economic profiling of wind energy

Marine & Offshore Renewable Energy, 26 – 27 September 2012, London, UK

ECONOMIC PROFILING OF WIND ENERGY S Yasseri, Safe Sight Technology, UK

SUMMARY This paper develops a practical framework for the economic appraisal of wind power generation. Economic evaluation provides a valuable insight in how to improve the viability of wind energy. This article also compares and contrasts the various valuation methods, and highlights their relative merits as a decision making support tool. Risks in renewable energy arise from many sources including state of knowledge, public attitude, site, regulations etc. By using Real Option Valuation (ROV) framework, we examine the risks and success factors in the energy sector so that the determinants of successful business can be identified. Then, we describe how the real option analysis can be applied for the valuation of offshore wind energy projects. A hypothetical case of a 300 MW offshore wind park is used to illustrate the method.

NOMENCLATURE DCF DTA EIS ENPV GNP NPV PV RO WACC OWP

T n

I q MAX

S σ

X I , II , III and IV u and d Rf

1.

Discounted Cash Flow Decision Tree Analysis Environmental Impact Study Expected Net Present value Gross Present Value Net Present value Present value Real Option Weighted Average Cost of Capital Offshore Wind Park life of the compound option number of periods Cost payoff structural mass stock price Volatility exercise price Project phases Up and Down states Risk-free rate

INTRODUCTION

To date energy generated from renewable sources has been more expensive than other modes of energy production, rendering investment unprofitable under free market conditions. A number of European countries have introduced additional policy instruments to increase investments into renewable energy facilities. In their climate policy goals, EU governments have included a renewable energy target. By 2020, 20 % of all primary energy in the EU is to be produced from renewable sources. EU policy instruments fall into two categories: quotas and feed-in tariffs [15, 16 & 19]. Regulators introduce or withdraw incentives aiming at development of a desired mix of energy generation. Thus, investors are exposed not only to the project risk,

© 2012: The Royal Institution of Naval Architects

but also to the risk of energy price and government policies. In this uncertain market, investors need to consider strategic options stemming from operation and management flexibility to decide whether to carry out the project, modify it during its inception, design, construction and operation phases, or simply postpone it await more information. The traditional discounted cash flow (DCF) technique is appropriate in situations with low degrees of economic and technical uncertainties. The objective of DCF is to sum the net cash flows and discount them at the weighted average cost of capital (WACC), or other discount rate, giving a static net present value (NPVstatic). The DCF decision rule states that a company should invest in projects that have a positive NPVstatic. Alternatively, decision tree analysis (DTA), developed in the 1950s [7 and 20], is more suitable for the valuation of projects that have a high degree of technical (technology) uncertainty and a low degree of economic uncertainty. DTA is a dynamic version of DCF, where discrete probabilities are assigned to potential outcomes at each stage of the valuation. It happens that projects with technical uncertainty have a symmetric pay-off structure. As a result, it is possible to assign discrete probabilities to the various outcomes, making DTA the most appropriate valuation technique for these types of projects. The difficulty with DTA lies in obtaining reliable discrete probabilities of success at each stage in the valuation. Some firms’ capital-budgeting decisions are based on the expected net present value (ENPV) model, which is a variation of decision tree models [20]. It was specifically developed to capture the effect of technical uncertainty (represented by probabilities to succeed for each phase) on the value of projects. If the prediction of future cash flows of the project is uncertain the decision tree model was supplemented with sensitivity and scenario analyses. These two types of analyses enable the impact of economic uncertainty on the project value to be taken into consideration [1].


2.

ECONOMIC PROFILE

The key variables that govern wind electricity generation costs are [18, 19, 20 & 22]: •

•

•

•

2.1

Capital costs- including wind turbines, foundations, transportation and grid connection, which can be as much as 80% of the total cost of the project over its entire lifetime. Variable costs- the most significant being operation and maintenance (O&M); but also includes other categories such as royalties, insurance and taxes as well as management and administration. Variable costs are relatively low and will oscillate around the 20% level of the total investment. The electricity produced- which in turn depends on the local wind climate, turbine technical specifications, site characteristics and power generation losses. This is characterized in terms of the capacity factor, which expresses the percentage of time that a wind energy park produces electricity during a typical year [3, 14 and 17]. The discount rate and economic lifetime of the investment. These reflect the perceived risk of the project, the regulatory and investment climate in the country and the profitability of alternative investments [1, 5 and 13]. CAPITAL COSTS

The capital costs of wind projects can be divided into several categories, such as [18 and 19] • The cost of the turbine itself (ex works). This comprises of manufacturing costs for the blades, and transformer, together with their transportation to the site and installation; • The cost of grid connection, including cables, substation, connection and power transmission systems; • The construction cost, including the foundations, transportation, installation and commissioning; • Other capital costs, including development and engineering costs, licensing procedures, consultancy and permits, SCADA (Supervisory, Control and Data Acquisition) and monitoring systems. Morthorst et al [18] state that on average, investment costs of a new offshore wind farm in near-shore areas are expected to be in the range of € 2.0–2.2 million/MW. Therefore, we assume an initial investment of £1.71 million per MW/h of capacity installed- this is a departure from the worst-case assumption for the required initial capital. Economics of scale justify a lower initial cost. 2.2 O&M COSTS Morthorst et al. [18] proposed an annual cost 16€ per MW/h installed as the yearly operations and maintenance (O&M) costs for wind parks. The value for O&M costs is estimated to account for the average expenditure of

insurance, regular maintenance, repair, spare parts and administration. Sometimes the site rental (or royalties) is also considered as part of O&M costs. In this study we consider the site lease as an independent cost item, using an assumed value of 4% of cash flow as an average. O&M costs are modelled as being “pegged” to the power generated in each year, assuming the effective working hours per year is related to the O&M costs. 2.3

ELECTRICITY PRODUCTION

Electricity production depends on the individual turbines’ capacity, the number of turbines and the local wind climate. The wind climate effectively determines what fraction of the total turbines nominal generation capacity is accentually produced- this depends on the windiness of the site. A load factor of around 34-37% is acceptable for a windy site. 2.4 TURBINE LIFECYCLE Manufacturers quote 20 years as the design life, although not many turbines have actually been operating for long. Several authors use the 20 years as the service life (e.g. Morthorst et al. [18]). 2.5 DISCOUNT RATE The discount rate should reflect the risk the investor is taking when financing the offshore wind park. As this technology is still relatively new, the risks are considered to be fairly high, and those companies responsible for the project have to manage risks regarding permitting, construction, technology failures, O&M costs and the capacity factor among others. Morthorst [18] assumes a discount rate range between 510 %, for calculation of the net present values (NPV), and other valuation measures. Gerdes et al [16] found that the rate of return for wind farms in Europe is about 9%. Some firms have a minimum rate of return which they use for the discount rate. This depends on their exposure to the market risk and hence their cost of capital may exceed the 10% mark. 2.6 TAX Tax rates vary from country to country in Europe and may change during the project life time. The corporate tax rate is about 20 to 25 % on the net profit. This would change during the service of the park. Any loss may be carried forward to the next year. This rate depends on the government decisions and hence may change during a project’s lifetime [17]. 2.7

DEPRECIATION

Depreciation is an important variable, as it allows some tax benefit based on the book value of the investment. The residual value of the park after 20 years is not zero. It may carry on for a few more years. In addition there is



still value in the foundation and established grid connections. In this study no allowance is made for decommissioning. 2.8

INCENTIVES

The revenue stream is dependent on the incentive scheme used in each country. A report by Mott Macdonald [19] describes several incentive policies commonly used in European countries. Europe has generally committed to cover 20% of primary energy consumption by renewable energy by 2020. While there is consensus on this goal they differ on how to achieve it. The European policy instruments fall into two categories: quota systems and feed-in tariffs. A quota system has been introduced, in the U.K. Under the rules of this system, electricity companies have to ensure that a fixed quota of the electricity they sell is generated from renewable sources. They are not obliged to produce this electricity themselves. Eligible renewable energy plants receive the so called Renewables Obligation Certificates (ROC) corresponding to the amount of electricity they produce. The ROCs are then sold in a certificate market so that any electricity supply company can fulfil its quota. For renewable energy plants, there are thus two sources of revenue stemming from the sale of electricity and the sale of certificates. Other countries, such as Germany has opted for the introduction of feed-in-tariffs. Under this system, the electricity generated from renewable energy is sold to power supply companies at a fixed minimum price (e.g. in 2010 for wind energy the price was set at 9.1 c/KWh in the first five years of operation and at 6.19 c/KWh for a further 15 years [19]. The additional costs for renewable electricity are covered by an additional per KWh charge on all consumers. Feed-in-tariffs decline over time to take account of technological progress. While ROCs represent a market based instrument that at least in theory should be able to achieve its goals more efficiently than command-and-control instruments such as feed-in-tariffs, in practice ROCs quotas often fail to be met and companies prefer to pay a fine than buying ROCs. Although there is a lot of divergence in terms of what is written to be the incentive system in the UK, the ultimate revision as approved currently stands at 2 ROCs per MW/h for Offshore Wind projects, with each ROC trading at an average of €54 (£48 GBP) per MW/h (Mott Macdonald 2011). 2.9

ELECTRICITY PRICE

Europe has long been preparing for a unified internal electricity market. This liberalized electricity market aims for the introduction of cross-country competition


according to a uniform legal framework, in order to maximize production and trading efficiency. The market price or the spot price is the hourly price for electricity traded on the market. As electricity cannot be stored efficiently, it must be used instantly after generation, which makes the spot price very sensitive to shifts in the consumer demand and the supply of electricity. The price can vary considerably even within a short time interval. If wind power is a large proportion of the total electricity generation, then the fluctuating nature of wind resources does not only cause problems in balancing costs, but it can also have a significant impact on the market price. On very windy days the increased supply of electricity will shift the supply and push down the equilibrium price of electricity. This impact is a very important fact for wind park investors, as OWPs will generally receive a low average price for its electricity, as they will produce a significant amount of its annual revenue on windy days, where the electricity supply is high and hence the market price is low. 3.

PROJECT PHASES

The life cycle of a wind park can generally be divided into four phases, a pre-development phase, a development phase, operational phase and decommissioning phase, which are illustrated in Figure 1. During pre-development phase, suitable locations are identified and the phase ends when the site is selected and authorities have agreed in principle with the proposal. The operational phase of a wind farm is typically expected to be 20years. The development phase generally requires 3 years but sometime may extend to 5 years. For the case study we use a 3 year development phase as shown Figure1. The development phase will vary significantly between different countries, but generally it includes some sort of feasibility study, the likely costs, concept selection and a process of obtaining the necessary permits. In general, the development phase can be divided into 4 stages as shown in Figure 1, which are feasibility studies, Selection, front end engineering, detailed engineering, fabrication and installation. The preapproval and the final approval take place at the feasibility phase. The four stages of the development phase are described in the following subsections. The probabilities of succeeding in the first two stages are relatively low, making the investment risky. However, the costs of the first three stages are low compared with the installation costs; hence the cost of the installation


can actually be avoided if the project fails before the final stage. The cost estimation in stage 3 only includes the cost of undertaking the study. Proceed

Proceed Abandon

Review Gate

Stage 2 Approval &Concept Selection Exit

Feasibility Studies

Proceed

Abandon

Abandon

Review Gate Stage 1 Feasibility studies

Proceed

Review Gate

Stage 3 Feasibility studies

Exit

Development 3 years

Stage 4 Construction

Operation 20 years

PROJECT VALUATION METHODS

The most common project valuation techniques are listed below. The first set of valuation techniques is quite well known: • Net Present Value (NPV) • Internal Rate of Return (IRR) • Payback period • Sensitivity/Scenario Analysis • Monte Carlo Simulation • Real Option Valuation 4.1

4.2 EXPECTED NPV MODEL The expected net present value (ENPV) is a simple extension of NPV model and can primarily be seen as an attempt to improve the standard NPV valuation of staged investments.

Exit

Figure 1: Project phases and four stages of the development phase.

4.

way to estimate the cost of equity within corporate finance is by use of the Capital Asset Pricing Model (CAPM). The CAPM states a linear relationship between the return on a stock and its beta, the market risk premium and the risk-free rate [1 & 20]. Out of these, only beta is individually defined for an investment [10]. The market risk premium and the risk-free rate should be the same for all stocks.

STANDARD NPV MODEL

To date the standard discounted cash flow (DCF) model is the benchmark valuation model, because of its simplicity. The model calculates the net present value (NPV) of an investment, based on its future cash flow, adjusted for the time-value-of-money and risk [6 & 13]. Time, initial investment, free cash flow and discount rate are inputs, of which the first two inputs are known, whereas the last two are estimates. The free cash flow is the profit after tax less capital expenditures and changes in working capital, but with depreciation added back. The discount rate is used to adjust the cash flows for market risk and time-value of money. At any given point in time there can only be one discount rate and one cash flow although both estimates can change over time, so the modelling is one estimation point per time period. The discount rate in the DCF model addresses the timevalue of money and a market risk. The most common

The expected net present value can be seen as a hybrid between the standard NPV model and the decision tree analysis. Hence, it is an extension of the NPV model rather than a distinct valuation model of its own. It models the future DCF values of the project as a string of nodes. 4.3

DECISION TREE ANALYSIS

The ENPV and the standard NPV models implicitly assume the passive holding of assets after the initial investment decision. However, it is often possible for a corporation to actively alter or abandon a project. 4.4

REAL OPTION VALUATION

The theory of real options is an extension of financial option [10]. A call option gives the holder the right, but not the obligation, to buy a security at a specified price in the future. The buyer of the call option is taking an optimistic view of the stocks underlying the call option. Similarly, a capital investment today that gives the investor the future right, but not the obligation, to make a further investment is a real option. A variety of factors can influence the value of the option. For example, as the value of the stock (or the present value of the expected cash flows) increases, so does the value of the call option. Another critical difference between NPV and real options is the effect of uncertainty (or risk) on value. Uncertainty typically is considered bad for the valuation of traditional cash flows. In contrast, uncertainty increases the value of real options. So, in today’s uncertain environment, the value of options actually increases. After an investment is made, time passes, uncertainty is resolved and the present value of cash flows (analogous to the future value of a stock) can be calculated more accurately. If the environment is volatile, then the chance that the value of the project in the future will exceed the necessary investment (or, in other words, that the NPV will be positive) is higher.



5.

REAL OPTION DEFINITIONS

The following are a list of most common terminology used in ROV [1, 5, 10, 11 and 13].

of agility or control; and this will affect their ability to exercise and capture the option payoffs from their project. 6.

Value of the underlying asset: This variable is the foundation from which the option derives its value. For a stock option, it is the current stock price. In the case of real options on a project, it is the future cash flow. Given that the call option holder has the right to purchase the share for the fixed exercise price, the higher the current stock price (or the future cash flow for real assets) the more valuable is the call stock option. Risk-free interest rate: This variable is the return on holding the risk-free asset such as government bonds, and remains identical in both financial options and real options framework. An increase in the risk-free interest increases the value of the call option. Maturity (or expiry) date: This variable is the time remaining until the option expires, and after which the option ceases to exist. In general, a longer term to maturity increases the value of the call option, because the option provides its flexibility over a longer period of time. Exercise price: For a stock call option, this is the fixed price at which the call option holder can purchase the stock. In the case of real options on projects, this represents the cost to develop the project. It should be noted that the exercise price is typically fixed throughout a financial option’s life. However in real option the exercise price (the cost to develop) may vary through time. A lower exercise price increases the value of a call option. Volatility: The value of a financial (or real) option is influenced by the uncertainty of returns on the underlying stock. Even though many systematic and nonsystematic factors influence returns, a reasonable estimate of volatility for the purposes of computing the option value can be calculated by simply measuring the variation in historical returns on the traded stock; however, such information is not available for real assets. Techniques for doing this may vary, but the volatility implied in a competitive financial market are most likely going to reflect some reasonable estimate of future return volatility. On the other hand, there is no market exchange for real option opportunities that can be referenced to produce an implied volatility. There is no widely accepted technique that captures the systematic and nonsystematic risks affecting the cash flows of real investments. Exercise: Financial investors can exercise options almost instantaneously. Real investment opportunities can be much more complex and time consuming to act on. Companies, for many reasons, maintain varying degrees


BINOMIAL REAL OPTION MODEL

The binomial model (Figure 2) represents the price evolution of the option’s underlying asset as the binomial tree of all possible prices at equally-spaced time steps from today under the assumption that at each step, the price can only move up and down at fixed rates and with respective pseudo-probabilities Pu and Pd [7]. In other words, the root node is today’s price, each column of the tree represents all the possible prices at a given time, and each node of value S has two child nodes of values Su and Sd , where u and d are the factors of upward and downward movements for a single time-step dt . Variables u and d are derived from volatility σ [7]

u = e −σ and d = eσ

dt

dt

(1) (2)

Pd is simply equal to 1 − Pu and Pu is derived from the assumption that over a period of dt the underlying asset yields the same profit as a riskless investment on average, so that if it is worth S at time t, then it is worth

Se rdt at time t + dt . This leads to the following equation: Se rdt = (Pu uS + (1 − Pu )dS ) from which we deduce

Pu =

Se rdt − d u−d

(3)

(4)

From the binomial tree representation, we can then iteratively derive the option price for each node of the tree, starting at the leaves. At each leaf of the tree (i.e. at option expiry) deriving call and put option price is simple [13]: (5) Vcall = max ( X − S , 0 ) Indeed, if market price S at expiry date is greater than strike price X , a call option returns for its holder S −X dollars of profit — for a same-day sale transaction — or zero profit otherwise. (6) V put = max ( X − S , 0 ) Similarly, if market price S at expiry date is lower than strike price X, a put option gives its holder X -S dollars of profit, or zero profit otherwise. Having calculated all possible option prices at expiry date, we start moving back to the root, using the following formula


Vt = (PuVu ,t +1 + Pd Vd ,t +1 )e − rdt ,

(7)

where Vt is the option price for one of the nodes at time t and V u , t +1 and V d ,t +1 are the prices of its two child nodes. This formula is derived from the observation that an option which is worth Vt at time t , is worth V t e rdt at time, and its expected value on the other hand, which is, Pu V u ,t +1 + Pd V d ,t +1 , by definition. Period 1

S

Period 2

Period 3

Period 4

Period 5

Su

Su2

Su3

Su4

Sd

Sud

Su2d

Su3d

Sd2

Sud2

Su2d

Sd3

Sud3

To determine the Net Present Value, we deduct all development and hardware costs. The NPV would indicate if the project is a profitable venture. Since NPV does not account the probability of success in any of four stages, we also discuss the Expected Net Present Value (ENPV) approach. However, neither NPV nor ENPV account for the market uncertainties, hence we also consider the Real Option Valuation (ROV) approach to gain more insight. 6.2

ASSUMPTIONS

A wind park generally has one only investment in fixed assets, namely the purchase and installation which is assumed to be 1.71m£/MW. Any spare parts costs are included in the service and insurance agreements. A wind park does not usually have any significant change in working capital either, and hence not included in the FCF. We assume the inflation rate is 2.5% which is applied to all variables in the free cash flow. It could be argued that the expected growth rate of the long term electricity price is higher than 2.5%. Since the turbine will not be purchased and constructed until 2.5 years into the development phase, the cost has been adjusted for an expected inflation of 2.5%. The construction cost is estimated to be M£513. See Table 1 and Table 2 are given in Appendix.

Sd4

Table 1 Probabilities of success and cost estimates Figure 2: Binomial tree. Only four 4 steps are shown The up multiplier u predicts that if the stock price makes an up movement, it will rise by an incremental amount −σ

dt

related to time and volatility. The expression u = e actually has its roots in physics of "Brownian motion", not statistics or finance. Brownian motion is the motion of gas molecules as they travel randomly through space, having their direction of motion altered by chance as they bang into each other.

6.

CASE STUDY

6.1

GENERAL

A 300MW offshore wind park (OWP) is considered for a near shore location in the North Sea. The proposed development will consist of fifty 6 MW turbines and it is expected the development to take 3 years, for the park to become operational on the January of 2015. The useful life of the turbine is 20 years, though the foundations (probably also the tower) and the grid connection have a residual value as a support for new wind turbines. This case study only considers the development phase which, as discussed, is divided into four stages (See Figure 1). We discount the estimated future free cash flows to year 2015, assuming that the development phase is successful.

Duration (Months) Cost (M£) Probability of proceeding

Stage 1

Stage 2

Stage 3

Stage 4

6

12

12

6

0.4 50%

2.0 50%

2.0 80%

513.0 100%

The average expected annual production in the lifetime of the wind park is assumed to be 37%. It is further assumed that the availability of turbines is 97%, and the transmission loss is around 2%. The total price received per produced kWh is a combination of the market price and a subsidy premium. The market price of electricity is the hourly spot price, which is very volatile. To simplify calculations we apply an estimated average price of 5pence/kWh. In addition to the market price, the OWP owner receives subsidies on the electricity generation. An average of 55 pence/kWh for the first 22,000 full load hours (approximately 10 years production in the first 20 years). The UK subsidies may last for the entire life, are not fixed and they depend on the market condition. This example limits the subsidies to 10 years.



The corporation tax is 25% and the capital cost of OWP can be depreciated in accordance with the accelerated depreciation principle by up to 25% of the remaining book value each year. Any remaining book value is depreciated in the last operational year of the turbine. Furthermore, such depreciation tax shields can be carried forward. This is relevant to this case study, where the tax shields in the first years are large compared with profits, and hence they cannot be fully utilized. Debt is a method of project financing, which is supported by the underlying cash flow of the project itself. There is no tax on the interest paid which is known as the interest tax shield. Each wind park is structured as a limited company making it possible to allow the wind park service the debt, instead of the holding company. How much finance can be arranged depends on the perception of project riskiness in its ability to generate the needed cash flow. In this example we assume 30% of capital investment can be raised by debt. The holding company may have to secure debt against its other assets. Only a large electricity generating company will be able to raise a large percentage of the capital investment as debt.

Electricity Price DCF Value Relative Change

Expected Electricity price (£/kWh) -20% -10% £ 0.050 +10% +20% £ 0.045 £ 0.045 £ 0.050 £ 0.055 £ 0.060 £ 528,634,979 £ 570,282,696 £ 611,749,475 £ 653,135,739 £ 694,341,902 -13.6% -6.8% 6.8% 13.5%

Cost of Equity DCF Value Relative Change

Discount Rate -20% -10% 8.00% +10% +20% 6.40% 7.20% 8.00% 8.80% 9.60% £ 668,868,135 £ 639,101,779 £ 611,749,475 £ 586,557,480 £ 563,302,961 9.3% 4.5% -4.1% -7.9%

Figure 3 Results of sensitivity analysis Stage 1

Stage Stage 2 3

Stage 4

End of 2034 S5 q5 S q2 4

Jan 2012 Preliminaries

The value of the operational phase free cash flow in year 2013 using the standard DCF model is £ 611,749,475. The cost of building the wind park (-£517,400,000) must be deduced from this in order to determine the Net Present value of the project. This will be discussed later. 6.3

SENSETIVITY ANALSYS

In order to gain more insight into estimated PV we performed sensitivity analyses. For this purpose, two primary variables are used, which are the electricity price and the discount rate. The nominal and relative changes in the PV value are given in Figure 3. 6.4

EXPECTED NET PRSENT VALUE

Two PV based models are currently used for wind parks valuation; i.e. the ENPV model and DTA. We discuss results ENPV only. Figure 4 illustrates the basic concept. ENPV assumes the market conditions have no effect. Thus, the probabilities reflect the possibility of failing in


q3 S3

100%

q2

80%

S2

55% 50%

(1-0.8)

S1

q1

(1-0.0.55) (1-0.5)

5.2 PV OF FUTURE CASH FLOW The present value calculation of future income (minus all operational costs) is shown in Table 2 (in Appendix).

Operations

Failure State

Scenarios

The DCF usually accounts for the value created due to debt financing by adjusting the discount rate. The standard way of accounting for this side effect is by the weighted average cost of capital (WACC). WACC calculations are based on interest rates for the 10-year bond adjusted with the Company’s credit risk premium. We assume the market risk premium 5.1% and the Company’s Beta is 1- adapted from Damodaran [11]. These give WACC=8%.

the development phase due to technical issues or the Authority’s rejection. The estimated probabilities of success for each stage are given in Table 1 and noted on Figure 4.

Figure 4 Decision tree for the wind park The probabilities used in the ENPV model reflect the events in the development stages. But probabilities such as the entire subsidy system being replaced or a new technology being invented, are not accounted for in the probabilities; since the probabilities of such events cannot be derived meaningfully. An advantage of the ENPV model is that it explicitly separates the development and operational phase. The separation leads to a debate of which discount rate to be used for discounting in each phase. If the assumption of no correlation between events and the market condition is accepted, then the risk-free rate should be used to discount the costs of development. But, the cash flows of the operational phase should be discounted at the company’s cost of capital, since the cash flows will be subject to market risk. The assumption that events are not correlated to the market, allows the problems of estimating the discount rate to be avoided.


