Green-X MODELLING RISKS OF RENEWABLE ENERGY INVESTMENTS

Transcription

1 Report of the project Deriving Optimal Promotion Strategies for Increasing the Share of RES-E in a Dynamic European Electricity Market Green-X MODELLING RISKS OF RENEWABLE ENERGY INVESTMENTS Work Package 2 within the 5 th framework programme of the European Commission supported by DG Research Contract N o : ENG2-CT Hans Cleijne, Walter Ruijgrok KEMA (The Netherlands) FINAL VERSION JULY 2004

2 This Energie publication is one of a series highlighting the potential for innovative non-nuclear energy technologies to become widely applied and contribute superior services to the citizen. European Commission strategies aim at influencing the scientific and engineering communities, policy-makers and key market players to create, encourage, acquire and apply cleaner, more efficient and more sustainable energy solutions for their own benefit and that of our wider society. Funded under the European Union s fifth framework programme for research, technological development and demonstration (RTD), Energie s range of support covers research, development, demonstration, dissemination, replication and market uptake the full process of converting new ideas into practical solutions to real needs. Its publications, in print and electronic form, disseminate the results of actions carried out under this and previous framework programmes, including former JOULE-Thermie actions. Jointly managed by the European Commission s Directorates-General for Energy and Transport and for Research, Energie has a total budget of EUR million over the period Delivery is organised principally around two key actions, Cleaner energy systems, including renewable energies and Economic and efficient energy for a competitive Europe, within the theme Energy, environment and sustainable development, supplemented by coordination and cooperative activities of a sectoral and cross-sectoral nature. With targets guided by the Kyoto Protocol and associated policies, Energie s integrated activities are focused on new solutions which yield direct economic and environmental benefits to the energy user, and strengthen European competitive advantage by helping to achieve a position of leadership in the energy technologies of tomorrow. The resulting balanced improvements in energy, environmental and economic performance will help to ensure a sustainable future for Europe s citizens. ENERGIE with the support of the EUROPEAN COMMISSION Directorate-General for Research LEGAL NOTICE Neither the European Commission, nor any person acting on behalf of the Commission, is responsible for the use which might be made of the information contained in this publication. The views expressed in this publication have not been adopted or in any way approved by the Commission and should not be relied upon as a statement of the Commission s views. European Communities, 2004 Reproduction is authorised provided the source is acknowledged. Printed in Austria Elaborated by KEMA (The Netherlands). List of Authors: Hans Cleijne Walter Ruijgrok

3 The Green-X project: Research project within the 5 th Research framework programme of the European Commission, DG Duration: October 2002 September 2004 Co-ordination: Reinhard Haas, Energy Economics Group (EEG), Institute of Power Systems and Energy Economics, Vienna University of Technology. Project partners: - EEG - Energy Economics Group, Institute of Power Systems and Energy Economics, Vienna University of Technology, Austria; - IT Power, United Kingdom; - KEMA - KEMA Nederland B.V., The Netherlands - RISOE - Risoe National Laboratory, Denmark - CSIC - The Spanish Council for Scientific Research (Institute of Economy and Geography), Spain - FhG-ISI - Fraunhofer Institute for Systems and Innovation Research, Germany - WIENSTROM GmbH, Austria - EGL - Elektrizitäts-Gesellschaft Laufenburg AG, Switzerland - EREC - European Renewable Energy Council, Belgium Contact/Information: web-site: or directly by contacting one of the project partners Imprint: The report Modelling risks of renewable energy investments (D6 Report) has been carried out by KEMA (Hans Cleijne, Walter Ruijgrok). Its completion was achieved in July 2004.

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5 3 CONTENTS Summary 6 1 Introduction 10 2 Dealing with uncertainties Risk and upward potential Sources of risk in renewable energy investments Relevance of risks for investors in renewable energy projects Relevance of risks in buying green energy 18 3 Modelling and measuring risks Quantifying risk through scenarios Quantifying value at risk Quantifying required green price Examples 28 4 Measures to deal with risks Why risks influence cost Project developer perspective Banker s perspective Independent ratings Risk management 46 5 Stake holder s perception of risk and renewable energy Introduction Results of survey Interviews 54 6 The role of risk in financing renewable energy projects Introduction 60

6 4 6.2 The relation between support mechanism and project risk Project finance through commercial bank debt Return on Equity Weighted average cost of capital 70

