Evaluating the Enhanced Flexibility of Lignite-Fired Power Plants: a Real Options Analysis

FCN Working Paper No. 10/2016

Evaluating the Enhanced Flexibility of Lignite-Fired Power Plants: A Real Options Analysis

Barbara Glensk and Reinhard Madlener

August 2016 Revised June 2017

Institute for Future Energy Consumer Needs and Behavior (FCN)

School of Business and Economics / E.ON ERC

FCN Working Paper No. 10/2016

Evaluating the Enhanced Flexibility of Lignite-Fired Power Plants: A Real Options Analysis

August 2016 Revised June 2017

Authors´ addresses:

Barbara Glensk, 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]

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] Evaluating the Enhanced Flexibility of Lignite-Fired Power Plants: A Real Options Analysis

Barbara Glensk 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 2016, Revised June 2017

Abstract

In this paper we develop a decision tool that is based on real options analysis and that supports flexibility investment decisions concerning conventional lignite-fired power plants. The value of the power plant is influenced both by technical and economic variables, the latter including subsidies. The four-step approach proposed allows to determine the optimal operation strategy in light of electricity and fuel price developments, to simulate the project value, to determine the binomial lattice of the expected project value, and finally to infer the optimal management decision (based on the option to choose). For the case of an existing lignite-fired power plant in Germany, we find that the plant can be operated profitably without any modifications until the end of its technical lifetime only if current government subsidies persist. In the absence of subsidies, however, it is preferable to stop operation immediately. The analysis also shows that profitable reinvestments in a number of flexibility retrofit measures are possible, the ranking of which depends, however, on the costs related to the retrofitting measures and their implementation time.

Keywords: real options, ﬂexibility options, energy market, lignite-ﬁred power plants ∗Corresponding author. Tel: +49 241 8049 820, fax: +49 241 8049 829, e-mail: [email protected] (R. Madlener).

1 1 Introduction

The current situation in the German energy market can be characterized in brief as follows: extensive government promotion of renewable energy sources (Erneuerbare-Energien- Gesetz – EEG), a rapidly increasing share of renewable electricity, a decrease in electricity wholesale prices, difficulties in the profitable operation of conventional power plants, and unfavorable conditions for new investments (especially in conventional power generation). The underlying driving process of these facets is the sustainable energy transformation referred to as “Energiewende”. The fundamental question, especially for electrical energy providers, is that of what the future electricity market will look like. Which electricity market design will be able to ensure the security of power supply, to cover the production costs, and to enable innovation and sustainability, when a large share of the electrical power is derived from intermittent renewable energy sources? According to the German Federal Ministry for Economic Affairs and Energy (BMWi), the existing electricity market needs to be converted into an “electricity market 2.0”. In this new concept the conventional power plants will be assigned a new role of complementing the renewables (so-called “back-up capacities” to cover the variability of net electricity demand), and will be remunerated by the market mechanisms introduced (BMWi, 2015). In this context the flexible and efficient operation of the conventional power plants will become more important for their owners as well as for the entire electricity system. But what does “flexibility” mean with respect to a power system? According to IEA (2014, p.23) “In its widest sense, power system flexibility describes the extent to which a power system can adapt the patterns of electricity generation and consumption in order to maintain the balance between supply and demand in a cost-effective manner. In a narrower sense, the flexibility of a power system refers to the extent to which generation or demand can be increased or reduced over a timescale ranging from a few minutes to several hours in response to variability, expected or otherwise. Flexibility expresses the capability of a power system to maintain continuous service in the face of rapid and large swings in supply or demand, whatever the cause. It is measured in terms of megawatts available for changes in an upward or downward direction.” In other words, with respect to power generation, the flexible operation of power plants means the ability to react to a change of situation. The greater the speed of this reaction, the higher the flexibility needed. Because in the traditional context, the flexibility of the power system is associated with immediately dispatchable generators, the major potential resources – flexibility options – that may be used for balancing between demand and supply

2 are: (1) energy storage availability; (2) demand-side management (DSM) and response; (3) grid infrastructure (its upgrading or renovation, which enhances transmission interconnec- tion capacity to adjacent power systems) – to export surplus, or to import supplementary power; and (4) flexible power plants, whose flexibility can be enhanced through retrofitting or new construction (see Figure 1).

Figure 1: Categories of system ﬂexibility options Source: Ecofys (2014), adapted

Apart from the technical availability of the flexibility options, the way in which these are operated is also important, i.e. how the technically existing flexibility is actually supplied when it is needed. Power plants are more flexible if they can: (1) start their production at short notice; (2) be operated at a wide range of different generation levels; and (3) quickly switch between different generation levels (IEA, 2014). In the case of conventional power plants, such as lignite-fired power plants, the increase in flexibility can either be achieved through the construction of new power plants designed for optimal flexibility, or through retrofit measures applied to existing power plants. The construction of a new power plant today is rather unlikely, because of excessive payback periods (of about 30 years), high investment costs, risks, and the uncertain role of conventional fossil-fueled power plants in the German energy transition process. The demolition costs of existing power plants are about 5% of the investment costs (Blesl et al., 2012). The most frequently used option, which enables a more flexible operation of power plants, is the retrofitting of an existing power plant, requiring specific reinvestments in already operated power plants.