The ENPV of the wind park is calculated using the probabilities and costs as well as the value of cash flow during the operational phase (see Figure 5). Payments for all stages are assumed to occur at the beginning of each stage, whereas the cash inflow is resolved at the end. For example, we decide to invest £400,000 in an EIM report without knowing if the final approval will be granted 12 months later. The chance of entering stage 2 requiring an investment of £1000,000 is 50%, as there is 50% chance that stage 1 is successful. The chance of entering stage 3 (viability and permission), requiring a further investment of £1000,000 is thus 25%; because both of the prior stages have a 50% chance of succeeding.

Stage

Time

Stage 1: Analysis and Pre-approval 0 Stage 2: EIS & Final Approval 0.5 Stage 3: Complaints and Compensation 1.5 Stage 4: Construction 2.5 Value of Operational Phase 3 Value of ITS 3

Prob. Probability of Cash Flow (t) PV (t=0) Stage Construction 50% 50% 80% 100% 0% -

Probability Weighted PV

100% -400,000 -400,000 -400,000 50% -2,000,000 -1,964,754 -982,377 25% -2,000,000 -1,896,115 -474,029 20% -513,000,000 -469,362,507 -93,872,501 20% 611,749,475 485,626,457 97,125,291 20% 17,479,587 14,513,345 2,902,669 ENPV (Including Debt)

4,299,053

Success stage 2

Figure 5 ENPV for the case study 1,396,384 0.4000 0.4500 0.5000 0.5500 0.6000

40.00% 592,506 814,806 1,037,107 1,259,408 1,481,709

Success Probability Stage 1 45.00% 50.00% 716,569 840,632 966,657 1,118,508 1,216,746 1,396,384 1,466,834 1,674,260 1,716,923 1,952,136

55.00% 964,695 1,270,359 1,576,023 1,881,686 2,187,350

60.00% 1,088,758 1,422,210 1,755,661 2,089,112 2,422,564

Success Probability Stage 3 72.00% 80.00% £ 1,071,105 £ 1,396,384

88.00% £ 1,721,663

96.00% £ 2,046,942

Success Probability Stage 1 -10% 50.00% 45% 50% £ 1,216,746 £ 1,396,384

+10% 55% £ 1,576,023

+20% 60% £ 1,755,661

Probability Stage 2 ENPV

-20% 40% £ 840,632


+10% 55% £ 1,674,260

+20% 60% £ 1,952,136

Probability Stage 3 ENPV

-20% 64% £ 745,826


+10% 88% £ 1,721,663

+20% 96% £ 2,046,942

1,396,384 2

64.00% £ 745,826

-20% Probability Stage 1 40% ENPV £ 1,037,107

Results of ENPV sensitivity analyses are given in Figure 6. The intention is to test the sensitivity to the probabilities of events, which we introduced for the ENPV calculation. There are two reasons why we test the probabilities. The probabilities have been estimated for an average wind farm, and it is therefore relevant to see what the value would be with other probabilities. The sensitivity analysis has only been performed for stages 1 to 3; since stage 4 has a 100% probability of succeeding. 6.5

BINOMIAL REAL OPTION VALUATION

At the end of each stage, we can decide whether to abandon the project (i.e. not exercising the option) or proceed with an investment in the next stage. Such flexibility has the potential to add a large value to the investment, since the majority of a wind park investment is made at the last stage of the project, when the market uncertainty is clearer. In terms of ROV investing in a wind park is a compound option, namely it consists of several options, and at the end of each stage we have the option to continue or not. We can enter the stage 1 by investing £400,000 in studies and preapproval, this money purchases the right but not the obligation, to continue to the next stage if the economics are favorable at the end of stage 1. We can choose to exercise the option by paying £2000,000 to enter stage 2, if the value of the option is higher than the exercise price. At the end of stage 2, we have the option of continuing or abandoning the project. Similarly, at the end of stage 3, we have the option to construct the wind park or abandon the project. We use the PV of the project as the underlying asset. This means that the value of the underlying asset is the value of the operational phase income discounted back to the beginning of year 2012. This PV does not include the development and construction costs, as these are accounted for differently in the binomial tree. The value of a wind park is highly correlated with the electricity price. We have therefore chosen the price volatility of electricity as the twin-security for our volatility [1]. Variables of Binomial Tree

Figure 6 Sensitivity analysis of ENPV

In the last column of Figure 5, the probability weighted PVs of each stage are given. The four development stages have been discounted with the risk-free rate, whereas the value of the operational phase has been discounted with the WACC. The total value of this column is £ 4,299,053, which is the expected net present value of the wind park when events are taken into consideration. A negative expected net present value indicates that the project does not yield the return on capital.

√

=

∆×

=

= 1.045 1 d= = 0.957 1.045

√

=

− −

= !−

√ .

u=e

p=

e

.

× .

− 0.957 1.045 − 0.957 = 0.5921 q = 0.4079

Figure 7 Numerical values of the binomial tree



The binomial model is built in discrete time; thus the length of time steps must be decided. It is undesirable to have too many time periods as this will make the binomial tree very large. However, increasing the number of time steps improves the precision of the model. Based on these considerations, our time step is set as a quarter of a year, giving a total of ten steps in our model. Using Formulae in Section 6, the risk-neutral probabilities the up and down movements of the binomial model are calculated as shown in Figure 7. The underlying asset value in the binomial tree (shown in Figure 8) is the present value of the operational wind farm of M£485,626. The binomial tree for the underlying asset is then constructed by letting this value follow the up and down movements (Figure 8). As we move to the right in the tree, more nodes are introduced representing a larger spectrum of possible outcomes. This means that the value of the project could end up being much higher or lower than the initial value. But the possibility of ending up in one of these extreme scenarios is very low. Figures given in thousands

Value t=0 Jan-12 485,626

Feasibility Studies and Pre-approval Q1 2012 Q2 2012 507,496 530,351 464,699 485,626 444,673

EIS and Final Approval Q3 2012 554,236 507,496 464,699 425,511

Q4 2012 Q1 2013 579,195 605,279 530,351 554,236 485,626 507,496 444,673 464,699 407,174 425,511 389,627

Detail Design and Placing Contracts Q2 2013 632,538 579,195 530,351 485,626 444,673 407,174 372,836

Q3 2013 661,024 605,279 554,236 507,496 464,699 425,511 389,627 356,769

Q4 2013 690,793 632,538 579,195 530,351 485,626 444,673 407,174 372,836 341,395

Q1 2014 Q2 2014 721,902 754,413 661,024 690,793 605,279 632,538 554,236 579,195 507,496 530,351 464,699 485,626 425,511 444,673 389,627 407,174 356,769 326,683

372,836 341,395 312,605

Figure 8 ROV binomial tree for the case study In Figure 9 the option value tree is constructed. The sequence of solving this tree is the reverse of the asset value tree, as it is solved working backwards, starting with the values from the final nodes of the asset value tree. At each node, we discount the option value with the risk-free rate and weight it with the risk-neutral probabilities p and q, to estimate the value one step backwards. At the nodes where the option can be exercised, the exercise price is subtracted from the option value. If this value is greater than zero, the option is exercised, if not, we discontinue the project which can be seen in Figure 9; in the nodes with no value. In this way the model incorporates the characteristic asymmetric payoff of an option, as the active management will only choose to continue the project when the payoff is higher than the exercise price Figures given in thousands

Value t=0 Primo 2012 31,816

EX

400 ROV Value

Feasibility Studies and Pre-approval Q1 2012 Q2 2012 43,108 56,439 17,122 24,712 6,484

EIS and Final Approval Q3 2012 74,325 36,678 12,839 2,351

Q4 2012 Q1 2013 92,682 113,288 49,330 64,830 19,128 27,926 3,993 6,782 20 34 -

2,000

Detail Design and Placing Contracts Q2 2013 135,776 83,164 39,656 11,518 57 -

Q3 2013 161,764 106,020 56,783 20,621 3,506 -

Q4 2013 186,994 128,739 75,397 31,025 5,975 -

Q1 2014 Q2 2014 213,524 241,413 152,645 177,793 96,901 119,538 45,857 66,195 10,182 17,351 -

2,000 31,816,488


513,000

Figure 9 Option value tree for the case study The real options value of the wind farm (only including the market uncertainty) can be seen in the far left node in the tree, and is equal to M£31,816- the probability of failure is ignored in this calculation.

Finally we perform a sensitivity analysis on the main value driver of the ROV, which is the volatility estimate of the underlying asset, as shown in Figure 10 below. 31,816,488 2

Volatility ROV Relative Change

£

14.10% 26,322,700

-20% 14.10% £ 26,322,700 -17.3%

Volatility Estimate 15.86% 17.62% 19.38% 21.14% £ 29,086,589 £ 31,816,488 £ 34,559,814 £ 37,276,248 Volatility -10% 17.62% 15.86% 17.62% £ 29,086,589 £ 31,816,488 -8.6% -

+10% +20% 19.38% 21.14% £ 34,559,814 £ 37,276,248 8.6% 17.2%

Figure 10 Sensitivity of results due to changes in the volatility As can be seen, the real option value is highly sensitive to the volatility. A 10% increase of the estimate (equal to a nominal change in volatility of approximately 2 percentage point) causes a 9.7% increase in real option value. This makes sense, as a higher volatility increases the possibility of ending up with a very valuable wind farm, whereas the loss can never be greater than the option value. 7.

CONCLUSIONS

Advances in pricing methods for financial securities have served to benefit investors in the valuation of the strategic options. There is great potential for using such asset pricing theories for the evaluation of real assets, and making capital allocation decisions, businesses valuation, and assess performance. Pricing real options arguably involves as much art as science, and the application of traditional models can produce misleading output. This paper has outlined DCF, NENPV as well as the binomial approach which is designed to bring about better understanding of strategic values of real option opportunities. The specific numerical examples in this paper, demonstrated the value of flexibility in decision making. This result is not surprising because of the asymmetric risk structure in the model that arguably corresponds with the underlying reality. The benefit of flexibility-tochange in an upward scenario may be quite substantial while the benefit in a downward situation may be comparatively small. Real options recognize that the ability to delay, suspend, expand or abandon a project is valuable, if there is flexibility in decision making. ROA offers a better tool to guide investment decisions in the context of uncertainty


and flexibility. In practice, managers realize that the value of timing in making investments is significant considering the rapidly evolving market conditions and uncertain business climate. A key contribution of our analysis involves the understanding the complexities of the projects in order to determine their interdependencies, how one project can be leveraged to launch other projects, and its impact on projected business benefits. We conclude that the practical measurement of the value of flexibility should include real option thinking that can provide valuable information to management. 8.

ACKNOWLEDGEMENTS

The author is grateful for many helpful comments of his colleagues, especially Chris Millyard and Sam Lau. 9.

Projects Int. J. Emerg. Sci., 1(4), 659-681, December 2011 13. Dixit and Pindyck. Investment under Uncertainty. Princeton University Press, Princeton, NJ, 1994. 14. Deng, S. J., ‘Pricing electricity derivatives under alternative spot price models’, In Proceedings of the 33rd Hawaii International Conference on System Sciences, 2000. 15. Fernández P., Valuing Real Options: Frequently Made Errors. Working Paper. IESE Business School, Madrid, 2005. Accessed at (18-08-2008) http://papers.ssrn.com/sol3/papers.cfm?abstract_id= 274855 16. Gerdes, G., Albrecht Tiedemann, and Zeelenberg, S. Case Study: European Offshore Wind Farms - A Survey for the Analysis of the Experiences and Lessons Learnt by Developers of wind farms, 158

pages, last accessed on 25-07-2012

REFERENCES 17.

1.

Amram, M., Kulatilaka, N., Real Options – Managing Strategic Investments in an Uncertain World, Harvard Business School, 1999. 2. Andersen, T.G., Bollerslev, T., Christoffersen, P.F., Diebold, F.X., Volatility forecasting’ National Bureau of Economic Research Working Paper 11188, 2005. 3. Bianco, C., Choi, S. and Soronow, D. ‘Energy Price Processes Used in Derivatives PricingCommodities Now: 74-86, 2001. 4. Bhattacharya, K., Bollen, M.H., Dallder, J.E., ‘Operation of Restructured Power Systems’, Kluwer Academic Publishers, Boston, 2001, Black, F., Scholes, M. S., ‘Pricing of Options and Corporate Liabilities’, Journal of Political Economy, 81 (3), 637-654, 1973. 5. Bogdan, B. and Villiger, R., Valuation in Life Sciences: A Practical Guide, Springer; 2nd edition 2008. 6. Brach, M. A., Real Options in Practice, John Wiley & Sons, Inc., 2003. 7. Brandão, L. E., , James S. Dyer, J.D. and Warren J. Hahn, W.J, ‘Binomial Decision Tress to Solve Real Option Valuation Problems’, Decision Analysis Journal, Vol. 2, No. 2, , pp. 69-88 June 2005. 8. Carr, P., ‘The Valuation of Sequential Exchange of Opportunities’, Journal of Finance, Vol. 43, 12351256, 1988. 9. Cox, J. C.; S. A. Ross; and M. Rubinstein. Option pricing: A simplified approach. Journal of Financial Economics, 7(3), 1979. 10. Copeland T.; Antikarov, V., Real options: a practitioner's guide,. New York, Texere Publishing, 2000. 11. Damodaran on line, last accessed on 25-07-2012 http://pages.stern.nyu.edu/~adamodar/ 12. de Oliveira, W. S. Antonio Jorge Fernandes, A. J., Economic Feasibility Applied to Wind Energy

18.

19.

20.

21.

22.

9.

http://www.offshore-power.net/Files/Dok/casestudyeuropeanoffshorewindfarms.pdf Levitt, A. C., Kempton, w Aaron P. Smith, P, alt Musial, W. and Firestone, J., Pricing offshore wind power, Journal of Energy Policy, 2011. Morthorst, PE, Auer, H, Garrad, A & Blanco,I, 'Wind Energy - The facts, Part III: The Economics of Wind Power', Intelligent Energy - Europe, Executive Agency for Competitiveness and Innovation, European Wind Energy Association. 2009. Last accessed 26-07-2012, http://www.wind-energy-thefacts.org/documents/download/Chapter3.pdf MottMacDonald, UK Energy Update, June 2010, last accessed 25-07-2012. http://www.decc.gov.uk/assets/decc/statistics/project ions/71-uk-electricity-generation-costs-update-.pdf Mun J., Real Options Analysis: Tools and techniques for valuing strategic investment and decision. John Wiley & Sons, 2nd edition, 2006. NYISO, The New York Independent System Operator (NYISO), 2007. Energy Market Information Web page. Available from: hhttp://www.nyiso.com/public/products/energy_mar ket/index.jsp?display=0i. Toke, D. Explaining wind power planning outcomes: Some findings from a study in England and Wales, Energy Policy 33, 1527-1539, 2005. AUTHORS BIOGRAPHY

Sirous Yasseri is a senior technical adviser working within the UK oil and gas industry. Dr Yasseri’s research focus is on valuation of large engineering systems, system architecting and integrating with business objectives. He has more than 40 years experience in oil and gas industry and over 140 publications in this field.



Value of Operational Phase Correction for Mid-year Factor DCF Value of Operational Phase

Free cash Flow

Asset Depreciation for Period

Operating income/EBIT

588,656,207 23,093,268 611,749,475

83,755,021

83,755,021

-

44,494,979

Remaining Carry forward

-

-

384,750,000

128,250,000 -83,755,021

£ 513,000,000

-

-

Asset Depreciation Carry forward Depreciation total

Book value after depreciation

Profit after tax

Tax

Taxable income:

-44,494,979 -44,494,979

Book Value, Tangible Assets Asset Depreciation Operating income/EBIT

-

83,755,021

513,000,000 128,250,000 -44,494,979

Net operating income / EBITDA

Carry Forward Previous Period Carry Forward for Period Remaining Carry Forward

7,963,179 3,882,167 807,668 484,601 161,534 13,299,148

Costs Service (From Year 3) Technical Management Land Lease Insurance Administration Own Energy Consumption Total Cost

2015

46,216,271 50,837,898 97,054,169

01-Jan-15

Revenue Electricity Sales (Market Price) Electricity Sales (Subsidy Premium) Total Revenue

Year

DCF Value of Operational Phase

84,628,787

84,628,787

-

56,053,693

96,187,500 44,494,979 -84,628,787

288,562,500

-

-

-

-44,494,979 -11,558,713 -56,053,693

384,750,000 96,187,500 -11,558,713

84,628,787

8,162,258 3,928,383 827,860 496,716 165,572 13,580,789

47,371,678 50,837,898 98,209,575

2016

84,339,770

84,339,770

-

43,854,548

72,140,625 56,053,693 -84,339,770

216,421,875

-

-

-

-56,053,693 12,199,145 -43,854,548

288,562,500 72,140,625 12,199,145

84,339,770

1,184,627 8,366,315 3,975,755 848,556 509,134 169,711 15,054,098

48,555,970 50,837,898 99,393,867

2017

85,228,155

85,228,155

-

12,731,862

54,105,469 43,854,548 -85,228,155

162,316,406

-

-

-

-43,854,548 31,122,686 -12,731,862

216,421,875 54,105,469 31,122,686

85,228,155

1,214,242 8,575,473 4,024,311 869,770 521,862 173,954 15,379,612

49,769,869 50,837,898 100,607,767

2018

77,931,803

53,310,964

32,827,785

-

40,579,102 12,731,862 -53,310,964

121,737,305

24,620,839

8,206,946

32,827,785

-12,731,862 45,559,647 -

162,316,406 40,579,102 45,559,647

86,138,749

1,244,599 8,789,860 4,074,081 891,514 534,909 178,303 15,713,264

51,014,115 50,837,898 101,852,013

2019

72,912,663

30,434,326

56,637,782

-

30,434,326 -30,434,326

91,302,979

42,478,336

14,159,445

56,637,782

56,637,782 -

121,737,305 30,434,326 56,637,782

87,072,108

1,275,714 9,009,606 4,125,095 913,802 548,281 182,760 16,055,258

52,289,468 50,837,898 103,127,366

2020

71,728,037

22,825,745

65,203,057

-

22,825,745 -22,825,745

68,477,234

48,902,292

16,300,764

65,203,057

65,203,057 -

91,302,979 22,825,745 65,203,057

88,028,801

1,307,606 9,234,846 4,177,384 936,647 561,988 187,329 16,405,802

53,596,705 50,837,898 104,434,603

2021

39,570,594

17,119,308

29,935,047

-

17,119,308 -17,119,308

51,357,925

22,451,286

7,483,762

29,935,047

29,935,047 -

68,477,234 17,119,308 29,935,047

47,054,356

1,340,297 9,465,717 2,482,854 960,063 576,038 192,013 15,016,982

54,936,623 7,134,715 62,071,338

2022

34,117,487

12,839,481

28,370,674

-

12,839,481 -12,839,481

38,518,444

21,278,006

7,092,669

28,370,674

28,370,674 -

51,357,925 12,839,481 28,370,674

41,210,155

1,373,804 9,702,360 2,252,402 984,065 590,439 196,813 15,099,883

56,310,038 56,310,038

2023

34,087,710

9,629,611

32,610,798

-

9,629,611 -9,629,611

28,888,833

24,458,099

8,152,700

32,610,798

32,610,798 -

38,518,444 9,629,611 32,610,798

42,240,409

1,408,149 9,944,919 2,308,712 1,008,667 605,200 201,733 15,477,380

57,717,789 57,717,789

2024

34,277,867

7,222,208

36,074,211

-

7,222,208 -7,222,208

21,666,625

27,055,658

9,018,553

36,074,211

-

28,888,833 7,222,208 36,074,211

43,296,420

1,443,353 10,193,542 2,366,429 1,033,883 620,330 206,777 15,864,314

59,160,734 59,160,734

2025

34,638,287

5,416,656

38,962,174

-

5,416,656 -5,416,656

16,249,969

29,221,630

9,740,543

38,962,174

21,666,625 5,416,656 38,962,174

44,378,830

1,479,437 10,448,381 2,425,590 1,059,730 635,838 211,946 16,260,922

60,639,752 60,639,752

2026

35,131,849

4,062,492

41,425,809

-

4,062,492 -4,062,492

12,187,476

31,069,356

10,356,452

41,425,809

16,249,969 4,062,492 41,425,809

45,488,301

1,516,422 10,709,590 2,486,230 1,086,224 651,734 217,245 16,667,445

62,155,746 62,155,746

2027

35,730,849

3,046,869

43,578,639

-

3,046,869 -3,046,869

9,140,607

32,683,979

10,894,660

43,578,639

12,187,476 3,046,869 43,578,639

46,625,508

1,554,333 10,977,330 2,548,386 1,113,379 668,028 222,676 17,084,131

63,709,640 63,709,640

2028

36,414,647

2,285,152

45,505,994

-

2,285,152 -2,285,152

6,855,455

34,129,496

11,376,499

45,505,994

9,140,607 2,285,152 45,505,994

47,791,146

1,593,191 11,251,763 2,612,095 1,141,214 684,728 228,243 17,511,235

65,302,381 65,302,381

2029

37,167,909

1,713,864

47,272,061

-

1,713,864 -1,713,864

5,141,592

35,454,046

11,818,015

47,272,061

6,855,455 1,713,864 47,272,061

48,985,925

1,633,021 11,533,058 2,677,398 1,169,744 701,846 233,949 17,949,016

66,934,940 66,934,940

2030

37,979,279

1,285,398

48,925,175

-

1,285,398 -1,285,398

3,856,194

36,693,881

12,231,294

48,925,175

5,141,592 1,285,398 48,925,175

50,210,573

1,673,847 11,821,384 2,744,333 1,198,988 719,393 239,798 18,397,741

68,608,314 68,608,314

2031

38,840,390

964,048

50,501,789

-

964,048 -964,048

2,892,145

37,876,342

12,625,447

50,501,789

3,856,194 964,048 50,501,789

51,465,837

1,715,693 12,116,919 2,812,941 1,228,962 737,377 245,792 18,857,685

70,323,522 70,323,522

2032

39,745,121

723,036

52,029,447

-

723,036 -723,036

2,169,109

39,022,085

13,007,362

52,029,447

2,892,145 723,036 52,029,447

52,752,483

1,758,585 12,419,842 2,883,264 1,259,686 755,812 251,937 19,329,127

72,081,610 72,081,610

2033

41,095,749

2,169,109

51,902,186

-

2,169,109 -2,169,109

-

38,926,640

12,975,547

51,902,186

2,169,109 2,169,109 51,902,186

54,071,295

1,802,550 12,730,338 2,955,346 1,291,179 774,707 258,236 19,812,355

73,883,650 73,883,650

2034


PV, Interest Tax Shield Mid-year Factor PV, Financial Side Effects:

16,946,131 533,456 17,479,587

-

352,890,059

Cash Flow from Interest Tax Shield

352,890,059 -

410,400,000

Remaining Principal End of the Year (Corrected for ITS Cash Flow)

-

Remaining Principal (End of the year) Cash Flow from ITS

-

290,828,592

290,828,592 -

6,561,270 5,641,830 12,203,100

-

6,561,270 6,561,270

-

ITS Carry Forward Primo ITS Generated in Period Cash Flow from ITS ITS Carry Forward (End of the year)

Tax

352,890,059 62,061,467 290,828,592

410,400,000 57,509,941 352,890,059

410,400,000

2016

22,567,319

Remaining Principal (Begining of the year) Installment Remaining Principal (End of the year)

2015

26,245,080

Begining 2015

Interest Expense

Year

Financial Side Effects 2017

-

225,087,311

225,087,311 -

12,203,100 4,649,622 16,852,722

-

290,828,592 65,741,281 225,087,311

18,598,488

2018

-

154,253,490

154,253,490 -

16,852,722 3,598,583 20,451,305

-

225,087,311 70,833,821 154,253,490

14,394,334

2019

8,206,946

77,979,252

86,186,198 8,206,946

20,451,305 2,466,128 -8,206,946 14,710,487

8,206,946

154,253,490 68,067,292 86,186,198

9,864,511

2020

14,159,445

-

10,053,362 14,159,445

14,710,487 1,246,693 -14,159,445 1,797,735

14,159,445

77,979,252 67,925,889 10,053,362

4,986,773

1,797,735

-

1,797,735

1,797,735 -1,797,735 -

16,300,764

-

-

2021

-

-

-

-

7,483,762

-

-

2022

-

-

-

-

7,092,669

-

-

2023

-

-

-

-

8,152,700

-

-

2024

-

-

-

-

9,018,553

-

-

2025

-

-

-

-

9,740,543

-

-

2026

-

-

-

-

10,356,452

-

-

2027

-

-

-

-

10,894,660

-

-

2028

-

-

-

-

11,376,499

-

-

2029

-

-

-

-

11,818,015

-

-

2030

-

-

-

-

12,231,294

-

-

2031

-

-

-

-

12,625,447

-

-

2032

-

-

-

-

13,007,362

-

-

2033

-

-

-

-

12,975,547

-

-

2034