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8 6 Summary Background and aim The project Green-X, which is sponsored by the European Commission, aims at developing a model which can describe the dynamics of the implementation of renewable energy in Europe. An important element of the dynamics concerns the behaviour and decision making by stakeholders on investments in these renewable energy technologies. A major driving force in this decision making process by stakeholders is related to the risks which stakeholders perceive in the market. The aim of this report is to describe methods to describe and quantify the risks of investments in renewable energy technology. These methods and their results for various business cases will be used later as input for the model Green-X so this model is able to take investor behaviour and risk awareness into account. What is risk? Risk in relation to investments in renewable energy projects can be described by the negative impact which uncertain future events may have on the financial value of a project or investment. Risks form the counterpart of the upward potential: the increase in value due to future events. Although both risk and upward potential are related to uncertainty of future events, risks usually play a more dominant role in investment decisions since investors are risk averse in most cases. When it comes to investment risks for renewable energy projects, three categories seem to play the most dominant role: regulatory risks which can be found in project development or are related to possible changes in the financial support for renewable energy due to changing government policies market and operational risks which are related to for instance increasing costs for operation or feedstock, such as biomass technological risks which follow from malfunctioning of the technology used and potentially can be large for some renewable energy technologies since these have entered only recently on the market.

9 7 How to quantify risks? As risks are closely related to the financial value of an investment, risks can best be quantified in financial terms and how they affect the value of a project. The net present value (NPV) is the most commonly used measure of the financial value of a project. To quantify risks we describe three methods in this report: scenario analyses value-at-risk or profit-at-risk assessments required green price calculations Table S.1 shows some advantages and disadvantages of these methods. Table S.1 Overview of advantages and disadvantages of various approaches to estimate risks scenario analysis Value-at-Risk (or profit-at-risk) advantages simple method easy to understand both risk and its probability is measured can calculate probability of loss/inadequate financial returns disadvantages may overestimate risk no information on probability of risk complex requires information on distribution of uncertain input required green allows calculation of risk use is limited to some price premiums situations only Stake holder perception The policy instruments which EU member states have currently put in place, aim at promoting investments in renewable energy sources by removing barriers and reducing risks. We have approached a group of more than 650 stakeholders who are involved in RES investments to obtain their views on the

10 8 risks and barriers for investments. The group we approached consisted of representatives in the electric power industry, renewable energy project developers and investors, manufacturers of RES technologies, banks, NGOs and governmental agencies across current and candidate EU member states. In addition to the questionnaire we held a number of interviews with representatives from the renewable energy industry to discuss the relation between risk and investments. These included representatives from International banks specialised in financing in project finance and more particular finance of renewable energy projects Project developers in the fields of offshore wind energy, onshore wind energy and biomass Topics discussed were the role in investment and debt provision decisions of Technology risk of the various renewable electricity options Regulatory risk in terms of support mechanisms The role of the support mechanism The two predominant support mechanisms in the EU are: systems where a guaranteed feed-in tariff is paid for renewable electricity for a period of time generators receive certificates when renewable electricity is fed into the grid. These certificates may be sold in the market to (1) offset a renewable portfolio obligation or (2) to provide buyers of electricity with certified green electricity. In terms of financial return and risk these schemes have different characteristics, which are listed in table S.2.

11 9 Table S.2 Overview of financial return and risk under different support mechanisms Feed in tariffs Certificate scheme Common characteristics Fixed rates Usually fixed period Fixed technologies Fluctuating prices Period not determined Fixed technologies Guarantees Government Supplier IRR Maximised by law Minimum set by investors and banks Maximised by market conditions Minimum set by investors and banks Largest risk Site/technology Regulatory change The influence of risk on the Weighted Average Cost of Capital These risks were combined and in the Weighted Average Cost of Capital for the technologies biomass, wind onshore and wind offshore under the support mechanisms of a feed-in tariff and a tradable green certificate scheme (see Table S.3) TabelS.3 Estimated Weighted Average Cost of Capital for different technologies and support mechanisms Wind onshore Biomass Wind Offshore TGC FIT Wind fund TGC FIT TGC FIT ß eq = ß base.a tech.a support Required Return on Equity 10.4% 9.5% 6.3% 13.6% 12.5% 15.3% 14.0% Post tax cost of debt 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% 3.3% Weighted Average Cost of Capital 5.4% 4.9% 4.0% 6.4% 5.6% 6.9% 6.0%

12 10 1 Introduction Background The project Green-X, which is sponsored by the European Commission, aims at developing a model which can describe the dynamics of the implementation of renewable energy in Europe. An important element of the dynamics concerns the behaviour and decision making by stakeholders on investments in these renewable energy technologies. A major driving force in this decision making process by stakeholders is related to the risks which stakeholders perceive in the market. Aim of this report The aim of this report is to describe methods to assess and quantify the risks of investments in renewable energy technology. The methods presented are used to analyse different business cases in order to obtain risk profiles and parameters which relate to different technologies and policy support mechanisms for renewable energy. A second important item is the risk perception by stakeholders and how this influences their behaviour as entrepreneurs in the renewable energy market. These results give important insights in how the different aspects of risk (technological risk, regulatory and market risk) interplay and influence decision-making by stakeholders. Finally the results of the analysis and the market consultation are combined in a model to quantify the costs of risk. The Weighted Average Cost of Capital is proposed as the factor to absorb the influence of risk. In this way it is possible to incorporate the results of this study into the Green-X model. The WACC influences the cost of renewables in the model and hence a higher or lower WACC results in a higher or lower development rate. Structure of this report Chapter 2 describes the concept of risk and upward potential. It gives an overview of the various sources of risk and its influences on the renewable energy market. The position of different market players is described.

13 11 Chapter 3 describes different methods to assess and model the effects of risk on the financial performance of a project. It introduces scenario analysis, Value-at-Risk and risk premiums. Chapter 4 gives an overview how different stakeholders handle risk and how it influences overall costs of renewable energy development. Chapter 5 describes the main conclusions from the stakeholder consultation. The stakeholder consultation consists of an inventory around important topics of decision making and the role of risk and a number of interviews where these aspects were discussed in more detail. Chapter 6 is devoted to the role of risk in the financing of renewable energy projects. The cost of debt and the cost of equity are treated separately and then combined into a model for the WACC. The model is applied to different technologies under different support mechanisms.

14 12 2 Dealing with uncertainties 2.1 Risk and upward potential Risk in relation to investments in renewable energy projects can be described by the negative impact which uncertain future events may have on the financial value of a project or investment. Risks form the counterpart of the upward potential: the increase in value due to future events. It is important to note that risk is not identical to uncertainty. Uncertainty of the financial value of a project can be both positive and negative in comparison with the expected value. The term risk, however, relates exclusively to the events which might occur and would lower the expected financial value. Events which may take place and would increase the expected value, form the upward potential. Although both risk and upward potential are related to the uncertainty of future events, risks usually play a more dominant role in investment decisions since investors are risk averse in most cases. When it comes to investment risks for renewable energy projects, three categories seem to play the most dominant role: regulatory risks which can be found in project development or are related to possible changes in the financial support for renewable energy due to changing government policies market and operational risks which are related to for instance increasing costs for operation or feedstock, such as biomass technological risks which follow from malfunctioning of the technology used and potentially can be large for some renewable energy technologies since these have entered only recently on the market. It is important to note that we discuss risks as perceived by stakeholders in renewable electricity. This is a different viewpoint than the one taken by

15 13 Awerbuch et al 1. who have applied portfolio theory to EU electricity planning and policy-making. They remark that adding wind, PV and other fixed-cost renewables to a portfolio of conventional generating assets serves to reduce overall portfolio cost and risk, even though their stand-alone generating cost may be higher. For energy planning purposes the relative value of generating assets should be determined not by evaluating alternative resources, but by evaluating alternative resource portfolios. 2.2 Sources of risk in renewable energy investments A serious issue in the development of renewable energy projects is how future events affect the value of the project and which risks are involved for the investment planned. Figure 2.1 illustrates the total risk a company in operation may face. Dealing with risk (i.e. uncertainties in future developments which have a negative impact on the operation and profit of a company) is a key element when it comes to value a new project and decide on investing in it. This situation applies not only to the actual investor, but also to other stakeholders who are involved, such as banks, insurance companies, suppliers of the technology and the off-takers of the energy. The sources of risk and its impact, however, can differ substantially for each of these stakeholders. 1 Applying portfolio theory to EU electricity planning and policy-making, Simon Awerbuch and Martin Berger, IEA working paper EET/2003/03, Paris February 2003

16 14 Product market risk customor loss product obsolesence compitition increases prpduct demand decreases Operational risk installation breaks down product defects increase weather affects operation grid imbalance increases Input risk input prices rise labour strikes key employees leave supplier fails Total company Financial risk capital costs increase risk exchange rates change inflation covenant violation default debt Legal risk product liability restraints of trade changes shareholder law suits Tax risk income tax rises revenue bonds end sale tax rises Regulatory risk environmental laws change price support ends import protection ceases stricter anti trust enforcement Figure 2.1 A variety of risk sources make up the total risk of a company 2 2 Taken from L. Meulbroek (2000). Total strategies for company-wide risk control. In: Mastering risk (vol. 3). Financial Times Mastering Series, London.

17 15 The importance of each risk category depends strongly on the nature of the company and the sector and market in which it operates. For trading in green electricity, we may distinguish the following risk sources which are relevant for sellers and buyers on the market: operational risk product market risk input risk regulatory risk financial risk producer of green energy prediction capability of load and grid imbalance is crucial for less mature technology performance is a risk factor demand expected to rise (favourable), but at same time competition may increase strongly as well bio-energy options: fuel costs important wind and hydro: varies with climate very important: profit strongly depends on price support import protection in particular relevant without long-term contracts and for sources where capital costs dominate (wind, hydro) buyer of green energy (in case of a certificate system) depends on banking strategy of certificates, otherwise possibly relatively low risk demand expected to rise (favourable), but at same time competition may increase strongly as well large profits at risk when longterm contracts are closed and prices change very important: profit & sales strongly depends on price support / tax breaks depends on inventory size (banking) and portfolio of contracts 2.3 Relevance of risks for investors in renewable energy projects A more detailed overview of risk elements which are relevant for investors in renewable energy projects in table 2.1. This overview includes a selection of measures which can be taken to minimise these risks. For generators trading in green electricity, we may distinguish the following risk sources which directly affect prices (besides other risks we mentioned): price development of electricity which is determined by supply and demand of electricity on the one hand and by fossil fuel prices on the other

18 16 price development of green certificates which is determined in part by supply and demand, but to a large extent also by changes in government policy, subsidies and regulation. Uncertainties in price developments due to supply and demand are relatively easy to quantify and translate into a risk premium based on historic developments. Fluctuations in prices due to developments on the world s fossil fuel market are more difficult to capture, but a tradition for this has been developed and can be incorporated through different scenarios. Also, for the value of green electricity, such developments are probably of lesser importance. The most important and most difficult source of uncertainty, in particular for the long term, concerns the role of government policy. This is most likely to change, as can be learned from the past, but it is difficult to predict when, in what direction and to which extent. However, for the value of green electricity these developments can be crucial.

19 17 Table 2.1 Overview of risks with which an investor in renewable electricity is confronted when his installation is in operation. For every risk we have indicated whether the generator can influence this risks and which measures he can take to limit the risk (within the company or externally on the market) type of risk description of the risk can be influenced external measure internal measure operational risks imbalance in delivery to the grid yes outsource load balancing planning & management load forecasting larger maintenance yes guarantees equipment supplier maintenance strategy lower plant availability yes guarantees equipment supplier load management apply conservative budgeting lower generation efficiency yes guarantees equipment supplier optimise apply conservative budgeting market risks lower demand partly hedging pricing policy higher fuel purchase prices yes contract length & conditions hedging market monitoring marketing pricing policy market monitoring purchase policy lower market prices yes ditto market monitoring pricing policy load forecasting new entrants no ditto market monitoring regulatory risks change in renewable energy policy changes in specific regulation decrease in financial support no no no long-term contracting fixed pricing apply risk analysis monitor value-at-risk profit requirements rate of return requirements

20 Relevance of risks in buying green energy Buyers of green energy, such as green suppliers for voluntary demand or suppliers with a government obligation to buy and sell a certain amount, are confronted, like investors in projects, with risks. However, the risk profile can be different. First of all, buyers of green energy are likely to be exposed to risks of the wholesale market for electricity (or heat or gas). Also, regulatory risks due to changes in government policies related to price support of renewables apply as well and are equally important and dominating. In addition, however, green suppliers also face risk on the retail market. This risk is in part determined by changes in government policies to promote and support renewables and in part by the dynamics of the retail market. Considering the liberalisation process of the energy market, risk on the retail market can be expected to increase in the near future compared with the present situation. Driving forces for this risk are: switching behaviour of customers increased competition between suppliers new entrants in the market. The short-term dynamics of developments on the retail market (in e.g. prices, market share, customer preferences) may impose conflicts with the current situation to contract renewable electricity in long-term contracts. When furthermore regulatory risk comes on top of retail market risk, green suppliers have to consider a suitable bidding and pricing strategy to close longterm contracts with generators. Otherwise their profit-at-risk (PaR) may run out of control and a potential loss-making situation can emerge if certain risks become reality in the future.

21 19 3 Modelling and measuring risks In order to understand and quantify risks which may affect investments in renewable energy a number of methods is used in the market. A qualitative assessment of potential risks and threats forms the basic step in understanding the importance of risk factors and how they may affect the financial value of an investment. When a picture is available of the risk factors affecting a (potential) project, an attempt can be made to quantify these risks. For investments the most logical step is to make this analysis in financial terms. In this way, the feasibility of a project can be judged and options for mitigation or improvement considered. A number of methods is available and used to quantify risks for investments, such as: scenario analyses value-at-risk or profit-at-risk assessments required green price calculations Table 3.1 shows some advantages and disadvantages of these methods. Table 3.1 Overview of advantages and disadvantages of various approaches to estimate risks scenario analysis Value-at-Risk (or profit-at-risk) advantages simple method easy to understand both risk and its probability is measured can calculate probability of loss/inadequate financial returns disadvantages may overestimate risk no information on probability of risk complex requires information on distribution of uncertain input required green price allows calculation of risk premiums use is limited to some situations only

22 Quantifying risk through scenarios The simplest approach to estimate the impact of risks on the value of a renewable energy investment is by calculating the net present value (NPV) of the expected cash flow under various scenarios. These scenarios express different future developments which may be possible. Usually, these scenarios reflect a worst case, best guess and optimistic estimate of future development which affect the value of the project. Assumptions may include elements such as the future price of electricity, green prices available for renewable production, the expected output of the project (in terms of kwh or GJ produced) and costs for operation and maintenance. When simple cash flow calculations are used with multiple scenarios, risks can be determined by the difference between the net present value which is considered the best guess and the worst case or lowest outcome. The difference between the best guess and the highest outcome does not represent a risk for the investor, but gives an indication of the upward potential if the actual developments turn out to be more optimistic than expected. It is important to remark that risk and upward potential do not have to be equal in magnitude. Net present value upward potential (expected in scenario) risk worst case best guess optimistic case Figure 3.1 Schematic representation of the risk and upward potential derived from scenario analyses with a cash flow model

23 21 Text box 1 explains how the net present value of a project can be calculated from its expected cash flow under a given scenario. Such a cash flow model with scenarios can be easily implemented in standard spreadsheets. Because of its simplicity, the approach also allows to analyse the impact of single state variations of each variable of scenario. The results of these single state variations can help to understand which factor has the largest influence on the risk and value of the project. When ranked on magnitude in a graph, the outcomes for each single state variation form a so-called tornado diagram (see fig. 3.2). In this way, the scenario approach provides information on the magnitude of potential risks and which factors contribute most to the overall risk of the project. There is, however, one important drawback of the scenario approach. It does not provide any information on the probability of events. To include probabilities the method has to be modified and turns in the approach which we will describe in the next section. net present value all parameters electricity price expected production maintenance costs etc. Figure 3.2 Tornado diagram showing the largest risk factors (red bars) in a scenario based risk assessment. The thick black line shows the expected value under best guess estimates.

24 22 Text box 1: Cash flow calculations The cashflow or income of a project before tax is calculated as follows: IBT t = (GP t + PE t + PP t - VC t)q t - RC t (1) With: IBT t GP t PE t PP t VC t RC t Income Before Tax in year t Green Price in year t Reference electricity price in year t Production support level per unit production in year t Variable production costs per unit production in year t Fixed production costs in year t Q t Production in year t The taxable income can be reduced by the tax deductions: the depreciation and the interest payments to the bank. Using linear depreciation (constant over time) the income after tax will be: IAT t = (1-t) IBT t + t (DEP t + R t) (2) DEP t = C/L if t L (3) DEP t = 0 if t > L With: DEP t Depreciation in year t IAT t τ L Income After Tax in year t Tax rate Depreciation time R t Interest payment over debt in year t The net present value (NPV) of the casfhlow after tax, can now be calculated as: PV p = IAT n t t t = 1 (1+ rp ) (4)

25 23 NPV With: n IATt = PVp C = C = 0 t (1+ r ) p (5) t = 1 p PV p The Present Value of the project r p Project rate of return n Lifetime of the project If only the equity part of the investment is considered these equations are as follows: PV e = IAT A n t t t = 1 (1+ re ) (6) NPV With: n IATt A = PVe E. C = E. C = 0 t (1+ r ) e (7) t = 1 e PV e The Present Value of the equity part of the project r e The required Return On Equity A The annuity of the debt part of the investment E The Equity share Furthermore: A = R t + P t (constant over time) (8) D.C + E.C = C With: P t The payoff of the debt part of the investment in year t D The Debt share

26 Quantifying value at risk In the previous section we described a simple approach to quantify the risks which may lower the value of an investment in renewable energy. An important drawback of this approach is the lack of probability of outcomes. To characterise the role of risks and provide information on probabilities on occurrence, Monte Carlo simulations have been introduced in cash flow analyses. Instead of building separate scenarios which describe different views on the future, Monte Carlo analyses build on distribution functions for each input variable which is subject to uncertainty. In a Monte Carlo analysis a large number of cash flow calculations are made (10,000+). For each calculation, a new set of input data is drawn randomly from the distribution function for each input parameter. Each calculation results in an outcome for the net present value of the project which can be used in the final analysis. By ranking all outcomes (from to small to large) it then becomes possible to characterise the probability function of the net present value of the project under uncertain conditions. The expected net present value is then given by the median value of the distribution function: Expected NPV = NPV (P = 50%) It is important to note that the expected NPV defined in this way, may differ (strongly) from the average NPV in case of a skewed distribution function. Under these conditions, the median value represents a better estimate for the expected value than the average value. The available distribution function for the NPV of the project also allows to examine the certainty of the expected value. We can define an uncertainty range which tells between which values the expected value may vary at a certain probability level. Similarly, we can identify a measure of risk: risks correspond to the lower band of the uncertainty range, while opportunities correspond to the upper band of the uncertainty range. In this way, we can calculate the value-at-risk (VaR) which can be defined by: VaR = NPV (P=50%) NPV (P=10%).

27 25 The value-at-risk, defined in this way, covers 80% of the investment risk and reflects a commonly used definition. This definition, however, leaves a possibility that the actual risk may be larger. For stricter measures, one may apply a different definition which includes the impact of events with a low probability. Finally, the Monte Carlo approach allows an estimate of the probability, that the investment does not meet the requirements of the investor on financial returns. (i.e. the net present value is negative). This probability is given by: P(loss) = S [ P (NPV<0) ] Figure 3.3 gives an illustration of the Monte Carlo approach applied to an investment in a biomass plant. In this example, the expected net present value is calculated at 14 million Euro. The Value-at-Risk (VaR) lays around 24 mln Euro (i.e. the difference between the NPV with 10% probability and the expected value). In this case the VaR is larger than the expected NPV indicating that this investment has a large probability of not meeting the requirements on financial returns. This situation has a probability of 30% in this example.

28 26 100% cumulative probability 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% net present value (in mln euro) Figure 3.3 Example of the probability distribution for the net present value of a renewable energy project based on a Monte Carlo approach. The expected value (P=50%) is 14 mln Euro. The Value-at-Risk(VaR) is 24 mln Euro in this case (VaR = NPV(50%) NPV(10%) = 14 (-10) = 24 mln. The analysis also shows that this investor has 30% probability of not meeting his internal financial return targets (NPV < 0). 3.3 Quantifying required green price RGP and cash flow calculations The Required Green Price (RGP) is the average minimal green price that investors wants to obtain from the market over the lifetime of the project so his demands regarding financial returns are met and the investment is feasible. The RGP is calculated from the cash flow of the project, in which all relevant factors are included such as O&M costs and policy parameters. An estimate of the required green price is particularly useful in markets where the financial support

29 27 for renewable energy is not fixed by the government (e.g. through feed-in tariffs), but depends on market dynamics. The result of a cash flow calculation is usually the rates of return of a project. When the potential income is given over the lifetime of the project, the attractiveness of the project can be measured by the project rate of return or, if only the equity part is considered, the return on equity (ROE). The internal rate of return indicates the situation in which the Net Present Value (NPV) of the project is zero; the price for which the product (in this case electricity) is accounted for in the calculations is then the cost price. However, if a RGP calculation has to be made, the potential income is not known because the RGP is not known. Therefore the calculations have to be made with required rates of return to be able to calculate the RGP backwards. The resulting price (sum of market price of electricity, the RGP and production support) will equal the cost price; with this price the required returns on investments are just met. Therefore the RGP will be calculated for the situation in which the NPV is zero, using required rates of return: NPV(RGP) = PV(RGP) - C = 0 (1) With: NPV(RGP) Net Present Value as a function of the RGP PV(RGP) Present Value as a function of the RGP C Total investment Determination of the RGP from the cash flow of a project Equation (7) in the text box on cash flow calculations can be rewritten as a function of the required green price (RGP) in the following way: RGP E. C n (1 τ )( Q ( PE t= 1 = n t t + PPt VCt ) RCt ) + τ ( DEPt + Rt ) A t (1+ re ) Qt (1 τ ) t (1+ r ) t= 1 e

30 28 The calculation of the RGP can be combined with a Monte Carlo approach. This combined method then results in risk premiums, which an investor (or any other stakeholder which is involved in a renewable energy project) may try to ask in the market to cover his risks. Table 3.2 gives an illustration of the results one can obtain with a combined RGP- Monte Carlo approach. In this case risk premiums have been calculated for three types of risks: operational, market and regulatory risks of wind and biomass projects in the Netherlands. The example shows that regulatory risks translate into substantial risk premiums for wind energy and biomass (in comparison with required risk premiums for operational and market risk). The value of these risks will vary between individual generators, sources, countries and, most importantly, with the period for which they are considered. To cover all these risks, a generator would like to see them covered by a risk premium on top of the minimum price he needs for a profitable operation under normal (i.e. less risky) conditions. Table 3.2 Indication of risk premiums for various risk sources for investments in wind energy and biomass 3 type of risk wind energy biomass operational risk 0,1 ct/kwh 0,5 ct/kwh market risk 0,2 ct/kwh 0,2 ct/kwh regulatory risk 1,2 2,5 ct/kwh 1,2 2,5 ct/kwh 3.4 Examples Scenario versus VaR-analysis To illustrate the differences between the scenario based discounted cash flow (DCF) analysis with the Monte Carlo based Value-at-Risk (VaR) approach, we 3 From: W. Ruijgrok e.a. (2001). How profitable is renewable energy? Need and possibilities for financial support with a view on Europe. KEMA, Arnhem, report for Minsitry of Economic Affairs.

31 29 turn to a simplified example for a wind project which is considered of being developed. The project size intended is 20 MW, but the investor has key uncertainties regarding: investment costs expected wind speed and power production maintenance costs power price available. Based on available information at the planning stage, the investor has formulated three scenarios with input parameters for a DCF analysis: worst case best guess optimistic case investment costs 24 mln 22 mln 18 mln power production 42 GWh 50 GWh 55 GWh O&M costs 1,0 mln/year 0,8 mln/year 0,7 mln/year power price 5.9 ct/kwh 6.3 ct/kwh 6.7 ct/kwh As input for the Monte Carlo based VaR-approach, the following distribution functions are used which are consistent with the ranges used in the scenario cases: distribution mean standard deviation investment costs normal 22 mln 2.5 mln power production normal 50 GWh 3.5 GWh O&M costs normal 0,8 mln/year 0,05 mln/year power price normal 6.3 ct/kwh 0.3 ct/kwh Table 3.3 Differences in outcomes for the scenario-based DCF analysis and the Monte Carlo based VaR analysis of the risk for a wind farm Scenario DCF Monte Carlo worst case -6.7 mln VaR (p=10%) -3.1 mln best guess 0.6 mln Expected NPV 0.6 mln

32 30 optimistic case 6.5 mln Upward potential (p=90%) 3.4 mln Probability not meeting return on equity 43 % The scenario-based DCF analysis and the Monte Carlo approach both result in a similar estimate of the value of this wind project. Large differences, however, exist in the estimated uncertainty in this expected value. The worst case estimate is substantially smaller than the Value-at-Risk, while the optimistic case is substantially larger than the upward potential. The risk derived from a scenario-based DCF analysis reflects a combination of risk events with a very low probability and projects the worst case situation. According to the results of the Monte Carlo approach such an outcome would have a probability of less than 1% for this case. The Monte Carlo approach perhaps shows a more realistic view on the risk profile of this case. Figure 3.4 shows how the value for this project is distributed for this imaginary case. Unlike the scenario-based DCF analysis, the Monte Carlo approach also is capable of showing the likelihood that the value of the project does not meet the requirements of the investor regarding the return on equity. For this situation, this probability is around 42%. This implies serious concerns about the financial feasibility of this project.

33 31 100% 90% 80% Cumulative Frequency 70% 60% 50% 40% 30% 20% 10% 0% Net present value (in mln euro) Figure 3.4 Cumulative probability distribution of the net present value of the wind energy case Risk in various stages of project development The risk of a renewable energy project depends strongly on the stage of its development. Clearly, risks are larger for a project which is in the identification or planning stage only. At that stage, the investor has to take into account that his plan will never mature and gets realized. He faces a risk of failure which is related to the (environmental) permitting process and acceptance by authorities on the one hand and financial, site and technological conditions which have to be met on the other hand. All these factors may provide reasons to abandon the project during the planning stage. These risks of failure will change over time when plans and conditions get more shape. If the conditions are good or improve, then the risk of failure will decrease. Besides the risk of failure, there are also many other possible risks in the project, since little is known yet for certain. Items like investment costs, expected power production, operation and maintenance costs are known only up to a certain degree. This implies that, even if the project may succeed, the investor faces additional risks because the information available is not complete. At certain point in time, however, more information will become available if project development continues. Permits to build and operate will be given removing the

34 32 risk of failure due to regulatory restrictions. Investment, operation and maintenance costs will become clear when a supplier has been selected and contracts have been closed. These steps will also remove parts of the risk for the investor, but not all. Some items still may remain uncertain and provide risk. For instance, the expected price for power sales 4, actual power production level, inflation rate and maintenance in the long run. Some of these risk elements may be removed when the project starts to operate. To illustrate the evolution of these risks during project development we have constructed a case based on a real life example from the Netherlands. In this example an investor has the intention to build a wind farm of 20 turbines. Table 3.4 describes how some the key data which are relevant for the value of the project change during the different stages of this case. Table 3.4 Key data for the planned investment in an example wind farm in the Netherlands during three stages of project development. A fixed value indicates a firm figure. A range gives the estimate available at that point in time. first plans halfway permitting permits obtained in operation development costs mln Euro mln Euro mln Euro 3.0 mln Euro investment mln Euro mln Euro 49 mln Euro 49 mln Euro expected production GWh GWh GWh GWh maintenance costs mln Euro /year mln Euro /year 2.3 mln Euro /year 2.3 mln Euro /year power price (1-10yr) ct/kwh ct/kwh ct/kwh 9.3 ct/kwh power price (11-15yr) ct/kwh ct/kwh ct/kwh ct/kwh risk of failure 70% 30% 5% 0% 4 This may differ for projects in countries with a feed-in tariff which is fixed for the lifetime of the project. These cases do not provide any uncertainties to investors, unless laws and regulations are changed and the tariff system is abolished. Such an event would pose a huge risk for the investor.