3 Such reinvestments incur some extra costs (depending on the retrofit measure, up to 25- 30% of the construction costs of a new power plant, see FDBR, 2013) related to a more efficient operation of the power plants, which may or may not bring economic benefits to the power system as a whole. Thus, the relevance of retrofit projects with a shorter payback period is higher. The investigations of the investments in the flexibility options can be undertaken using real options analysis (ROA) instead of the traditional now-or-never discounted cash flow (DCF) analysis. ROA applies option valuation techniques developed in finance (Black and Scholes, 1973; Merton, 1973). The extension of this method to “real assets” was proposed by Myers (1977) and also by Dixit and Pindyck (1994) and others. Applying options theory to “real assets” they extend its application to decision-making processes under uncertainty. Moreover, ROA – compared to now-or-never discounted cash flow analysis – can take managerial flexibility appropriately into account. Since the 1990s, ROA has been applied in order to support decision making processes in many different industries and also in the energy sector (see Fernandes et al., 2011 for a useful review). In this study we adapt the model proposed by Deng and Oren (2003) as well as taking advantage of our previous studies regarding disinvestments (Glensk et al., 2016) and reinvestments (Glensk and Madlener, 2015) in already existing power plants. Our contribution does not have a methodological character; rather, we develop some kind of procedure which can support decision-making process and in our case give answers to the following four questions: (1) Can lignite-fired power plants be operated profitably without any changes until the end of their lifetime? (2) How can existing lignite-fired power plants be made more flexible (and thus profitable)? (3) Which flexibility options for lignite-fired power plants can be taken into consideration? and (4) What is the optimal investment time for these flexibility options? The remainder of this paper is organized as follows. Section 2 provides a short literature review regarding the use of ROA especially for power plant valuation. Section 3 presents the proposed model for retrofitting existing power plants based on the option of choosing. Section 4 gives a brief overview of possible flexibility options for lignite-fired power plants. In section 5 the case study is introduced, and the results illustrate the application of the proposed procedure for two selected retroffiting measures. Section 6 concludes.

4 2 Literature review

Most investment opportunities in real assets (such as: buildings, equipment, plant or power plant, etc.) involve options in themselves. Unfortunately, the valuation process of these options is rather difficult using traditional capital investment techniques such as net present value. In contrast, ROA, which applies option pricing theory, is a useful valuation tech- nique for supporting the decision-making process with the investments in real assets. A somewhat dated but interesting and useful review of the current state-of-the-art in the application of ROA in the energy sector (for different generation technologies) is provided by Fernandes et al. (2011). In their paper the authors begin with basic principles of real options theory, which include the definition of real options, their common types, and valuation methods. The authors demonstrate various application fields of ROA, such as the oil industry, power generation, energy markets, as well as greenhouse gas emission mitigation policy. Moreover, they compile the table with different studies which use ROA solved by applying different real options solution methods, such as: partial differential equation modeling, binomial option valuation, Monte Carlo simulation, and dynamic programming. Finally, the authors point out the considerable need for further research and the application of ROA to renewable energy sources. Due to the promotion of renewables, the conventional generation technologies face difficulties in the profitable operation and are confronted with the question about further operation. Glensk and Madlener (2015) use ROA to determine the optimal time to continue or stop the electricity generation by conventional power plants (disinvestment option). However, the question can be reformulated. Instead of asking when the operation of the power plant should be stopped, it can be asked how the flexibility of the power plant can be improved in order to be able to continue power plant operation. To answer this research question, Glensk and Madlener (2017) apply the option to invest, and investigate possible retrofitting options which can improve the flexible operation of gas-fired power plants. The authors tackle the question of when investments in flexibility measures should take place, if at all. In the context of our research questions, and the application of a real option to choose, Gatfaoui (2015) considers a firm which has to choose between different energy sources to run its business. The choice is between crude oil and natural gas, and the goal is to minimize energy or production costs of the firm. The author finally proposes the use of the option to switch from crude oil to natural gas, which allows the establishing of a hedging strategy. Another interesting application of the real options approach can be found in de Oliveira

5 et al. (2014), where the authors analyze the feasibility of installing a cogeneration unit in an industrial plant in Brazil. This investment should make the firm more flexible and should allow it to make a choice between an increase in production or the generation of surplus energy which can be sold in the short-term electricity market. This study is a very good example of ROA being applied in the context of flexibility improvements. Regarding possible flexibility improvements of different technologies as well as different parts of the energy system (see Figure 1), a comprehensive overview is provided by Eco- fys (2014). In this report, the authors present flexibility requirements necessary for the transition to power systems with high shares of variable renewable energy sources (VRES). Moreover, the report provides an overview of the broad spectrum of flexibility options for different generation technologies and their advantages, but also identifies key barriers to their deployment. In contrast, Maaß (2013) presents a useful review of flexibility measures for thermal power plants with realization time, costs of installation, as well as the different aims which can be achieved by their application. Taking into account the lessons learned from the different studies as well as information about retrofitting measures enhancing the flexible operation of a power plant, in the following we propose a new valuation model for lignite-fired power plants based on real options approach and using an option to choose.

3 Option to choose model for lignite-ﬁred power plants

In real options theory, different types of options (to invest, defer, abandon, switch, grow, choose, contract, shut down, expand) or multiple interacting options, etc. can be considered at various stages of a project’s lifetime. Also, for the flexible operation of a power plant, ROA – and especially the application of the option to choose – seems to be justified. With the option to choose, the decision-maker has the possibility to decide between different decisions depending on the development of the market situation. In the proposed model three situations (options) are distinguished: (1) the further operation of the power plant without any changes (option to continue); (2) reinvestment in technical measures which increase the flexible operation of the power plant (option to expand); (3) disinvestment (option to abandon). Real options models can be solved by applying different solution approaches, such as partial differential equations with a closed-form model, simulations, or the binomial lattice method. Depending on the style of the option (e.g. American- or European-style, call

6 or put, Bermuda (see Franzen and Madlener (2016))), and the formulation of the real options model, one of these solution methods should be applied. The binomial lattice as an option pricing model is based on the simple formulation that the price of the underlying asset, in any time period, can move to one of two possible new stages (“up” or “down”) with certain probabilities. It is a common method applied for American-style options. The major advantages of the binomial lattice method are: (1) its ease of use and better tractability; (2) its flexible use for different types of real options problems. The closed-form (Black-Scholes) approach, in contrast, can only be applied to European-style options. This method is also considered as the limiting case of the binomial approach. The application of this method is justified if the distribution of the underlying asset is normal and if the price trajectory of the underlying asset is continuous and has no jumps. Monte Carlo simulation methods are used especially for high-dimensional option models, i.e. when the model incorporates multiple sources of uncertainties. Applying the Monte Carlo simulation method, the differential equations describing the process of the sources of uncertainties are no longer needed because their “physical” processes can be directly simulated. However, this method is computationally expensive. Note that the proposed model for the option to choose is the American-style option, i.e. it can be exercised at any time. In such a situation, it is more adequate to apply the binomial lattice model compared to the closed-form model, the latter of which allows only one exercise date. Furthermore, the proposed model is not a simple set of equations, but rather a procedure which supports the decision-making process. In the case of the investigation of the flexibility options for power plants, the model consists of several steps: (1) determination of the operation strategy; (2) simulation of the expected value of the project (i.e. underlying asset); (3) definition of the binomial lattice of the underlying asset; and (4) definition of the binomial lattice for the option to choose.

