paper No. 15/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Höfer T., Sunak Y., Siddique H., Madlener R. (2014). Wind Farm Siting Using a Spatial Analytic Hierarchy
Process Approach: A Case Study of the Städteregion Aachen, FCN Working Paper No. 16/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Garnier E., Madlener R. (2014). Day-Ahead versus Intraday Valuation of Demand Side Flexibility for Photovoltaic
and Wind Power Systems, FCN Working Paper No. 17/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Sluzalek R., Madlener R. (2014). Trade-Offs when Investing in Grid Extension, Electricity Storage, and Demand
Side Management: A Model-Based Analysis, FCN Working Paper No. 18/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Galassi V., Madlener R. (2014). Identifying Business Models for Photovoltaic Systems with Storage in the Italian
Market: A Discrete Choice Experiment, FCN Working Paper No. 19/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Illian K., Madlener R. (2014), Short-Term Energy Storage for Stabilizing the High Voltage Transmission Grid: A
Real Options Analysis, FCN Working Paper No. 20/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Oberst C.A., Madlener R. (2014). Regional Economic Determinants for the Adoption of Distributed Generation
Based on Renewable Energies: The Case of Germany, FCN Working Paper No. 21/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Oberst C.A., Madlener R. (2014). Prosumer Preferences Regarding the Adoption of Micro-Generation Technologies: Empirical Evidence for German Homeowners, FCN Working Paper No. 22/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Harmsen – van Hout M.J.W., Madlener R., Prang C.D. (2014). Online Discussion among Energy Consumers: A
Semi-Dynamic Social Network Visualization, FCN Working Paper No. 23/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Madlener R., Heesen F., Besch G. (2014). Determination of Direct Rebound Effects for Building Retrofits from
Energy Services Demand, FCN Working Paper No. 24/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Heesen F., Madlener R. (2014). Technology Acceptance as Part of the Behavioral Rebound Effect in Energy
Efficient Retrofitted Dwellings, FCN Working Paper No. 25/2014, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Schulz S., Madlener R. (2014). Portfolio Optimization of Virtual Power Plants, FCN Working Paper No. 26/2014,
Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
2013 Grieser B., Madlener R., Sunak Y. (2013). Economics of Small Wind Power Plants in Urban Settings: An Empirical
Investigation for Germany, FCN Working Paper No. 1/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, January.
Madlener R., Specht J.M. (2013). An Exploratory Economic Analysis of Underground Pumped-Storage Hydro
Power Plants in Abandoned Coal Mines, FCN Working Paper No. 2/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Kroniger D., Madlener R. (2013). Hydrogen Storage for Wind Parks: A Real Options Evaluation for an Optimal
Investment in More Flexibility, FCN Working Paper No. 3/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Petersen C., Madlener R. (2013). The Impact of Distributed Generation from Renewables on the Valuation and
Marketing of Coal-Fired and IGCC Power Plants, FCN Working Paper No. 4/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, February.
Oberst C.A., Oelgemöller J. (2013). Economic Growth and Regional Labor Market Development in German
Regions: Okun’s Law in a Spatial Context, FCN Working Paper No. 5/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March.
Harmsen - van Hout M.J.W., Ghosh G.S., Madlener R. (2013). An Evaluation of Attribute Anchoring Bias in a
Choice Experimental Setting. FCN Working Paper No. 6/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.
Harmsen - van Hout M.J.W., Ghosh G.S., Madlener R. (2013). The Impact of Green Framing on Consumers’
Valuations of Energy-Saving Measures. FCN Working Paper No. 7/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.
Rosen C., Madlener R. (2013). An Experimental Analysis of Single vs. Multiple Bids in Auctions of Divisible
Goods, FCN Working Paper No. 8/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April (revised November 2013).
Palmer J., Sorda G., Madlener R. (2013). Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An
Agent-based Simulation, FCN Working Paper No. 9/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Bruns S.B., Gross C. (2013). What if Energy Time Series are not Independent? Implications for Energy-GDP
Causality Analysis, FCN Working Paper No. 10/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, June.
Bruns S.B., Gross C., Stern D.I. (2013). Is There Really Granger Causality Between Energy Use and Output?,
FCN Working Paper No. 11/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Rohlfs W., Madlener R. (2013). Optimal Power Generation Investment: Impact of Technology Choices and
Existing Portfolios for Deploying Low-Carbon Coal Technologies, FCN Working Paper No. 12/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Rohlfs W., Madlener R. (2013). Challenges in the Evaluation of Ultra-Long-Lived Projects: Risk Premia for
Projects with Eternal Returns or Costs, FCN Working Paper No. 13/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.
Michelsen C.C., Madlener R. (2013). Switching from dFossil Fuel to Renewables in Residential Heating Systems:
An Empirical Study of Homeowners' Decisions in Germany, FCN Working Paper No. 14/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Rosen C., Madlener R. (2013). The Role of Information Feedback in Local Reserve Energy Auction Markets, FCN
Working Paper No. 15/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Himpler S., Madlener R. (2013). A Dynamic Model for Long-Term Price and Capacity Projections in the Nordic
Green Certificate Market, FCN Working Paper No. 16/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Weibel S., Madlener R. (2013). Cost-effective Design of Ringwall Storage Hybrid Power Plants: A Real Options
Analysis, FCN Working Paper No. 17/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Budny C., Madlener R., Hilgers C. (2013). Economic Feasibility of Pipeline and Underground Reservoir Storage
Options for Power-to-Gas Load Balancing, FCN Working Paper No. 18/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Johann A., Madlener R. (2013). Profitability of Energy Storage for Raising Self-Consumption of Solar Power:
Analysis of Different Household Types in Germany, FCN Working Paper No. 19/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Hackbarth A., Madlener R. (2013). Willingness-to-Pay for Alternative Fuel Vehicle Characteristics: A Stated
Choice Study for Germany, FCN Working Paper No. 20/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Katatani T., Madlener R. (2013). Modeling Wholesale Electricity Prices: Merits of Fundamental Data and Day-
Ahead Forecasts for Intermittent Power Production, FCN Working Paper No. 21/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Baumgärtner M., Madlener R. (2013). Factors Influencing Energy Consumer Behavior in the Residential Sector in
Europe: Exploiting the REMODECE Database, FCN Working Paper No. 22/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Charalampous G., Madlener R. (2013). Risk Management and Portfolio Optimization for Gas- and Coal-Fired
Power Plants in Germany: A Multivariate GARCH Approach, FCN Working Paper No. 23/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Mallah S., Madlener R. (2013). The Causal Relationship Between Energy Consumption and Economic Growth in
Germany: A Multivariate Analysis, FCN Working Paper No. 24/2013, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
2012 Ghosh G., Shortle J. (2012). Managing Pollution Risk through Emissions Trading, FCN Working Paper
No. 1/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, January. Palzer A., Westner G., Madlener M. (2012). Evaluation of Different Hedging Strategies for Commodity Price Risks
of Industrial Cogeneration Plants, FCN Working Paper No. 2/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March (revised March 2013).
Sunak Y., Madlener R. (2012). The Impact of Wind Farms on Property Values: A Geographically Weighted
Hedonic Pricing Model, FCN Working Paper No. 3/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May (revised March 2013).
Achtnicht M., Madlener R. (2012). Factors Influencing German House Owners' Preferences on Energy Retrofits,
FCN Working Paper No. 4/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, June.
Schabram J., Madlener R. (2012). The German Market Premium for Renewable Electricity: Profitability and Risk of Self-Marketing, FCN Working Paper No. 5/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July.
Garbuzova M., Madlener R. (2012). Russia’s Emerging ESCO Market: Prospects and Barriers for Energy
Efficiency Investments, FCN Working Paper No. 6/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July (revised September 2012).
Rosen C., Madlener R. (2012). Auction Design for Local Reserve Energy Markets, FCN Working Paper No.
7/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July (revised March 2013).
Sorda G., Madlener R. (2012). Cost-Effectiveness of Lignocellulose Biorefineries and their Impact on the
Deciduous Wood Markets in Germany. FCN Working Paper No. 8/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.
Madlener R., Ortlieb C. (2012). An Investigation of the Economic Viability of Wave Energy Technology: The Case
of the Ocean Harvester, FCN Working Paper No. 9/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Hampe J., Madlener R. (2012). Economics of High-Temperature Nuclear Reactors for Industrial Cogeneration,
FCN Working Paper No. 10/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Knaut A., Madlener R., Rosen C., Vogt C. (2012). Effects of Temperature Uncertainty on the Valuation of
Geothermal Projects: A Real Options Approach, FCN Working Paper No. 11/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Hünteler J., Niebuhr C.F., Schmidt T.S., Madlener R., Hoffmann V.H. (2012). Financing Feed-in Tariffs in
Developing Countries under a Post-Kyoto Climate Policy Regime: A Case Study of Thailand, FCN Working Paper No. 12/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Blass N., Madlener R. (2012). Structural Inefficiencies and Benchmarking of Water Supply Companies in
Germany, FCN Working Paper No. 13/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Madlener R., Schabram J. (2012). Predicting Reserve Energy from New Renewables by Means of Principal
Component Analysis and Copula Functions, FCN Working Paper No. 14/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Harzendorf F., Madlener R. (2012). Optimal Investment in Gas-Fired Engine-CHP Plants in Germany: A Real
Options Approach, FCN Working Paper No. 15/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Schmitz M., Madlener R. (2012). Economic Feasibility of Kite-Based Wind Energy Powerships with CAES or
Hydrogen Storage, FCN Working Paper No. 16/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Dergiades T., Madlener R., Christofidou G. (2012). The Nexus between Natural Gas Spot and Futures Prices at
NYMEX: Do Weather Shocks and Non-Linear Causality in Low Frequencies Matter?, FCN Working Paper No. 17/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December (revised September 2013).
Rohlfs W., Madlener R. (2012). Assessment of Clean-Coal Strategies: The Questionable Merits of Carbon
Capture-Readiness, FCN Working Paper No. 18/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Wüstemeyer C., Bunn D., Madlener R. (2012). Bridging the Gap between Onshore and Offshore Innovations by
the European Wind Power Supply Industry: A Survey-based Analysis, FCN Working Paper No. 19/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Fuhrmann J., Madlener R. (2012). Evaluation of Synergies in the Context of European Multi-Business Utilities,
FCN Working Paper No. 20/2012, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
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 (revised October 2012).
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 (revised July 2011).
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.
Madlener R., Ruschhaupt J. (2011). Modeling the Influence of Network Externalities and Quality on Market Shares
of Plug-in Hybrid Vehicles, FCN Working Paper No. 5/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March.
Juckenack S., Madlener R. (2011). Optimal Time to Start Serial Production: The Case of the Direct Drive Wind
Turbine of Siemens Wind Power A/S, FCN Working Paper No. 6/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March.
Madlener R., Sicking S. (2011). Assessing the Economic Potential of Microdrilling in Geothermal Exploration, FCN
Working Paper No. 7/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.
Bernstein R., Madlener R. (2011). Responsiveness of Residential Electricity Demand in OECD Countries: A Panel
Cointegration and Causality Analysis, FCN Working Paper No. 8/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.
Michelsen C.C., Madlener R. (2011). Homeowners' Preferences for Adopting Residential Heating Systems: A
Discrete Choice Analysis for Germany, FCN Working Paper No. 9/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May (revised January 2012).
Madlener R., Glensk B., Weber V. (2011). Fuzzy Portfolio Optimization of Onshore Wind Power Plants. FCN
Working Paper No. 10/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Glensk B., Madlener R. (2011). Portfolio Selection Methods and their Empirical Applicability to Real Assets in
Energy Markets. FCN Working Paper No. 11/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Kraas B., Schroedter-Homscheidt M., Pulvermüller B., Madlener R. (2011). Economic Assessment of a
Concentrating Solar Power Forecasting System for Participation in the Spanish Electricity Market, FCN Working Paper No. 12/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.
Stocker A., Großmann A., Madlener R., Wolter M.I., (2011). Sustainable Energy Development in Austria Until
2020: Insights from Applying the Integrated Model “e3.at”, FCN Working Paper No. 13/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July.
Kumbaroğlu G., Madlener R. (2011). Evaluation of Economically Optimal Retrofit Investment Options for Energy
Savings in Buildings. FCN Working Paper No. 14/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.
Bernstein R., Madlener R. (2011). Residential Natural Gas Demand Elasticities in OECD Countries: An ARDL
Bounds Testing Approach, FCN Working Paper No. 15/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.
Glensk B., Madlener R. (2011). Dynamic Portfolio Selection Methods for Power Generation Assets, FCN Working
Paper No. 16/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Michelsen C.C., Madlener R. (2011). Homeowners' Motivation to Adopt a Residential Heating System: A Principal Component Analysis, FCN Working Paper No. 17/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November (revised January 2013).
Razlaf J., Madlener R. (2011). Performance Measurement of CCS Power Plants Using the Capital Asset Pricing
Model, FCN Working Paper No. 18/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Himpler S., Madlener R. (2011). Repowering of Wind Turbines: Economics and Optimal Timing, FCN Working
Paper No. 19/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November (revised July 2012).
Hackbarth A., Madlener R. (2011). Consumer Preferences for Alternative Fuel Vehicles: A Discrete Choice
Analysis, FCN Working Paper No. 20/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December (revised December 2012).
Heuser B., Madlener R. (2011). Geothermal Heat and Power Generation with Binary Plants: A Two-Factor Real
Options Analysis, FCN Working Paper No. 21/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.
Rohlfs W., Madlener R. (2011). Multi-Commodity Real Options Analysis of Power Plant Investments: Discounting
Endogenous Risk Structures, FCN Working Paper No. 22/2011, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December (revised July 2012).
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.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 and December 2011).
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 (revised December 2010).
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 (revised May 2011).
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 (revised June 2011).
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 (revised July 2011).
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 (revised July 2012).
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.
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 (revised September 2010).
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., 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 November 2011).
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.
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 10, 52074 Aachen, Germany.