35 33 30 NPV in mln euro VaR Expected NPV Upward potential First plans Halfway Permit In operation Figure 3.5 Value-at-Risk (VaR), expected NPV and upward potential estimated using a Monte Carlo approach for the development of a wind farm with 20 turbines in the Netherlands Figure 3.5 shows how Value-at-Risk, expected NPV and upward potential change during the different stages of project development. The expected value of the project (in NPV terms) is negative in the initial stages of development due to the large risk of failure of the project, leaving the investor with developments costs and no returns from the project. When plans become more mature and the risk of failure decreases, the expected value will start to rise. At that stage, there is, however, still a possibility that the project has to be abandoned, which leaves the investor with a loss as indicated by the negative VaR. This situation improves when a permit has been obtained and quotations from the supplier for turbines and maintenance are available. The VaR shows a large shift upwards, illustrating the significant reduction of risk failure and risks in investment and maintenance costs. Also note that the expected value of the project improves because more information is available. However, the upward potential is slightly reduced when more is known. When the project has entered the stage of operation, again additional information has become available. As a result, uncertainty decreases, which brings the valueat-risk up and upward potential down. In this case, the expected value is slightly affected upward. Due to a conservative approach in estimating uncertain factors, expectations which were previously used were slightly lower than the actual

36 34 values. This leads to a positive effect on the expected NPV when better information came available. Risk in the portfolio of a green supplier The previous examples took a view on the risk of an investor who is considering an investment in a single renewable energy project. When an investor wants to build a portfolio of projects, for instance in different renewable energy options in different European countries, he may be confronted with additional issues such as: how do you build a sensible portfolio from different projects and different countries how do risks accumulate in building a portfolio is there a limit in portfolio size (considering risks and return on investment or value of the portfolio)? We will illustrate the possibilities of the Monte Carlo based profit-at-risk approach to assess the risks of a portfolio of projects. The example is based on the evaluation of a portfolio with candidate projects of a European investor 5. This portfolio of candidate projects contained projects in wind energy, biomass (landfill, co-firing in coal powered station, biomass CHP) and small scale hydro power in England, Norway, Sweden, Finland, Denmark, Germany, the Netherlands, France, Spain and Italy. For each potential project, the expected net present value and a measure of risk were assessed using a Monte Carlo approach. A plot of these results provides a risk return diagram (see fig. 3.6), which shows how the various projects are distributed. There appears to be a selected group of projects (contained in the green circle in fig. 5.3) which share a relatively high return and low risk in comparison with all other projects. Similarly, there is a group of projects with a relatively poor ratio of returns versus risk. 5 Results are based on projects which have been analysed for a European investor in KEMA, 2003 (M. Vosbeek, W. Ruijgrok and H. Cleijne). The market for renewable energy in Europe. Country and price information on wind energy, biomass and hydro power. Confidential report.

37 35 Figure 3.6 Risk return profile for a selection of possible renewable energy projects in Europe Average NPV [ in EUR-ct/kWh ] 1,50 1,25 1,00 0,75 0,50 0,25 0,00-0,25-0,50-0,75 high return low risks poor ratio of return versus risk BE-biok -1,00 0,00 0,02 0,04 0,06 0,08 0,10 0,12 0,14 0,16 Risk level (measured by variation in NPV) which have been considered by a green energy supplier to incorporate in his portfolio. The amount of risk is measured in this case by the variation in NPV calculated in a Monte Carlo analysis. The return is measured by expected median NPV. The yellow line indicates an optimal risk-return profile. European evaluation of different renewables options highest possible return at a given risk level The diagram also indicates a kind of optimal risk-return relation : for each risk level there seems to be a project with a maximum return. This relation is, however, shaped differently than relations found in for instance the stock market, where higher levels of risk on average provide better returns (see fig. 3.7). The efficient investment frontier which is found in the stock market shows that at each risk level a best performer exists. The efficient investment frontier allows a trade-off between risk and returns. This can be used in a portfolio to manage risks by diversification of the stocks in which one invests by spreading investments over sectors, countries and companies. Our analysis for the renewable energy projects portfolio did not show such an efficient investment frontier, but a reverse relation. Returns seem to decrease at higher risk levels. So, this implies that there is no trade-off between risks and

38 36 returns. Risk management for this portfolio means that managing risks always means opting for the projects with the highest possible returns. Stock market Renewable projects return return risk risk Figure 3.7 Difference in risk-return relation between stock market and investing in projects in renewable energy market. In the stock market an efficient investment frontier exists, which allows higher returns at higher risk levels. The question now is how a portfolio of projects can be built and how much risk is associated with possible sizes of that portfolio. Considering the risk management lesson we noted earlier ( always opt for the projects with highest returns to minimize risk ), projects were ranked according to their return-risk ratio with best performing projects selected first. As the next step, for each project the expected net present value and downside risk were calculated based on project size. In this way it becomes possible to assess the cumulative value of the portfolio and absolute risk level (see fig. 3.8). Results shows that the value of the portfolio rise up to a certain size of the portfolio (around 1,700 GWh in this case), then stabilize more or less if the portfolio increases in size and after a certain size starts falling again. There appears to be a maximum value of the portfolio (around 1,700 GWh) beyond which further growth either increases the absolute risk to which this investor is exposed or risk increases and value even decreases. At least this end of the portfolio should always be avoided in a sensible investment strategy.