3.1 Determination of the optimal operation strategy

For the determination of the operation strategy, the operational load level of a power plant for each hour needs to be defined. In the proposed model the application of the dark spread, as the profitability indicator and source of uncertainty, and its comparison to the marginal cost of the technology (MCT ) are used to find the optimal operation strategy of 1 the power plant . The dark spread (Spreadt) defines the difference between the electricity

1The proposed approach for the deﬁnition of the optimal operation strategy is simpliﬁed and not a goal of our present study. It is intended to aid the better understanding of the model.

7 price and the fuel price regarding the load-level-dependent net eﬃciency factor:

P P Spread = P − β · fuel,t + (1 − β) · fuel,t , (1) t elec,t η(load max) η(load min) where Pelec,t and Pfuel,t denote the electricity price and the price of the fuel (in our case, lignite), respectively, η(load) is the load-level-dependent net efficiency of the power plant, where load = [load max, load min], and β ∈ [0, 1]2. The dark spread defined in eq. (1) is evaluated using the arithmetic Brownian process3, and its values are compared with the marginal cost of the technology for some given operation load level (MCTspread, load) which is based on the approach proposed in Traber and Kemfert (2011) and defined as follows:

P · e MCT = CO2 spec + OM , (2) spread, load η(load) var

where PCO2 denotes the mean value of CO2 price, espec is the speciﬁc emission factor for

the fuel used in the power plant, OMvar denotes the variable operation and maintenance costs, and η(load) is the load-level-dependent net eﬃciency of the power plant. Assuming, for simplicity, that only three possible operational load (output) levels exist, i.e: load = [load max, load min, 0], the operation strategy of the power plant can be deﬁned as:

if Spreadabm,t ≤ MCTspread, load max op level = 0 (3)

if MCTspread, load max < Spreadabm,t ≤ MCTspread, load min op level = load min (4)

if MCTspread, load min < Spreadabm,t op level = load max, (5)

where Spreadabm,t is the dark spread modeled as an ABM process, op level is the operational load level whose values can be: load max – the maximal operational load level (equal to 100% of installed net capacity), and load min – the minimum operational load level at which the power plant can be operated, and zero (for no operation). The minimum load level achievable today is at about 60% of the installed power; for

2We assume in our case study that β = 0.5. 3The arithmetic Brownian motion (ABM) process is an alternative to the standard geometric Brownian motion process typically used in ROA which, in contrast to the GBM process, enables negative prices to be taken into account. For the long-term evolution of the dark spread, an ABM process (St) is deﬁned as: St = St−1 + αdt + σdZt with a constant drift (α) and volatility (σ), and Zt representing a standard Brownian motion process. For more information, see Alexander et al. (2012, p.122) and Glensk and Madlener (2015).

8 the state-of-the-art technology it is about 50% of installed power, and the best achievable level presently lies at about 40% (Brauner, 2012; Hille, 2012).

3.2 Monte Carlo simulation of the expected project value

In the second step of the proposed procedure, the defined operation strategy is used for the cash flow (CFt) calculation and the Monte Carlo simulation of the expected project values (E(PV )). The expected project value is calculated for an existing power plant without any retrofit measure (i.e. some technical element which can improve the flexible operation of the power plant), and with a particular retrofit measure, according to the formula:

T X CFt − OMfixed,t − Dept E(PV ) = , (6) (1 + W ACC)t t=0 where OMfixed,t represents ﬁxed operation and maintenance costs, Dept denotes depreciation, the weighted average cost of capital (WACC) is used as the discount rate, and

CFt is the cash ﬂow calculated by taking into account the forecasted operating load levels from the ﬁrst step of the procedure, CO2 price development (PCO2 ) and variable operation and maintenance costs (OMvar). Start-up, shut-down, as well as marginal ramp-up costs

(cramp-up), decomposed into the ramping fuel requirement (rf) and decreased depreciation due to ramping (d), are given as follows:

cramp-up = rf · (Pfuel + PCO2 · espec) + d (7) and also taken into consideration (cf. Traber and Kemfert, 2011). More details regarding the calculations of CFt for diﬀerent load levels can also be found in Glensk and Madlener (2015).

3.3 Binomial lattice of the underlying asset

Some assumptions regarding the underlying asset are necessary to use the binomial lattice approach as a solution method for our real options model. In our case the project value at time t (PVt) deﬁnes the underlying asset, whose distribution parameters can be obtained from the Monte Carlo simulation (see Section 3.2). Assuming a normal distribution of the

9 expected project value, the “up” and “down” movement parameters are deﬁned as follows:

√ √ up = e(σ ∆t) and down = e(−σ ∆t), (8)

with σ as the associated volatility and ∆t as the incremental time. Figure 2 illustrates the

binomial lattice of the project value (PVt) and how its value changes over time.

Figure 2: Binomial lattice of the project value

Furthermore, it is assumed that the investors are risk-neutral and that the “up” and “down” movements occur with probabilities prob and (1 − prob) respectively, which take the value between h0,1i and sum up to unity. It has to be noticed that in ROA the values of the underlying asset are very often non-traded. The adequate formula for the prob value calculation in such a situation is given as follows:

K − down prob = , (9) up − down where K is the risk-adjusted growth factor of the non-traded underlying asset (cf. Guthrie, 2009, pp.33-38).

10 3.4 Binomial lattice for the option to choose

In the last step of the proposed model, the binomial lattice of the underlying asset is used for the backward calculation of the option value (binomial lattice with option value). The calculation begins at the last period (t = T ) on the lattice of the underlying asset, and the option value for each time is equal to the maximum between the continuation, abandonment, or expansion values given as follows:

 CV (continuation value)  t OVt = max AVt (abandonment value) (10)   EVt (expansion value)

where ( PV for t = T CV = t (11) t prob·PVt+1,up+(1−prob)·PVt+1,down PVt = erf·∆t for t = T − 1,T − 2,..., 0

AVt = DisinvestmentF actor · InvCosts (12)

EVt = ExpansionF actor · PVt − ExpansionCosts. (13)

PVt+1,up and PVt+1,down denote the project’s present values after an “up” or “down” movement in the subsequent time period t + 1, respectively, and rf the risk-free interest rate. The DisinvestmentF actor in eq. (12) is related to the technology and, the coal-fired power plants, about 5% of the new investment (InvCosts) (Blesl et al., 2012). The ExpansionF actor in eq. (13) is the difference between the expected power plant value with and without the retrofit measure.

4 Flexibility options for lignite-ﬁred power plants

According to BMWi (2015), the lignite-fired power plants in Germany will constitute so- called back-up capacities in the “electricity market 2.0” and ensure security of supply through their flexible operation. The flexible operation of the power plants is constrained by the technical restrictions of those technologies, and defined by ramping capability, minimum load, as well as must-run requirements (Ecofys, 2014). Furthermore, each conventional power plant has a certain flexibility in its original design which can be divided into three properties: (1) time of the shutdown and startup; (2) minimum load; and (3) load

11 gradient. The reduction by the time of the shutdown and the startup can be achieved by the application of various technical measures and an increase in the flexibility of the power plant. In the case of the minimum load, it may be economically preferable to operate at minimum load, accepting a short period of losses, if the market price is below the minimal marginal production costs, rather than to shut down the power plant for this time period. By reducing this level, periods of operation become feasible that were prevented by a few hours with low prices within a sequence of hours with profitable prices. Therefore, decreasing of the minimum load is desirable. Furthermore, the increase of the load gradient makes the individual load cycles faster and thus better adapted to the volatile electricity prices. Based on these properties as well as the list of measures proposed in Maaß (2013), the following important effects of retrofit measures to increase flexibility can be considered:

1. Reduction of the shut-down and start-up time – can be achieved by: an increase in the performance of the coal-grinding plant; external steam heating or equipment for keeping hot gas carrying components hot (e.g. air preheater); circulation mode for keeping the minimum mass ﬂow density on the water/steam side in the evaporator; or improvement of the measurement and control technology.

2. Reduction of the shut-down and start-up costs – can be reached by the optimization of the burner; or adoption of an indirect ﬁring system (e.g. additional pulverized coal storage between the mill and the burner).

3. Reduction of the minimum load – can be obtained by using: a thermal storage system in the low-pressure or high-pressure preheating; circulation mode for keeping the minimum mass flow density on the water/steam side in the evaporator; an economizer bypass to keep the flue gas temperature limit upstream of the DeNOx catalyst; support for the feed-water temperature by using a preheater bypass (circulation around the entire steam generator); an indirect firing system with additional pulverized coal storage between mill and burner; transition to the one-mill operation; improved coop- eration of coal mill and burner to stabilize the combustion process through optimized combustion chamber sensors for active fire area monitoring.

4. Increase of the efficiency at part-load – can be manged by: the application of fre- quency control of pumps and blowers; or changing the water/steam system with a retrofit of the turbines or a boosted gas turbine (e.g. using high-quality materials to increase the steam parameters, optimization of blading, and flow channel).

12 5. Increase of the load gradient – can be realized by: an increase in the number of parallel steam lines through higher quality materials and reduction of the wall thickness (only for new build); the optimization of the preheating by use of gas turbines for preheating the feed-water; the thermal storage system in the low-pressure and high- pressure preheating; the indirect ﬁring system by additional pulverized coal storage between the mill and the burner; the warming facilities for the steam turbine and other steam-carrying components of the water/steam cycle.

With these individual measures, or a combination of these measures, the flexibility of conventional power plants can be increased. Nevertheless, the limits of measures to increase flexibility are not only dependent on the economy; some measures may additionally encounter technical or politically established boundaries. From a technical point of view, for example, lowering the minimum load increases the risk of combustion instabilities on the firing side. The furnace of a coal-fired power plant has often more than one mill and each burner has a stable firing system down to a power level of about 50%. In the situation where a mill exceeds this limit, it has to shut down, and the power of the remaining mills has to rise so that the remaining mills are able to achieve a stable fuel combustion. When decreasing the minimum load, the change to the one-mill operation is necessary, which is considered to be a security risk because of problems such as blow-off of the flame. The other possibility is the bypass mode. Here, the hot steam from the superheater is again redirected to the preheater train instead of to the turbines, and each burner can be operated with a stable combustion, and the minimum load can be decreased further. Of course, the losses are very high for this procedure for as long as the market price is positive. Furthermore, each increase in the flexibility is accompanied by a lifetime consumption change of the inflicted components. In general, the power plant will be charged more, for example, through frequent load changes, so that the lifecycle of the power plant decreases (Litau, 2015). Regarding the policy side, to preserve the required flexible dispatchable fossil power plant capacity for providing the residual load, incentives for investments in flexibility must be present. The current market design in Germany, which is based on the merit order and the energy-only market, will encounter some problems if the generation of renewable energies in the coming decades exceeds the demand for electricity more often. In such a situation, the spot market price will drop sharply because the marginal costs4 of wind and solar energy are almost zero. For that reason, adequate policy measures are necessary to

4Note that the marginal costs do not contain capital costs of the technology.

13 promote competition in the generation market, and the current energy system should be modified such that investments (reinvestments) in options that enable the balancing of the supply of fluctuating renewable energies become profitable (Litau, 2015). From the economic point of view the retrofit measures are less complex and faster to realize than new investments, but also expensive (typically about 30% of new investments; Maaß, 2013). Already comprehensive retrofitting can be realized in two years but the costs depend on the power plant component where the retrofit measure should find application. Table 1 gives some examples of the estimated costs and realization times of different retrofit options applied in different parts of the thermal power plants, and the improvements that can be achieved regarding the increase of the flexibility of the power plant (more details about different technical solutions can be found in Plewnia, 2014, pp.42-53; Maaß, 2013).

5 Case study

Considering the federal state of North Rhine-Westphalia (NRW) – Germany’s “Energieland” and “Industrieland” No.1 – the lignite- and hard-coal-fired power plants are still the dom- inant power and heat generation technologies (almost 11 GW of installed net capacity of lignite-fired power plants and more than 11 GW of installed net capacity of hard-coal-fired power plants, see BNetzA, 2015). Their flexibility is key for the reliable operation of the whole power system and the security of supply for existing industries as well as households. For that reason, the real options model proposed was applied to the one existing lignite-fired power plant in NRW – Goldenberg, owned by RWE – to analyze the economic viability of investments in retrofit measures that can increase the flexibility of the power plant. All information and techno-economic data used in the case study were obtained from the available information about this power plant on the BNetzA and RWE websites, as well as from literature research. Table 2 reports on the data used for the real options analysis.

14 Table 1: Effects achieved by retrofit options applied in different parts of the power plant

Eﬀects of the retroﬁt measure

Component where the Realization Shutdown Costs retrofit measure can be time time built in [months] [months] Load band Partial load efficiency Fuel saving Ramp-up rates Reduction of power Efficiency Lower O&M costs Emission reduction Load gradient Start-up times Life expectancy Overload capacity 15 Firing system X X X X X X X X 6–8 2–18 30% of new plant

Steam generator X X X X X X X 6–24 0.5–12 30% of new plant

Mill X X X X X X X 12 n.a. 30% of new plant

Steam turbine and water steam cycle X X X X X X X X 6–24 0.5–3 e1-5 million

Flue gas cleaning and second structure X X X X 3–18 0.5–2 tens of millions of e Table 2: Technical and economic parametrization used in the case study

Parameter Value Total installed power(4) 171 MW (gross) Commissioning time(4) 1993 Total lifetime(6) 35 a Minimum load level 60% Net thermal efficiency for max load level(8) 43% Net thermal efficiency for min load level(1) 36% (2) Specific power plant CO2 emission 0.929 t CO2/MWh Investment costs(8) 1500.00 e/kW Fixed O&M costs(3) 62.85 e/kW Variable O&M costs(3) 3.00 e/MWh WACC(7) 8.75% Corporate tax(5) 29.72% Electricity price (hourly) Time series Jan 1, 2004 – Dec 31, 2012 Lignite price (daily) Time series Jan 1, 2004 – Dec 31, 2012

CO2 price (daily) Time series Mar 3, 2005 – Dec 31, 2012

1 Bine Informationsdienst (2015) 2 Erdmann and Zweifel (2010) 3 Hermann et al. (2014) 4 http://www.rwe.com/web/cms/de/60098/rwe-power-ag/energietraeger/braunkohle/standorte/edz-kw- goldenberg/ 5 http://www.tradingeconomics.com/germany/corporate-tax-rate 6 Konstantin (2009) 7 Pretax cost of capital, see RWE (2015) 8 Wissel et al. (2010)

Furthermore, the ROA is undertaken on an hourly basis to capture the impact of the flexibility measures. The number of hours needed for the start-up, as well as the ramp-up costs have to be taken into account. For a typical lignite-fired power plant today the start-up takes about 6 hours when the power plant has been turned-off for less than 8 hours. In a switch-off situation of more than 48 hours, the start-up takes approximately 10 hours. Depending on the state of the technology, the times needed for the start-up are 4 and 8 hours, respectively, but the optimization potential lies at 2 and 6 hours (Brauner, 2012; Hille, 2012). The start-up costs depend on the state of the power plants i.e. how long the power plant was shut down for. We refer to a “hot start” when the lignite-fired power plant has been switched off for less than 12 hours. In this situation the start-up costs are 63 e/MW.

16 For a so-called “warm-start” (the power plant has been switched off for more than 12 but less than 72 hours) the start-up costs are 84 e/MW. In the situation where the power plant has been switched off for more than 72 hours, the “cold start” of the power plant is needed, with start-up costs of 123 e/MW (Götzet al., 2014). It is assumed that the ramp-up costs are constant for a given technology. They are calculated according to eq. (7). We assume rf = 6.2 kWh/kW for the ramping fuel requirements and d = 3 e/MW for the depreciation of the ramp-up costs, which are assumed to be 102.51 e/MW, following Traber and Kemfert (2011).

5.1 Subsidies

In order to bring a new technology into the market and make it profitable, governments around the world try to support the development of such technologies by using different preferential treatments (subsidization). In Germany, the Act on Granting Priority to Renewable Energy Sources (Erneuerbare Energien Gesetz – EEG) promotes development of technologies for the generation of electricity from renewable energy resources. But also conventional technologies, such as lignite and hard-coal power plants, are supported by the German federal government in order to make them profitable. As was found by Küchler and Wronski (2010), the last four decades’ substantial subsidies, amounting to $538 billion between 1970 and 2014, were directed toward lignite and hard-coal power generation, whereas subsidies for renewables were estimated to amount to only $130 billion over the same period. The subsidization of lignite power plants can take different forms. We can distinguish between subsidies which have a direct effect on the public budget, e.g. direct financial aid and tax reduction, or indirect effects (so-called implicit support) and external costs. The broad range of different studies and reports – such as Lechtenböhmeret al. (2004), Storchmann (2005), Küchler and Meyer (2010), Meyer et al. (2010), Wronski and Fiedler (2015), or van der Burg and Pickard (2015) – give a more detailed overview of the exact form of these subsidies as well as what they financially amounted to over the last decades. Regarding the already existing power plants it is very difficult to find out which kind of subsidies they have received. For that reason we assume in our analysis that the subsidies for lignite-fired power plants amount to 1.1 e-ct/kWh (see Küchler and Meyer, 2010; Küchler and Wronski, 2010).

17 5.2 Examples of ﬂexibility measures

Figure 1 shows that the flexibility options can be subdivided into different groups, such as spatial flexibility options (regarding the electricity distribution network), storage, or temporal flexibility options (for the supply and the demand side) (Connect, 2014) . How- ever, regarding the supply side of the energy system, the most frequently used flexibility options are retrofitting measures, i.e. reinvestments in the technical components, which can improve the flexible operation of the power plant and make it better applicable in a new energy system dominated by variable renewable energy resources. One of those improvement possibilities is the reduction of the minimum load (see Section 4). The part-load efficiency as well as the minimum load can be improved by a stabilized firing, and additionally, creates the possibility for power plants with long start-up sequences to take part in the balancing market, because of the reduced reaction time when increasing the load level after operating at minimum load. To achieve these improvements for the lignite-fired power plant, the reinvestments in mill, burner parameter, flame detector, firing system, or cooling flow reduction can be undertaken. The first considered improvement is related to the reinvestments in the firing system, which are very complex and can improve not only the minimum load of the power plant but also have other effects (see Table 1). The costs associated with these measures are about 30% of the investment costs in a new power plant; additionally, the power plant needs to be shut down for several months. A second group of retrofit measures analyzed in this case study which can improve, for instance, start-up speed, minimum load level, or life expectancy (for more effects, see Table 1), are improvements in the steam turbine and water steam cycle. The costs of such a reinvestment are estimated to be about e1-5 million, and the shutdown time is significantly lower.

5.3 Results from the case study

In the first step of the analysis, the operation strategy of the power plant is determined using the dark spread as a profitability indicator and source of uncertainty according to the simplified method presented in Section 3.15. Figure 3 presents this strategy for two selected months. For the strategy for the month of February 2016, notice the interrupted operation of the power plant. Here, we see how often the power plant should be shut

5The model presented in Section 3 was implemented in Python 2.7.

18 down and how often it should be operated on the minimum load level. For December, for example, Christmas time is also visible from the operation plan of the power plant. Here, it can be recognized how the operation plan of the conventional power plants can change because of a high penetration of renewable energy resources in the power market. Moreover, this deﬁned simpliﬁed operation strategy presents the expected future role of the conventional power plants as back-up capacities. In this role more frequent shut-downs and start-ups by power plant operation are needed. Figure A.1 in the Appendix presents, for example, the operation strategy for each month in the year 2016.

Figure 3: Optimal operation strategy for February and December 2016

In the second step of the proposed procedure the expected project value of the power plant (see eq. (6)) is determined by using the simulated operation strategy of the power plant from the previous step. The calculations were done for six diﬀerent situations presented in Table 3. The situations depend on the considered minimum load level, which

impacts the net thermal eﬃciency factor (ηmin), and the inclusion of subsidies in the calculations. The maximum net thermal eﬃciency factor (ηmax) for all situations is equal to 43%.

19 Table 3: Situations considered for the expected project value calculation

Min load level [%] ηmax/ηmin [%] Subsidies No subsidies

60 43/36 Situation 1 Situation 4

50 43/35 Situation 2 Situation 5

40 43/34 Situation 3 Situation 6

The expected project values obtained for the so-called base situations, i.e. “Situation 1” – with subsidies – and “Situation 4” – without subsidies – define the underlying asset. Their distribution parameters are used for the calculation of the binomial lattice of the underlying asset. Using backward calculation, the binomial lattice for the option to choose can be determined by applying eq. (10). Here, the option value is defined as a choice between the three possible situations, i.e. the further operation of the power plant without any changes (option to continue), reinvestment in the retrofit measure to improve the flexibility of the power plant (option to expand), and shut-down disinvestment of the power plant (option to abandon). As mentioned above (Section 5.2), two different flexibility measures were analyzed to find out which is the best decision option. The first flexibility option concerns the reinvestment in the firing system, which improves the minimum load level of the power plant but is relatively expensive (see Table 1). The reinvestments undertaken in this part of the power plant increase the power plant’s present value by about 12% if the minimum load level is reduced to 50% and by about 24% when the minimum load level is reduced to 40% (comparison of the project values from Situations 1 and 2 as well as from Situations 1 and 3). These changes can be observed for the case where the subsidies of the lignite-fired power plants are included in the calculations. In the case without subsidies the increase of the power plant’s present value is only about 8% for the reduction of the minimum load level to 50% and about 15% for the reduction to 40% (comparison of the project values from Situations 4 and 5 as well as from Situations 4 and 6). The decision obtained analyzing the first retrofit option when subsidies were included for each year is “continue”, i.e. the further operation of the power plant without any changes. Even though the reduction of the minimum load level causes a significant increase of the power plant’s present value (12% increase at min load level of 50% and a 24% increase at

20 40%), the maximal option value always corresponds to the decision “continue” (eq. (10)). The results were obtained for the situation where the minimum load level is reduced to 50% as well as to 40%. Figure A.2 in the Appendix presents the more detailed development of the binomial lattices. For the same retrofit measure but in the situation where subsidies are excluded from the analysis, the decision for all considered periods is to stop the operation of the power plant. This decision is reached for the reduction of the minimum load level to 50% as well as to 40%. The option value according to eq. (10) is always equal to the residual value (5% of total investment costs) which can be reached when the operation of the power plant is stopped. The power plant’s present value calculated without subsidies is always negative, and the retrofit measures are unable to increase that value because the costs connected with this reinvestments are relatively high (30% of new build). Figure A.3 in the Appendix reports the values obtained from the calculations. The second flexibility option concerns the reinvestment in the steam turbine and water steam cycle. Also in this case the minimum load level of the power plant can be improved but the reinvestment costs are definitely lower, i.e. about e1-5 million (see Table 1). This kind of reinvestment increases the power plant’s present value by about 12% when the minimum load level is reduced to 50%, and by about 24% when the minimum load level is reduced to 40%, including the subsidies in the calculations. Without subsidies, the increase of the power plant’s present value is only about 8% for the reduction of the minimum load level to 50% and about 15% for the reduction to 40% (and thus the same as the results obtained for the firing system). Because the reinvestment costs of the steam turbine and water steam cycle are not as high, the results look different especially when the subsidies are taken into account. Moreover, the results differ slightly for the reduction of the minimum load level to 50% and to 40% (see Figures A.4 and A.5 in the Appendix). From Figure A.4 in the Appendix it can be noticed that already in the first considered time period the reinvestments are suggested to improve the flexible operation of the power plant. Nevertheless, the last time periods considered in the analysis6 result in the decision “continue”. This difference depends on the simulated expected power plant values for the different periods in the future. There, one can also see when exactly the switch between the decisions “expand” and “continue” takes place (viz. between periods 13 and 14). Figure A.5 in the Appendix presents the results for the same upgrading measure, but the min load level which can be achieved is 40%. In this case, for all considered time periods, the

6The number of analyzed time periods depends on the number of years when the power plant can still be in operation – i.e. the lifetime of the power plant.

21 optimal decision is “expand”. Analogically to the reinvestments in the firing system, the exclusion of subsidies from the calculations for the reinvestments in a steam turbine results in the same values and decisions, i.e. “stop” to operate the power plant (see Figure A.3 in the Appendix). Also here, the exclusion of subsidies has a significant impact on the calculated power plant values. This provides evidence that in the changing situation in the energy sector and on today’s electricity market, the expensive conventional power plants planned for base- load operation need financial support in order to be profitable with or without (flexibility) retrofitting options. The overview of the results obtained in the analysis can be found in Table 4. Here, it can be noticed that the reinvestment costs of the retrofit measures (Table 1) have a significant impact on the project values (see Figures A.2 – A.5 in the Appendix) and thus also the decision. Moreover, the negative project values always lead to the decision “stop” the generation, even when reinvestments in the retrofit measures are considered. Retrofitting investments increase the power plant’s efficiency and its present value, but that is not enough to cover their costs. Regarding the situation when the project values are positive all three situations from the proposed real options model to choose can appear. For very small project values the relationship between project value and power plant’s residual value is crucial. Here, the second market for power plants or the methodologies for the calculation of the power plant’s residual values can be taken into consideration. For the decision “expand” two factors are particularly important: the reinvestment costs and the expansion factor. The following conclusions can be drawn from the analysis of these two selected retrofitting measures: (1) the proposed procedure works as expected and can give different results depending on the considered retrofitting measures, and the development of the underlying asset which depends on the economic and technical parameters necessary for the calculation of the power plant value; (2) the reinvestment costs and realization time for the retrofit measures play a significant role regarding the decision; and (3) the inclusion of subsidies significantly impacts the value of the underlying asset (expected power plant value) and also the optimal decision.

22 Table 4: Decision obtained from the real options analysis Decision with subsidies Decision without subsidies

Flexibility measure: Retroﬁt of the ﬁring system

Minimum load level reduced to:

50% Continue operation of the power Stop operation of 40% plant without any changes the power plant

Flexibility measure: Retroﬁt in the steam turbine and water steam cycle

Minimum load level reduced to:

50% Retroﬁt (expand) the power plant Stop operation of for periods 1 till 13 the power plant Continue operation of the power plant for periods 14 till 15

40% Retroﬁt (expand) the power plant Stop operation of in period 1 the power plant

6 Conclusions and Policy Implications

Extensive promotion of renewable energy sources, an increasing share of renewable electricity, and decreasing electricity wholesale prices cause difficulties in the profitable operation of conventional power plants – also lignite-fired ones – in Germany. Such a situation is also not favorable for new investments. Moreover, in this context the security of supply becomes more important than before. This is also one of the fundamental aspects of the “electricity market 2.0” concept proposed in BMWi (2015). In order to be able to achieve a positive contribution margin with an existing conventional power plant and thus to survive in the future “electricity market 2.0”, the flexibility of conventional power plants needs to be adjusted to the new challenges. Flexibility is the ability of the remaining conventional generation system to provide the residual load,

23 which is determined by the supply of variable renewable energy sources (Sander, 2012). It is necessary to ensure both the security of supply and the integration of VRES into the electricity system. Because of the increase of electricity supply from fluctuating renewable energies, the importance of flexibility among the conventional power plants becomes significant. As mentioned in the introduction, flexibility options do not only exist on the generation side, but also on the demand side, in the electric network and in the form of energy storage possibilities. In our study, we focused on the supply side only, i.e. on electricity generation. From this perspective, flexibility of power plants – in our case lignite-fired power plants – can be achieved by improving different parameters of the power plants through the implementation of adequate technical elements (see Section 4). For the decision about which technical measure should be implemented (and when), we applied a procedure based on the real options approach. Specifically, we developed a four-step procedure which allows us to simply determine the optimal operation strategy of the power plant by considering the development of the electricity and fuel prices, to simulate the expected project value, to define the binomial lattice of the expected project value, and finally to find the optimal management decision by using the option to choose (see Section 3). Applying the proposed procedure to two selected retrofit measures, we can conclude the following:

• Lignite-ﬁred power plants in Germany can be operated proﬁtably without any changes until the end of their lifetime only if they are subsidized.

• Regarding the support system for lignite-fired power plants it can be observed that in cases where retrofitting options are not too expensive, and if the installation does not take too much time (especially when the power plant has to be shut down), they can increase the profitability of the power plant, as measured by the ExpansionF actor and the option value, respectively.

• Without subsidies the present value of the operated power plant becomes negative with and without a retroﬁt measure.

Furthermore, the proposed procedure was shown to constitute a useful tool for the decision-making process because:

• it allows the determination of a simpliﬁed operation strategy for the power plants; in our case it shows the expected future role of lignite-ﬁred power plants as back-up

24 capacities (interrupted operation, electricity delivered on demand, and more shut- downs and start-ups);

• by using the real options approach, market uncertainties (such as the stochastic

development of electricity, fuel, or CO2 prices) can easily by incorporated into the model structure and can positively impact the decision-making process;

• it allows changes in the values of some parameters of the model, such as the sub- sidy level, and shows direct implications of policies and policy changes, which are important for market participants and their optimal decision-making.

The proposed procedure can be recommended as a useful tool in decision-making processes also for other technologies. Nevertheless, it would be beneficial to improve the first step, where the operation strategy of the power plant is determined, or to implement a user interface for those parameters which have to be defined for the power plant value calculations. Another option to improve the proposed approach is the possibility to integrate other flexibility options, such as energy storage, into the analysis.

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29 Appendix

Figure A.1: Operation strategy for 2016 with the dark spread as proﬁtability indicator and source of uncertainty

30 31

Figure A.2: Binomial lattices for ﬂexibility measures applied to the ﬁring system when the load level is reduced to 40% and 50% and subsidies are included 32

Figure A.3: Binomial lattices for ﬂexibility measures applied to the ﬁring system when the load level is reduced to 40% and 50% without subsidies 33

Figure A.4: Binomial lattices for ﬂexibility measures applied to the steam turbine when the load level is reduced to 50% and subsidies are included 34

Figure A.5: Binomial lattices for ﬂexibility measures applied to the steam turbine when the load level is reduced to 40% and subsidies are included

List of FCN Working Papers

2016

Frieling, J., Madlener, R. (2016). Estimation of Substitution Elasticities in Three-Factor Production Functions: Identifying the Role of Energy, FCN Working Paper No. 1/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March (revised September 2016).

Al-Saad F., Madlener R. (2016). The Role of Shale Gas in the EU Decarbonization Process, FCN Working Paper No. 2/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, March. de Graaf X., Madlener R. (2016). Optimal Time-Dependent Usage of Salt Cavern Storage Facilities for Alternative Media in Light of Intermittent Electricity Production and Carbon Sequestration, FCN Working Paper No. 3/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, April.

Weida J., Kumar S., Madlener R. (2016). Economic Viability of Grid-Connected Solar PV and Wind Power Systems in Germany: A Multi-Region Analysis, FCN Working Paper No. 4/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, May.

Franzen S., Madlener R. (2016). Optimal Expansion of a Hydrogen Storage System for Wind Power: A Real Options Analysis, FCN Working Paper No. 5/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, June.

Hammann E., Madlener R., Hilgers C. (2016). Economic Feasibility of Compressed Air Energy Storage Systems: A Real Options Approach, FCN Working Paper No. 6/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July.

Kirmas A., Madlener R. (2016). Economic Viability of Second-Life Electric Vehicle Batteries for Energy Storage in Private Households, FCN Working Paper No. 7/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, July.

Peters L., Madlener R. (2016). Economic Evaluation of Maintenance Strategies for Ground-Mounted Solar Photovoltaic Plants, FCN Working Paper No. 8/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.

Falcke L.F., Madlener R. (2016). Potential Impacts of the Planned Market Stability Reserve on Speculators’ Behavior in the EU Emissions Trading System. FCN Working Paper No. 9/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.

Glensk B., Madlener R. (2016). Evaluating the Enhanced Flexibility of Lignite-Fired Power Plants: A Real Options Analysis, FCN Working Paper No. 10/2016, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August (revised June 2017).

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 (revised February 2016).

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.

Rosen C., Madlener R. (2015). An Option-Based Approach for the Fair Pricing of Flexible Electricity Supply, FCN Working Paper No. 10/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.

Glensk B., Madlener R. (2015). Real Options Analysis of the Flexible Operation of an Enhanced Gas-Fired Power Plant, FCN Working Paper No. 11/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, August.

Madlener R., Lohaus M. (2015). Well Drainage Management in Abandoned Mines: Optimizing Energy Costs and Heat Use under Uncertainty, FCN Working Paper No. 12/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, September.

Chang N.C.P., Oberst C.A., Madlener R. (2015). Economic Policy Evaluation for the Deployment of Alternative Energy Sources in Brazil, FCN Working Paper No. 13/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October.

Köhnke Mendonça C., Oberst C.A., Madlener R. (2015). The Future Expansion of HVDC Power Transmission in Brazil: A Scenario-Based Economic Evaluation. FCN Working Paper No. 14/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, October (revised September 2016).

Stähr F., Madlener R., Hilgers C., Holz F. (2015). Modeling the Geopolitics of Natural Gas: The Impact of Subsidized LNG Exports from the US to Eastern Europe, FCN Working Paper No. 15/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.

Schäfer D., Madlener R. (2015). Economic Evaluation of Ultra-Long Investments: A Case Study of Nuclear Waste Disposal, FCN Working Paper No. 16/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.

Kammerlocher M., Bostanjoglo N., Madlener R., Kurrat M. (2015). Revenue Analysis of Electric Vehicles as Pooled Ancillary Service Providers under Uncertainty, FCN Working Paper No. 17/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.

Esteve Soldado J.F., Wolff S., Madlener R. (2015). Environmental Impact of Electrifying Postal Delivery Fleets in Inner-City Districts: A Life-Cycle Assessment of the StreetScooter, FCN Working Paper No. 18/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.

Voss A., Madlener R. (2015). Auction Schemes, Bidding Strategies and the Cost-Optimal Level of Promoting Renewable Electricity in Germany, FCN Working Paper No. 19/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.

Weida J., Kumar S., Madlener R. (2015). Financial Viability of Grid-Connected Solar PV and Wind Power Systems in Germany, FCN Working Paper No. 20/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.

König M., Madlener R. (2015). Assessing Local Power Generation Potentials of Photovoltaics, Engine Cogeneration, and Heat Pumps: The Case of a Major Swiss City, FCN Working Paper No. 21/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.

Wessel M., Hilgers C., Madlener R. (2015). Turning Brown into Green Electricity: Economic Feasibility of Pumped Storage Hydro Power Plants in Open Pit Mines, FCN Working Paper No. 22/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.

Walden R., Madlener R., Oberst C.A. (2015). Model-Based Economic Evaluation of the Participation of Private Households in a Local Energy Cluster, FCN Working Paper No. 23/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.

Kumar S., Madlener R., Lehman A. (2015). A Multi-Scenario Cost-Benefit Analysis of the German Electricity Market in Light of New Energy and Environmental Policies, FCN Working Paper No. 24/2015, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, December.

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 (revised February 2016).

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.