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1 University of Freiburg Faculty of Environment and Natural Resources in cooperation with ZEE Centre for Renewable Energy MODEL-BASED ANALYSIS OF THE LONG-TERM DEVELOPMENT OF THE TURKISH ELECTRICITY MARKET Master thesis submitted in partial fulfilment of the requirements for the Degree of Master of Science in Renewable Energy Management by Pinar Korkmaz Student No

2 First Examiner: Prof. Dr. Sermin Onaygil Second Examiner: Dr. Roderich v. Detten Scientific Supervisor: Dr. Valentin Bertsch Freiburg i.br., Date of submission November 11, 2013 i

3 Declaration of own work I, Pinar Korkmaz, declare that this master thesis titled Model-Based Analysis of the Long- Term Development of the Turkish Electricity Market has not been submitted elsewhere and was produced without external aid and is entirely my own work. All materials which have been used in the research are quoted or acknowledged as appropriate. Freiburg, November 11, 2013 Pinar Korkmaz ii

4 Abstract The Turkish electricity market is undergoing a period of liberalization, bringing important structural changes. In addition, electricity consumption increased 6 % on average per year in the last ten years and this increase is expected to continue in the next decades. Therefore, new generation capacities will definitely be needed to supply the increasing demand. This creates many opportunities for national and international investors who would like to be a part of this growing market. In this study, the Turkish electricity market is modeled to calculate possible future capacity expansions and electricity prices according to four scenarios; Clean Transition (CT), Conventional Development (CD), The Restless Years (RY), and Moderate Development (MD). To do so, first the market structure is modeled in the software PLEXOS and backtested against three previous years of available data for verification. According to the scenarios, long term investment opportunities in renewable and conventional energy are analyzed and possible outcomes of new nuclear facilities are evaluated. CO 2 emissions and national fossil fuel dependency are also discussed. High electricity prices are obtained both in CD and CT due to high demand and fuel prices in the former and the additional cost of CO2 in the latter. Conversely, low prices were seen in RY and medium prices in MD reflecting the demand and fuel price of each scenario. Investment opportunity is strong in Turkey, under all scenarios. Conventional power growth is substantial under all circumstances, while renewable energy growth is dependent on supportive policies such as the EU ETS, even with low technology costs and moderate fuel prices. Under moderate demand growth, shown in MD and CT, nuclear energy reduces the electricity cost and stabilizes prices for the first decade of expansion, but under high growth, used in CD, only slight price reductions are observed. In the MD scenario, CO 2 emissions does not significantly increase over 2012s but for CD and RY, CO 2 emissions increase by more than 100%. With the application of the EU ETS and high growth of renewables, emissions are halved by Dependence on fossil fuels, most of which are imported, is only stabilized or reduced under MD and CT whereas CD and RY scenarios result in increased exposure to worldwide price fluctuations. iii

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6 Acknowledgements First, I would like to thank my technical supervisor Dr.ValentinBertsch who provided the opportunity to write my master thesis at Karlsruhe Institute of Technology. Through my study, his recommendations, guidance and comments were really valuable. I would like to express gratitude Prof. Dr. Sermin Onaygil at Istanbul Technical University Energy Institute and Dr. Roderich von Detten at University of Freiburg who agreed to be my supervisors and examiners and helped to complete my study with appreciable suggestions. Furthermore, I would like to express thanks to Mehmet Isiksoy at SMA Management Consulting Company, Huseyin Yegin at Bosphorus Gaz Corporation, Sezer Bekar and Utku Korkmaz at ELECTURK Energy for helping me to collect the necessary data to model Turkish electricity market and their prized advices through my study. I would also like to mention the name of the experts, Riza Güngor and Ilker Ucler at Energy Marketing Regulatory Authority, for sharing their valuable opinions about Turkish electricity market and recommendations for my study. Moreover, I would like to acknowledge Peny Panagiotakopoulou for her technical support concerning with the software, which was applied in this study. I would also like to acknowledge Tyrell Prouty for his contributions and advices in my study. Finally, I would like to thank Ozge Aldikacti and my family for their moral support through my MSc Thesis study. v

7 Table of content Declaration of own work... ii Abstract... iii Acknowledgements... v Table of content... i List of Tables... 4 List of Figures... 5 List of Abbreviations Introduction Problem Statement State of the Data Objectives Structure Background Information History of Turkish Electricity Market Operational Structure of the Market Renewables and Current Incentives Country Potential for New Electricity Generation Technologies Methodology Introduction to Electricity System Modeling Decision Process of the Software Modeling of Turkish Electricity Market in PLEXOS Modeling of Thermal Power Plants Modeling of Natural Gas Plants Modeling of Coal Power Plants Modeling of Fuel Oil Power Plants Modeling of Biomass & Geothermal Power Plants i

8 3.3.2 Modeling of Run-of River HydroPower Plants Modeling of Wind Power Plants Modeling of Hydroelectric Dam Power Plants Introduction to Scenario Development Results of Back Testing Backtesting Results of Backtesting Results of Backtesting Results of Analysis of Market Prices Analysis of Back-Testing Long-Term Analyses According to Different Scenarios Analyses of Scenario 1-CLEAN TRANSITION Description of CLEAN TRANSITION Results of the Scenario-CLEAN TRANSITION Analyses of Scenario 2-CONVENTIONAL DEVELOPMENT Description of Scenario-CONVENTIONAL DEVELOPMENT Results of the Scenario-CONVENTIONAL DEVELOPMENT Analyses of Scenario 3-THE RESTLESS YEARS Description of Scenario-THE RESTLESS YEARS Results of the Scenario- THE RESTLESS YEARS Analyses of Scenario 4-MODERATE DEVELOPMENT Description of Scenario-MODERATE DEVELOPMENT Results of the Scenario-MODERATE DEVELOPMENT Discussion Long Term Analysis Critical Assessment of the Study Further Work ii

9 7 Conclusion References Appendices iii

10 List of Tables Table 2.1 : Installed Power in Turkey Table 2.2 : Installed Power According to Generation Institution Table 2.3 : Installed Capacity Growth According to Institutes Table2.4: Average Energy Prices in terms of TL/MWh& USD/MWh in 2010, 2011 and Table 2.5 : Feed-in Tariff in Turkey Table 3.1 : Installed Capacity of Natural Gas Plants Table 3.2 : Installed Capacity of Coal Plants Table 3.3 : Installed Capacity of Fuel Oil Power Plants Table 3.4 : Installed Capacity of Geothermal Power Plants Table 3.5 : Installed Capacity of Biomass (Renewables+Waste)Power Plants Table 3.6 : Installed Capacity of Run-of River Power Plants Table 3.7 : Installed Capacity of Run of the River Power Plants Table 3.8 : Installed Capacity of Hydro-Dams Table 3.9 : Storage Capacities of Hydro-Dams Table 3.10: Annual Generation of Hydro-Dams Table 5.1 : Scenarios with the Characteristics of the Key Drivers Table 5.2 : Thermal Power Technologies Table 5.3 : Emission Characteristics of the Fuels Table 5.4 : Installed Capacity of Nuclear Energy Table 5.5 : Short Term Thermal Power Plant Installation

11 List of Figures Figure 2.1 : Present Market Structure Figure 2.2 : Electricity Generation ( ) Figure 2.3 : Electricity Generation Share ( ) Figure 2.4 : Generation of Electricity According to the Sources Figure 2.5 : Solar Map of TurkeyAccording to Yearly Radiation Values Figure 2.6 : Turkey Monthly Average Global Radiation Values Figure 2.7 : Turkey Monthly Average Sunshine Duration (hours) Figure 2.8 : Wind Speed Map of Turkey Figure 3.1 : Efficiency Learning Curve Figure 4.1 : Comparison of Natural Gas Generation in Figure 4.2 : Comparison of Lignite Generation in Figure 4.3 : Comparison of Hydro Dam Generation in Figure 4.4 : General Comparison in Figure 4.5 : Comparison of Natural Gas Generation in Figure 4.6 : Comparison of Lignite Generation in Figure 4.7 : Comparison of Hydro Dam Generation in Figure 4.8 : General Comparison in Figure 4.9 : Comparison of Natural Gas Generation in Figure 4.10 : Comparison of Hydro Dam Generation in Figure 4.11 : General Comparison in Figure 4.12 : General Comparison of Market Prices in 2010,2011& Figure 5.1 : Lignite Price Projections Figure 5.2 : Electricity Demand of Clean Transition Figure 5.3 : CO 2 prices Figure 5.4 : Solar PV System&Wind Energy Capital Cost Figure 5.5 : Natural Gas and Import Coal Prices Figure 5.6 : Installed Capacity Growth in Clean Transition Scenario Figure 5.7 : Electricity Market Prices in Clean Transition Scenario Figure 5.8 : Emission Production in Clean Transition Scenario Figure 5.9 : Electricity Demand of Conventional Development Figure 5.10 : Natural Gas and Import Coal Prices Figure 5.11 : Installed Capacity Growth in Conventional Development Scenario Figure 5.12 : Electricity Market Prices in Conventional Development Scenario Figure 5.13 : Emission Production in Conventional Development Scenario Figure 5.14 : Natural Gas and Import Coal Prices Figure 5.15 : Electricity Demand of Restless Years Figure 5.16 : Installed Capacity Growth in the Restless Years Scenario Figure 5.17 : Electricity Market Prices in the Restless Years Scenario Figure 5.18 : Emission Production in the Restless Years Scenario Figure 5.19 : Electricity Demand of Moderate Development Figure 5.20 : Installed Capacity Growth in Moderate Development Scenario Figure 5.21 : Electricity Market Prices in Moderate Development Scenario Figure 5.22 : Emission Production in the Restless Years Scenario Figure 6.1 : Electricity Demand Comparison Figure 6.2 : Natural Gas Price Comparison Figure 6.3 : Import Coal Price Comparison Figure 6.4 : Installed Capacity of Turkey According to Different Scenarios in

12 Figure 6.5 : Electricity Price Comparison Figure 6.6 : Yearly Emission Production Comparison

13 List of Abbreviations TEIAS Turkish Electricity Transmission Company EMRA Energy Marketing Regulatory Authority TEAS Turkey Electricity Distribution Company EPK Energy Marketing Regulation EUAS Turkey Electricity Generation Company TETAS Turkey Electricity Trading and Contracting Company BO Build Operate Power Plants BOT Build Operate Transfer Power Plants TOR Transfer Operation Right Power Plants IEA International Energy Agency SMP System Marginal Pricing EU ETS European Union Emission Trading System OECD The Organization for Economic Co-operation and Development 7

14 1 Introduction 1.1 Problem Statement Energy is one of the most important factors in determining the financial & economic prowess of countries. Electricity, along with petrol and natural gas, is one of the main actors in the field of energy. From the beginning of 2000s, Turkey has been undergoing a period ofliberalization and privatizations in the energy field. The current developments of the electricity market willnot be enough to meet the energy demand of the country in the next 40 years. According to a report published by Turkish Electricity Transmission Company (TEIAS), Turkish energy consumption will double in the next 8-10 years1,and by 2035, according to the Ministry of Energy and Natural Resources in Turkey, electricity consumption could even triple the current demand.2 Therefore, new generation facilities must be built in the comingyears to meet the country s growing energy demand. This brings promising investment opportunities not only for the national companies but also for international companies who would like to invest in Turkey. Considering the high potential of hydro, wind and solar PV power in Turkey, cost of fuels in the next 40 years and dropping cost of renewable energy(re), the future role of RE investment in the field are also going to be considerably important. At this point, it is crucial to identify the most profitable investments among the capacity expansion options to build new ones in the next 40 years. With the liberalization of the electricity system, an electricity market was also created and electricity trading was made possible. Therefore, it is also important to project future electricity market prices to compare different investment opportunities.in this study, long term planning is essential to decide between capacity expansion options in such an electricity market system since the feasibility study of electrcity expansion power plants are made in the long term. 1.2 State of the Data Recently, there have been some reports publishedwhich forecastdemand until TEIAS published itslast report in December, 2012.This report projects the next ten years of new 1 Energy Marketing Regulatory Authority, 2012, p: 33 2 Ministry of Energy and Natural Resources, 2012, p:1 8

15 electricity generation capacity. 3 Installed capacity and demand projections until 2021 were included according to the license applications to the Energy Marketing Regulatory Authority (EMRA) for new generation facilities as well as expert opinions. Since the market has been experiencing a liberalization period for only a couple of years, any detailed academic research or studypublisheduses these market prices. It is predicted that companies who are active in the market would be carrying on their own analysis; however, these analyses have been quite confidential. Therefore, they are not cooperative with policy makers and public for long term analysis. Moreover, expert opinions in the field would only provide information until Beyond this date, there is not any expert opinion for long term planning or published reports are available. 1.3 Objectives As mentioned in the previous section, overthe next 40 years, Turkey is going to need new electricity generation facilities to meet the growing demand. The goal of this thesis is to develop a detailed analysis of the long-term scenarios of the Turkish electricity supply system. In this analysis, the aim is to answer how market prices would vary in the next 40 years according to different generation expansion options. Therefore, different renewable energy scenarios regarding solar, wind and hydro power are going to be important part of this study to analyze the effect of their generation on the market prices. With this in mind, a principal market model of the Turkish energy market structure is to be built. The factors which could affect the electricity price in 40 years will be the key drivers to answer the research question. Moreover,factors such as the technical data of existing power plants, electricity demand and load profile, renewable energy expected share of the electricity production, fuel prices, and the currency in the country are going to be vital parameters in this research.therefore, it is pointed to evaluate the future of the electricity market in the country and develop different scenarios. Following the market structure modeling and renewable energy investment scenarios, profitable investment opportunities for the next 40 years are identified. According to the scenarios and power plant options, the variation of wholesale electricity market prices will be obtained. 3 TEIAS, 2012, p:

16 Developing the model for the future price projections and investment opportunities according to different scenarios would be valuable for policy makers in the Turkish electricity market to develop future steps in the energy field. Furthermore, the study is aimed to provide general overview of the market and glance of the plausible future prices for investors who would like to be a part of Turkish electricity market, and identify feasible investment opportunities. One of the main aims of the thesis is to provide brief information to the public about the electricity market structure in Turkey and its future, effects of increasing share of renewables and nuclear and different approaches in the energy sector. 1.4 Structure The aim of the thesis is to model the Turkish wholesale electricity market and to analyze long-term electricity generation investment opportunities according to the modeled electricity market as presented in the Objective section. At the beginning of the study, it is necessary to understand the Turkish electricity market structure with its historical developments. In this section, the structure of the Turkish electricity market is explained with the duties of different organizations in the system. Historical information such as electricity demand and installed capacity growths is given in detail. Moreover, current developments, especially in renewable energy is clarified. The maximum utilization potential from these sources isalso declared. The second part of this thesis describes the methodology. Different electricity market modeling approaches is analyzed and the software PLEXOS, which was used in this research,is reviewed. Modeling of the Turkish electricity market structure in the software is explored. This modeling is detailed according to different electricity generation sources in the country. In the last section of this part, scenario construction is going to be declared for future analyses of the market. The third part of the thesis is going to include back-testing of the model in PLEXOS and ananalysis of the scenarios for the future of Turkish electricity market structure in In the back testing part, the model is validated. The program results for the previous years (2010, 2011 & 2012) are compared with the historical data in terms of the electricity generation and 10

17 market prices. The data obtained in the back testing is used as a basis to model the future structure. After back testing analysis, The fourth part of the thesis is scenario analysis. Different parameters in scenario construction are explained and according to these parameters and future expected world trend, four different scenarios are created. Modeling of theturkish electricity market structure and investment decision analysis are done and the software is run according to these different models. In each scenario, wholesale market prices, installed capacity of each energy source and yearly emission production are obtained and discussed. In the last part of the thesis,the general discussion, critical assestment of the study, future research possibilities, and the conclusions of the master thesis are presented. 11

18 2 Background Information 2.1 History of Turkish Electricity Market In the first years of the Turkish Republic (est. 1923), some institutions were founded in order to develop the energy industry and use domestic energy sources. In 1935, the General Directorate of Mineral Research and Exploration was founded to explore mineral resources in Turkey. In order to search and develop electricity and energy sources, the Electrical Power Resources Survey and Development Administration was founded. Furthermore, in order to finance electricity production in the country, Etibank was established the same year.4 In 1963, the Energy and Natural Resources Ministry was established and in 1970the Turkey Electricity Authority was charged with all activities related to electricity except transmission. In 1984 the private sector was incorporated and the Turkish Electricity Authority monopoly ended. Private sector activities started to take place with Build-Operate-Transfer Plants and Plants with Transferred Operating Rights. WhileBuild-Operate-Transfer Plants were valid for the new generation facilities, the case was quite different for plants with transferred operating rights. For these plants, the property belonged to the Turkish Electricity Authority but the operation was managed by private sector participants.in 1994, Turkish Electricity Authority was divided into two main companies.the Turkey Electricity Generation and Transmission Company wasin charge of electricity generation activities and the Turkey Electricity Distribution Company (TEAS) was responsible fordistribution. In 1997, a new regulation granted private sector participants permission to establish thermal power stations with the model of Build-Operate. 5 In 2001, the Energy Marketing Regulation (EPK) 4628 was passedto make a competitive electricity market structure for private sector participants. Under this regulation, TEAS wasdivided into three organizations to provide better market conditions. The Turkey Electricity Generation Company (EUAS) became responsible for generation activities.teias for transmission andturkey Electricity Trading and Contracting Company (TETAS) was founded for electricity trade. After the publication of EPK, EMRA was founded to regulate electricity and natural gas markets in Turkey.Private sector participants were allowed to 4 Yavuz, H.; Gürkan, F;, Şimşek, Z., 2010, p: 2 5 Camadan, E.; Erten, I.E., 2010, p: 55 12

19 participate in market activities such as generation or wholesale of electricity by obtaining the relevant license. The aim of this regulation was to create amarket structure in which having bilateral contracts would be possible. Moreover, consumers gained the right to choose their ownsuppliers. To support this market structure and to balance instability in the market, a balancing market was also established. With these developments, the liberalization period of the market structure started. At the end of 2009, wholesale electricity prices were in the market Operational Structure of the Market As mentioned in the Section 2.1.,the Turkish electricity market has entered a liberalization period commencing with the new regulation and foundation of EMRA. During this period, some important structural changes have been integrated into the market. The current electricity market structure in Turkey can be seen below in Figure 2.1. Figure 2.1 : Present Market Structure 6 Camadan, E.; Erten, I.E.; 2010, p: 56 13

20 There are four different electricity producers in the country. In Turkey, EUAS has the highest share with 36.5%. In this structure, TETAS is responsible for the security of energy supply in the country. TETAS was founded to purchase the electricity from EUAS as well as Build- Operate Power Plants (BO), Build Operate Transfer Power Plants (BOT), and Transfer Operation Right Power Plants (TOR). It sells the electricity to the Distribution & Retail Sale Companies which are private.when the supply does not meet the demand according to the market forecasts done by TEIAS; it makes special agreements and operates back-up power plants. 7 The privatization period of the distribution companies has been completed resulting in 21 distribution companies according toregion. 8 In this market structure, wholesale companies can buy electricity from Independent producers, auto-producers, who have the license to generate their own electricity consumption and can sell the excess amount to the market,and the daily spot market. Wholesale companies can also import or export the electricity. Along with TEIAS, they provide electricity to the eligible customers. Non-eligible customers buy their electricity from the distribution companies in their region. The day-ahead market structure was implemented to balance supply and demand in the country and to regulate wholesale electricity activities in December, 2011.The transactions are determined in this structure hourly for the next day. The spot price can change during the day. In this market system, the predictions of the transitions are considerably important since inaccurate predictions impact theprice. 9 In Turkey, the electricity market transition period is still underway with the deregulated market environment aimed to be finished before Lastly, EMRA decreased the threshold of being eligible customers to 5000 kwh 11 electricity consumption per year while the limit was kwh in Mostly natural gas, lignite, fuel oil, hydro power and mineral coal are used to produce energy. The percentage of electricity production, according to various sources, and their installed capacity is shown in Table TETAS, 2010, p: RetrievedOctober 2, Unal,K.,2011, p: Gokgoz, F.;Atmaca, M.E.; 2012, p: ResmiGazete [Official Newspaper, P.K.], ResmiGazete [Official Newspaper, P.K.],

21 Power Plants Table 2.1 : Installed Power in Turkey 13 Installed Power Percentage Number of Units (MW) (%) Fuel Oil/Naphtha/Motorin Import Coal/Lignite Natural Gas/LNG Biomass Multi Fueled (solid + liquid) Multi Fueled (liquid +natural gas) Geothermal Hydro ( Dams ) Hydro (Run off River) Wind TOTAL Turkey buys most of the natural gas and petroleum from other countries. TEIAS statistics also show that the biggest part of the energy is produced by governmental organizations, as shown in Table However, this situation has been changing and independent power producers have been playing a role as well. 13 Retrieved 24 April, Retrieved 24 April,

22 Table 2.2 : Installed Power According to Generation Institution Ownership Installed Power (MW) Percentage (%) Number of Units EÜAŞ(Governmental) EÜAŞ and its affiliates Plants with transferred operating rights Build Operate Power Plants Build Operate Transfer Power Plants Independent Power Plants Auto-production Plants TOTAL As it mentioned in the Section 2.1., the privatization period was commenced in Until this date, only governmental institutions had the right to generate electricity. This character has been changing since 1984 with the new regulations. This transition and the installation capacity growth in the country can be seen in Table

23 Table 2.3 : Installed Capacity Growth According to Institutes 15 INSTALLED CAPACITY (MW) GOVERNMENTAL PLANTS PRIVATE SECTOR PLANTS TOTAL (MW) Share (%) (MW) Share (%) Total (MW) % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % TEIAS, 2012, p:11 17

24 Since 1984, not only the installed capacity of the power and private sector share in this but also electricity generation and consumption has been rising rapidly in the country. The growth of electricity generation can be seen in Figure 2.2 below Figure 2.2 : Electricity Generation GWh( ) 16 In this period, the shares of private sector plants and governmental plants in electricity generation have altered as well. This revision is shown in Figure 2.3 below % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Governmental Private Figure 2.3 : Electricity Generation Share ( ) TEIAS, 2012, p:12 18

25 Share of the electricity generation sources have changed since 1984 with the growing demand. As it can be seen in Figure 2.4 below, while the generation of electricity from hydro has been tripled, generation from thermal power plantshas experienced eightfold increase Thermic Hydro Figure 2.4 : Generation of Electricity According to the Sources (GWh) 18 After the EPK law was passed, private sector obtained the rights not only to generate electricity but also to trade the electricity in the wholesale structure. Therefore, the spot market system emerged and the price mechanism was also adapted to the market. Especially after 2010, the electricity prices have been rising rapidly with the economic growth in Turkey. The monthly average energy prices in 2010,2011 & 2012 can be seen in Table TEIAS, 2012, p:12 18 TEIAS, 2012, p:11 19

26 Table 2.4 : Average Energy Prices in terms of TL/MWh& USD/MWh in 2010, 2011 and AVERAGE PRICES 2010 TL USD 2011 TL USD 2012 TL USD January January January February February February March March March April April April May May May June June June July July July August August August September September September October October October November November November December December December Renewables and Current Incentives In previous years, fossil fuels dominated electricity generation but along with the global trends Turkey now uses renewable energy to generate electricity and significant renewable energy projects are currently under construction. Furthermore, an important regulation was put in place on 10 March, 2012 that supports renewable energy investments in Turkey.20According to this regulation, a license is no longer required to install a renewable energy power plant under 1000 kw, and the entities are able to generate power for their own usage. The final legislation was passedin August, 2013 and now the various projects are under 19 Retrieved 24 April, T.C. ResmiGazete [Turkish Republic Official Newspaper, P.K.], 2012, Number:

27 way to benefit from unlicensed electricity generation.21 In the middle of 2012, data collection was initiated for the implementation of 600 MW of solar energy. This capacity will start constructionin The energy experts in EMRA expect their construction to end until the beginning of According to the current renewable energy regulation in Turkey, a special feed in tariff was determined for each type of renewable energy source and special incentives were introduced to support investments in this area. The new feed-in tariff, which is guaranteed to be paid by government for 10 years, can be seen in Table 2.5 below. A detailed table for the government incentives for local content are shown in the Appendix A. These incentives are guaranteed to be paid for 5 years.22 Table 2.5 : Feed-in Tariff in Turkey 23 ( & 6094 number regulation) Renewable Energy Facility Type Feed in Tariff ($cent/kwh) Hydropower Facility 7,3 Wind Energy Facility 7,3 Geothermal Facility 10,5 Biomass Facility 13,3 Solar Energy Facility 13,3 2.4 Country Potential for New Electricity Generation Technologies Since the beginning of the century, the electricity generation and demand have almost doubled in Turkey and according to the statistics done by Energy Ministry in Turkey, electricity demand is projected to continue growing with the developing economy. Therefore, Turkey is an appealing place for the new energy investors. With the increasing electricity demand, domestic sources have become more important for the new investors. Turkey has 21 T.C. ResmiGazete [Turkish Republic Official Newspaper, P.K.], 2013, Number: Ministry of Energy and Natural Resources Renewable Energy General Directorate, p: Ministry of Energy and Natural Resources Renewable Energy General Directorate, p:8 21

28 great potential for renewable energy and domestic coal, lignite and hard coal. 24 To identify feasible investments, it is necessary to know the economic potential of these resources. Solar energy is one of the most promising generation technologies in the coming decades with zero green gases emission to fight with the climate change. Turkey has one of the highest solar irradiations in Europe; especially in the Southern part. The annual electricity production is predicted to be between 1100 and 1330 kwh per installed kwp; compared to kwh/kwp in Germany. 25 The detailed solar map of the country, monthly average global radiation values, and sunshine durations can be seen in Figure 2.5 and 2.6 below. Solar Radiationk Wh/m 2 - year Figure 2.5 : Solar Map of TurkeyAccording to Yearly Radiation Values Ozer B.,Gorgun E., Incecik S.,2013, p: Suri M., Huld A. T., Dunlop E.D., Ossenbrink H.A., 2007, p: Retrieved June 10,

29 Figure 2.6 : Turkey Monthly Average Global Radiation Values (KWh/m 2 -day) Figure 2.7 : Turkey Monthly Average Sunshine Duration (hours) 28 Turkey is a promising country for solar development, as can be seen from Figure 2.5,2.6 and 2.7 and current regulations. In 30 years, it will definitely bean important energy player. Another important renewable energy source is wind energy in Turkey. According to EUAS, the economic potential is estimated as 48 GW. 29 Although the country has enormous potential 27 Retrieved June 10, Retrieved June 10,

30 forwind energy, the installed capacity is only 2.4 GW. 30 The detailed wind potential map of the country according to different city boundaries is shown in Figure 2.8. Figure 2.8 : Wind Speed Map of Turkey 31 As can be seen from Figure 2.8, especially the west coast has considerable wind energy potential. However, this coast is also very popular fortourism. In order to overcome these conflicting interests,there should be long-term planning balancing the country requirements and social benefits in both sectors. 32 Hydropower is another important renewable energy source in Turkey withthe economic potential estimated at around 36 GW. 33 Today, the installed capacity of hydropower is 19.6 GW including dams and run of river hydro stations. 34 The Turkish Government aims to install most of the potential by Hydropower is alos an appealing option especially for small investors with run of river options. 29 Ozer B.,Gorgun E., Incecik S.,2013,p: Retrieved April 24, Caliskan M.,2007, p:11 32 Akova I.,2011, p: Retrieved May 1, Retrieved April 25,

31 Lignite is another domestic resource in Turkey. The calorific value of domestic lignite varies between kcal/kg. 68% of the total country reserve has low calorific value and the calorific value of 23% of this reserve is between kcal/kg. 5.1% of it has kcal/kg and 3.4% has more than 4000 kcal/kg calorific value. 35 The total installed capacity of lignite is 8.3 GW. According to the experts in EMRA, Turkey has reserves to install another 12 GW economically. Today, natural gas plants have the highest share; 30% of installed capacity. 36 Turkey buys most of the natural gas from Russia with long term agreements. There are foreign natural gas trader companies who have already invested or would like to invest in short-term future.currently, worldnatural gas price forecast have been fluctuating due to the new reserves of alternatives fuels to natural gas.it appears, with different price projections and the foreign investors in the field, natural gas will be a part of the Turkish energy market. Turkey does not have a nuclear plant yet. However, the construction of the first nuclear plant is about to start in Akkuyu, Mersin. The agreement was finalized in May12, 2010 with Russia Federal Government. The nuclear plant will have four units with 1200 MW each. The commision of the first unit is expected to in 2019.The plant will generate 35 billion kwh per year when the whole commisioning is completed for an expected lifetime of 60 years. 37 The commisioning of the whole plant will be completed in Turkey has also completed agreements to install the second nuclear plant with the Japanese Government in 3 May, The location was chosen as Sinop for this power plant andthe capacity will be also 4800 MWe. The final timeline has not yet been determined for the construction and commissionbut according to the experts in EMRA, the first unit of the second plant will start to generate electricity in 2023 or A third nuclear plant is also being considered , Retrieved May 1, Retrieved April 24, Retrieved July 18, Retrieved July 19,

32 3 Methodology 3.1 Introduction to Electricity System Modeling Energy is one of the key elements to define being a developed country; therefore, modeling of energy market has become more crucial. Electricity system modeling is challenging due to unstable electricity demand, fuel prices, the cost of different technologies, and increasing environmental awareness. Therefore, it is vital to accurately model these variables. 39 There are many tools to model electricity systems for the short, mid and long term futures. These tools help manage electricity systems, electricity demand, calculate market prices, and determinegeneration expansion alternatives. Another challenge which has been rising lately in electricity modeling is to model renewable energy electricity generation since generation cannot be projected exactly. These models also help to come up with assumptions to model renewable energy generation. 40 Electricity demand is different at each hour, each day, each month and each year. In future projections, factors such as economic growth, seasonal fluctuations and consumer behaviors have to be considered. Generally, there are system operators in charge of managing electricity systems who make short, medium and long term planning. 41 There are different techniques used to manage different market structures. As mentioned in the previous Section 2.1, before 1984 Turkish electricity market was managed only by governmental organizations and as such followed a monopoly structure. Currently there are multiple electricity suppliers and customers can choose their suppliers. In liberalized market structures, bilateral contracts, power exchanges, derivatives, ancillary service types are enabled. For example, most of the markets in United States, bilateral contracts are driven to supply electricity. On the other hand,system Marginal Pricing (SMP) and bilateral agreements are employed in Europe. System Marginal pricing is determined as a single island-wise price for each half four trading period. This price is defined by market operators. 42 In Europe, market structures also vary according to different countries. Therefore, different tools model different electricity market systems with different uncertainties and parameters. In amonopoly electricity market, the 39 Sarica K., Kumbaroglu G., Or I.,2012, p: Foley A.M., Gallachoir B.P.Q., Hur J., Baldick R., McKeogh E.J., 2010, p: Foley A.M., Gallachoir B.P.Q., Hur J., Baldick R., McKeogh E.J., 2010, p: Retrieved September 5,

33 main aim was to ensure that installed capacity is able to meet demand. The market mostly driven by traditional governmentally and there is only one electricity supplier for monopoly customers.the electricity price is determined by taking basis of revenue requirements and covering the construction, operation and maintenance cost of the system. The liberalized market structure works different than monopoly. The objective in liberalized electricity markets is to keep unserved energy, which is the amount of energy that is demanded but cannot be supplied in a region, in minimum and to decrease the production cost. Yet, this is not always the obtained result at the liberalized electricity markets. When the liberalized market structure is analyzed in reality, it seems that to sell the electricity with the profit is the main motivation. Therefore, when the electricity price equates the individual suppliers long-term investment and short-term production costs, economic stability can be observedin a liberalized market. As a result of this market mechanism, long-term investments are more complex than in monopoly markets. Generation expansion planning is also another important issue in the liberalized markets.the price of electricity is mainly identified in a wholesale market.43a naturaloutcome of liberalmarkets is price fluctuations.unexpected system disturbances can cause these fluctuations such as ancillary services problems. With new renewable energy policies and emission reduction targets, most of the electricity market models have focused on modeling renewable energy generation, their contribution in green gas emission reduction, and the effects of fuel price escalation. There are always other parameters which are not stable in the long term such as currency fluctuations and varying cost of the different technologies. Especially, for renewable energy;the unit cost of systems have been declining with improving technology. There are important skills which are still missing in the field and it is expected to gain these and decrease the cost of the technologies in the future.planning renewable energy generation is another important challenge in the electricity market modeling since the generation is diversified and cannot be accurately forecasted. In addition, electricity systems modeling should include socio-economic, policy and environmental constraints. Different electricity system scenarios should also be able to integrate green gas emissions, policies to reduce CO 2, and the growth of different renewable energy technologies. 43 Foley A.M., Gallachoir B.P.Q., Hur J., Baldick R., McKeogh E.J., 2010, p:

34 There are two main methodologies in electricity system modeling, Stochastic Optimization and Dynamic Programming. In the dynamic programming method, complex issues are solved by dividing them into sub topics. This canbe from bottom to top or from top to down. This methodology s algorithm only works with special purpose software.in Stochastic Optimization, the algorithm includes stochastic probabilistic elements in the objective or in the constraints. Stochastic optimization has also other functions such as Mixed Linear Programming, Mixed Objective Linear Programming, Linear Programming, Nonlinear Programming and Integer Linear Programming. 44 There are different models which have been developed to model different electricity systems. These are: I. AURORAxmp II. EMCAS III. GTMax IV. UPLAN V. WASP IV VI. WILMAR VII. EFOM-ENV VIII. MARKAL IX. TIMES X. PLEXOS Each model has different properties and different time horizons inside of the model. In this section, each model is explained briefly. I. AURORAxmp AURORA Electric Market Model was developed in 1997 by EPIS, Inc. The software can model competitive wholesale electric markets. 45 The software is used in North America, Asia and Europe and works with MOSEK solver. AURORAxmp forecasts electricity market price day-ahead, monthly and long term. It can differentiate prices according to market areas, zones or trading hubs. Long-tern capacity expansion modeling can be made as well including lifecycle analysis, and retirement of the plants. Risk and uncertainty analysis can be done for 44 Foley A.M., Gallachoir B.P.Q., Hur J., Baldick R., McKeogh E.J., 2010, p: Retrieved September 7,

35 the fuel price escalation, hydro and load volatility, hedging strategy. The program works with genetic algorithms and linear programming. The model also has atool for wind generation. This tool can save the hourly wind speed and create wind generation by stochastic treatment. It also has an advanced hydro shaping tool. Verbund, an Australian Power Company, is an example of an AURORAxmpuser. The software models different carbon regimes according to European Union Emission trading Scheme. 46 II. EMCAS Electricity Market Complex Adaptive System (EMCAS) was developed by Argonne National Laboratory in 2004 and is now used in 20 different countries around the world. Agent Based Modeling is used in this software which enables the electricity market to be modeled with various participants. Each of the participants has their own business structures, risk inclinations, aims and decision mechanisms. The software also enables these agents to make decisions based on the past experiences for the new conditions. This feature makes the tool different than other traditional tools since it is not possible to observe each individual customers decision in traditional models. It is possible to run the model hourly, real time, dayahead, week ahead, month ahead, year ahead and multi-year ahead. There are three main physical participants; generators, transmission buses, and transmission lines; and seven decision making participants; consumers, demand agents, distribution companies, generation companiesand transmission companies, Independent System Operators, Regional System Operators, and Regulators. It uses a special tool called VALORAGUA, to model hydro plants generation in an hourly schedule. There are also two approaches to model wind generation, deterministic based and stochastic based. Moreover, observing the effect of alternative smart grid options is possible. It also shows the effectof smart grid options on electricity prices, operation of the plants, consumption and green gas emission. Bilateral contracts in the markets, ancillary services and spot market structures can be integrated to the model as well. Therefore, long term integrated resource planning up to 50 years is promising with this tool. In long term planning, it identifies type, location and working schedule of the new technology as an investment option. The primary objective is to maximize the profit and to minimize the cost for the individual participants of the system Retrieved September 7, Retrieved September 8,

36 III. GTMax Argonne National Laboratory developed Generation & Transmission Maximization (GTMAx) in GTMax have been used by the World Bank, the European Union and the U.S. Agency for International Development (USAID) and recommended as a useful tool to model regional interconnection, electricity market analysis, and generation & transmission planning. It is used in liberalized and regulated electricity markets and driven by LINGO solver in the latest version 5.6. Thermal power plants can be modeled in detail in this tool by integrating their min/ max hourly output, hourly up and down ramp rate restrictions, start-up and shut-down costs, minimum up/down times, and daily change constraints. GTMax can optimize the generation schedule of hydro/thermal power plants. It also has detailed hydropower modeling including, run of river, storage units, and pumped storage. 48 IV. UPLAN LGG Consulting Energy developed UPlan in The software has different modules : UPLAN-NPM (Network Power Model), Wind, UPLANG for the gas industry, PLATO, and UPLAN MAM( Multi Area Model). It is used in Asia, North America and Europe to model different market structures. Within the software, it is possible to make regional trades and to model ancillary services. It is employed by Federal Energy Regulatory Commission (FERC), Natural Research Environment research Corporation (NERC), the Department of Environment in the United States of America, the World Bank,USAID, and the European Bank for Reconstruction and Development (EBRD). It can be used in the hourly, day-ahead, week-ahead, month-ahead, year-ahead and multi-year ahead markets. UPLAN has a merchant plant model. Merchant plant model can decide the location, size and timing of a new plant in generation expansion planning. The electricity network system can be also modeled by this tool. The software can incorporate alternating current(ac) power flow to the system as well which is only applied in this software.risk & error analyses can also be applied Foley A.M., Gallachoir B.P.Q., Hur J., Baldick R., McKeogh E.J., 2010, p: Retrieved September 8,

37 V. WASP Wien Automatic System Planner was developed in 1973 by thetennessee Valley Authority and Oakridge National Laboratory with maintenance by the International Atomic Energy Agency. The main purpose was to perform long term generation expansion planning forelectricity systems. It has users in more than 100 countries and has been used for academic purposes and real projects. The World Bank uses the tool especially for long term capacity expansion planning. WASP works for medium to long term up to 20 years and long term demand projections can be also made. The plant types can be specified within the tool according to fuel types and technologies. It was actually created to model monopoly electricity markets; however, it has been used to model liberalized electricity market systems in different studies as well to make long term generation expansion planning. 50 VI. WILMAR RISØ National Laboratory with its partners created Wind Power Integration in Liberalized Electricity Markets (WILMAR) in The software was developed as a part of the European Commission fifth framework project with the aim of modeling the integration of wind energy generation from short term to medium term in liberalized electricity system. It was planned to apply in Denmark, Finland, Germany, and Norway with hydro power generation planning. It works for day-ahead and intraday market structures since the countries have this type of electricity market systems. The software makes wind energy generation projections. There are different sub-tools and databases such as a scenario tree tool,scheduling model, and a long term model within WILMAR. Each database needs different information to forecast energy generation. For example, scenario tree tool model needs wind speed, electricity demand in the past, load projections and outage data. 51 VII. EFOM-ENV Energy Flow Optimization Model-Environment is dynamic optimization model and employs linear programming. It can be used to model different energy sectors as well as electricity markets. The model represents energy producing and consuming sectors in each state or province. It optimizes the development of these sectors according to the fuel prices and usefule energy demand through a pre-defined time horizon. The development of national 50 Foley A.M., Gallachoir B.P.Q., Hur J., Baldick R., McKeogh E.J., 2010, p: Ibid. 31

38 energy systems can be dependent on energy and environmental constraints such as availability of fuels, penetration rates of technologies, emission standards, and emission ceilings. The model databases include various type of conversion and end-use technologies such as conventional technologies, renewable energy technologies, efficient fossil fuel burning technologies, combined heat and power technologies, and energy conversion technologies in the demand sectors. The main aim of this software is to make energy and environment policy analysis and planning to reduce pollutant emissions. 52 VIII. MARKAL Market Allocation Model (MARKAL) was created in the late 1970s at Brookhaven National Lab as a reaction to the oil crisis. In 1978, the International Energy Agency, with the adoption of MARKAL, developed the Energy Technology and System Analysis Program (ETSAP). The U.S. Department of Energy s adopted MARKAL as a basis of the Analysis of Global Energy Markets(SAGE) model. With all these applications, MARKAL is used almost 40 countries all around the world. MARKAL is an energy system optimization model which is data-driven. Resource supplies, energy conversion technologies, end use demands, and the technologies are uploaded to the software by user. To specify each of the technology, fixed and variable costs, technology availability and performance and pollutant emissions must be also provided by user. According to all the inputs, software calculated the least cost set of technologies over time for the specified demand. It uses straightforward linear and mixedinteger linear programming techniques.the other outcomes of the models area determination of the technological mix at intervals into the future, estimataion of total system cost, energy demand, estimation of criteria and GHG emissions, and estimation of energy commodity prices. 53 IX. TIMES The Integrated MARKAL-EFOM System (TIMES) is used by the Energy Technology Systems Analysis Programme (ETSAP). It implements the agreement of International Energy Agency. The software includes all the advanced characteristics of MARKAL and some of EFOM. TIMES is a tool can be used to model local, national or multi-regional energy systems. It can be applied to analyse entire energy sector as well as single sectors such as 52 Office of theprincipalscientificadvisergovernment of India, 2006, p: Retrieved 1 November,

39 electricity. TIMES is a deterministic linear programming model generator and it computes a dynamic inter-temporal partial equilibrium on integrated energy markets. Energy service demands, resource potential and costs, policy scenarios, different technologies and their costs are the key inputs. The software is mainly applied for medium and long term modeling but it can be also possible to use for the short term. In the TIMES model, the electricity generation sector is characterized according to the voltage levels of lines, transformers, and power plants. Within TIMES, most of the renwable energy technologies are treated with 100% efficiency and the availability of the source is integrated as capacity or activity constraints. 54 X. PLEXOS Primarily, Glen Drayton created PLEXOS for Power Systems to model electricity systems. Now, Energy Exemplar has the patents of the software. Energy Exemplar branches are located in Australia, England and United States. Originally, AMMO was the solver but now MOSEK and Xpress-MP, with databases in Microsoft Access and XML data formats, are used as a slover. The latest version is PLEXOS in the market and in this study this version was used. It is a linear, mixed integer model which uses quadratic optimization and game theory which is based on Nash-Cournot (Hobbs) and Bertrand techniques. The market mechanism works with different pricing approaches such as locational market pricing, regional or uniform pricing, generator and load settlement. Different contracts in transmission rights or power purchasing issues can be also associated with the program. PLEXOS has varied clients from around the world,especially in the USA, where it is preferred by electricity market participants.it is used in 15 countries all around the world. Moreover, it has also been used in studies submitted to the Federal Energy Regulatory Commission. PLEXOS models thermal, hydroand renewable generation facilities, transmission systems, and ancillary servicesaccording to different regions. Daily market modeling can be done in hourly schedule. Long term generation expansion planning and portfolio optimizations are also possible in PLEXOS. It needs different parameters to integrate different generation sources to the model. The thermal generation facilities need the number of units of the plant, heat rate functions, fuel properties, fuel price escalations, emission characteristics of the fuel and taxes. The number of the constraints can be increased with other operating features. The software gives the opportunity to select necessary constraints for each model. Therefore, it would depend on the user to decide which constraint must be used in the model. The previous version of the 54 Deane J.P.,Chiodi A., Gargiulo M., Gallachóir B., 2012, p:306 33

40 software, PLEXOS 4.0, was able to make generation expansion planning only for 1 year lookahead period. In the latest versions of the tool, PLEXOS 5.0 and 6.0, capacity expansion planning can be modeled in the longer period. This period generally is expressed between 10 to 30 years. However, longer term expansion planning is probable with shorter step sizes. It uses Mixed Integer Planning, which minimizes net present value of the total cost of expansion and production, to solve long term expansion problems. Different constraints can be incorporated to the transmission module. Hydro and renewable modeling is quite different than thermal power plants modeling in this software. In hydro modeling, there are different options to model big dams. One of them is the head storage method which requires natural inflow for the specific periods. Wind, solar and run off river technologies are modeled considering their locations and installed capacities. According to the potential of the location and installed capacity of the facility, hourly electricity generation can be integrated.in PLEXOS, CO 2 emission production, trading and pricing can be also included. 55 Modeling can be done according to different time scales, from short-term (less than a year) to medium term (1-5 years) to long term (1-40 years).depending on the time scale simulation uses four different phases. LT plan PASA MT Schedule ST Schedule LT plan optimizes the combination of generation expansion facilities by minimizing the net present values of the total costs of the system to expand generation facilities. It generally works for between 10 to 30 years. LT Plan runs before the PASA, MT & ST Schedule. LT Plan can run separately or with other phases as well. PASA schedules maintenance events for MT & ST Schedule and computes the reliability of the system. MT Schedule models the electricity market in medium to long term. Especially, hydro planning, fuel supply for thermic power station and emission constraints cannot be analyzed in the short term. Therefore, the software needs to run with MT Schedule to take into account effects of hydro planning in a long term. 55 Foley A.M., Gallachoir B.P.Q., Hur J., Baldick R., McKeogh E.J., 2010, p:

41 ST Schedule is based on chronological optimization. It is mixed-integer programming. Unlike MT Schedule & LT Plan, ST Schedule models the electricity system market at the full resolution of the horizon. Generally, the horizon length is chosen as an hour and it gives the hourly market price in the chosen length. However, the horizon length can be adjusted shorter like 5-minute intervals Decision Process In this study, the software is required, could make detailed short, medium, and long term modeling. Different renewable energy technologies, their generation modeling, and different thermal technologies integration are also necessary. In addition, short and long term market prices could be calculated. Different capacity expansion options could be integrated and the software could choose the most profitable investment opportunities according to the variables for the future modeling. The calculation of CO 2 emission production and CO 2 price mechanisms are other features essential to observe the possible effects of these mechanisms. The results should be available in the required period. In the short therm analysis, they must be available hourly and monthly; whereas, in the long term analysis annualy data must be existing. All these characterics are available in PLEXOS and its features match with Turkish electricity market structure and long term expected developments. Therefore, PLEXOS is applied in this study to model Turkish electricity market and its future investment opportunities. ST & MT Schedules ware used to model the past, & 2012, for backtesting and LT Plan is used to decide future investment opportunities along with the market prices according to the model in Turkey. 3.3 Modeling of Turkish Electricity Market in PLEXOS To make enhanced long term expansion decisions and future modeling, an accurate system model is needed. System models should be able to work close to the reality and model result should be comparable with real data. Therefore, it is crucial to make back-testing to have accurate electricity system to make future projections in PLEXOS. 56 PLEXOS Documents, 2012, p:3-4 35

42 Naturally, in this study data management was quite important. Therefore, the first step was to collect the data from the previous years. Since the liberalization period and the new electricity market structure do not have a long history in Turkey, it was decided to start back-testing from 2010.Detailed system data was needed at the beginning of the study to build the model. Monthly electricity generation data and yearly installed capacity data were collected through multiple institutes and companies, working with the Turkish electricity market,. These organizations are thanked in the acknowledgement section. In PLEXOS, each electricity generation source is modeled with different constraints. Therefore, to integrate each technology different information and techniques were used. These techniques were detailed below according to each technology Modeling of Thermal Power Plants A thermal generator in PLEXOS works with the constraints of unit number, fuel, heat rate function, and rating/rating factor. Unit number defines the number of generating units in the particular generation station. Specific dates can be determined for the unit number, and desired time schedule for the specific generation station can be adjusted. Fuel is used to determine the fuel type of the plant which works with different technologies and different types of fuel such as natural gas, coal, fuel oil. Fuel constraints help to specify the price of each different fuel for each technology. Heat rate function is linked with the efficiency of the generation facility. The unit of the heat rate function is determined GJ/MWh in PLEXOS. Efficiency of the power plants is described with percentage. Therefore, unit conversion generally has to be made to fill up this constraint. Rating/rating factoris used to define the availability of the power plant, in other words, capacity factor of the power plant. Rating factor can be calculated for the desired period in the time horizon. It could be daily, monthly or yearly. In this study, each thermal plants capacity factor was determined monthly. In the Turkish electricity system, there are different thermal power plants work with different technologies and different fuel types. Natural gas, fuel oil, import coal, lignite, hard coals are used in thermal power plants to generate electricity. To model each type of power plants, different fuel prices, different capacity factor calculations and different heat rate functions had to be obtained. 36

43 Efficiency (%) Modeling of Natural Gas Plants To model natural gas power plants properly, the efficiency of each plant had to be obtained to calculate the heat rate function. As a first step, the list of the natural gas plants in Turkey was acquired and the efficiency value of the each power plant was obtained. The efficiency values of some power plants were available online and some were obtained by personal communications with the company. To estimate the missing values, an efficiency learning curve based on years, in which they were installed, approach was used to reach realistic values. This curve is shown in Figure 3.1. A detailed list of natural gas power plants & their efficiency values can be seen in Appendix B1. To make the list, several assumptions had to be made. All the auto producer power plants were counted as one large plant and one efficiency value was obtained for all of them. The other assumption made for the new generation facilities in each year, 2011 and Since according to learning curve approach generation facilities which would build up at the same year must have the same efficiency value, the new generation facilities were also assumed as one big power plant Figure 3.1 : Natural Gas Power Plants Efficiency Learning Curve Jugeler K., 1999, p:53 37

44 To convert the efficiency values to the required unit in PLEXOS, the efficiency value was divided to 3.6. Sample calculation of heat rate function Given:. Another constraint is rating factor, availability of the plant for the selected time length. During the data collection period, the amount of generated electricity according to each source in a monthly basis was obtained from the related organizations. Installed capacity of the natural gas power plants for each year was also identified as shown below in Table 3.1 according to their commissioning years.multi fueled power plants, solid-liquid & liquidnatural gas, were also included in this capacity. Table 3.1 : Installed Capacity of Natural Gas Plants Year Installed Capacity of Natural Gas Plants (MW) Since the exact dates, in which power plants start to generate electricity, could not be obtained accurately, it was assumed that installed capacities, would work during the whole year. According to these assumptions, rating factors, their availability, were calculated monthly for three years. The calculation of the rating factor in January, 2010, is shown below as an example. The price of natural gas was obtained from BOTAS (Petroleum Pipeline Corporation). BOTAS is the aunthority in the natural gas market to import, distribute, sale and price in Turkey. In PLEXOS, the unit of the price is $/GJ. However, in BOTAS website, prices were 38

45 published in TL/kWh. Therefore, the currency conversion had to be made. The currency was acquired from Turkish Central Bank. Since the natural gas prices are published monthly by BOTAS, the monthly average currency rate was used. Detailed price list and monthly average currency can be seen in Appendix B2. Unit conversion for the natural gas pricing in January, 2010 is shown below as an example. Sample Conversion of Natural GasPriceforJanuary 2010 Given ( ) ( ) ( ) ( ) Modeling of Coal Power Plants There are lignite, import coal, hard coal and asphaltic power plants, contribute considerably to the Turkish electricity market. To model these power plants in PLEXOS, the same constraintsused in natural gas power plants were needed. To obtain efficiency values of these power plants the same procedure was followed. A detailed list of the coal power plants and their efficiency values can be found in Appendix B3 section.to calculate the rating factors thesame procedure as natural gas plants was used. The assumptions,were also valid in this section. Installed capacity of coal power plants is shown in Table 3.2, 3.3 &

46 Table 3.2 : Installed Capacity of Coal Plants Year Installed Capacity (MW) Lignite , , ,174.7 Import coal , , ,881 Hard Coal To model asphaltite power plants in Turkey, the same procedure was followed. The installed capacity is only 135 MW and the average rating factor was calculated as 82%. Since lignite and hard coal are domestic products and import coal is bought from outside of the country, prices of these products are quite different from each other. Their calorific values are varied as well.the calorific values of the coals are essentialto convert the units and costswere different for each type of coal. Lignite pricing: The calorific value of lignite is assumed as 4165 kcal/kg which is internationally accepted for lignite. 58 The calorific values of lignite are different for each reserve in the country; therefore, this value was taken as average. The average price in Turkey was around 80 $in Sample calculation of Lignite price in 2012: = ( ) ( ) 58 Retrieved May 10, Retrieved May 11,

47 For the previous years, the price information was not available. Therefore, according to 7% average inflation rate per year, lignite price was calculated for 2010& 2011 as 4.04 $ / GJ and 4.33 $ / GJ, respectively. Import Coal Pricing: To calculate the price of import coal the European Association for Coal and Lignite (EURACOAL) standards and prices were used. The calorific value of the coal is accepted as 7000 kcal/kg. 60,61 A detailed price list can be seen in Appendix B4.The same unit conversion in lignite pricing was followed to calculate the import coal price in $ / GJ. Hard Coal Pricing: The calorific value of hard coal in Turkey varies between 6200 and 7200 kcal/kg. Accordingly, hard coal was calculated with the same method Modeling of Fuel Oil Power Plants To model fuel oil plants in PLEXOS, the same constraints which were valid for other thermal power plants were required.the Rating factor calculation was done in the same way as the other thermal power plants. Since most of the fuel oil plants work under the responsibility of EUAS, the efficiency values of these plants were mainly obtained from EUAS. BP Statistical Review of World Energy 2013 report was used to attain fuel oil prices for the period. 62 Monthly price list can be seen in Appendix B5. The calorific value of fuel oil isaccepted as 9200 kcal/kg which was taken from The Energy and Fuel Data Sheet published by the University of Birmingham, UK. 63 Installed capacities of fuel oil plants in the period modeled as seen below in Table European Association for Coal and Lignite, European Association for Coal and Lignite, BP, 2013, p:14 63 Staffell I.,

48 Table 3.3 : Installed Capacity of Fuel Oil Power Plants Installed Capacity of Fuel Year Oil Plants (MW) Modeling of Biomass & Geothermal Power Plants In PLEXOS, biomass & geothermal power plants work with the same principles asthermal power plants. Therefore, rating factor & efficiency values of the power plants were requiredto model them. Since the monthly generation values were already available, the rating factors were calculated in the same way according to yearly installed capacities which are shown below in Table 3.4 and 3.5. Monthly electricity generation values and installed capacities were provided by EUAS. Table 3.4 : Installed Capacity of Geothermal Power Plants Year Installed Capacity of Geothermal Plants (MW)

49 Table 3.5 : Installed Capacity of Biomass (Renewables+Waste)Power Plants Installed Capacity of Year Biomass Plants (MW) Modeling of Run-of River HydroPower Plants Hydro run-of the river power plants generate electricity according to seasonal river flows. Since they do not have any storage or any regular flow regime, it is quite hard to model them in such software. In PLEXOS, to model renewable energy sources, which do not have constant electricity generation, the hourly generation profile can be created and uploaded to the software. As run-of river plants are an appealing investment option in Turkey, creating hourly average profiles was needed both to model the past and for the future analyses. To create the profile, hourly generation values in one year were collected from three run of the river power plants. These three plants are located in different regions of the country.accurate modeling of run-of river generation in such a model is challenging. With these different generation values, the variety in the generation was provided. However, this profile might lead to some errors in the model but these three regions do provide a reflection of different genn profiles. By extending their generation values according to total installed capacity in Turkey, the average electricity generation profile was created and uploaded to PLEXOS. Since each year the installed capacity of run-of the river plants has increased in Turkey, each year new generation profiles were uploaded to the model. In PLEXOS, the installed capacities of run-of river plants are shown in Table 3.6 below between 2010 & 2012.According to this; unit, maximum capacity and hourly rating values of the plants are chosen as constraints to model run-of river power plants in the software. 43

50 Table 3.6 : Installed Capacity of Run-of River Power Plants Installed Capacity of Run Year of the River Plants (MW) Modeling of Wind Power Plants Modeling of wind power plants in PLEXOS has the same challenges with run of the river plants since they both do not have regular generation, storages or any reliable forecasted value to estimate electricity generation from these sources. The same method to model run of the river power plants was followed to model wind plants. The electricity generation data was collected from three different wind power plants and their generation values were averaged and extrapolated to the total installed capacity of the country. Same challenges in run-of river modelling were also valid wind power plants. The assumptions and possible errors are subject in this section as well. The installed capacity values in the modeled period can be seen in Table 3.7. Table 3.7 : Installed Capacity of Run of the River Power Plants Year Installed Capacity of Wind Power Plants (MW) The average country profile is also required for future modeling in the software. Since Turkey has high wind potential, it would be definitely essential to have the hourly generation profile of the country to make capacity expansion planning of this source. 44

51 3.3.4 Modeling of Hydroelectric Dam Power Plants Turkey generates almost 30% of its electricity from hydro power plants. 64 Dams have a higher share than run-of power plants in this generation. It is crucially significant to have proper hydro-dam modeling in the software due to the large in electricity generation. In this study, head storage method was applied to model dam geneartions. In the head storage method, seasonal natural inflow (in MW)for each generation facility and their storage capacity (in GWh) are needed. Since any available detailed data regarding with each dams could not be obtained, some assumptions had to be made to calculate the natural inflow. There are four big dams,altınkaya, Atatürk, Karakaya&Keban, which have installed capacity more than 600 MW and generate a significantamount of electricity. It is also easier to reach detailed data related with these dams. On the other hand, there are many dams with smaller storage for which detailed informationis not available. Therefore, it was assumed that there would be 5 big dams in Turkey in The remaining dams were modeledas one big dam called others. Dams, which were installed in 2011 & 2012, were called Dam2011 & Dam2012 in the model. According to this, the installed capacities of dams in the system are shown below in Table 3.8. Table 3.8 : Installed Capacity of Hydro-Dams 65 Name of the Dam Installed Capacity (MW) Altınkaya 700 Atatürk 2400 Karakaya 1800 Keban 1330 Others 6905 Dam Dam TEIAS, 2012, p: Retrieved May 8,

52 There are some parameters which can be used to calculate storage capacities & natural inflows for each specific dam. To calculate the storage capacities, the equation below was used. ( ) ( ) ( ) In this equation; ( ) ( ) ( ) ( ) As it can be seen from the equation, to calculate energy capacity of the dam, the mass of water which could be stored in the pool must be calculated. Therefore, the volume of the pool must be known. In addition, height of these dams should be also identified to for the calculation. For the specific dams, this information was obtained from the State Water Supply Administration (DSI) web site. In the web site, the height of the dam identifies height of the dam from the bottom until the top. However, to be able to generate energy some amount of water must be stable at the bottom of the pool. Therefore, in the calculations the height was multiplied with 0.67 to take into account this effect. Turbine efficiency was accepted as 90%. This is the average value for the turbine efficiencies in dam systems. 66 In Table 3.9, storage capacities of dams, according to their pool volume and height,are shown. Table 3.9 : Storage Capacities of Hydro-Dams Name of the Pool Volume Height Storage Capacity Dam (hm 3 ) (m) (GWh) Altınkaya Atatürk Karakaya Keban Others** Dam2011** Dam2012** Eurelectric, 2003, p:12 46

53 After determining the storage capacities of the plants, the next step was to upload monthly natural inflow regimes for each one. The monthly generation values from all the dams for the period, , were obtained from the related institutes.moreover, thenatural inflow, which was collected at the entire dams, on monthly basis was also reached. The next concern was to distribute this natural inflow to each dam. To do this, annual generation values of Altınkaya, Atatürk, Karakaya, Keban were found. 67 Since the yearly total generation value wasknown, generation of Others, Dam2011 & Dam2012 were calculated by subtracting generation of these four dams. Average yearly generation values of each dam areshown below in Table According to these annual generation values, total monthly natural inflow was distributed to each dam. Table 3.10 : Annual Generation of Hydro-Dams Name of the Dam Annual Generation (GWh) Altınkaya 1.63 Atatürk 8.90 Karakaya 7.50 Keban 6.30 Others Dam Dam Retrieved May 8,

54 3.4 Introduction to Scenario Development After completing back testing, the next step is to model the electricity market until Prior to modeling, different scenarios must be developed to determine uncertainties and the effect of these parameters. Therefore, scenario structuring must be summarized to construct different scenarios. Peter Schwartz defined scenario in his book The Art of the Long View : 68 "A tool for ordering one s perceptions about alternative future environments in which one s decision might be played out. Alternatively: a set of organized ways for usto dream effectively about our own future. Concretely they resemble a set of stories, either written out or often spoken. However, these stories are built around carefully constructed "plots" that make the significant elements of the world scene stand out boldly. This approach is more a disciplined way of thinking than a formal methodology." Scenario structuring and its methods need strategic management and an organizational learning tool. 69 Strategic management is necessary for composing realistic stories in the scenario time line. The Organizational learning tool is significant to determine the scenario constraints and to mark different predictions for their future. Scenarios have become essential since the effects of climate change are starting to be felt with changing weather temperatures and natural disastrous. It is important to point out that scenarios are distinct from forecasts. In forecasts, uncertainties are predicted in the most probable way based on current trends. On the other hand, scenarios present several different but possible results according to the identified uncertainties. 70 In this case, the aim to develop different scenario storylines is to explore possible outcomes from different perspectives to construct future energy systems. 71 Long term energy scenarios are generally developed based on current problems, challenges and trends. Scenarios are helpful to make cause-effect declarations. In the energy sector, scenarios are generally made in the long-term to reduce uncertainties. A scenario explores uncertain set of future conditions such as fuel prices, electricity demand, and technology costs. Scenario analysis develops different strategies and future indicators as well. 68 Schwartz P, Ghanadan R., Koomey G.J., 2005, p: Bertsch V., 2013, p:4 71 Bertsch V., 2013, p:5 48

55 Scenarios can have different target groups and perspectives. They can be developed for policy makers, companies, investors or regulators. From the point of policy makers, resource security, sustainability and security of supply can be the priorities and main objectives. On the other, if the scenarios are developed for companies, profit maximization, risk minimization, securing of long-term market share can be the main targets. From the governmental point, the most essential target can be to be able to supply the demand. Using different scenarios in energy markets, decision makers obtain useful information regarding with future. Therefore, scenarios must be imaginable to attain possible impacts of this information. There are six step of a scenario construction process.the first step is to determine the key parameters for possible scenarios. These parameters will decide the trend of the future system. These parameters must be important and uncertain in the intended time period. The second step is to connect key parameters into a logical framework. It is vital to link the critical drivers together consistently to achieve more feasible results. Group drivers are meaningful when they are combined according to their charaterisctics.the third step is to construct mini scenarios; developing a different scenario for every possible outcome for each parameter. The fourth step is to reduce the mini scenarios to a maximum of four main scenarios. The number of mini scenarios are decreased according to their importance considering the main objective of scenario construction. The fifth step is to further develop the main scenarios around these mini scenarios. The scenarios must be developed in the most suitable way to be used by planners and managers who benefit from these scenarios to plan future strategies. The last step is to identify the most critical outcomes and issues which could have biggest influence on the future planning. 72 To model wholesale market prices and decide feasible investment opportunities, key uncertainties have to be related with electricity generation and the factors which can affect electricity generation cost and market prices. To develop different scenarios to model the Turkish electricity market, the key factors were determined as fuel prices, environmental policies, renewable expansion targets and policies, renewable energy technology prices, electricity demand.according to different mini scenarios of these drivers, four main scenarios were constructed to make the future analysis in Turkish electricity market. The scenarios were modeledin the PLEXOS according to their parameters. Electricity market prices and installed capacity were calculated for each scenario and the results explored in the Section 5.1.Scenario Analyses. 72 Bertsch V., 2013, p:10 49

56 4 Results of Back Testing In the methodology section, modeling of each generator type was explained according to different constraints and properties. In this section, back-testing will be analyzed to verify the accuracy of the model for the future projection. Besides the parameters mentioned in the methodology section, hourly electricity demand was also required in PLEXOS to calculate market prices. Hourly electricity consumption values for the determined period, , were acquired from PMUM. 73 Each year was considered as one system and each run was done for one year. The results from three years were compared with the real values. To achieve amodel close to theactual market, the process was repeated,adjusting the rating & rating factors. Monthly generation values and average market prices were analyzed according to these runs. 4.1 Backtesting Results of 2010 As most of Turkish electricity is met by natural gas, lignite and hydro-dams, the detailed comparison is madebetween the historical data of these and model results. In 2010, according to the data obtained from 47% of Turkey electricity consumption, which is 98471GWh, was met by natural gas power plants. The comparison between the historical and PLEXOS monthly generation can be seen below in Figure 4.1.Natural gas monthly generation from model results and historical data are almost equally each other as it can be seen in Figure Retrieved April 30,

57 Generation (GWh) Generation (GWh) 10,500 10,000 9,500 9,000 8,500 Hist. Natural Gas 8,000 7,500 Model Natural Gas 7,000 6,500 Figure 4.1 : Comparison of Natural Gas Generation in 2010 Another important share in electricity generation belongs to lignite in % of electricity consumption, GWh, was generated from lignite plants. The comparison can be seen below between the historical and model results in Figure ,000 3,800 3,600 3,400 3,200 3,000 2,800 2,600 2,400 2,200 2,000 Hıst. Lignite Model Lignite Figure 4.2 : Comparison of Lignite Generation in

58 Generation (GWh) Generation (GWh) 5,000 4,500 4,000 3,500 3,000 2,500 Hist. Hydro Dam Model Hydro Dam 2,000 Figure 4.3 : Comparison of Hydro Dam Generation in % of electricity consumption, GWh was generated from hydro dam power plants in Due to the considerable contribution of hydro power, accurate hydro modeling was one the key factors in back-testing. The comparison of the model results with the historical value can be seen in Figure 4.3. The detailed graph comparison of other generators in 2010is shown below in Figure ,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 Hist. Sum Model Sum Figure 4.4 : General Comparison in

59 Generation (GWh) According to the historical data, GWh electricity was generated in Turkeyin PLEXOS results shows that GWh electricity was demanded and needed to generate. The reason for this difference comes from the assumption that electricity trading with neighboring countries was ignored.in 2010, historical monthly electricity generations of natural gas, hydro dam and lignite had the same fluctuations and too close generation values with the model results. The same matching can be seen in Figure 4.4 for other generation sources as well. 4.2 Backtesting Results of 2011 With the new generators which were built in 2011, the software was run for According to historical data, 46% of the electricity production, GWh, was generated from natural gas plants. Monthly comparison can be seen in Figure 4.5 below. 10,500 10,000 9,500 9,000 8,500 8,000 7,500 7,000 6,500 6,000 Hist. Natural Gas Model Natural Gas Figure 4.5 : Comparison of Natural Gas Generation in % of electricity generation, GWh, was provided from lignite power plants in Detailed monthly generation comparison is shown in the Figure

60 Generation (GWh) Generation (GWh) 4,000 3,700 3,400 3,100 2,800 Hist. Lignite Model Lignite 2,500 Figure 4.6 : Comparison of Lignite Generation in 2011 Hydro dam plants generated 18% of electricity demand,41363 GWh in Turkey in Monthly generation distribution can be seen in Figure ,400 3,900 3,400 2,900 2,400 Hist. Hydro Dam Model Hydro Dam 1,900 Figure 4.7 : Comparison of Hydro Dam Generation in 2011 General comparison including all the sources in 2011 is also shown below in Figure

61 Generation (GWh) 110, ,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 Hist. Sum Model Sum Figure 4.8 : General Comparison in 2011 According to the historical data generated electricity was GWh; while, it was calculated as GWh by the software. The reason for this difference could be the same fact which was mentioned in the analysis of 2010 section. In 2011, historical monthly electricity generations of natural gas, hydro dam and lignite had the same fluctuations and too close generation values with the model results. The same matching can be seen in Figure 4.8 for other generation sources annually as well. 4.3 Backtesting Results of 2012 At the end of 2011, the electricity market structure was changed to Day-Ahead Market System, the effects of which were seen on the electricity generation 44% of the electricity consumption, GWh, was generated from natural gas plants. The actual data and model results for electricity generation from natural gas plants is shown below in Figure

62 Generation (GWh) Generation (GWh) Hist. Natural Gas Model Natural Gas Figure 4.9 : Comparison of Natural Gas Generation in 2012 Hydro dam power plants generated 19% of electricity consumption, GWh, in Turkey. Data comparison between the actual & model results can be seen in Figure 4.10 for hydro dam generation. 6,000 5,500 5,000 4,500 4,000 3,500 3,000 2,500 2,000 Hist. Hydro Dam Model Hydro Dam Figure 4.10 : Comparison of Hydro Dam Generation in 2012 General comparison in 2012 can be also seen in the Figure 4.11 below. 56

63 Price($/MWh) Generation (GWh) 110, ,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 Hıst. Sum Model Sum Figure 4.11 : General Comparison in Analysis of Market Prices In the back testing, a market price comprasion made as well to validate the model. The annual market price comparison between the historical data and model can be seen in Figure 4.12 below $ 72.01$ $ Figure 4.12 : General Comparison of Market Prices in 2010,2011&2012.Each years calculations based on USD average for that year. Hist. Model 57

64 As explained in the history section 2.1, the Turkish electricity market has been undergoing liberalization for a couple of years and it has not yet been completed. The majority of the installed capacity is still owned by EUAS with 36.5%, These power plant have not yet been generating power according to the day-ahead market principle. Most of the big hydro dams and some of natural gas plants in the country belong to this group and their opration does not consider the market conditions. On the other hand, PLEXOS simulates a 100% competitive market and always optimizes the least costly approach to meet the demand. This characteristic of the software does not yet match the Turkish electricity market completely. Since only monthly generation values could be obtained from the market, capacity factor and generation profile of the power plants were done monthly. However, when it comes to matching the peak demand, the software may have used different power plants than the reality according to its working principle. Therefore, some prices differences between historical data and market prices were experienced. There are other issues in the Turkish electricity market which could have affected the results of the whole period. There are long term contracts between Build Operate & Build Transfer Operate power plants and TEIAS to purchase their generation. According to these contracts, their electricity generation is always in the system even if these generations are not the most economical ones. Some of these power plants generate electricity from fuel oil. Considering the fuel oil prices and the efficiency of these power plants, their generation can not have the least generation cost. Another challenge throught the back-testing process was to obtain fuel oil prices. Generally, there are special contracts which are mentioned different prices to purchase the fuel between the supplier and the power plants. In this study, it was not possible to obtain these prices exactly and the international values were used. The same issue also arose for lignite and import coal power plants. It was not also possible to find the exact price in these contracts. Some price assumptions had to be made in the model. These price assumptions and the long term contracts could explain some of the price differences seen in Figure 22. Day-ahead market structure was implemented in the Turkish market at the end of It can be said that this structure does not have a long history as well. Considering that the hourly market prices mechanisms were developed in 2009 and the day-ahead market was implemented at the end of 2011, the accurate supply-demand planning can not be mentioned 58

65 yet. Since the electricity market prices are also affected by supply-demand balance, the effects of this were seen on the market prices in last years as well. 4.5 Analysis of Back-Testing Back testing is always important part in this type of study since future modeling are done by using the results obtained from back-testing and it validates the model. Therefore, the results of the back-testing must be accurate with the actual data. At the end of the back testing, capacity factors and generation profiles of wind and run of river plants for each year were acquired. Natural inflows for the dam storages were determined monthly. Assumptions were validated to tell if they will be also valid in future modeling. Historical hydro, natural gas and lignite generation values mainly matched with the model results. Generations from other sources compared annually and the historical and mosel results were mostly equal to each other. The generation balancebetween historical values and model results is one of two key drivers to validate the models in this type of studies. The another driver is the market prices. Model market prices were not having the exactly same trend with the historical data. The reasons for these differences were explained in the Section 4.4. Until the liberalization is completed, 100% competitive electricity market can not be mentioned for Turkey. The difference in the market prices does not make the obstacle to validate the model. Therefore, it can be said that with the generation balance between historical data and market results and the fairly close market prices, the model was validated for the future analyses. 59

66 5 Long-Term Analyses According to Different Scenarios The back-testing was carried for three years periods and the results were explained in the Section 4.The hourly electricity market has only history for 3.5 year. Therefore, it was not possible to go beyond than 201. Since the software works as one year sytem, average values of these parameters in three years were calculated to create one year Turkish electricity market system in PLEXOS.In this process, average values were used to obtain more accurate model The software does Finally, one year system was obtained and moved to future projections. To answer the research question accurately, long term analyses was done until the end of To make detailed long term analyses, four different scenarios were constructed according to the key drivers of Turkish electricity market. According to the methodology of scenario construction which was explained in the Section 3.4., key drivers were determined in Turkish electricity market. These drivers were fuel prices, especially natural gas & import coal, environmental policies, renewable energy technology prices and expansion targets and electricity demand in Turkey. According to mini scenarios about these drivers, four scenarios were raised and structured. The report,long-term scenarios and strategies for the expansion of renewable energies in Germanyin the light of developments in Europe and in the world, published by Fraunhofer IWES, was used to obtain fuel price projections. In the report three different fuel price scenarios; high, middle, low, were developed to project future prices. The same report also provided CO 2 prices. These price projections are meaningful in the countries in which European Union Emission Trading System (EU ETS) is applied;whereas,it is not applied in Turkey yet. Since one of the main drivers was chosen as environmental policies, to observe their effect on renewable energy expansion, emission production and electricity prices, theywere applied in this study. International Energy Agency (IEA) scenarios were used to derive renewable energy technology prices. According to World Energy Outlook, they mainly worked on four different scenarios. 74 According to their scenarios, they forecasted technology costs according to 74 IEA, 2012, p:

67 different type of fuel and renewable energy sources. These costs were applied in this study as well. Since one of the main drivers was determined as electricity demand in Turkey, some assumptions had to be made to project electricity demand in each scenario. As it mentined, electricity demand has grown with 6% average in the last ten years. Based on this growth rate, different demand projections were made to be used in the scenarios. According to different future scenarios of these drivers, the main characteristics of four scenarios can be seen in Table 5.1 below. Table 5.1 : Scenarios with the Characteristics of the Key Drivers Scenario 1 Scenario 2 Scenario 3 Scenario 4 Name of the Clean Conventional The Restless Moderate Scenario Transition Development Years Development Demand Middle High Low Middle Fuel Cost Middle High Low High RE Technology Cost Low High Middle Low EU ETS Applied NA NA NA There were constant parameters which were applied in all the scenarios. One of these parameters was type of conventional power plants, which were the options, generation expansion candidates, to extend installed capacity during the period, their technology cost and their life time. Name of the technologies, their technology price, life time and thermal efficiency values can be seen in Table

68 Table 5.2 : Thermal Power Technologies Name of the Technology Capital Cost ($/kw) Life Time (Year) Thermal Efficiency (%) Combined Cycle Natural Gas Turbine % Natural Gas Turbine % