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UNIVERSIDAD PONTIFICIA COMILLAS ESCUELA TÉCNICA SUPERIOR DE INGENIERÍA (ICAI) OFFICIAL MASTER'S DEGREE IN THE ELECTRIC POWER INDUSTRY Master’s Thesis Diffusion of new renewable power in Brazil: A Real Options Approach Author: Athir Nouicer Supervisor: Luciano Losekann Co-Supervisor: Edmar Luiz Fagundes de Almeida Florence, July, 2015 UNIVERSIDAD PONTIFICIA COMILLAS ESCUELA TÉCNICA SUPERIOR DE INGENIERÍA (ICAI) OFFICIAL MASTER'S DEGREE IN THE ELECTRIC POWER INDUSTRY Master’s Thesis Diffusion of new renewable power in Brazil: A Real Options Approach Author: Athir Nouicer Supervisor: Luciano Losekann Co-Supervisor: Edmar Luiz Fagundes de Almeida Florence, July, 2015 UNIVERSIDAD PONTIFICIA COMILLAS INSTITUTO DE ECONOMIA DA UNIVERSIDADE FEDERAL DO RIO DE JANEIRO Master in Economics and Management of Network Industries (EMIN) ABSTRACT A major problem in the expansion of renewable energies sources is finding the most adequate way to support them. The design of a suitable support scheme is necessary for an efficient development of renewable energies sources (RES). In Brazil, Many projects are delayed after getting the construction license due to financing problems. Some of them are even abandoned. Experiences of two European countries (Germany and UK) were analyzed in order to find efficient alternatives for RES expansion in Brazil. These countries are currently implementing new RES support schemes in order to increase electric systems’ efficiency; Contract for differences for UK and a tendering scheme in Germany. The recent results show mitigated outcomes. They are however encouraging considering investors caution against new regulatory policies. Brazil has already adopted a similar scheme, the auction mechanism, since 2004. Still, its share from RES apart from hydro and biofuels is very small, 4% for wind energy and less than 1% for solar energy. It has, nevertheless, an attracting potential. To investigate on this problem, we applied a Real Options approach to value the investment opportunities in a wind farm project. Compared to the traditional NPV calculation, The Real Options method excels in terms of covering the managerial flexibility for delaying the investment decision. The considered project is subject to a multistage investment strategy consisting of design, construction and operation phases. A binomial approach through a decision tree was elaborated to model the investment opportunity. Two scenarios were adopted for wind farms that will participate in the 2017 A-3 auction. The results suggest that the option of delaying the project has significant value, since the investor can wait until the uncertainties get revealed. This study can serve as a guide to ANEEL, the Brazilian electricity regulatory agency, and to RES investors for the type of strategy to undertake in order to increase RES generation in Brazil. Good planets are hard to find... List Of Figures Content List Of Figures.................................................................................................................................................. 3 List Of Tables ................................................................................................................................................... 4 ABBREVIATIONS .......................................................................................................................................... 5 1. 2. INTRODUCTION..................................................................................................................................... 6 1.1. Motivation ....................................................................................................................................... 6 1.2. Objectives of the Master Thesis ........................................................................................................ 7 1.3. Methodology.................................................................................................................................... 7 LITERATURE REVIEW .......................................................................................................................... 8 2.1. 2.1.1. Regulatory entities ....................................................................................................................... 9 2.1.2. Regulatory scheme to increase renewable integration .................................................................... 9 2.1.3. Characteristics of RES-E technologies ........................................................................................ 10 2.2. 3. Support Mechanism for Renewable Energies .................................................................................. 11 2.2.1. Price-driven strategies ................................................................................................................ 11 2.2.2. Quantity driven strategies ........................................................................................................... 13 2.2.3. Indirect strategies ....................................................................................................................... 14 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS .................... 16 3.1. European regulatory framework...................................................................................................... 16 3.2. Historical development and current status of RES-E deployment in EU countries ............................ 17 3.2.1. Historical development at EU level ............................................................................................ 17 3.2.2. Progress at country level ............................................................................................................ 17 3.3. 4. Regulation ....................................................................................................................................... 8 Country-specific learned lessons..................................................................................................... 19 3.3.1. Case study Germany .................................................................................................................. 19 3.3.2. Case study UK ........................................................................................................................... 22 THE BRAZILIAN CONTEXT ................................................................................................................ 26 4.1. Overview of Brazil ......................................................................................................................... 26 4.2. Electricity policies of Brazil ........................................................................................................... 27 4.2.1. First reform of the electricity market: 1990’s .............................................................................. 27 4.2.2. Second reform of electricity market: 2004 .................................................................................. 28 4.2.3. National Policy on Climate Change: 2009 .................................................................................. 28 4.3. Renewable energy potential ............................................................................................................ 28 4.4. RES support mechanisms ............................................................................................................... 29 4.4.1. Proinfa: quota & feed-in tariff scheme ........................................................................................ 29 4.4.2. Incentives on wire costs for selling energy contracts at the free market ........................................ 29 4.4.3. Current Structure: Technology-specific auctions ......................................................................... 30 4.5. Investment decision in renewable energies in Brazil........................................................................ 31 4.6. Lessons from European experience to Brazil................................................................................... 32 4.6.1. The auction system .................................................................................................................... 32 1 List Of Figures 4.6.2. 5. Possible improvement for Brazil................................................................................................. 32 THE REAL OPTIONS METHOD ........................................................................................................... 34 5.1. Presentation of the method ............................................................................................................. 34 5.2. Renewable energy policy evaluation using real option models ......................................................... 34 5.3. Real Options method for the Brazilian Market ................................................................................ 36 5.4. Mathematical modeling .................................................................................................................. 37 5.4.1. Discounted cash-flow (DCF) method.......................................................................................... 37 5.4.2. Estimation of wind farm cash-flow return volatility .................................................................... 37 5.4.3. The Event tree............................................................................................................................ 39 5.4.4. The Decision Tree ...................................................................................................................... 40 6. A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm ....................................................................................................................................... 41 6.1. Project description.......................................................................................................................... 41 6.2. Methodology.................................................................................................................................. 42 6.2.1. Estimation of cash flow return volatility ..................................................................................... 42 6.2.2. The Cash flow............................................................................................................................ 43 6.2.3. The Event Tree .......................................................................................................................... 45 6.2.4. The project decision tree ............................................................................................................ 47 6.3. Analysis of the failure of the last wind auction ................................................................................ 50 6.4. Model discussion ........................................................................................................................... 52 CONCLUSION .............................................................................................................................................. 53 REFERENCES ............................................................................................................................................... 55 ANNEX ......................................................................................................................................................... 60 ANNEX A .................................................................................................................................................. 60 ANNEX B .................................................................................................................................................. 62 2 List Of Figures List Of Figures Figure 1 : Differences between the RES support mechanisms .......................................................................... 13 Figure 2 : Tradable green certificates scheme .................................................................................................. 14 Figure 3 : Diversity of RES-E support schemes in the EU-28 ........................................................................... 17 Figure 4 : EU member states RES targets for 2020 .......................................................................................... 18 Figure 5 : RES Installed Capacity and Cost in Germany .................................................................................. 20 Figure 6 : Wind and Solar energies installed capacities in Germany ................................................................. 21 Figure 7 : The operation of an intermittent FiT with Cfd .................................................................................. 23 Figure 8 : RES Installed Capacity and Cost in UK ........................................................................................... 24 Figure 9 : Brazil Installed Capacity in 2014 ..................................................................................................... 26 Figure 10 : Price serie and deseasonalized serie for 2020-2050 ........................................................................ 42 Figure 11 : Free Cash Flow of the first scenario’s investment........................................................................... 44 Figure 12 : Free Cash Flow of the second scenario’s investment ...................................................................... 45 3 List Of Tables List Of Tables Table 1 : EU Member States RES indicative trajectory .................................................................................... 18 Table 2 : The Binomial tree ............................................................................................................................. 39 Table 3 : Characteristics of the project ............................................................................................................. 41 Table 4 : The two scenarios ............................................................................................................................. 42 Table 5 : S1 Traditionnal Investment Analysis ................................................................................................. 43 Table 6 : S2 Traditionnal Investment Analysis ................................................................................................. 44 Table 7 : Compound Option features ............................................................................................................... 45 Table 8 : S1 Event Tree (R$) ........................................................................................................................... 46 Table 9 : S2 Event Tree (R$) ........................................................................................................................... 46 Table 10 : S1 Second Investment Option (construction phase) valuation Tree .................................................. 47 Table 11 : S1 First Option (invest R$4 million design phase) valuation Tree .................................................... 48 Table 12 : S1 Project Decision Tree ................................................................................................................ 48 Table 13 : S2 Second Investment Option (construction phase) valuation Tree .................................................. 49 Table 14 : S2 First Option (invest R$4 million design phase) valuation Tree .................................................... 49 Table 15 : S2 Project Decision Tree ................................................................................................................ 50 Table 16 : Event Tree (R$) .............................................................................................................................. 50 Table 17 : Second Investment Option (construction phase) valuation Tree ....................................................... 51 Table 18 : First Option (invest R$4 million design phase) valuation Tree ......................................................... 51 Table 19 : Project Decision Tree ..................................................................................................................... 51 4 ABBREVIATIONS ANEEL Brazilian Electricity Regulatory Agency BNDES Brazilian National Development Bank CCEE Brazil's power trade chamber CCGT Combined Cycle Gas Turbine COFINS Contribution to Social Security Financing CO2 Carbon dioxide DCF Discounted cash flow EDF Electricité de France (French Company) ETS European Emission Trading System EPE The Energy Research Company EU European Union FCE/ACL Free Market FCF Free Cash Flow FINAME Financing of machinery and equipment FIT Feed-in Tariff FIP Feed-in Premium IMF International Monetary Fund KW Kilowatt KWh Kilowatt-hour O&M Operation and Maintenance ONS National Electric System Operator NPV Net Present Value PIS Social integration program PPA Power Purchase Agreement PROINFA Programme of Incentives for Alternative Electricity Sources (Brazil) PV Photovoltaic RCE /ACR Regulated Market RES Renewable energy sources RES-E Electricity from renewable energy sources RO Real options RPS Renewable Portfolio Standard R$ Brazilian Real SHP Small Hydro Plant TGC tradable green certificates TSO Transmission System Operator UK United Kingdom WACC Weighted average cost of capital 5 INTRODUCTION 1. INTRODUCTION 1.1. Motivation Worldwide, the diffusion of renewable power sources is the main drive to mitigate CO2 emissions. It justifies the use of subsidies to promote those sources, especially wind and solar. EU countries, like Germany and Spain, have led this process. New laws and norms are continuously being approved following successes and failures of the previous ones. Also, new regulatory instruments are continuously under study to cope with the generated challenges to power system operation and expansion. Intermittency of renewable generation is a challenge. As to provide security of supply, it is necessary to keep backup units to compensate renewable generation when weather conditions are inappropriate. However, when the diffusion is intense, traditional thermo-power plants dispatch is lower and their average cost increases. Many countries are orienting funds into renewable energies sources, such as wind turbines, solar farms and geothermal plants. However, countries, like Denmark and Germany, which reached large share of renewable generation face the highest energy prices. Without efficient and attractive support schemes, investors won’t take the risk and put their money in a renewable energy farm. In Brazil, the diffusion of renewable sources apart from hydro is peculiar. The Brazilian power system presents an unusual composition. The Brazilian power mix is dominated by hydropower. Hydropower generates 80% of Brazil’s electricity. So, the drive to mitigate CO2 emissions is less significant. The large reservoirs of hydropower plants constitute in fact a way to deal with intermittence of new renewable sources. However, the challenge is to reduce the dependency on hydro sources and rain falls. Moreover, distances from the best generation sites to the main load centers are long. This gives makes challenging for the Brazilian Regulator ANEEL to design efficient RES support schemes in order to boost solar and wind energy expansion. In electricity power systems regulation, policy learning between countries is an important driver for improving policy design. In this project, we will start by analyzing the European experience to get lessons for the Brazilian power system. Previous European experiences suggests that a well-designed feed-in tariff can generate rapid growth for targeted RE technologies by creating conditions that attract capital to those particular sectors. However, in the long run, the best way to encourage renewable would be by means of taxation and/or introducing market-conform instruments such as a wellfunctioning system of competitive auctions. The use of auction schemes is increasing when compared to other mechanisms, as FiT and FiP, although they still the most popular mechanisms, especially in developing countries. This confirms the country specific aspect of the support mechanism design. The choice of support scheme by the government and any alterations to this choice can have a considerable impact on both the timing of investments and the generating capacity of projects. The associated regulatory uncertainty creates incentives for investors to wait until the type of support scheme and the corresponding level of support is sufficiently attractive. Thus, answering how and to what extent political factors, i.e. RES support schemes, influence the renewable energy integration turns out to be a critical question. To assess this impact, we will use a real option analysis. This concept has slowly but surely been gained academic ground. Real options valuations specifically provide an alternative to the standard net present value assessment when investment involves some underlying uncertainty and built-in 6 INTRODUCTION flexibility. In particular, in contrast to the static nature of the net present value, real option valuations allow the investor to exploit the flexibility in the dynamic decision process as uncertainty evolves. Indeed, with uncertainty and irreversibility, NPV rule is often wrong and option theory gives better answers. 1.2. Objectives of the Master Thesis The goal of the thesis is in a first step to use the EU experience to identify advantages and difficulties to increase renewable share in the Brazilian power mix. Second, a real option analysis will be used in order to assess investors’ behavior under the current Brazilian auction scheme for renewable energies projects. 1.3. Methodology In the first part of this thesis, we investigate the different European countries’ experiences on renewable energy support schemes’ design and their effects on the behavior of investors. Advantages and Disadvantages of each experience and their adaptability with the Brazilian power system are analyzed to point out the qualitative findings that would benefit to the renewable energy expansion in Brazil. Analyzing the Brazilian electricity power system and investment decisions is necessary to better assess the adaptability of these experiences. A valuation of renewable energy project timing is carried out through real options. This analysis has been previously used in Oil refinery and conventional power plants valuation. There are different factors that take part in the valuation of a renewable energy project. Mainly; upfront costs, electricity price uncertainty and public incentives. 7 LITERATURE REVIEW 2. LITERATURE REVIEW 2.1. Regulation Classical microeconomic theory is based on the assumption of pure and perfect individuals’ rationality that maximize their welfare by minimizing their costs and with a perfect and transparent information at any time. In reality, these conditions are difficult to reach: to maximize their profits, producers have often the incentive in making their consumers irrationals to make their products more attractive. To fix the market imperfections, regulation is necessary, but the level of government (or public body) intervention between producers and consumers is not so obvious. One main hurdle is to keep incentives to an efficient behavior. Nevertheless, the word “regulation” is not easy to define. People might hold several interpretations about regulation, but they do not know what ‘regulation’ means. Oxford dictionary define it: A rule or directive made and maintained by an authority, in other words it is an act or legislation issued by a government minister or a charged entity, whose aim is to organize, guide or secure the application of an activity. Regulation should be simple, however, simplicity is not easy to attain. Steve Jobs figured out that “you have to work hard to get your thinking clean to make it simple”. In Antifragile (Taleb, 2014) , Nassim Nicholas Taleb described it through the Arab expression for trenchant prose: no skill to understand it, mastery to write it. As a general policy, the deregulation of energy markets led to the shift of the risk from the governments’ side to the private parties’ side. It gave the last a right to carry the investment instead of the governments. However the more complex the regulation, the more bureaucratic the network, the more an agent who knows the loops and misshapes would benefit from it later. On the contrary, several examples of energy industry shows that with the evolution of private expertise in energy markets, investors tend to have more knowledge than their regulator and trying to use this asymmetry of information to have more convenient regulations. This is a franchise, an asymmetry one has at the expense of others. For example, according to Nassim Nicholas Taleb (Taleb, 2014) Toyota Cars Company hired former U.S. regulators and used their “expertise” to handle investigations of its car defects. When talking about regulation we should reorient our views in two ways: first with respect the regulator attitude regarding the impact of the implemented policies on the national budget and on the investors’ behavior and second with respect to focusing on the resiliency and adaptability of the financial system established following these procedures. The electricity industry is not an exception. As any other industry, it is subject to the laws and principles that govern both the physical characteristics of electricity and also to the fulfillment of the expectations of utilities and consumers. For instance, to insure the stability of the power system, the regulator might consider attracting more heterogeneous power producers, so that different generators might hold different compositions of assets, parameters and costs. The regulation of the power industry is based on three elements (Pérez-Arriaga, 2013) ; the design of rules to control the agents’ behavior, the structure of the power industry and the supervision of agents’ performance. The design of the rules is the main tool to guide the different market agents towards the goal decided by the regulator. An example of the rules is the design of the remuneration of renewable 8 LITERATURE REVIEW project. Different schemes have been used to increase the integration of RES. Each one has different characteristics; either a price driven strategies, quantities driven strategies or tax exemption in order to make the investment in RES attractive to private parties. The structure refers to the generation, transmission, distribution and supply activities’ organization especially after the privatization of the electricity market. In a well functioning market, a sufficient number of similarly sized competitors must participate to enhance market competition. If not, the regulators must set rules that prevent adverse effects of large agents and natural monopolies on market efficiency. The last element is the supervision of agents’ performance. Market agents are not altruistic. The regulator should supervise them to assure the respect of the rules he sets. 2.1.1. Regulatory entities Chile was the first country to change the traditional power sector regulation in 1981 (Pérez-Arriaga, 2013). Then, a wave of reform was led in many other countries to transform the institutional framework, organization, and operating environment of the infrastructure of electricity industry. Each country has adopted a different structure of its power sector and the approaches to reform vary across the countries but the main objective is common; to improve efficiency. Regulatory entities are set to put the national policies into application. They organize and supervise the functioning of the electricity markets for the benefit of the consumers and producers with being consistent upon the objectives of energy policy. To exercise their function, regulatory entities (Pérez-Arriaga, 2013) have several instruments of different nature (economic, structural, etc.). They attempt to manage Cost-of-service subject to regulatory oversight, benchmarking of regulated monopolies, Price or revenue caps, unbundling of the electricity industry’s activities, enhancing competitiveness and application of other incentives such as setting quality standards (ISO- 9001) and other regulatory measures that may also be deployed, such as command and control (standards, targets, penalties, etc.) and operating license requirements. 2.1.2. Regulatory scheme to increase renewable integration The major characteristic of an energy sources to be defined as renewable energy is its sustainability. One of the most interesting definitions comes from Mr. Bernard L Cohen, former professor at University of Pittsburg (Cohen, 1983). He introduced the term 'indefinite' (the necessary time period for an energy source to be sustainable enough to be considered as renewable energy) in numbers by using the relation between the sun (solar energy) and the earth. According to Professor Cohen, if the energy source could last as long as the relationship between the Earth and Sun is supposed to last, about billion years, then it is considered as a renewable energy source. Another characteristic of a renewable energy source is its environmentally-friendly features. The expansion of renewable energies limits the dependence in fossil fuels and therefore contributes in the reduction of CO2 emissions. In recent years, a considerable number of countries are adopting different policies to increase renewable energy share in their power mix. The main advantage is their limited environmental impact comparing to fossil sources. Thus, the power grid is getting greener through a profound transformation that will continue over the next few decades to decarbonize the world energy model. Consequently, different challenges are raising for the system operators from the technical, economic and regulatory perspectives; RES-E induces changes in the management of generation systems (C.Batlle, 2012) and grid operations (Wholesale market functioning, Price dynamics...). In addition to that, the design of markets and grid regulation has an impact on the deployment of RES, as well as the design of support mechanisms for RES-E affects the system operation and wholesale market outcomes. RES-E have a highly variable and unpredictable output and they are frequently called ‘‘intermittent generation’’. Most of these technologies have high investment costs but very low (or null) variable costs. The wind energy has experienced the highest growth in the last years due to technological advances leading to lower costs and the incentivizing government policies for renewable investments (The Wall Street Journal, 2013). While there are several significant numbers of experiences and literature that can help us to describe the efficiency of the different previous alternatives for promoting renewable energies, the mutual 9 LITERATURE REVIEW implications of electric power systems and RES-E related regulation has not been sufficiently studied yet. An explanation could be that the regulatory design of electric power systems has been conceived without considering the impacts that a large penetration of RES-E has on them (C.Batlle, 2012). Indeed, since some countries have experienced a high RES-E penetration, the weakness of their regulatory schemes has shown up. Countries like Spain has suffered from an over investment in RES obliging the Spanish regulation authority to review their RES support scheme which were excessively attractive for investors but costly to consumers. The main issue is to extract a previous experience from its context and adapt it in another one. This may rise from the countries’ topological nature and socio-economic environment. In addition to that, different policies will induce different types of RES, with different characteristics, and this will result in different impacts on electricity power system. The design of support schemes is critical: On the one hand, the support schemes and the investors’ revenues need to be predictable and last for the whole regulatory period. On the other hand, the regulator, i.e. the policy maker, should design flexible support schemes that can adapt to new situations (Río, 2015). As a matter of fact, support schemes need to have flexibility property without raising uncertainty and doubt among investors. Thus, the regulation authorities need to deal with the new changes in the electricity power system through adapting the market design and grid regulation. These changes should be different according to the RES-E policy and consequently on the type of RES-E technologies which is being promoted. Previous studies have analyzed RES expansion largely as a market process influenced by governmental policies. However, analyzing directly the influence of political institutions and government political ideologies, in the RES integration process, has been given less consideration. 2.1.3. Characteristics of RES-E technologies In addition of being very sensitive to policy instruments, the RES-E expansion is highly dependent on the characteristics of the technology which is being promoted and its potential in the given area. It is not easy to say which technology is better. Technologies have their advantages and drawbacks. The choice between one of them is highly dependent on the application and the location of the system. This study will focus in two renewable energy sources; wind and solar. 2.1.3.1. Wind energy Sailing vessels can be considered as the first human use of wind energy (Wikipedia, 2015). In 3500 B.C, the Sumerians have already sailed with sails. The use of wind energy has evaluated across the years. The first Wind Turbine Generator was invented in the IXXth century. Becoming more and more powerful and efficient, wind energy is now an important RES. It is the renewable source that has experienced the most impressive growth in recent years. Mostly, in every country in the world, there are wind turbines. Nevertheless, the amount of installed capacities and the share in the energy mix vary highly among them. It depends mainly on the policies adopted by each country to increase the renewable integration. China, USA and Germany have the largest installed capacity with respectively 114,763 MW, 65,879 MW and 39,165 MW. However, for the wind energy share, Denmark, Portugal and Spain have the highest percentages with 33.8, 24.6 and 20.9% in 2013 (Roney, 2014). Denmark was the only country to produce one third of its electricity from wind energy in 2013. In Germany and the UK, wind contributed nearly 8% of electricity generation in 2013. Moreover, four states in northern Germany get half or more of their electricity from wind. An important factor of this increasing integration is that the cost of producing wind energy has come down steadily over the last few years. The main cost is the installation of wind turbines. However, costs of electricity from wind energy depend strongly on wind speed and regularity. Wind farms located in regions with high wind potential might generate much more profits than the ones located in less advantageous regions. Thus, location-specific support mechanisms might be set depending on the energy source condition to avoid windfall profits. 10 LITERATURE REVIEW 2.1.3.2. Solar energy As the name indicates, solar energy transforms the sun’s radiation into electricity. It presents an important potential especially in regions with high exposition to sun radiation. This gross potential can cover 8,000 times the human primary energy demand (Letcher, 2008). The main issue is the efficiency of the methods used to transform this energy. There are two main methods for producing electricity from solar radiation; first, solar photovoltaics (PV) and second concentrated solar power (CSP). Solar PV uses cell arrays to produce direct current electricity from solar radiations, and CSP consists on concentrating solar energy in order to heat water or another liquid to produce steam that feeds a turbine. Solar energy is the most socially accepted way of distributed generation (Devine-Wright, 2008). It is preferred to wind energy in isolated areas or small installations to make them autonomous being though a very good investment for individuals. However, the main drawback for solar energy is its intermittency. This is mainly because of the weather conditions. The panels produce much in the summer when demand is lower. On the contrary, the production of energy in winter is lower while the consumption is higher. Solar energy produces in the day and depending on the weather, not depending on energy demand. There is a need therefore to invest in energy storage or backup energy means which are very expensive. 2.2. Support Mechanism for Renewable Energies The economic attractiveness is a critical part of renewable energies deployment and expansion strategy. If RES do not present a financial return to investors, they will not be able to compete with the conventional resource technologies. However, it is not obvious to compare a unit cost of renewable energy to conventional sources. External costs, such as the social and environmental costs, are included. Support instruments for RES-E are characterized by three main parameters – the type of support instrument chosen, the degree of harmonization and the specific design elements (C.Batlle, 2012). All of these have an indirect influence on electricity markets and on grids flows through their impact on the technology mix and geographical location but also can have a direct influence for example by setting rules for the participation of RES in the market. The support mechanisms used to foster renewable energy projects can be classified into direct and indirect policy instruments. Direct policy measures (Haas, 2010) aim at the stimulation of RES-E on the short run, however indirect ones focus on developing long-term more favorable framework. Another layer is considered in the support schemes classification, is whether they are price or quantity driven mechanism and whether they address the installed capacity or the generated electricity. Support mechanism design vary from technology-neutral mechanism to technology specific ones, the differentiation depends mainly on the RES required targets to meet. The differentiation is made on purpose so that it doesn’t allow windfall profits for the cheapest technology. Nevertheless, this is a source for complexity for policies’ design parameterization and transaction costs. 2.2.1. Price-driven strategies The price driven strategies are a financial support in terms of a subsidy received by RES generators. It can be for the installed capacity (per kW) or a payment for the energy produced (per kWh). Pricedriven strategies such as feed-in tariffs or feed-in premiums tend to be technology-specific instruments (Haas, 2010), different contracts are offered according to the generation technology and regulator’s target. 2.2.1.1. Investment focused strategies Entrepreneurs aiming to invest in renewable energy projects can benefit from investment subsidies or low interest loans. Development banks like BNDES in Brazil, offer attractive financing for renewable energy projects. Generally, they offer favorable terms including, for example, sixteen-year loans with 11 LITERATURE REVIEW interest rates lower than the other banks. Loans and non-tax mechanism encourage new RES-E capacity expansions; they lower the cost of investments for entrepreneurs. Another example is the European Regional Development Fund (A.Poullikkas, 2012) for renewable energy projects. It supports investors under certain conditions like maximum amount and subsidy’s percentage from the eligible costs. These conditions differ according to the location and the size of the project or company with eventual ceilings to prevent windfalls remunerations. Whether they are fiscal or financial measures, non-tax mechanisms play an important role in RES projects’ expansion today. They are tools used by regulators to trigger supply or demand. ‘Ecotaxes’ and ‘carbon taxes’ for example are imposed on conventional electricity generators. Consequently, they benefit to RES producers. In addition to that, they send direct message to end-users about the added value of RES-E. Tax incentives should, nevertheless, be a supplement to attract investors, but not be the principal focus. Indeed, usually, they don’t provide a long-term certainty for investors (A.Poullikkas, 2012). 2.2.1.2. Generation based strategies Feed-in tariffs (FIT) A feed-in tariff (FIT) support scheme is a fixed payment to RES generators for each unit of electricity generated. The price is fixed for a certain period under a contract and it is independent of the electricity market price. FITs can be differentiated according to the environment they are implemented in. Different contracts durations, settlement of a cap for particular technologies, installed capacity differentiation levels, and in some countries, combining this schemes with auctions are factors subject to differentiation. In the European Union countries, the introduction of FIT was a way to boost renewable energy expansion, as well as the R&D activities related to the development of this sector. The energy consumption in European countries is among the highest in the world. They have targeted to generate 20% of their energy production from renewable energy by 2020. Recently, FIT rates in Germany were reviewed and lowered, the same case happened also in other European countries. Today, FIT for photovoltaic KWh in Germany pays between 13.50 and 19.50 €cents/KWh, depending on the size of photovoltaic projects. These prices are becoming closer to gridparity1. Since Feed-in tariffs are more applicable in a technology-specific form, it promotes market development of less mature technologies, leading to potential cost reductions and therefore allow a high dynamic efficiency. In some cases, the remuneration may be paid for the installed capacity instead of the generation. It is used to trigger investors’ reaction to the regulators’ targets. FITs have shown remarkable achievements in term of RES expansion as a support mechanism according to previous experiences. The main factor of this success is reducing uncertainty for investors; stable revenue flows are offered to them. The level of the feed-in tariff is typically determined by an administrative procedure on the basis of levelized costs or using an auctioning mechanism referring to the potential benefits of using RES (Erika de Visser, 2014). Feed-in premiums (FIP) In a feed-in premium (FIP) support scheme, RES generators receive a fixed payment on top of the market price. They have to sell the electricity directly in the market and receive an additional payment, called premium. It can be a fixed payment or dependent of market price in order to limit both the price risks for producers and the risks of making excessive profits at the same time (Held, 2014). To 1 Grid parity is a state, at which a developing technology (i.e RES) will be able to produce electricity at the same cost as conventional technologies. 12 LITERATURE REVIEW understand the difference between FITs and FIPs; for fixed FITs the total feed-in price is fixed, for premium scheme, only the amount on top of market price is fixed (see figure 1). For the renewable plant owner, the total price received per kWh, in the premium scheme (electricity price plus the premium), is less predictable than under a feed-in tariff because it depends on a volatile electricity price. A FiP with cap and floor prices can minimize both the upside and the downside risks as only a certain revenue range is allowed for RES generators. The cap and floor feature aims to avoid large divergences between profits and losses. In case of the sliding premium or contract for difference (CfD), the premium is a function of the market price. The higher the market price, the lower the Premium (Ragwitz, 2012) . Figure 1 : Differences between the RES support mechanisms (Meeus, 2012) 2.2.2. Quantity driven strategies Quantity driven strategies are used by regulators to define the desired level of generation of RES. They are partly implemented in a technology-neutral manner. In the last years, several countries like, Italy, Poland, Sweden started implementing quotas mechanisms through technology-differentiation. This differentiation is established through accrediting different number of certificates to each technology or via splitting up the target in sub-targets 2 (Held, 2014). Other countries are moving to auction and to tendering system since they believe it fosters competition and price disclosure. The financial support for this schemes can either be investment focused or generation based. A differentiation between tender mechanism and auction is that in auctions, the selection of offers (bids) is based on price, while tenders may include additional qualitative and quantitative criteria. By contrast, tender mechanisms use an auction (Grau, 2014) to determine the required remuneration levels. The tendering process is considered competitive if the total cumulated capacity or generation bidden exceeds the capacity or the generation that is being tendered. The remuneration of the chosen bids can be pay-as bid or a common (clearing) price, which correspond to the highest accepted Price. A maximum Price is set by the regulator or the government to limit the risk of excessively high bids which can induce a costly support scheme. 2.2.2.1. Investment focused strategies: Tendering system Under this scheme, regulator, announce the amount of capacity to be installed. A bidding process is defined and winners benefit from a set of favorable investment conditions, including investment grants per installed kW. The level of the incentives to invest is usually technology-specific. In auctions mechanisms there are three key rules: bidding, clearing, and pricing (Luiz T. A. Maurer, 2011). The bidding rules organizes how offers can be submitted. The clearing defines the methods of comparison of the bids and the designation of the winner as well as the allocation of the product. Finally the pricing determines the Price at which bidders will be paid, for example, there is pay-as-bid type where winners will be paid by the Price they have bidden with. A second type is the uniform price sealed-bid auction where the winners are paid the highest accepted bid. 2 called carve-out, where individual markets for tradable certificates are created for each technology. 13 LITERATURE REVIEW Tendering systems for long term contracts use government or regulator established system to meet planned targets. Potential investors submit bids with €/kWh and they are evaluated by the government and most suitable bidder is selected and has the exclusive right to benefit from the support granted. Local electricity distributors or incumbent suppliers are then obliged to buy electricity from the successful plants on the basis of a long-term contract. Tendering and auctions system has been gaining ground over the other support schemes due to successful experiences in countries like Brazil and China. Thus, during the last years, Germany and UK started for instance migrating to auction system to increase competition among RES generators and to reduce the cost of supporting renewable energies generators. European countries started using these schemes very lately, starting by Spain in 2007 and UK and Germany in 2013. 2.2.2.2. Exchangeable quotas Exchangeable quotas or Quota certificate schemes are used for supporting renewable energy in e.g. UK (ROC), Italy (Certificati Verdi). They can be compared to the European Emission Trading System (ETS) with the exception that they depend on the power system regulation and promote RES expansion, instead of limiting CO2 emissions (Grexel Systems Ltd, 2014) . Exchangeable quotas introduce binding targets for electricity suppliers to buy either green electricity directly from the RES-E producers (Adrien De Hauteclocque, 2011), or green certificates issued by RES-E producers. These targets are defined as a percentage of their electricity deliveries. A compensation mechanism for the opportunity cost incurred by purchasers is usually introduced. The basic functioning of energy certificate systems can be divided into three steps: first, producers are issued electronic certificates for units (typically per MWh) on electricity they inject into the grid. Second, they can then sell these certificates separately from electricity. Third, the value of the certificates is derived from their end-use (see figure 2), which is either to comply with a set green quota and/or to prove that sold or consumed energy originates from the source identified in the certificate. Non-compliance with the quota leads to a financial penalty higher than the certificates’ market price. Figure 2 : Tradable green certificates scheme (Meeus, 2012) In a quota system (such as Renewable Portfolio Standard (RPS) or Renewable Purchase Obligation (RPO) an obligation to buy energy certificates is imposed on a suitable party such as electricity suppliers and large electricity consumers. Combination of price and quantity driven strategies happens in practice (e.g. UK and Germany recently). Indeed, in countries using a quota obligation or auction mechanisms as main support mechanism, the more small PV farms can be supported through feed-in tariffs. 2.2.3. Indirect strategies The support for RES can have other form from the one already presented which can be less explicit. These strategies can have indirect impact on RES diffusion. Aside from strategies which directly address the promotion of one (or more) specific renewable electricity technologies, there are other strategies which may have an indirect impact on the dissemination of RES. The most important are: • Eco-taxes on electricity produced with non-renewable sources; • Taxes/permits on CO2 emissions; 14 LITERATURE REVIEW •Removal of subsidies previously given to fossil and nuclear generation; •The promotion of renewable electricity via energy taxes or environmental taxes, two options exist: - The exemption from taxes (energy taxes, sulfur taxes, etc.); - If there is no exemption for RES, taxes can be (partially or wholly) refunded. Regulatory Voluntary Summary of the fundamental types of promotion strategies (Haas, 2010) Direct Indirect Price-driven Quantity-driven Investment focused Investment incentives Tendering system for Environmental taxes Tax credits investment grant Simplification of Low interest/soft loans authorization procedures Simplification of authorization procedures Generation based FIT/FIP Tendering system for long term contracts Tradable green certificate system Investment focused Shareholder programs Voluntary agreements Contribution programs Generation based Green Tariffs 15 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS 3. EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS For historical reasons, the European electricity network was mainly built on a national basis, or local. But soon after the war, one of the first acts of the reconstruction of the European countries was to interconnect their national grids, on the principle of energy solidarity (ENTSO-E, 2013). Since then, a certain percentage of electricity can cross the countries’ borders allowing the complementarities of different networks and different sources of production. Energy policies are different from a European country to another; however, they fall under the same objectives set by the European Commission; - Ensuring the security of supply in the long-term. The Green Paper on energy efficiency points out that “by 2030, based on the present trends, the EU will be 90% dependent on oil imports and 80% on gas ones”. So, diversification is needed. - Fulfilling the Kyoto commitments on reducing greenhouse gas emissions - Improving energy efficiency - Developing renewable energy - Ensuring sustainable development To meet these objectives, every European country has implemented different support schemes to increase the RES integration. This is due to the industrial, political and geographical characteristics of these countries. Member States are implementing a single or hybrid support scheme by combining all or some of the RES support schemes. These support schemes have increased penetration of the RES-E in Europe, making them interesting to the Brazilian case. However, there is still a long way in order to achieve the 2020 target. 3.1. European regulatory framework The EU energy policies are set under the same framework through common successive directives set in order to establish a common market design that aims to the promotion of energy efficiency and the use of RES. With 96/92/ EC Directive of December, 19th 1996 and the Directive 03/54/EC of June 26th 2003 related to internal electricity market, the European Union concretized the idea of setting up an integrated electricity market at the level of EU member countries. Yet some countries have not waited for the EU to liberalize their energy sector, they started setting market reforms for several years before in a move to liberalize their energy markets. These countries experiences 3 are the inspiration of these directives of the European Union. The liberalization of electricity markets in Europe passed by three major steps. The first one is the stated above 96/92/EC directive. This directive established the grounds for a competitive market for electricity. It requires a separation of the accounts (of the generation, transmission and distribution activities) but not ownership of the vertical integrated utilities. It hasn’t stated anywhere a requirement to privatize the electricity sector. Besides, it organizes third party access to the transmission and the distribution network which can be negotiated or regulated towards a completing the electricity market. The electricity Directive (2003/54/EC) established the basic regulatory framework for the European regional market (EP, 2003). It emphasizes on the fair access to the network in order to trigger the 3 UK started market deregulation on 1989 and Norway on 1990 16 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS market competitiveness that was seen as not sufficient after the previous directive. It defines the organization of the power sector as well as market access, auctions procedure and network access conditions. The third directive 2009/72/EC and the regulation 714/2009 organize the cross-border exchanges in electricity. The 713/2009 establishes the organization and cooperation schemes between regulatory authorities. This directive was considered as a completion of the internal European market. It is supposed to separate the electricity industry’s activities; Generation, transmission, distribution and supply. 3.2. Historical development and current status of RES-E deployment in EU countries 3.2.1. Historical development at EU level The European Union (EU) has already tuned its energy policy into achieving maximum carbon dioxide (CO2) emissions reduction from power generation plants. In this context, it has already set out a strategic objective of achieving at least a 20% reduction of greenhouse gases by 2020 compared to 1990 levels (Poullikkas, 2011). This strategic objective represents the core of the new European energy policy. Recognizing the positive effects of renewable energy sources (RES) technologies towards achieving this goal, the EU has taken a range of specific actions in the direction of enhancing the integration of RES in the existing European power generation system as a major step towards the reduction of global warming and climate change phenomena. 3.2.2. Progress at country level The EU policy has set through the Directive 2009/28/EC of the European Parliament and of the Council on RES a mandatory national target for each Member country for the share of energy from RES in the final energy consumption. Thus every country has adopted different RES support schemes to achieve theses target. These differences are due to the particular characteristics of each country: RES potential, existent energies, political orientation. The main support mechanisms at the national level in Europe: -Feed-in tariffs (FIT), Feed-in premiums (FIP), Quota obligations with TGC, Loans, Investment grants, Tax incentives and Tendering schemes Figure 4 shows the policies choices by country at the European level. Figure 3 : Diversity of RES-E support schemes in the EU-28 (Ecofys, 2013) 17 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS The goal is to reach the 2020 targets. National target levels are different from a country to another according to the initial generation mix of the year 2005. Table 2 shows the indicative trajectories of the EU member states over the years from 2010 to 2020. The target’s percentage is calculated by the equations in table 1 is reported in Figure 5 that presents the percentage of RES in the gross consumption in year 2005 and the target set for 2020. It indicates the average share of RES that the member states should have in every two years period until 2020. The concerned States have to implement adequate policies to reach or exceed the share of energy from RES. Table 1 : EU Member States RES indicative trajectory Indicative trajectory S2005 + 0.2 (S2020-S2005) Notes As an average to 2012 S2005 + 0.3 (S2020-S2005) As an average to 2014 S2005 + 0.45 (S2020-S2005) As an average to 2016 S2005 + 0.65 (S2020-S2005) As an average to 2018 S2005= The share of the member state RES in 2005 S2020= The target share of the member state RES in 2020 for the two year period 2011 for the two year period 2013 for the two year period 2015 for the two year period 2017 source: The Renewable Energy Directive 2009 Figure 4 : EU member states RES targets for 2020 According to Eurostat News release of 10 March 2015, Sweden with 52.1% of RES share has by far the highest share of energy gross final consumption at the EU level. It is followed by Latvia with 37.1% and Finland with 36.8%. In those countries, renewable energy share is historically high due to hydropower. In contrast, the lowest proportions of RES are in Luxembourg 3.6%, Malta 3.8% and the Netherlands 4.5%. The EU countries achievements as for 2013 differ between countries. Energy from RES in gross final consumption reached 15.0% compared to 8.7% in 2005. Three countries Bulgaria, Estonia and Sweden out of the 28 have already reached their 2020 targets according. Moreover, countries like Italy, Lithuania and Romania are less than 0.5 percentage points from their 2020 targets. On the opposite side of the scale, the United Kingdom (9.9% from 2020 target), the Netherlands (9.5%) and France (8.8%) are on the bottom of the ladder. 18 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS 3.3. Country-specific learned lessons In order to focus into specific countries experiences in RES support schemes, we chose to focus in two different European experiences; Feed-in Tariffs in Germany and Quota mechanism in United Kingdom. This choice is driven by the type of the mechanisms they have deployed, their achievements and their future plans. The German experience has achieved interesting performance in matter of RES expansion. The FIT chosen has reignited controversy due to the high cost of this mechanism. The British experience has been chosen to analyze the performance of the quota mechanism deployed there. Both of the countries are currently passing by a transitory period where they are changing their support schemes to boost RES expansion in a competitive and cost efficient environment. 3.3.1. Case study Germany 3.3.1.1. Overview of the German experience Germany is one of the countries that have experienced a rapid growth of renewable energy integration in the last years. The German experience can be portrayed as a success story. In addition to European goals, Germany has, for many years, its own energy policy promoting renewable energy: The “Energiewende” or the energy transformation. After Fukoshima disaster in 2011, the German government has decided the complete cessation of nuclear plants by 2022. It seeks for establishing a nuclear-free and a low-carbon economy through increasing RES share. Germany’s national goals are the reduction of carbon dioxide emissions by 90 % compared to 1990 levels and to reach 80% of its electricity generation by RES in 2050. In 1989, Germany launched a market stimulation program to boost the installation of 250 MW of wind power. It offered a fixed payment per kWh in addition to investment incentives for private operators such as farmers. This program was effective until 1995. In parallel, Germany has set in 1991 a fixed FIT support scheme for RES-E through the Electricity Feed Law of December 1990. This program aimed at the direct market integration of RES started promoting significant amounts of RES by promoting its investments. The significant advantage of the Electricity Feed-In Law is its simplicity. It stated that grid operators pay about 80 % of electricity retail prices as FIT for RES-E (Held, Feed-In Systems in Germany, Spain and Slovenia, 2007). Furthermore, it obliged electricity suppliers to accept the electricity fed into the grid as well as priority scheduling and dispatch,. In year 2000, a “Renewable Energy Act” succeeded to the fixed FIT and a target of 12.5% for the RES-E was set for 2010. This act introduced the uncoupling of the tariff level from the electricity retail price. A differentiation for tariffs was set on technology level and also within the same technology: the location for wind farms and fuel type for biomass. This new tariffs were based on the real generation costs of a technology. Contracts up to 20 years for the FIT were proposed to attract risk-averse investors. In a later stage, the regulatory authorities introduced a cap to prevent excessive charges on grid operators. A cap of 5% of the share of RES-E in the grid to be paid by grid operators was set. The “Renewable Energy Act” introduced also a tariff digression for new installations to encourage cost reduction also called the ratchet effect. The FITs for new projects decrease each year by a legal percentage (or by law amendments). It encourages technology learning and R&D improvements in order to decrease the policies’ costs. In 2011, the German decision to phase out nuclear power has led to the shutting down of eight reactors. First, Germany imported more electricity to compensate these plants. However, after one year, there was a reversal trend, particularly related to the ongoing development of renewable energy, which helped to drag the wholesale electricity market prices down in Germany. But not only that, the decline in the price of coal and CO2, rooted in the electricity prices in Germany participated in falling the prices (Agora Energiewende, 2014). 19 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS Another current challenge in Germany is the location of the majority of their wind plants. They are concentrated in the northern part of the country. They are adding grid constraints not only to the national network operators but also in operators in the neighboring countries. Indeed, Central and Eastern European countries like Czech Republic and Poland are moving to disconnect their power lines from Germany as wind-generated electricity is overloading their network and might cause blackouts (The Institute for Energy Research, 2013). However, supporters of RES integration dissociate this problem and put it on the grid operators’ side. In 2014, a new reform of the German renewable energy law has introduced an auction model aiming to replace over the feed-in tariff. Several previous experiences in Brazil, California and China showed interesting results in competitive auctions. Indeed, due to longer project durations for large PV projects there are high uncertainties about price modules and FIT levels. The German renewable energy law of 2014 states that support for RES will be determined in competitive tendering procedures beginning no later than 2017 (European Commission, 2013). Only the details of solar PV are disclosed so far. An amount of 600 MW of solar PV capacity per year will be tendered in two or three auctions starting from 2015. It targets PV arrays larger than 100 kilowatts but smaller than 10 megawatts. The first tendering was done on April 2014 and resulted in a winning Price of US$101.91/MWh for 25 projects and a capacity of 157 MW. The authorities received 170 bids being 4 times the tendered amount for a ceiling Price of US$130/MWh (Business spectator, 2015). The result was however marginally above the market Premium model for solar power prices which is $100/MWh. 100000 25 80000 20 60000 15 40000 10 20000 5 0 0 Cost (b€) Installed capacity (MW) 3.3.1.2. Cost of the mechanism During the last decade, German policy makers have created different mechanism and policies to support renewable energy. Generous subsidies and purchase tariffs were imposed to finance energy policy "Energiewende" which targeted a nuclear-free economy and low CO2 emissions. Although these subsidies have fostered an impressive deployment of renewable energy sources since the 2000s, they have also created an imbalance of energy markets affecting the reliability of production. This results in increases in electricity prices for most users, and distortions on investments decisions. Every new megawatt (MW) of RES is subsidized which make them not market price sensitive. Moreover, policy makers have underestimated the cost of subsidies to renewable energy and its impact on the national economy. As figure 6 shows, the cost of RES has been increasing considerably since its establishment reaching 21.7 bn€ in 2014 for a total of more than 300 bn€. On the other hand, conventional energy generators are now operating in less stable conditions to compensate the intermittency of renewable energy sources generation to maintain the balance between supply and demand. Moreover, expensive renovations are needed for these plants to enable them to answer quickly to any unforeseen change in their operational requirements. As a result, gas production units receive financial compensation in order to remain economically viable in case of temporary needs. 20002001 20022003 200420052006 20072008 200920102011 201220132014 Installed capacity of RES Cost of the RES Figure 5 : RES Installed Capacity and Cost in Germany (Oxera and Energiewende, 2014) 20 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS 3.3.1.3. Social acceptance The energy transition in Germany is driven primarily by citizens. According to a survey done by Forsa in 2010, 95% of German citizens ask for more RES deployment and 73% agree on having RES farms on their neighborhood (RWE, 2012). However, it isn’t guaranteed to continue if the electricity prices keep increasing. On the other hand, Citizens investment in RES distributed generation appears as a promising alternative that governments support to increase renewable energies diffusion. In Germany, the number of cooperatives jumped from 77 in 2005 to 1000 in 2015 with about half of the renewable energies’ installed capacity. The involvement of German citizens focuses on photovoltaic, onshore wind and biomass. 3.3.1.4. Lessons The RES integration in Germany has allowed the saving of tones of CO2 and the creation of about 370,000 jobs. However, an important drawback of the FIT support schemes is it is high cost, one of the highest at the European level during the last 15 years. Globally, the German experience is considered a success story so far. Thanks to the implemented support schemes, the installed capacity of wind and solar energies has been continuously increasing. However, we can see in figure 7 that there are different trends between wind and solar plants deployment. The wind energy has experienced an important growth between the year 1999 and 2010 while the solar energy diffusion started latter. Indeed, the solar energy installed capacity has only grown after 2006. But, it experienced an exponential increase to surpass the wind capacity for the first time in 2012. This is due to the preferential FiT that expresses the government’s objectives. 40000 35000 30000 25000 20000 15000 10000 5000 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 0 Wind installed capacity (MW) Solar installed capacity (MW) Figure 6 : Wind and Solar energies installed capacities in Germany However due to the high cost of this support, the regulatory authorities are moving towards a tendering mechanism for electricity investment. This is done in order to limit windfall gains from which benefited for some investors under the FiT scheme. Even though, the auction of April 2015 has resulted in a price marginally higher than the market premium. It is still not very positive according to the expected results. Another problem with the recently implemented tendering is the lower limit for projects capacity, which is 100 KW; it is hard to expect small businesses to go through the auction paperwork. Auctions should be for professional investors (FGinsight, 2013). The German model of cooperative society could serve as support for reflection on how this can be implemented in other framework. It is clear that this is due first to the general awareness of German citizens. However the German case is leading due also to a simplified legal framework and less hampered economic viability. Indeed, only three members are needed to create a cooperative, the fact of limited liability of members is recognized, the capital is variable without a minimum of 21 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS establishment and the association of new members is done without notarial requirements. Finally, if there can be democratic governance (1 member = 1 vote), it can also be based on the shares held. The involvement of local actors is a key asset for the success of community projects. Such participation allows the community to achieve its goals of energy policy in the context of a shared approach with the locals. In Germany, cooperatives are exempted from the obligation of financial prospectus publication and thus the need to obtain authorization from the Financial Regulator. However, they are audited by the regional federation of cooperatives control. In addition to that, the profitability of cooperative societies isn’t something to hide, but instead represents a key element of massive deployment of renewable projects by citizens taking advantage of relatively favorable regulatory framework and access to preferential loans. The cooperatives show a return on investment of 4% on average (Actu-environnement, 2014). The self-support feature of the cooperatives and the direct benefit of ownership to the cooperative members raised the popular acceptance of the energy transition and made people more aware about the process leading them to change their behavior. The benefit isn’t only financial; people are directly involved by owning a part of the energy system. This social complicity in the energy transition boosted the private investment in RES generation and formed a helping hand to the government in its policy to increase the RES share. 3.3.2. Case study UK 3.3.2.1. Overview of the UK experience The UK is on track to meet its renewable energy targets. With a quarter of its existing generation capacity set to close over the next 10 years due to environment barriers, new regulation or lifecycle limit, wind and solar power are trying to replace conventional generation farms. To support this transition, there are different RES support schemes for electricity generation: feed-in tariffs, Contracts for Difference 4(Cfd) and a quota system in terms of a quota obligation and a certificate system. The Quota Obligation was set in 2002 in England and Wales, and Scotland, followed by Northern Ireland in 2005 (Ofgem, 2013). It was the main support mechanism for RES projects until 2014. It places an obligation on UK electricity suppliers to allocate an increasing amount of the electricity they supply from RES. The quota scheme concerns electricity suppliers with a capacity higher than 5 MW. A quota is satisfied if he presents a certain number of green certificates. These certificates are traded between conventional and renewable energies suppliers. If suppliers do not present a sufficient number of TGC to meet their obligation, they must pay a penalty. Smaller scale generation, plants with a capacity under 5 MW, is mainly supported through the Feed-In Tariff. Eligible plants must undergo to an accreditation process (RES-Legal, 2014) and the scheme may differ according to generating plant size and energy source. Once this process is completed and the plant has been accredited, the electricity is fed into the grid and bought by the FiT licensee. The FiT rates are corrected yearly by the Gas and Electricity Markets Authority (Ofgem). This scheme is applicable to England, Wales and Scotland only. In 2014, The UK government has published the final framework governing how it will allocate contracts for difference (CfDs), a new support scheme that will replace the existing ones including the Quota Obligation (QO), which will be phased out by the year 2017. 4 A Contract for difference as defined in wikipedia is a contract where both parties agree a “strike” price for defined time periods. Then when the spot Price in any time period is higher than the strike price, the generator will refund the difference. Similarly a retailer will refund the difference to the generator when the actual price is less than the strike price. 22 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS Under the Contracts for Difference (CfD) scheme, a RES-E generator and a CfD Counterparty enter into a contract, which is based on a difference between the market price and an agreed “strike price”. Currently, the scheme is applicable in England, Wales and Scotland. It is expected to be introduced in Northern Ireland in 2016. Starting from April 2017, the CfD scheme will be the only support scheme for all new RES-E plants exceeding 5 MW. Figure 7 : The operation of an intermittent FiT with Cfd (EMR White Paper, 2014) The figure above explains the functioning of a Cfd. A strike price with the top redline is set at £70/MWh and the wholesale price is represented by the black line. When the wholesale price is below the “strike price”, the public authority pays the generator the difference (green area). On the contrary, when the wholesale price exceeds the strike Price — on the right of the graph— the generator pays the public authority the difference shown in the red line down. The replacement of quota obligation by Cfd came since the QO and Final investment decision (FID) enabling scheme failed to bring the expected results and defending consumers’ interests. They caused relatively windfall gains for generating (RES-Legal, 2014). The FID was actually introduced to enable investors take investment decisions impacting on the time to commissioning the Project. It was established especially to reduce the uncertainty caused by the transition to the CfD regime. 3.3.2.2. Cost of the Mechanism The main mechanism adopted for RES support in UK was Renewables Obligation deployed since 2002 to enhance RES expansion and attract private investors. It is a complex mechanism that puts an obligation on suppliers to purchase a percentage of their electricity from renewable producers. The value of this scheme can be divided in two layers; the value of the fine avoided, and the expected share of the fines paid by other competitors (REF, 2011). There is theoretically a risk for the value of the certificate, however according to data history this value would remain stable over the life of the scheme with £50 approximately. The wholesale electricity Price has varied in the recent years from £25 to over £40 now. As a consequence, the renewable generator expects to receive between £75 and £100/MWh which is around 50% above of the electricity wholesale price. This RO can be seen though as a cost pass-through to costumers. The costs of the Renewables Obligation were nearly £1.8bn a year in 2011 to 2012, rising to £3.2bn a year in 2014 as shown in Figure 9. 23 30000 3,5 25000 3 2,5 20000 2 15000 1,5 10000 Cost (b£) Installed capacity (MW) EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS 1 5000 0,5 0 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Installed capacity of RES Cost of the RES Figure 8 : RES Installed Capacity and Cost in UK (Ofgem, 2013) This generous scheme has attracted several investors and resulted in a high deployment of Renewable energies generators as it can be seen in figure 9. Installed capacity has grown especially after 2010 but its level is still below the annual targets. It indicated that regulators needed to change this scheme to meet the 2020 targeted capacity. The quota mechanism has however led to huge differences between renewable technologies overcompensation for ones (wind energy) while giving not sufficient remuneration for others (solar). For this reason and others, OFGEM, the British regulator for electricity and gas, and the British government decided to replace the Renewables Obligation scheme with the Contract for Difference (CfD) based on auctions starting from 2014. However, the RO scheme will be kept open as an alternative for new RES projects until March 2017 and the tradable green certificate market will be running until the end of their period, 2037. 3.3.2.3. Social Acceptance Public acceptance is as an important issue shaping RES deployment by influencing political decisions. In UK, some regional surveys have attempted to identify levels of public understanding and awareness opposition towards renewable energy. There is a common trend for supporting RES among UK population with 73% of them holding positive attitudes towards RES-E. The main driver is environmental concern about climate change (Mastropietro, 2014). However, levels of awareness and opposition change between age groups being lower in younger and older ones (16–24 and 65+) compared to middle-aged respondent class (35–44 and 55–64 years). On the other hand, the social class, income and age present a positive correlation with the levels of support for RES-E expansion. In addition to that, Devine-Wright empirical studies (Devine-Wright, 2008) show political decisions are in phase with social acceptance of RES technologies. Solar technology is however more supported than other type of RES with 84% of the population supporting the use of it compared to 72% for wind and 64% for biomass according to the British Department of Energy & Climate Change data of 2012. Since 2012, a growing number of people start choosing to start renewable energy cooperatives in their communities. This shows that the British population made a further step in their support of RES being directly active in RES diffusion. With financial crisis, this is a considerable help for governments in order to achieve their RES targets set by EU. 24 EUROPEAN EXPERIENCES FOR RENEWABLE ENERGY SUPPORT MECHANISMS 3.3.2.4. Lessons The CfD regime is considered as a massive improvement compared to the previous support schemes. It is an enhancement in both budget management and RES plants’ competitiveness. CfDs will provide guaranteed payments to operators and thus decreasing uncertainties for them without risking excessive remuneration when market prices are high. The payments are set according to the technology strike Price and the market reference Price. The chosen generators are the ones with lowest bids below the maximum. To better evaluate the increase of competitiveness, an analysis of the Contract for difference auction results is necessary. The results published on February 26th 2015 shows the acceptance of 27 projects with a total capacity of 2.1GW sharing subsidies of £315million per annum by 2020/21.The resulting strike prices were around 17-18% lower than the auction starting prices (Howard, 2015). For distributed power generation projects, UK has in turn made the choice of a fund with 10 million pounds (about € 12m) to support them. Considering the German cooperatives’ experience, the British government wants to encourage the development of cooperatives and collective actions to reduce energy consumption. For now, only 60 MW of projects have been financed by citizens. Studies (Actuenvironnement, 2014) estimate between 0.5 and 3 GW the volume of projects that could be financed collectively by 2020 in the solar, wind and hydropower, accounting for 2.2 to 14% of installed renewable capacity by the same date. 25 THE BRAZILIAN CONTEXT 4. THE BRAZILIAN CONTEXT 4.1. Overview of Brazil Brazil is the world's seventh largest economy in term of GDP and the largest in Latin America. The economical growth that happened over the last two decades is due to the abundant natural resources (oil, gas, minerals, hydropower potential) and dynamic industrial sectors (food industry, biofuels, aviation, automobile). The current Brazilian government’s priorities are accelerating growth and reducing inequality in compliance with fundamental economic balances. However, it needs to cope with the recession in which the Brazilian economy fell the last year. Indeed, after a very strong growth, the economy showed signs of slowing since 2010 (GDP growth was 7.5% in 2010, 2.7% in 2011, 0.9% in 2012, 2.3% in 2013, 0.1 % in 2014), (BBC, 2014) "This recession shows the exhaustion of a growth model that has been centered on internal consumption," said Eduardo Velho, chief economist at investment firm INVX Global in Sao Paulo to BBC. The decrease of commodities export prices and the decline of household consumption, industrial activity and investment are the main factors that led the Brazilian economy to enter recession in the first half of 2014. Over the year, economic growth was 0.1% and IMF forecasts a reduction of Brazilian GDP in 2015. In addition to that, Brazil is facing difficulties to control inflation, a devaluation of the Real and a degradation of Public Accounts (deficit of 5.9% GDP in 2014). Concerning the electricity sector, the Brazilian interconnected system has an installed capacity of 125 GW, which is expected to reach 200 GW by late 2023 (Melo, 2007) . The RES share is 80% of total installed capacity as presented in figure 3. This is mainly due the important share of Hydropower which accounts for about 67% of existing capacity in Brazil. Some of the hydropower plants have multiannual reservoirs. In addition to that, the main conventional sources used are nuclear, oil, natural gas and coal plants. Industrial gas 1% Wind 4% Biogas 0,1% Solar 0,01% Coal 3% Nuclear 1% Hydro Fossil 6% Natural Gas Biomass Fossil Biomass 9% Coal Natural Gas 9% Nuclear Hydro 67% Industrial gas Wind Biogas Solar Figure 9 : Brazil Installed Capacity in 2014 (Ministério de Minas e Energia, 2014) . 26 THE BRAZILIAN CONTEXT 4.2. Electricity policies of Brazil The Brazilian energy sector was partly privatized in the 1990s. The decision to privatize the power sector was made by President Cardoso at the beginning of his first term in 1995 (Ferreira, 1999). This reform took place due to the need for boost investments as to ensure the security of supply. Privatizing the electric power sector meant also to reduce public sector debt. Brazil main focus was to ensure enough thermal and renewable resources to provide firm energy during a dry period, as hydropower’s share in the power mix was about 90% of electricity generation at that time. It makes the Brazilian power sector one of the greenest or “bluest” in the world. However, this reliance on hydropower has shown its limit in 2001 after several consecutive drought years. Diversifying the Brazilian energy mix was necessary to avoid the impact of drought scenarios. In the last 22 years, electricity production was multiplied by 2.5 (Barroso C. B., 2011), however the production of renewable energy couldn’t follow this pace. Although hydroelectricity doubled its production and biomass increased nine times, the total RES growth was only 116%. The rest is supplied by conventional energy sources; nuclear power has multiplied its production by seven. Thus it was necessary to use fossil fuels to complete: their production increased by eight, and their share in the energy mix grew from 10% to 14.6%. The largest power company in Brazil is Centrais Elétricas Brasileiras (Eletrobrás). The Federal government owns it. Through its subsidiaries, Eletrobras controls 40% of electricity production capacity in Brazil and 69% of the national grid. Since 2008, it was allowed by a new legislation to invest outside of Brazil. It is the largest electric utility in South America. Tractebel Energia is the major privately owned company in the Brazilian electricity sector. It owns 11 power plants in Brazil: 6 of them are hydroelectric and 5 are thermal. The different structural reforms adopted since the liberalization of the Brazilian electricity are cautious and gradual. However, this didn’t prevent some retrograding steps between the different reforms introduced in the regulatory framework. More details are given below. 4.2.1. First reform of the electricity market: 1990’s Brazil started to reform its power sector in 1995 with privatization of its major electricity distribution companies and the unbundling of the electricity main activities. The reform allowed IPPs to enter the electricity market and generation companies was partly or fully privatized. Besides, a nationwide system operator ONS, Operador Nacional do Sistema Elétrico, and an independent regulator (ANEEL) were created. Consequently a wholesale electricity market was established in 1998. This reform was introduced to attract private investment since it was difficult for the Brazilian government to follow the pace of the economic growth and the increasing need for new electricity generation. However, due to the complexity of the Brazilian electricity industry and the incomplete regulatory reform structure; an important part of the industry remained state-owned. Indeed, ANEEL was created by Law 9427 of December 26, 1996, about two years after the start of the reform. Due to the need for investments, government wanted to give a liberalization friendly image in order to attract investors. Thus, it started the reform even before an electricity regulator had been established. In two years, about 10 distribution companies had been divested for $12 billion (Almeida, 2005). This reform was encouraged by the economic crisis in the 1990’s and the perception of a lack of efficiency of the public utilities. It led, however, to a divestment of state assets. This market-oriented reform wasn’t much successful. It hasn’t covered all the generation investment needs and it resulted in higher consumer tariffs. As a matter of fact, in the last semester of the year 2001, Brazil faced a severe electricity supply crisis. A “reform of the reform” was led by the recently established Chamber of Management of the Electricity Crisis which brought some structural changes to improve the model. Many problems still existing however that’s why there was a need to revise the reform as a whole to correct the occurring problems. 27 THE BRAZILIAN CONTEXT 4.2.2. Second reform of electricity market: 2004 The second electricity reform process started in 2004 following an electoral commitment of President Lula. It was seen as a counter-reform, implemented to overcome problems of the previous reform. It came due to the gradual loss of control over electricity planning. It aimed to insure the security of supply and limit the rise of electricity prices. The short term wholesale market introduced in the first reform was replaced by long term contracts which became the only way to trade electricity (Melo, 2007). The main changes introduced by this reform were the creation of two energy trading markets; The Regulated Contracting Environment (ACR in Portuguese) where a distribution companies buy energy in public auctions and a Free Contracting Environment (ACL) where large consumers and generators are free to choose each other outside the centralized auctions. The energy is negotiated through bilateral contracts. The results of the second reform process remain somewhat ambiguous to date. It increased the pace of generating capacity expansion; however, the new power plants are expensive and bring high CO2 emitting sources. 4.2.3. National Policy on Climate Change: 2009 Between 1990 and 2005, Brazil’s greenhouse gas emissions rose by 60%. Thus, In December 2008, the Brazilian Government launched the National Climate Change Plan. It aims mainly to reduce GHG gas emissions through mitigation and adaptation efforts (UNESCAP, 2013), promotion of renewable energy, reducing deforestation and loss of forests and R&D. The plan’s objectives concerning energy are: • Increasing of ethanol’s domestic consumption by 11% per year by 2020 • Increasing the contribution of co-generation electrical energy, especially of sugarcane biogases, to 11.4 % of the total supply of electricity in the country by 2030. 4.3. Renewable energy potential There is a foggy issue that needs to be explained when dealing about renewable energy in Brazil; it is true that Brazil produces 83% of its electricity from RES. However, 77% is from hydropower. The wind energy share among the installed capacity is around 4% and the solar installed capacity is even lower. Brazil wants to increase the share of new RES to insure the security of supply. Diversifying its procurement sources next to the hydro ones will reduce the rain dependency and the fear from dry years. Brazil can rely on an interesting wind potential, variable according to estimates, particularly in the northeast of the country on the coasts and mountains where the average wind speed is the most important, averaging over 8 m/s. On the other hand, located on either side of the equator, the country enjoys a sunshine therefore greater than 5300 Wh/m2/day (E. Melo, 2010) in most parts of the country and therefore in favor of photovoltaic and sugar cane cultivation. Photovoltaic has a huge potential. The little use of it may seem surprising. However, the development of this energy in a centralized market is hard especially with the competition of other types of sources. The installation of photovoltaic panels connected to the grid would be a better energy security guarantee against a weakened network by a massive hydroelectric reliance produced at places far from the consumption. Moreover, Brazil, has a considerable advantage in a solar industry thanks to its high production of metallurgical silicon (3rd in the world), a basic raw material to manufacture solar PV. Although, there is a certain dynamic in the private sector investments with the development of several projects and the construction of power plants, the installed solar PV power current is still very limited. Brazil must invest in R&D, but also provide a framework that supports the deployment. This framework should be 28 THE BRAZILIAN CONTEXT both legal and in particular for connecting PV panels to the network via an inducement of financial assistance since the cost of this energy is still high. In 2011, UTE Norte Fluminense, a subsidiary of EDF has opened the doors of the first solar plant in the country. Located in Macaé, in the State of Rio de Janeiro, the installation consists of 1,800 photovoltaic panels with a capacity of up to 320 KW in times of strong sunlight. The solar energy provides power to the thermoelectric plant in Macaé and reduces CO2 emissions by 250 tons per year. 4.4. RES support mechanisms In 2002, following the first reform of the Brazilian energy sector, a FIT support scheme was set by the Brazilian government under PROINFA, the Programme of Incentives for Alternative Electricity Sources (IRENA, 2013). The PROINFA supported three sources; wind, biomass and small-scale hydro. 4.4.1. Proinfa: quota & feed-in tariff scheme The program, called Proinfa (Programa de Incentivos às Fontes Alternativas de Energia Elétrica in portuguese) helps RES independent producers to increase their contribution of the national grid supply. Proinfa was designed in two phases. The first involved the installation of energy infrastructure capable of producing 3,300 megawatts of electricity, a little more than 4% of electricity generating capacity in 2002, divided evenly between biomass, small hydro and wind power. The second phase of the program was to increase the share of these energy sources by 10%. However, new regulations on the energy sector forced the revision of these objectives and the second phase of PROINFA was not accomplished. Project submission was open until December 2006 in order to benefit from the PROINFA tariffs, however, the implementation has been gradually postponed until 2012. A 20 year contract was offered to each selected project. The generated electricity by RES producers is purchased by Eletrobras, which sells it to final consumers as a portion of their actual consumption. The cost of this FIT is levied to customers’ electricity bills. The average price paid under this scheme for wind farms in 2010 was about 140 US$/MWh, USD 96/MWh for small hydro, and USD 70/MWh for biomass (Barroso C. B., 2011). PROINFA led to the development of 1,136 MW of wind power, 111 MW of biomass, and 992 MW of small hydropower. Regarding the RES integration, PROINFA could be seen as a successful policy for starting the RES expansion in Brazil. However, if we focus in the cost of it, it turns out that this success was very expensive. In fact, it resulted in an increase the electricity prices. The efficiency of this reform is a big issue; information asymmetry between suppliers and government was the main hurdle for reaching efficient payment rates. In addition to that, projects were selected based on the date of environmental permit submission in a first-come, first-served base. This has led to a black market for environmental licenses (IRENA, 2013). In addition to that, obtaining the environmental license isn’t easy to take as their requirements change often. Moreover, due to grid connections issues, several RES projects have to be kept offline during some periods of the day. Several projects faced larger cost, delayed or even aborted. Another difficulty was the BNDES local content requirement to finance RES projects. It required that 60% of a RES project’s costs to be supplied by local Brazilian manufacturers. However, for wind projects at that time, there was only one supplier in Brazil, which caused several delays for wind projects. 4.4.2. Incentives on wire costs for selling energy contracts at the free market In 2007, under the framework of the second reform, an additional support mechanism took place in the form of a deduction to free consumers 5 on distribution and transmission tariffs for buying energy from RES. Indeed, depending on the distance between generation and consumption sites, the consumer pays the distribution companies for using the grid. These consumers get a discount in form of a cross 5 Consumers which has the right to choose their suppliers and whose demand exceeds 3 MW in voltage, equal or superior to 69 kV, or at any voltage level if the supply started after July 7th 1995 29 THE BRAZILIAN CONTEXT subsidy on wires’ tariffs paid by all captive consumers. It can be seen as an additional support for RES so that they can sell high-priced energy contract and compete with the other conventional producer. The tariff is set by the regulator, who is responsible for adjusting it so that he guarantees the economic balance of the distribution companies. 4.4.3. Current Structure: Technology-specific auctions Brazil with Chile, UK and Portugal were among the first countries to adopt renewable energies auction schemes. The Brazilian Federal government laid the foundations for a New Model for the Electricity sector between 2003 and 2004, based on different laws, and decrees. The 2004 Model created an entity to establish long term planning in the electricity sector (The Energy Research Company – EPE). It also implemented an institution to assess the security of electricity supply (the Electric Sector Monitoring Committee-CMSE) and an institution dealing with commercialization of electricity in Interconnected System (the Electric Energy Commercialization Chamber -CCEE), which substituted an old one (The Wholesale Energy Market – MAE). Other important changes occurred such as the definition of the Ministry of Mines and Energy (MME) as the Conceding Authority and expansion of the autonomy of the National Electric System Operator (ONS). With regard to energy commercialization, two environments were established for contracting energy: the Regulated Contracting Environment (ACR), with the participation of energy generation and distribution agents, and the Free Contracting Environment (ACL), in which Generation Agents, Traders, Importers & Exporters and Free Consumers participate. 4.4.3.1. The Regulated Contracting Environment (ACR) In Brazil, the auction process is centralized and project specific in order to attract new generating capacity in a competitive way to ensure efficiency. It takes place in the ACR, Regulated Contracting Environment. The auction scheme is a hybrid type one, where the first phase operates as a descending clock auction6 then the second phase operates as a pay-as-bid sealed-bid auction. In this case, the use of a hybrid auction aims at taking advantage of the benefits of both auction systems: price discovery in the descending clock auction and avoidance of collusion (Luiz T. A. Maurer, 2011). In descending clock auction, participants can exit at any specified intermediate Price. The Price is then known by the rest of the bidders. However, winners are not obliged to disclose their bottom lines. This type of auctions allows a more precise expression of bidders’ costs and provides valuable feedback to bidders of competitors’ prices in real time. Along with its transparency, descending clock auction selects the most efficient firms as suppliers7. The distribution utilities sign then bilateral contracts with the generators who offer winning bids in each auction. Since each producer signs contracts with every distribution utility, risks are spread out among sellers (as well as among buyers). ACR is expected to achieve gains of scale. The contracts duration are for 15 and 30 years in order to reduce risks for investors and generate efficient price signals to enhance the planning of the generation capacities expansion. There are three types of ACR Auctions separated for a better risk allocation between existing and new plants: • Auctions of Energy from Existing Power Plants; • Auctions of Energy from New Power Plants; • Auctions of Energy Adjustment (just for Existing Power Plants). 6 In decending clock auctions, all items are auctioned simultaneousl. In each round, the auctioneer announces the current price for each product (electricity) which is lower than the previous round one and bidders answer by staying “in” or going “out” of the process. Every exit is definitive. Prices go down until the aggregate supply reachs demand. At the end, everybody is paid the closing Price. 7 Latter the first phase was removed. 30 THE BRAZILIAN CONTEXT The objective of the auctions for existing power plants is to meet the current demand. However, for auctions of energy from the new plants, they must add capacity to meet the demand growth. The Auctions of Energy Adjustment correct any mismatch between contract and actual load. The existing energy auctions take place under contracts from 5 to 15 year and with energy delivery of one year ahead (“A-1”) (E. Melo, 2010). The New energy auctions contracts last between 15 (thermal) and 30 (hydro) years and with delivery five (“A-5”) or three (“A-3”) years ahead. For “Adjustment Auctions” the contracts don’t last more than two years and they are signed up to one year ahead between one seller and one distribution utility. Under the ACR framework, there are two modalities for auctions supply contracts (E. Melo, 2010): • Contracts for delivered energy in which all hydrologic risks are taken by producers to supply the energy contracted. • Contracts for energy availability, in which all risk of energy production deviations relative to the contract are assigned to the pool and then passed through to captive consumers. 4.4.3.2. The Free Contracting Environment (ACL) The Free Contracting Environment (ACL) was established by Law 9,074/1995, altered by Law 9,648/1998 and then by ANEEL’s Resolution 264/1998. Under this framework, free consumers can choose their electricity supplier. Electricity is freely traded between generators, IPP, self-producers, traders, importers and Free Consumers while paying transmission and/or distribution tariffs to the system operator. The ACL includes also bilateral contracts between generators and distributing companies, signed before the establishment of the ACL law. They remain effective until their expirations. The contracts last for different periods, however, short-term contracts are predominant (Sioshansi, 2013). Agents are free to define prices, quantities, durations and hedge clauses for these contracts. Only Public generators need to pass through public auctions when they contract in the ACL. The Law 10,848/2004 established new rules allowing free consumers who had contracts with distribution companies to migrate to the ACL. They must inform 1 to 3 years in advance to switch to the ACL. Once a consumer opts for the Free Contracting Environment they may only return to the regulated environment after notifying their local Distributer five years in advanced, or a shorter period, at the discretion of the Distributer. 4.5. Investment decision in renewable energies in Brazil An agent who is planning to invest in new power plants has now two options: • Bid in the ACR market (auctions) • Sell a long term bilateral contract in the ACL In 2014, auction prices were among the lowest ever recorded for solar energy (BNEF, 2014) which caused certain reluctance among RES investors. Auction prices for wind energy weren’t much more attractive. The auction for wind energy of last year resulted in the contracting of 769 MW across 31 projects at a clearing price lower than the estimated best-in-class. Which for a country seeking to increase its RES-E, apart from hydro, makes- the situation more challenging for regulators to set the suitable policy. Few years ago, electricity prices were also relatively low in Brazil; there have been only few projects with sufficiently low costs than could be implemented. However, the FIT scheme that was set until 2005 played an important role to support RES projects. Then with the auction schemes implemented, more uncertainty raised for investors. Indeed, there are a number of factors that influence expectations from electricity market. This makes future profitability highly uncertain in this industry and hence holds back investments. This relationship has always been known intuitively. Thus the actual decrease in auction prices is a real hurdle for attracting RES investors. 31 THE BRAZILIAN CONTEXT Each year, there are at least two rounds of auction; one for the existing plants and the other for new plants. For the existing plants, the contracts last for a maximum period of 8 years and starts from the next year. They cover usually the recently expired contracts or the new loads in the grid. Prices are correlated to the current spot market prices, thus, the option of selling in the spot market may be relevant. For the new plants, there is two types of contracts (Hammons, 2011) ; one with a delivery starting five years ahead and with a contract duration of 15 to 30 years (A–5) and the other who’s with a delivery starting three years ahead with the same contract duration (A–3). The latter can be seen as an adjustment of the energy contracted in A-5. The establishment of long term contracts trough auctions was a response to the Brazilian electricity crisis in 2001. It came to bring more stability for RES investment. However, the government contracts do not cover all the uncertainties that the investors deal with. The common reasons are interest rates, construction costs, equipment costs, labor costs, and environmental regulation which affect mainly hydro projects. 4.6. Lessons from European experience to Brazil 4.6.1. The auction system When analyzing RES support schemes, learning from other countries experiences is important and gives interesting insights for policies’ makers. However, it is necessary to take into account the particularities of the country under studies; i.e. Geographical, political, industrial and economic factors. The Brazilian electricity system, for instance, present very different characteristics compared to the European ones. However, a general overview from these experiences shows a common recent trend between the European countries to move towards energy auction for RES investments assessment to boost RES expansion and to reduce risk among investors. Indeed, Auctions are part of an integral part of a country’s energy policies. Brazil has chosen this mechanism about 10 years ago. The Brazilian auction system which happen in the ACR environment aim to ensure the energy supply to captive consumers in a equitable and economically efficient way through auctions. A particular auction design is needed for each type of energy sources such as wind, solar, SHP and others. It is a Taylor-made process aiming to a better risk allocation between the different sources. They provide transparent and sustainable outcomes that are robust towards political and institutional changes. Several conditions need to be respected to guarantee the success of an auction. An important one is the participation of a sufficient number of bidders to enhance competitiveness and consequently effectiveness. Setting a maximum bidding price for RES projects is an important factor for attracting bidder. In August 2015 auction, the solar price ceiling was 30% above of the R$ 260 cap in October 2014 one to reflect the dollar appreciation and increased interest rates from BNDES, the Brazilian Development Bank. Setting a price cap aims to ensure moderate tariffs. However, the price caps should be consistent with the aim of ensuring the feasibility of the projects with long useful duration. Another important element is that auctions should project the regulatory stability and transparency. Bids disclosure increases investors’ perception about the fairness of the mechanism. Thus one might say that Brazil has an efficient mechanism and should have attracted already many RES energy projects and have a considerable share of renewable energy generation (apart from hydro) in its energy mix. However, as stated in this thesis, this is not true yet. 4.6.2. Possible improvement for Brazil It is true that Brazil has shown to the world that wind energy doesn’t need direct subsidies. The Brazilian wind power plants, for example, have higher capacity factor compared to other countries being between 23-50%. In addition to that, Brazil has one of the lowest wind generation costs. For 32 THE BRAZILIAN CONTEXT solar energy, the current installed capacity is very low. This is due to the high cost compared to other energies, i.e. wind. To improve this, more solar only auctions should be organized. The competitive auctions process has driven onshore wind prices to world record lows. The reached levels are even too low to attract investors. Moreover, Brazil’s development bank, BNDES, the second largest in the world, is raising interest rates, making it more difficult to finance new RES projects. In addition to that, a RES project needs to comply with different rules to get licensed from the BNDES. The major one is the locally manufactured components, which enter under the FINAME8 program requirements of the BNDES. Up to 2012, each wind farm had to comply with 60% of local content or they will be disqualified from the BNDES financing. In December 2012, a new methodology was implemented fixing progressive local content goal for the main components of a wind turbine. The method is applied to gradually increase local components. However, for solar energy this is currently an important hurdle, due to the cost differences between local and imported equipment. Financing a new project is indeed one of the main hurdles for the Brazilian energy sector. Several projects that are accepted in the auctions are delayed or abandoned. For instance, according to LAS Research an American market research firm (Renewable Seenews, 2015), the vast majority of the winning solar projects in Brazil’s October 2014 auction will not be carried out due to financing issues. October 2014 solar auction resulted in an average electricity sale Price for the 20-year power purchase agreements of R$215 (US$ 71/€ 65.7)/MW. Both, low interest rates of BNDES loans and a higher number of auctions are necessary to attract more RES projects. This is getting even worse with the dollar appreciation since the majority of the products are imported, making the components more expensive. As a matter of fact, investors are facing increasing costs of imported components, local expensive ones and low auctions prices. Consequently, many projects that won the auction are cancelled. In the last auction of April 2015, only few bids for wind energy were done and from the 500 MW capacity auctioned, only 70 MW was contracted. To guarantee projects completion several additional requirements should be added to the selection process. To get registered in the next auction, ANEEL should set a longer list of technical prerequisites. Bid bonds9 and project completion guarantees are also necessary for insuring the fulfillment of ANEEL’s targets through each auction. In addition to that, delays of investment should be more controlled through penalties in case of delays and license withdrawal if construction delays are higher than 1.5 year without proper justification (Barroso L. , 2012). A reduction of contract price can be a solution while plant is delayed with providing a replacement firm energy for the delayed period. Expectations for August and November 2015 auctions indicate that higher prices will be reached resulting in more realistic and attractive projects. Indeed, the fast decrease of the prices in the last auction has driven investors to bid in non realistic projects. Future auctions might meet a higher success in the following years. 8 FINAME program provides financing to companies for the acquisition of new machinery and equipment manufactured in Brazil with subsidized rates. Financing is granted through accredited financial agents. 9 A written guaranty from a third party guarantor submitted to ANEEL by the investor (bidder). A bid bond ensures that on acceptance of a bid, the RES generator will proceed with the contract. Otherwise, the guarantor will pay the customer the difference between the contractor's bid and the next highest bidder. 33 THE REAL OPTIONS METHOD 5. THE REAL OPTIONS METHOD 5.1. Presentation of the method In order to analyze the Brazilian auction mechanism, we chose to study the Real Options approach and its application for investments behavior under the auction system. It is a quantitative method which is gaining ground in assets’ valuation in the energy sector. It addresses the investment decision problem by analyzing not only the expected net present value (NPV), but also adding flexibility to the decision maker by considering the value of the option to wait for a better timing, the option to discontinue, to increase or to decrease the investment (Damodaran, 2007). The real options value is thus the difference between the expanded present value (taking into account the managerial flexibility) and the traditional present value, which does not account flexibility. Real Options Value = Vexpanded - Vtraditional The Real Options theory is based on financial options from (Scholes, 1973) and (Merton, 1976)from which came the idea of incorporating investment valuation methods under uncertainty. According to (Antikarov, 2003), the analysis of real options method is more relevant when there is a large uncertainty and flexibility to adapt. (Siegel, 1986) was the first to realize the gaps in the discounted cash flow method (DCF), since this method assumes that all projects with positive NPV create value for shareholders. (A. K. Dixit, 1991) states that traditional methods do not consider two important characteristics in the majority of investment projects: investments can be delayed and the initial investments are often irreversible. This investment deferral option allows investors wait for new information. To be able to determine the value of real options such as expanding, delaying, switching or selling a project, a traditional valuation through NPV method should be done first without considering flexibility. Then when adding the uncertainties related to the project, it is possible to construct the different paths that its value may have in the future which will affect the investors decision. Proposed by (Antikarov, 2003), the above concept can be used by constructing a binomial tree containing the possible project values determined by the modeling. The different levels of the tree correspond to steps in the future and the probabilities describe how likely it is that certain jumps in the tree will occur. 5.2. Renewable energy policy evaluation using real option models The underlying stochastic variable of the investment option is the generating company revenue. This revenue depends on the RES support scheme decided by the regulator. After the investment is undertaken, the investor starts receiving fixed payments associated to the power plant operation during its lifetime. Assessing the impact of RES support schemes using real options approaches has been analyzed under different framework. In (Boomsma, 2012) paper, a real option analysis has been carried out to evaluate renewable energy project under FIT and Renewable energy certificates. Discrete changes of support scheme with Markov switching process were set to may occur at random points in time. The value of investment timing and capacity choice numerically using a least squares Monte Carlo approach to option pricing. The study is based on wind power and was applied to Norway which has a similar power mix as Brazil but a different electricity market design. It concluded that the feed-in tariff encourages earlier investment. However, when investment has been undertaken, renewable energy certificate trading creates incentives for larger projects. 34 THE REAL OPTIONS METHOD (I. Ritzenhofen, 2014) study has undertaken similar approach to evaluate the timing and the likelihood of RES investments of a single investor under three different scenarios: FiT, free market and switching regime10. The model has been applied to an onshore wind farm but it can be adapted to other RES projects. The paper states that under and attractive FIT schemes, future policy changes have a limited impact on current investment decisions. However, we find that after a regulatory decision to switch from a FIT scheme to a free-market, investment behavior changes dramatically: investment is delayed or even abandoned. Another study realized by (L. Abadie, 2014) analyzed also the wind projects investments’ behavior under different supports schemes including a fixed FIT, a premium on top of the electricity market price and transitory subsidy11 when there are futures markets with long maturities. The real option approach applied has shown that investors would benefit from delaying their projects when their remuneration comes from electricity market price only. A FIT will however, as in (I. Ritzenhofen, 2014), encourage early investments. When it comes to a lump-sum subsidy, a one-time or a transitory initial subsidy is better for foster an investment than a higher subsidy spread for a longer period. The one-time subsidy can also outperform a constant premium per MWh received over the project’s lifetime. Increasing the electricity price with the TGC price significantly raises the value of the project; nonetheless, it is proven to be similar to a fixed premium in that it does not contribute effectively to early investment. However, the different real option methods applied in previous works are focused on developed financial electricity markets where short and long term transactions take place regularly and it is possible to remunerate a RES project with pure or mixed market based schemes. For the case of Brazil this market doesn’t exist and applying the real option method to evaluate the implementation of different support schemes policies is complicated. Indeed, the problem is that real option method is based in continuous negotiation markets that currently don’t exist in Brazil. A solution would be either we imagine a market or to adapt the real option method to the Brazilian case. In addition to that, the renegotiation during the investment period doesn’t happen, the risk neutral approach won’t be realistic and a Modeling of a green certificate market is not possible. Using the short term liquidation market and identifying it as a spot market could be a possibility. The investment decision in Brazil is done through auctions. The decision of wait and see, means that the investor will analyze the project value during the period (3 years) preceding the production starting date and assess the best investment decision he can make. If there is no incentive to invest, the generator will not take part in the auction, skip it and wait for the following one. The study of (Caminha-Noronha, 2006) has undertaken this approach with valuating new hydraulic generation assets that will be traded in the new energy auction. Uncertainties were modeled in setting up the cash flow for the investments incorporating some possible managerial flexibility associated with waiting for investing or abandoning the project. The evaluation was taken in a multi-stage investment consisting of a first phase of design and licensing and a second phase of construction and operation phases. It was treated as a sequential compound option and a binomial approach was introduced to model this approach. The paper hasn’t used, however, stochastic dynamic programming such as the Dixit and Pindyck model stated in his book (A. K. Dixit, 1991). The reason it says is that it would bring more complexity to the problem without considerable gain. Another critic to this paper is that between auctions, different variables change and it is not necessary that you will get more information. There are other studies that have been carried out with applying Real Options method to evaluate the flexibility. The paper of (Juliana de Moraes Marreco, 2005) has used a similar approach to valuation study of operational flexibility in the Brazilian system. The Real Options approach used was for 10 The switching regime model is a scenario that starts with a fixed FIT for a certain period of time with an expected switching to to the free market regime in the future. The switching time is calculated through a on a combination of actual RES capacity, installation targets, and incurred support scheme costs. 11 A subsidy that is only available at the initial time but if the investor opts to postpone the investment the subsidy is foregone. 35 THE REAL OPTIONS METHOD calculating the fair value of a financial subsidy to be paid to thermal generators for being ready to produce when needed in dry periods. The problem was modeled with the Real Options method in order to calculate gains that could be obtained from the flexible system, a system with more thermo complementarities. The results should represent the fair value to be paid for the thermal generators. The payment is the capacity payment for the flexible generators currently implemented or being implemented in some power systems. The (Minardi, 2009) article has applied a Real Options approach to valuing the managerial flexibility of delaying a small hydro power (SHP) project during the 2004-2008 period. A simulation of energy prices’ behavior in the long run was done and prices were collected from electricity contracted by distribution utilities only for the ACR contracts since ACL contracts aren’t public. Then the authors estimated the volatility of returns of SHP cash flows through considering long term and short term scenarios and a simulation of projected five years of cash flows was run for each scenario. Finally, for the Real Options modeling a five-year horizon to price the deferral option was used and the process was started by underlying asset price tree. The results suggested that the deferral option has significant value, because the entrepreneur can wait until prices are high to sell the energy or the authorization. 5.3. Real Options method for the Brazilian Market Due to significant increase in electricity prices and growing environmental issues in Brazil, there is a focus on the possibility of introducing more RES-E generation capacity. Hydro-electricity is facing drought and other environmental hurdles. Wind and solar generation projects are indeed a good alternative to tackle this problem. Few years ago, when electricity prices were relatively low in Brazil, there have been only few projects with sufficiently low costs than could be implemented. Thus a FIT scheme was set until 2005 to support RES projects. Then with the auction scheme implemented, more uncertainty raised for investors since they were exposed to more risks. Indeed, there are a number of factors that influence expectations from electricity market. This makes future profitability highly uncertain in this industry and may hold back investments. This relationship has always been known intuitively, but with the introduction of real option theory, one has a tool for a more precise measurement of the uncertainty’s impact on investment behavior. In this study, we will focus on the ACR Market, in which a high percentage of transactions occur through the Auction system. Comparing the impact of different possible support schemes for the Brazilian electricity market isn’t as obvious as in other markets. The main cause is that there is not a daily electricity markets as in the majority of liberalized electricity systems. The risk neutral characteristic of real option in electricity market is not very relevant to analyze. Risk neutral probability in the Brazilian power system is not very useful because Brazil doesn’t have a liquid market where you can adjust your position every time. In Brazil RES project can compete in the free market. However, the free market, which is a market for large consumers, usually is not oriented to finance new projects in Brazil. Further, an option would be is to simplify the calculation and considering the NPV to assess the attractiveness of renewable energy projects under the existent RES support schemes. However this simplification would result in the loss of the advantages of the real options method. We analyzed also a method on which we consider a Brazilian market with more CCGT and other conventional plants, the opportunity cost refereeing the bid of the most expensive plant dispatched. With this we can model a risk neutral price process for gas, coal and transforming it to price of electricity. Then we generate scenarios for the different electricity prices. After that, we need to build a decision tree to implement the option valuation and the effect of changes that we can implement if we use green certificates or FIT. This option involves, however, the use of many approximations and thus the results won’t be very accurate. After reviewing different implementation of the Real Options method in Brazil in (Caminha-Noronha, 2006), (Juliana de Moraes Marreco, 2005) and (Minardi, 2009), the more appealing way to analyze 36 THE REAL OPTIONS METHOD investment behavior on RES, is to focus on investments opportunities under the auction scheme with highlighting the factors that influence the investment decision such as Electricity prices, WACC or currency exchange rates. Thus, the applied Real Options approach will evaluate a new wind power project that will participate in the 2017 A-3 energy auction. The auction’s winner has to sign long-term power purchase agreements (PPA) simultaneously with all distributors at the bidding-price. This approach models the uncertainties in setting up the future cash flow for an investment and incorporates some possible managerial flexibility associated with the decision taken along the investment forecast. This flexibility has a value; it represents the real options associated with the project. Our approach is based on (Caminha-Noronha, 2006) and (Minardi, 2009) studies, in which we incorporate the flexibilities regarding the waiting to invest in a new solar/wind farm and the abandon option, representing the transfer of concession rights. Since the project involves a multistage investment consisting of design, construction and operation phases, it can be treated as a sequential compound option. This approach models the uncertainties in setting up the future cash flow for an investment and incorporates some possible managerial flexibility associated with the decision taken along the investment forecast. 5.4. Mathematical modeling The main idea of real option is that Investment decision can be treated as the exercising of an option. A RES investor has option to invest. It exercises the option now or wait for more information that can be revealed by the time. One thing that should be kept in mind is that investing in electricity generation is irreversible. There is an opportunity cost of investing now rather than waiting. The value of option or the opportunity cost can be important. Moreover, the value of the option to invest gets higher when the uncertainty is higher. The model that will be used in this analysis is inspired from (Antikarov, 2003) and (CaminhaNoronha, 2006). The investment analysis regarding this plant will be made in four main steps that are usually followed when real options are incorporated into a binomial model (Meeus, 2012) , namely: -Calculation of the present value without flexibility, using the discounted cash-flow method; -Simulation of electricity prices and estimation of the uncertainties; -Building the event tree based on the set of combined uncertainties driving the volatility of the project; -Incorporation of the flexibilities, by building a decision tree and assessing the investment decision; 5.4.1. Discounted cash-flow (DCF) method In order to estimate the value that the power plant would have if it existed today, we will calculate the NPV through the Discount cash flow method. We used the following equations and assumptions. Cash Flow = Net Profit + Depreciation Net Profit = Earnings Before Taxes - Income Tax - Social Contribution on Net Income EBT= EBITDA - Depreciation EBITDA = Net Revenue- Operating and Maintenance Expenses – Fee for using the Transmission System - Administrative Expenses Net Revenue = Gross Revenue - PIS - COFINS – TFSEE More details on tax calculation will be given in the case study through a real example. 5.4.2. Estimation of wind farm cash-flow return volatility Different factors can explain the volatility of electricity prices. The main one is that production and consumption have to be continuously balanced due to the non storability characteristic of electricity. 37 THE REAL OPTIONS METHOD Thus, this imbalances cannot automatically be recovered without the participation of different generators with different costs which will have a direct effect on equilibrium prices (Seifert, 2007). Another factor which turns out to be relevant especially for the Brazilian case is the electricity demand and production is weather-dependent due to the hydro-generators rainfall precipitation dependency. 5.4.2.1. Simulation of Electricity prices From the well known techniques for parameter estimation, there are Least Square regressions and Maximum Likelihood. Both method are known to be good at estimating the standard deviation and the mean , but poor in estimating the jump intensity . In order to estimate cash-flow return volatility, we constructed 2000 long term energy sales scenarios by compiling the forecast of short term electricity price. The scenarios are a forecast of monthly electricity prices provided by EPE from January, 2020 to December 2024. An assumption of the Brazilian energy planning is that short term price must converge to the energy expansion cost. The model used in this study is the Maximum Likelihood parameter estimation derived from Simulating Electricity Prices with Mean-Reversion and Jump-Diffusion model (Mathworks, 2014). The electricity price is modeled as log( ) = ( ) + With P(t) being the short-term electricity forecasted price. The logarithm of electricity price is modeled with two components: f(t) and Xt. The component f(t) is the deterministic seasonal part of the model, and Xt is the stochastic part of the model. f(t) is modeled by trigonometric functions (Seifert, 2007)as follow: ( ) = sin(2 ) + (2 ) + sin(4 ) + (4 ) + With si,i=1..5, are constant parameters and t is the annualized time factors. The stochastic components Xt is modeled as an Ornstein-Uhlenbeck process (mean-reverting) with jumps. 5.4.2.2. Calibration First, the deterministic seasonality part is calibrated using the least squares method. After the calibration, the seasonality is removed from the logarithm of price. The second stage is to calibrate the stochastic part. The model for Xt, needs to be discretized in order to conduct the calibration. To discretize, we assume a Bernoulli process for the jump events. That is, there is at most one jump per day since we are calibrating against daily electricity prices. The discretized equation is: = ∆ + + With probability (1-λΔt) and, = ∆ + + + + With probability λΔt where and are independent standard normal random variables, and ∆ . The density function of given based on (Á.Escribano, 2002) and (Pablo, 2003). ( )=( ∣ ( ∣ )= 2 ( ∣ ) = (2 ) ( ∣ / − ( / + ) ) + (1 − exp ( ( ∆ ) ( ∣ − ∆ − 2 ) + = 1− ) − ) ) 38 THE REAL OPTIONS METHOD The parameters θ={ , , function in Matlab: − ∑ log ( ∣ , The first inequality constraint , , λ} are calibrated by minimizing the negative log likelihood ) Subject to < 1, > 0,0 ≤ > 0, < 1 is equivalent to ≤1 > 0, the volatilities and must be positive. Finally for has to be between 0 and 1 as it represents the probability of a jump occurring in ∆ time. In this model, ∆ is one month, thus there is 12 jumps a year. The Matlab function MLE from the Statistics and Machine Learning Toolbox™ is used to solve the above maximum likelihood problem. Then we will get the parameters needed for the Event tree analysis. 5.4.3. The Event tree The calculated uncertainty is applied to building an event tree. The event tree models the values that the project may along the time. Initially its present value is introduced in step 0. The price of the underlying asset can either increase by factor u or drop by factor d in each step as shown in Table 2. The underlying asset is the present value of the project. The factors u and d are related to the volatility of the underlying asset return according to the equations The equations from binomial model used for the event tree are: √∆ = √∆ = = We estimated the risk neutral probability according to the following expression (Antikarov, 2003): = ( ( )∆ − ) ( − ) Where rf is the risk-free interest rate. 0 1 Table 2 : The Binomial tree Steps 2 3 3 2 V0 V0*u V0*d V0*u V0*u*d 2 V0*d V0*u V0*u2*d V0*u*d2 V0*d3 4 5 V0*u5 V0*u4 3 V0*u *d 2 V0*u *d 2 3 V0*u*d 4 V0*d V0*u4*d V0*u3*d2 V0*u2*d3 V0*u*d4 V0*d5 Since the project involves a multistage investment consisting of design, construction and operation phases, it can be treated as a sequential compound option. A binomial approach was elaborated to model this investment opportunity analysis V0 is the present value of the project in the baseline case, i.e. in year 0 and without considering managerial flexibility. 39 THE REAL OPTIONS METHOD 5.4.4. The Decision Tree After the event tree, the following step is to build the decision tree. This is done through a backward analysis of the decision tree. We will analyze the investment decision in two phases; a design phase and a construction phase. The first one is at the end of the first year, where the investor can proceed to invest R$ 4M in the design phase which will give him the right to proceed to the construction and the second on is at the end of the third year where, the investor can make his final decision. The valuation begins in the last tree columns. Therefore, in the last period the option value is calculated as follows: V = Max [S - X; 0] If the project present value (S) of the event tree is higher than the invested value (X), the exercise price, the option should be exercised and its price will be the difference S – X. If not, the option shouldn’t be exercised, and its value is zero. Then, for each previous step is evaluated using the replicating portfolio method to estimate the value of the project if it was kept alive. The same process is done for the first option, which is the investment of R$ 4M at the end of the second semester. The two values of the first step will allow us to calculate the option value at the step 0 which is to invest R$ 1M. 40 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm 6. A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm 6.1. Project description The objective of this part is to illustrate by way of case study the use of the real option valuation model to assess investment opportunities for a wind farm project in Brazil. The applied method can be used for a wind or solar farm. However, since the data for solar projects are not available yet as not enough solar energy auctions took place so far, we will analyze investment opportunities in wind project. Precisely, a 30MW wind farm project that will be participating in the 2017 A-3 auction. The project considered is: Characteristics Installed Power Building Investment Cost Average Energy Price Generation O&M Cost TUST (in North East region) TUST wind discount TFSEE PIS/COFINS Administrative Expenses Tax (IRPJ + CSLL) Operation period Construction period Table 3 : Characteristics of the project Value 30 MW 1574,07 US $/KW 57,26 US $/MWh 21 R$/MWh 4,1 R$/KW 50% of TUST 3,09 R$/KW 9,15% of Gross Income 100,000 R$/Year (IRPJ + CSLL) 34% of EBT 20 years 6 months When the project gets accepted in the auction, it has 3 years for building it. However, since the construction period is about 6 months, we analyzed the investment decision in a 6 months period. We have though 6 steps for the decision tree of the Real Options method. The multistage investment allows treating the problem as a sequential compound option; R$1 M investment in the first semester creates the right to invest R$4 M in the second one, and the exercise of that choice creates the option to invest to build the plant or the option to abandon the project, representing the transfer of concession rights that worth, we suppose, twice the gross income of a year (Caminha-Noronha, 2006). Its value depends on the scenario chosen below. To better understand the added value of the Real Options Analysis, two scenarios will analyzed, an optimistic and a pessimistic one. Both of them under the simulated average energy price of R$ 142, 36/MWh from the previous wind energy auction of October 2014. The differences between them are the values of WACC, the Real exchange rate and load factor. As for the WACC, it depends on the share of the BNDES loan in the project financing and consequently on the NPV value. In the first scenario we consider a 70% share of BNDES resulting in a WACC of 6.1% and a WACC of 12% when only own capital is considered. Then, for the Real Exchange rate, it affects the investment cost. Indeed, recently the Brazilian Real to US Dollar exchange rate is subject to a high volatility. For the first scenario, we considered an Exchange rate of 3.08 which is the average of the last year values and for the pessimistic scenario we considered a rate of 3.12 which is the current exchange rate. Concerning the load factor, it affects the yearly gross revenue for the wind farm and varies according to the projects location. The weighted average for the wind farms that started operation after 2014, it is 46% and the minimum used for the pessimistic case is 32%. 41 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm Summary of the two scenarios: Table 4 : The two scenarios Scenario 1 (Optimistic) 6,1% 3,087533 46% Characteristics WACC Exchange rate ($ to R$) Load Factor Scenario 2 ( Pessimistic) 12% 3,12364 32% 6.2. Methodology 6.2.1. Estimation of cash flow return volatility To estimate the cash flow volatility, we simulated the electricity prices for the wind farm project, a forecast for future marginal cost provided by EPE was used. It is composed from a 2000 scenarios of monthly average prices for the period going from January 2020 to December 2024. The series are calculated by NEWAVE software, the computational model for the optimization of medium-term plans provided by EPE. Some operational restrictions need to be added such as minimum and maximum limits which are R$ 30.26 and R$ 388.04, respectively. A sample is drawn in figure 10. This database is used for estimating the volatility of the returns for the project. In our case we modeled the electricity as described in the part 5.4. log( ) = ( ) + The component f(t) is the deterministic seasonal part and Xt is the stochastic part. The logarithms of price and seasonality trends are plotted below. Also, the deseasonalized logarithm of price is plotted in Figure 10. log(price) and Seasonality log(Prices) 6 5 4 3 2020 2021 2022 2023 2024 2025 2024 2025 Date log(price) with Seasonality Removed log(Prices) 200 100 0 -100 2020 . 2021 2022 2023 Date Figure 10 : Price serie and deseasonalized serie for 2020-2050 Then to conduct the calibration of the stochastic part, the model for Xt needs to be discretized. Thus, we assume a Bernoulli process. The discretized equation is = ∆ + + With a probability of (1-λΔt) and, 42 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm With probability λΔt where ∆ and = ∆ + + + + are independent standard normal random variables, and = 1− The function MLE from the Statistics and Machine Learning Toolbox™ is well suited to solve the above maximum likelihood problem. After running the Matlab code from Annex A, this has been executed for the 2000 price series in order to calculate the standard deviation of the electricity prices for every series. Then the weighted standard deviation of the 2000 series found is σ =19,4448% 6.2.2. The Cash flow Modeling the Real Options method involves building an event tree, which projects the possible future values of the plant under two scenarios, in our case. Consequently, it is necessary to estimate the value of the plant as if it exists today. This is done through the discounted cash flow method for both scenarios to calculate the net present value of the project. 6.2.2.1. Cash flow of the first Scenario The first scenario considers relatively favorable investment conditions (Table 4). The WACC of 6.1% which considers a 70% share of BNDES, a common share value that has been used in among wind farm projects in Brazil. The BNDES has indeed a lower cost of capital than other banks that allows it to charge low interest rates on loans and still have a positive net interest margins. The Exchange rate R$/US$ used is an average of last year exchange rates values and the load factor is the weighted average load factor for the wind farms installed in Brazil that started operation from 2013 to 2015. (See Annex B) We conducted a traditional investment analysis (Table 5) to estimate the free cash flow and the NPV value. The total investment in the three years depends in the Real exchange rate. Thus, it is different between the two scenarios. The average investment cost US$ 1.574M/MW according to data provided by the Brazilian Wind Energy Association. The wind farm project will cost immediately R$1 M for environmental studies, which will take 6 months to complete. At the end of that year, the firm could invest R$ 4 M to complete the design stage. Even though, this step can be simulated using Computer softwares, field investigations are still a significant step for a full project valuation. This step provides with an estimation of possible environmental impacts that can influence the following construction phase. The latter, can be exercised for our project in the following four semesters, we assumed 13% of the capital cost for the each of the two first semesters and 32% for the two last ones. Table 5 : S1 Traditionnal Investment Analysis Period 1st semester 2nd semester 3rd semester 4th semester 5th semester 6th semester 3rd to 23rd year Gross Revenue 17 210 NPV= -3 638 R$ Million <0 Costs 4 275 EBITDA 12 935 Depreciation 6 548 Taxes 2 171 Free Cash Flow (1 040) (3 948) (19 323) (19 323) (50 906) (50 906) 10 763 Don’t invest 43 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm Millions Considering the investment flow illustrated in the figure 11 and a WACC of 6.1%, the NPV value is negative. According to the NPV analysis there is no incentive for this investment. For the case of Energy commodities and especially Electricity, which is subject to high price volatility, we should go further in the investment analysis to the Real Options analysis. 20 10 0 -10 0,5 1,5 2,5 4 6 8 10 12 14 16 18 20 22 -20 -30 -40 -50 -60 Figure 11 : Free Cash Flow of the first scenario’s investment 6.2.2.2. Cash flow of the second Scenario The second scenario is built under pessimistic circumstances that could happen, especially with the financial crisis that is occurring in Brazil. The WACC is calculated without any participation of the BNDES. Moreover, the exchange rate is high, however, it can get even higher in the future and the load factor is the lowest that has been deployed in the two last years. Table 6 : S2 Traditionnal Investment Analysis Period 1st semester 2nd semester 3rd semester 4th semester 5th semester 6th semester 3rd to 23rd year Gross Revenue Costs EBITDA Depreciation Taxes 11 972 3 023 8 949 -6 643 784 NPV = -54,147 R$ Million Free Cash Flow (1 053) (3 999) (19 573) (19 573) (51 566) (51 566) 8 165 Don’t invest For the Scenario 2, the pessimistic one, after considering the investment flow illustrated in figure 12 and a WACC of 12.1%, the NPV is negative and it has a much lower value. Thus, according to the traditional NPV value, the investment seems to be non-profitable. This is due to a high WACC 12%. We only considered own capital in this case and without the participation of BNDES. The higher exchange rates than the first scenario influence the investment capital since the main parts of the wind turbines are imported. Concerning, the low load factor it influence the annual gross revenue which is proportional to the energy generated. We used the lowest average load factor for the wind farms that started operation between 2013 and 2015. Its value is 32%. 44 Millions A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm 20 10 0 -10 0,5 1,5 2,5 4 6 8 10 12 14 16 18 20 22 -20 -30 -40 -50 -60 Figure 12 : Free Cash Flow of the second scenario’s investment 6.2.3. The Event Tree The calculated volatility in IV.5.1 is applied for building the event tree in each scenario. The tree shows the evolution of the power plant values that the asset may have along the time. The tree is constructed through the following equations: Growth rate: Reduction rate: With √∆ = = = 1,147396542 √∆ = = 0,871538272 = 19% and ∆ = 0,5 We estimated the rising movement probability according to the following expression (Antikarov, 2003): = ( ( )∆ ( ) ) = 0,538911042 Where rf is the risk-free interest rate. The compound option features are presented in the table 7 below: Table 7 : Compound Option features Scenario 1 Scenario 2 Tree Steps Quantity Option Expiration Time (Semesters) Base line Price Project PV Exercise Price Initial Investment Project PV Exercise Price Initial Investment First Option 5 1 142,36 R$ 121.944.257 R$3.948.405 R$1.040.000 142,36 58.085.463,2 R$ 3.999.683 R$1.053.000 Second Option 4 128.770.404 120.586.000 Due to the irreversibility of the investment, we assumed that all investment flows happen at one point in time, the beginning of the semester, and that the cash flow is generated at the end of the semester. 45 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm Scenario 1 has a higher starting present value which explains the higher value of the first event tree compared to the second one 6.2.3.1. First scenario’s event tree For the each scenario, we introduced initially the present value in the step and then the other elements are obtained by multiplying by u for the upper element and by d for the lower one. The difference between the first scenario event tree (table 8) and the second scenario one (table 9) is in the first step, the project present value. The equations and values used to build the trees are the same. For the first Scenario, the Project present value is 122.459.323. For the Step 1, it was multiplied by u and d as follow: Vup =122.459.323*1.14=140.509.403 Vdown= 122.459.323*0.87=106.727.986 Step 0 Step 1 Table 8 : S1 Event Tree (R$) Step 2 Step 3 Step 4 Step 5 243.533.964 212.249.170 184.983.275 161.220.004 140.509.403 122.459.323 184.983.275 161.220.004 140.509.403 122.459.323 106.727.986 140.509.404 122.459.323 106.727.986 93.017.525 106.727.987 93.017.525 81.068.333 81.068.333 70.654.155 61.577.800 6.2.3.2. Second scenario’s event tree For the second Scenario, the project present value is 60.986.985. For the Step 1, it was multiplied by u and d as follow: Vup= 60.986.985*1.14=69.976.255 Vdown= 60.986.985*0.87=53.152.491 Due to the difference of the of investments condition, we can notice that all the project values during the design and construction phases are lower in the second scenario than in the first one. Step 0 Step 1 Table 9 : S2 Event Tree (R$) Step 2 Step 3 Step 4 Step 5 121.284.373 105.703.973 92.125.058 80.290.514 69.976.255 60.986.985 92.125.058 80.290.514 69.976.255 60.986.985 53.152.491 69.976.255 60.986.985 53.152.491 46.324.430 53.152.491 46.324.430 40.373.514 40.373.514 35.187.063 30.666.872 46 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm 6.2.4. The project decision tree In order to build the decision tree, we started from a backward analysis starting from the last step of the tree. In the last step the option value is calculated as follow: V = Max[S - X; 0] where V = Real Option Value, S = Event tree PV and X = price to exercise the option. When the project present value (S) from the event tree is higher than the price to exercise (X) which is the invested value the option should be undertaken and its price will be S – X. When it is lower, the option shouldn’t be exercised and it will be equal to zero. Tables 10 and 11 for the first scenario and tables 13 and 14 for the second scenario illustrate the application of the method For the Table 10, above, the exercise price which is the Investment Present Value in the third year the last column value is calculated as follow: V=Max[243.533.964-128.770.404;0]=114763559 For the previous steps, we used the replicating portfolio method to assess the project value. 6.2.4.1. First scenario’s decision tree Table 10 : S1 Second Investment Option (construction phase) valuation Tree Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 114.763.559 87.235.251 63.616.258 56.212.870 44.946.086 36.206.085 30.972.142 22.901.827 11.738.999 20.916.057 14.284.331 6.548.544 8.810.043 3.653.074 0 2.037.849 0 0 0 0 0 The calculation of the values in the previous steps is made using the replicating portfolio method (Caminha-Noronha, 2006)as below. For example for the upper value in the step 4: We calculate 114763559 − 56212870 = 243.533.964 − 184.983.275 and Then = 56212870 − = ∗ 184.983.275 ∗ 212.249.170 + = 87.235.251 Finally, V = Max[S - X; Portfolio] = Max [212.249.170-128.770.404; 87.235.251] = 87.235.251 This calculation is made with Excel software for all the other steps. 47 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm Then, in order to evaluate the option to invest after the design stage, we integrated a second option value to invest the R$4 to finalize the design phase. The completion of the plant design through an American call option or the transfer of concession right through a European put option. In step 1 for example the lower value in the step 1 is calculated as follow: V= Max [8.810.043 – 3.948.405; 34.419.231-3.948.405-8.810.043; 0] = 21.660.783 The right to invest R$1 M in the first semester is also calculated by the replicating portfolio method for either 27.023.737or 21.660.783 resulting in 24.020.593. Table 11 : S1 First Option (invest R$4 million design phase) valuation Tree Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 114.763.559 87.235.251 63.616.258 44.946.086 27.023.737 24.020.593 56.212.870 36.206.085 22.901.827 14.284.331 21.660.783 11.738.999 6.548.544 3.653.074 2.037.850 0 0 0 0 0 0 Then, following the results of the Valuation tree, we build the final Project decision tree. Step 0 Table 12 : S1 Project Decision Tree Step 1 Step 2 Step 3 Step 4 Step 5 Invest R$128 M Wait Invest R$128 M Wait Wait Invest R$4 M Invest R$1 M Wait Invest R$128 M Wait Wait Transfer Rights Wait Wait Wait Don’t Invest Wait Wait Don’t Invest Wait Don’t Invest From the Project decision tree we can see that there are opportunities for investing in this project although the NPV is negative in this scenario. In fact, depending on the uncertainty of the market, there is a probability of 53% in each step to go to the next upper case. The option of selling the concession rights at the end of the first year is relevant since there is a lower probability for the investment to be worthwhile. Thus the value of the European put option of the transfer right should be 48 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm undertaken. If the option of investing R$ 4M is made and the design phase done, it can be seen that the investment has a higher probability of success. Indeed, the right to invest R$4 M is equal to 24.020.593 which is 23.020.593 more than the R$1 M which forms the initial project’s cost. 6.2.4.2. Second scenario’s decision tree For the scenario 2, which is the pessimistic one, the investment conditions are less attractive that the first one, the WACC, load factor and exchange rate. The NPV method doesn’t incentivize investing in the project with a value equal to -51.245.094. Table 13 : S2 Second Investment Option (construction phase) valuation Tree Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 698.641 446.939 285.918 0 182.909 0 117.012 0 0 74.855 0 0 0 0 0 0 0 0 0 0 0 The last column of the Valuation tree was constructed as follow: Max [121.284.373 -120.585.732; 0] = 698.641 i.e. when the project value is higher than the invested value, the option should be exercised. For the second value in the last column its value is calculated as: Max [92.125.058-120.585.732; 0] =0 This means that the option shouldn’t be undertaken and the same for the remaining values in the last column. The previous steps values are calculated with the same replicating portfolio method as in the first scenario. That is to allow us to complete the valuation tree in the second option. For the First option valuation tree in Table 14, either to invest R$4 million design phase with an American call option or the transfer of concession right with a European put option. The lower value in the first step is calculated as follow: V= Max [0-3.999.683; 23.943.813—3.999.683-0; 0] = 19.944.130 Table 14 : S2 First Option (invest R$4 million design phase) valuation Tree Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 698.641 446.939 285.918 182.909 19.827.118 18.770.575 0 0 0 0 19.944.130 0 0 0 0 0 0 0 0 0 0 49 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm The right to invest R$4 million in the second semester of the project is determined using the same technique; the replicating portfolio method. Then based on the value of the valuation tree, we build the final decision tree of the project. The right to invest R$1 M in the first semester is also calculated by the replicating portfolio method for either 19.827.118 or 19.944.130 resulting in 18.770.575. Table 15 : S2 Project Decision Tree Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 Invest R$120 M Wait Wait Don’t Invest Wait Wait Don’t Invest Invest R$4 M Wait Wait Invest R$1 M Wait Wait Don’t Invest Transfer Rights Wait Wait Wait Don’t Invest Wait Don’t Invest In the second scenario, it is clear that there is less probability that the project will turn out to be worthwhile. However, even though the NPV is negative (R$ -54 M), there is a small probability that the investment will proceeded in the future. Table 15 illustrates the optimal strategies for the investment forecasting. The right to invest R$4 M is equal to 18.770.575 which is 17.770.575 more than the R$1 M which forms the initial project’s cost. Thus the first phase of design investment (R$1 M) should be exercised 6.3. Analysis of the failure of the last wind auction In this part, we will apply the real option method to understand the reluctance of investors to participate in the last A-3 wind energy auction. During this auction, in April 2015, only 70MW was contracted from the 500MW auctioned. This happened, even though that the ceiling prices for wind was raised by the government to R$ 179 /MWh. For this we kept the condition of the first scenario and we changed only the baseline Price of 142.36R$/MWh to the ceiling Price of R$ 179 /MWh. Under these conditions, the Project has now a positive NPV equal to R$ 26M. The IRR found is 7.37% is higher than the WACC which is 6.1%. According to the NPV method, this investment is worthwhile. Thus we will conduct the Real Options Analysis to reevaluate the investment decision. Table 16 : Event Tree (R$) Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 303.626.105 264.621.771 230.628.001 201.001.129 175.180.177 152.676.229 230.628.001 201.001.130 175.180.177 152.676.229 133.063.176 175.180.177 152.676.229 133.063.176 115.969.651 133.063.177 115.969.651 101.071.989 101.071.990 88.088.107 76.772.156 From the event tree in the table 16 we can see that the project has higher value during its life compared to the two previously analyzed scenarios. One thing has to be high lightened is that we used the ceiling 50 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm price as a major factor for calculating the annual gross revenue. The investors have to bid below this price. Usually the average auction is two to three Reals lower than the ceiling price. However, this case presents the most attracting possible conditions. Under these conditions we build the following valuation trees: Table 17 : Second Investment Option (construction phase) valuation Tree Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 174.855.700 139.607.852 109.260.984 83.174.627 61.596.623 75.987.210 53.813.160 36.800.698 44.495.913 101.857.596 24.539.840 46.409.772 27.662.310 16.420.258 9.711.654 4.292.772 2.394.702 1.335.873 0 0 0 The second and first options valuation tree are constructed following the same process used in the two previous scenarios with calculating the option value in the last column and then using use of the replicating portfolio method for the previous ones. Table 18 : First Option (invest R$4 million design phase) valuation Tree Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 174.855.700 139.607.852 109.260.984 83.174.627 57.648.218 40.662.691 101.857.596 75.987.210 53.813.160 36.800.698 20.591.434 46.409.772 27.662.310 16.420.258 9.711.655 4.292.772 2.394.702 1.335.873 0 0 0 Finally according to the calculated values from the valuation trees, we, build the final Project decision tree (see table 19 below) Table 19 : Project Decision Tree Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 Invest R$128 M Wait Wait Wait Invest R$4 M Invest R$1 M Invest R$128 M Wait Wait Wait Invest R$4 M Invest R$128 M Wait Wait Wait Invest R$128 M Wait Wait Don’t Invest Wait Don’t Invest 51 A REAL OPTION APPROACH FOR RENEWABLE ENERGY INVESTMENT IN BRAZIL: A case study of a wind farm The right to invest R$4 M is equal to 40.662.691 which is 39.662.691 more than the R$1 M which forms the initial project’s cost. Thus the first phase of design investment (R$1 M) should be exercised. The option to finish the whole design phase should be exercised as well since in both cases the right to invest is higher than the investment cost. However, after this phase, the investor should wait until the uncertainties get revealed to process to the final investment. From the results of the Real Options analysis, we can perceive from the table 19, that although the NPV of the project is positive and the IRR higher than the WACC, there is a probability that the project will turn out to be unprofitable. It is true that the probability is lower than the previous scenarios where we used a lower average baseline scenario price. But the risk exists for these wind power plants that were supposed to start generating power on July 1st, 2017. These results can explain the fact that in the analyzed auction only 70MW from the 500MW auctioned were contracted. It also illustrates the growing hurdles for RES investors. Indeed, the weak Real is driving the investments cost up which affects the auctions prices. Thus, if the Real will continue falling with keeping the current market condition i.e interest rates, one should expect that the energies prices will grow from an auction to the other. 6.4. Model discussion For electricity projects, the studied wind farm is subject to price volatility and the project value changes over the time. In this case various external events can change the project value and consequently the investment decision. The established model presents a methodology to analyze investment opportunities in a wind energy farm. The model can be adapted to other type of energy plants by adapting the model inputs. The binomial approach was used to model the Real Options since we included combined option in the analysis. The use of stochastic dynamic method, such as Mean reverting method or the Geometric Brownian Motion in this problem, would bring more complexity to the problem especially since we used the forecasted prices provided from EPE. From the results obtained we can see that changes in currency exchange rate, WACC and annual gross income (trough load factor) have a substantial impact on investment decision. When changing these different factors, the NPV might become negative the investment should not be exercised since it can result in substantial loss for the company according to the NPV method. For the case of April 2015 last wind auction, the results show that even though we used the ceiling price for the baseline scenario for the optimistic scenario, the project is subject to uncertainty and the waiting option is important to calculate. An improvement of the model can be by including an American option in the investment decision which means that the project can be built before the end of the start of the PPA and the investor can trade in the free market. This however needs a data of the transactions occurring in the free market which are not easy to get since not all the details are disclosed. 52 CONCLUSION CONCLUSION The analysis of the European experiences in renewable energies support schemes has shown that quota mechanisms are a losing ground compared to the others support schemes. Feed-in Tariffs are also regressing and their levels, in Italy and Spain for example, are being reduced. On the contrary, Sliding Feed-in Premium, Contracts for differences and auctions schemes are getting more popular in Europe, respectively in Netherlands, UK and Germany. Considering country-specific aspects of the power market, renewable support schemes should be designed on a tailor made basis. Notwithstanding, learning from previous experiences has a considerable role in the prevention of falling into the similar other countries errors. In Brazil, the wind energy has experienced a fast development in the last years to reach 4% of the installed capacity. Its share still, however, far from the share of the European countries. The growth of the wind generating capacity is facing several problems. The major one is financial. For solar energy, the Brazilian policy is not clear yet. The solar energy market is growing very slowly. The first solar only auction was held in Pernambuco in December 2013 for 123MW of capacity. The winning Projects have faced licensing and granting problems and many of them were delayed. The Brazilian development bank, BNDES, is raising interest rates making the financing of new RES project more complicated. In addition to that, it imposes strict requirements for the investors in order to benefits from its loans. Indeed, many companies have been rejected from the energy markets for non compliances with the 60% quota of locally manufactured components. This quota is needed to benefit from the bank’s financial support. The local content requirement is reviving debates on its efficiency and fairness. It takes place in the context of the national economic strategy to promote the “made in Brazil” and protecting it from international competition. In addition to that, the weak Real is making the importation of necessary component more and more expensive To summarize, the scarceness of BNDES loans that are also requiring local content, the non stable Brazilian Real and the increasing returns on equity are making the auction ceiling prices very low to attract investors. Their bids are getting more risky causing several projects abandonment during the development phase. The attractiveness issue and the effects of financial instruments have been analyzed in the quantitative part of the thesis through the Real Options approach. Two scenarios have been built to assess investors’ behavior during the auction. We included managerial flexibilities in the investment decision allowing the investors to delay the project, to sell it at the end of the first year or to abandon it after the design phase. The option of delaying the investment is valuable for investors who wants to build a RES farm, but wants to check if the market investments conditions will become more favorable before entering such an irreversible investment plan. The managerial flexibility is especially valuable in electricity markets since it is subject to high uncertainties. The results showed that according to the traditional analysis NPV was negative and there was no incentive to invest. However, when considering all the possible flexibilities in the market such as transferring the concession or delaying the investment, the calculated NPV were positives due to the aggregated value of the options. We applied also the Real Options Method to understand the investors’ reluctance during the last April wind auction. We found that even though in average the projects have a positive NPV, the investment 53 CONCLUSION still have chances to be unprofitable. The ceiling price of 179 R$/MWh set by ANEEL to compensate other non attractive market condition isn’t sufficient and the price that will guarantee a secure investment with regards to market uncertainties is 304,39 R$/MWh, which is very high for the Brazilian market. The Real Options method is a good solution for companies that need a higher management tools, more accurate than the NPV method. The stochastic electricity market conditions can demonstrate that a project with a negative NPV has a probability to be profitable and that the Manager needs to wait before taking a yes or no decision. This study can serve as a guide to RES investors on how to plan their investment behavior. It can also allow the estimation of the auction ceiling prices with taking into account the market investment conditions. The analysis made in this thesis can be adapted to other RES project with changing the necessary input in the model. The results accuracy can be improved by modeling the long-term energy price and estimate more accurate costs of the project in calculating the free cash flow. Also we can add a deferral penalty if the project has been delayed more than the allowed time by ANEEL. Also, we can incorporate the ability for the project to trade in the free market before the PPA starting date, which will add dividends payments in the free cash flow method. 54 <REFERENCES REFERENCES A. K. Dixit, R. S. (1991). nvestment under Uncertainty. New Jersey: Princeton University Press. Á.Escribano. (2002). Modeling electricity prices: international evidence. Departamento de Economía Universidad Carlos III de Madrid . A.Poullikkas. (2012). An overview of the EU Member States support schemes for the promotion of renewable energy sources. International Journal Of Energy and Environment . Actu-environnement. (2014). Energies renouvelables : les citoyens (et leur épargne) appelés à la rescousse. Retrieved from www.actu-environnement.com/ae/news/energies-renouvelablesfinancement-citoyen-participatif-20560.php4 Adrien De Hauteclocque, Y. P. (2011). Law & Economics Perspectives on Electricity Regulation. EUI Working Paper RSCAS. Almeida, E. L. (2005). Reform in Brazilian Electricity Industry: The search for a new model. International Journal of Global Energy Issues . Antikarov, C. &. (2003). Real Options: a Practitioner’s Guide. New Yorker. Barroso, C. B. (2011). Review of Support Schemes for Renewable Energy Sources in South America. Elsevier . Barroso, L. (2012). Renewable Energy Auctions: the Brazilian Experience. Workshop on Energy Tariffbased Mechanisms, IRENA. BBC. (2014, August). Brazil's economy falls into recession, latest figures show. Retrieved from www.bbc.com/news/business-28982555 BNEF. (2014). Solar energy makes landfall in Brazil at record-setting low price. Retrieved from www.about.bnef.com/press-releases/solar-energy-makes-landfall-brazil-record-setting-low-price/ Boomsma, T. (2012). Renewable energy investments under different support schemes: A real options approach. Elsevier . Business spectator. (2015). Retrieved from www.businessspectator.com.au/news /2015/4/30/solarenergy/g erman-solar-pv-tender-sees-impressive-us1019mwh-bids C.Batlle. (2012). report on interactions between RES-E support instruments and electricity markets. intelligent Energy - Europe, ALTENER. 55 <REFERENCES Caminha-Noronha, J. C. (2006). Optimal Strategies for Investment in Generation of Electric Energy through Real Options. Munich Personal RePEc Archive . Cohen, B. (1983). Breeder Reactors: A Renewable Energy Source. Am. J. Phys , 51-75. Damodaran, A. (2007). trategic Risk Taking: A Framework for Risk Management. Wharton school publishing. Devine-Wright, P. (2008). Reconsidering public acceptance of renewable energy technologies: a critical review. E. Melo, A. d. (2010). he New Governance Structure of the Brazilian Electricity Industry: How is it possible to introduce market mechanisms? Ecofys. (2013). Experience with renewable electricity (RES-E) support scheme in Europe. Retrieved from http://www.leonardo-energy.org/sites/leonardo-energy/files/documents-and-links/ecofyssupport_policies_2014_04.pdf EMR White Paper. (2014). Electricity Market Reform – Contract for difference and Allocation Overview . Retrieved from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/233004/EMR__Co ntract_for_Difference__Contract_and_Allocation_Overview_Final_28_August.pdf ENTSO-E. (2013). Evolution of a European Interconnected Grid. Secretariat of UCTE. EP. (2003). Directive 2003/54/EC of the European Parliament and of the Council concerning common rules for the internal market in electricity and repealing Directive 96/92/EC. Erika de Visser, A. H. (2014). Methodologies for estimating Levelised Cost of Electricity (LCOE). Ecofys. European Commission. (2013). Staatliche Beihilfe SA.33995 (2013/C) (ex 2013/NN) – Deutschland. Förderung der Stromerzeugung aus erneuerbaren Energien und Begrenzung der EEG-Umlage für energieintensive Unternehmen’, Letter to the Member State. FGinsight. (2013). What will the renewable obligation review mean for farmers. Retrieved from www.fginsight.com/home/renewables/what-will-the-renewable-obligation-review-mean-forfarmers?/64459.article Grau, T. (2014). Comparison of Feed-in Tariff s and Tenders to Remunerate Solar Power Generation. DIW Berlin. Grexel Systems Ltd. (2014). Energy Certificate Systems. Haas. (2010). A historical review of promotion strategies for electricity from renewable energy sources in EU countries. Elsevier . Haas. (2010). Efficiency and effectiveness of promotion systems for electricity generation from renewable energy sources e Lessons from EU countries. Elsevier . Hammons, T. J. (2011). Market Mechanisms and Supply Adequacy in the Power Sector in Latin Americ. InTechOpen. 56 <REFERENCES Held. (2014). Design features of support schemes for renewable electricity. Ecofys. Held. (2007). Feed-In Systems in Germany, Spain and Slovenia. Fraunhofer – ISI. Howard, R. (2015). What can we learn from the renewables CfD auction results? Retrieved from Policy Exchange: www.policyexchange.org.uk/media-centre/blogs/category/item/what-can-welearn-from-the-renewables-cfd-auction-results I. Ritzenhofen, S. S. (2014). Optimal design of feed-in-tariffs to stimulate renewable energy investments under regulatory uncertainty — A real options analysis. Elsevier . IRENA. (2013). Renewable Energy Auctions in Developing Countries. Juliana de Moraes Marreco, L. G. (2005). lexibility valuation in the Brazilian power system: A real options approach. Elsevier . L. Abadie, J. (2014). Valuation of Wind Energy Projects: A Real Options Approach. Mdpi . Letcher, T. M. (2008). Future Energy: Improved, Sustainable and Clean Options for. Elsevier . Luiz T. A. Maurer, L. A. (2011). Electricity Auctions An Overview Of Efficient Practices. World Bank. Mastropietro. (2014). Electricity auctions in South America: Towards convergence of system adequacy and RES-E support. Elsevier . Mathworks. (2014). Simulating Electricity Prices with Mean-Reversion and Jump-Diffusion. Retrieved from www.mathworks.com/help/fininst/examples/simulating-electricity-prices-with-meanreversion-and-jump-diffusion.html?refresh=true Meeus, L. (2012). Renewable Energy: support mechanisms analysis. FSR Summer School on Regulation of Energy Utlities. Melo, E. (2007). A Perspective of the Brazilian Electricity Sector Restructuring. Câmara de Comercialização de Energia Elétrica - CCEE. Merton, R. C. (1976). Option Pricing When Underlying Stock Returns Are Discontinuous. Journal of Financial Economics 3 , 125-144. Minardi, L. M. (2009). Applying real options theory to the valuation of small hydropower plants. Revista de Economia e Administração . Ministério de Minas e Energia. (2014). Brazil Electricity Installed Capacity. Retrieved from www.mme.gov.br/documents/1138787/0/Capacidade+Instalada+de+EE+2014.pdf/cb1d150d-0b524f65-a86b-b368ee715463 Ofgem. (2013). Renewables Obligation (RO). Retrieved from www.ofgem.gov.uk/environmentalprogrammes/renewables-obligation-ro Oxera and Energiewende. (2014). Retrieved from http://www.oxera.com/LatestThinking/Agenda/2014/Almost-a-reform-the-new-German-support-scheme-for.aspx 57 <REFERENCES Pablo, V. a. (2003). Pricing Power Derivatives: A Two-Factor Jump-Diffusion Approach. Universidad Carloes III de Madrid. Pérez-Arriaga, I. J. (2013). Regulation of the Power Sector. Springer. Poullikkas. (2011). Electricity generation cost in isolated power systems. Accountancy Cyprus , 102, 104-105. Ragwitz, M. (2012). Recent developments of feed-in systems in the EU. The International Feed-In Cooperation. REF. (2011). The Probable Cost of UK Renewable Electricity Subsidies. Retrieved from www.ref.org.uk/publications/238-the-probable-cost-of-uk-renewable-electricity-subsidies-20022030 Renewable Seenews. (2015). Financing issues put at risk many Oct solar auction winners in Brazil. Retrieved from www.renewables.seenews.com/news/financing-issues-put-at-risk-many-oct-solarauction-winners-in-brazil-473505 RES-Legal. (2014). Promotion in United Kingdom. Retrieved from www.res-legal.eu/search-bycountry/united-kingdom/tools-list/c/united-kingdom/s/res-e/t/promotion/sum/204/lpid/203/ Río, P. d. (2015). What will be the main challenges for the design of renewable electricity policy in the EU? The European IEE project towards2030-dialogue. Roney, J. M. (2014, May). Denmark, Portugal, and Spain Leading the World in Wind Power. Retrieved from www.earth-policy.org/data_highlights/2014/highlights46 RWE. (2012). The Power of Participation. Retrieved from www.rwe.com/web/cms/mediablob/en/1716210/data/1701408/6/rwe/responsibility/acceptancestudy/blob.pdf Scholes, B. &. (1973). The Pricing of Options and Corporate Liabilities. The Journal of Political Economy, Vol. 81, No. 3 , 637-654. Seifert, J. U.-H. (2007). Modelling Jumps in Electricity Prices: Theory and Empirical Evidence. Review of Derivatives Research , 59-85. Siegel, M. R. (1986). The Value of Waiting to Invest. Quarterly Journal of Economics , 101, 707-727. Sioshansi, F. (2013). Evolution of Global Electricity Markets. Elsevier. Taleb, N. N. (2014). Antifragile: Things That Gain from Disorder. Penguin. The Institute for Energy Research. (2013). Germany’s Green Energy Destabilizing Electric Grids. Retrieved from www.instituteforenergyresearch.org/analysis/germanys-green-energy-destabilizingelectric-grids/ The Wall Street Journal. (2013). The Experts: What Renewable Energy Source Has the Most Promise? Retrieved from The Wall Street Journal: www.wsj.com/articles/SB10001424127887324485004578424624254723536 58 <REFERENCES UNESCAP. (2013). Case study: Brazil’s National Plan on Climate Change and Law. Low Carbon Green Growth Roadmap for Asia and the Pacific . Wikipedia. (2015). History of wind power. Retrieved from en.wikipedia.org/wiki/History_of_wind_power 59 ANNEX ANNEX ANNEX A load('simprices') labels=datestr(simprices(1,:)); minp=30.26; maxp=388.04; for k=1:60 xtic{k}=([(labels(k,4:11))]); end data= [xtic{:}]; for fg=2:2001 fg Prices=simprices(fg,:); for i=1:60 if Prices(i)==0 Prices(i)=1; end end PriceDates=simprices(1,:); figure(1) plot(PriceDates,Prices) datetick(); set(gca, 'XTickLabel', xtic(:)) xlabel('Date'); Obtain log of prices logPrices = log(Prices); % Obtain annual time factors from dates PriceTimes = yearfrac(PriceDates(1), PriceDates(60)); PriceTimes =5; % Calibrate parameters for the seasonality model seasonMatrix = @(t) [sin(2.*pi.*t) cos(2.*pi.*t) sin(4.*pi.*t) cos(4.*pi.*t) t ones(size(t, 1), 1)]; C = seasonMatrix(PriceTimes); seasonParam = C\logPrices; % Plot log price and seasonality line figure(2); subplot(2, 1, 1); plot(PriceDates, logPrices); datetick(); title('log(price) and Seasonality'); xlabel('Date'); ylabel('log(Prices)'); %T % Plot de-seasonalized log price 60 ANNEX [X,S,P] = remst(Prices,12,1); subplot(2, 1, 2); plot(PriceDates, X); datetick(); title('log(price) with Seasonality Removed'); xlabel('Date'); ylabel('log(Prices)'); % Prices at t, X(t) Pt = X(1:end); % Prices at t-1, X(t-1) Pt_1 = X(1:end)/10; Pt_1(60) =0; % Discretization for monthly prices dt = 1/12; % PDF for discretized model mrjpdf = @(Pt, a, phi, mu_J, sigmaSq, sigmaSq_J, lambda) ... lambda.*exp((-(Pt-a-phi.*Pt_1-mu_J).^2)./ ... (2.*(sigmaSq+sigmaSq_J))).* (1/sqrt(2.*pi.*(sigmaSq+sigmaSq_J))) + ... (1-lambda).*exp((-(Pt-a-phi.*Pt_1).^2)/(2.*sigmaSq)).* ... (1/sqrt(2.*pi.*sigmaSq))+minp; % Constraints: % phi < 1 (k > 0) % sigmaSq > 0 % sigmaSq_J > 0 % 0 <= lambda <= 1 lb = [minp minp minp minp minp minp]; ub = [maxp maxp maxp maxp maxp maxp]; % Initial values x0 = [100 100 100 100 100 100]; % Solve maximum likelihood params = mle(Pt,'pdf',mrjpdf,'start',x0,'lowerbound',lb,'upperbound',ub,'optimfun','fmincon'); % Obtain calibrated paramters alpha = params(1)/dt; kappa = params(2)/dt; mu_J = params(3); sigma = sqrt(params(4)/dt); sigma_J = sqrt(params(5)); lambda = params(6)/dt; sigmas(fg)=sigma; end 61 ANNEX ANNEX B Power Plant Instaled Capacity (MW) Load State Factor[57] Araças Curva dos Ventos Igaporã Alvorada Igaporã Guirapá Igaporã Licinio Igaporã Conceição Igaporã Planaltina Morrão Colônia Embuaca Faísa I Faísa II Faísa III Faísa IV Faísa V Icaraí I Icaraí II Mundaú São Jorge são Cristovão Santo Ant. de Padua Taíba Águia Taíba Andorinha Ceará II Fonte dos Ventos Areia Branca Mar e Terra Miassaba 3 Rei dos Ventos I Rei dos Ventos 3 Caracará Dreen Asa Branca Atlantic Copel CPFL 167,7 56,4 38,4 52,8 73,6 76,8 52,8 117,6 18,9 27,3 29,4 27,3 25,2 25,2 29,4 27,3 37,8 30 24 26 14 23,1 14,7 87 79,9 27,3 23,1 68,47 58,45 60,12 90 94 160 60 75,6 108,2 48,0% 44,1% 53,6% 46,7% 42,3% 44,5% 45,6% 51,6% 43,7% 40,8% 31,9% 34,9% 33,0% 33,9% 30,9% 47,7% 34,4% 50,7% 55,0% 54,6% 58,6% 46,3% 44,8% 40,0% 45,2% 43,0% 36,3% 33,4% 49,4% 35,0% 52,1% 49,3% 44,1% 50,3% 53,8% 48,5% BA BA BA BA BA BA BA BA CE CE CE CE CE CE CE CE CE CE CE CE CE CE CE CE PE RN RN RN RN RN RN RN RN RN RN RN 62 Dobreve Modelo Morro dos Ventos Renascença Santa Clara Índios 2 Índios 3 Quinta 1 Quinta 2 Palmar Total/Average 89,16 56,4 145,2 150 188 29,9 23 105,3 64 334 3192,8 52,6% 50,2% 47,3% 45,8% 42,1% 38,5% 38,3% 50,0% 41,3% 50,6% 46,0% RN RN RN RN RN RS RS RS RS RS 63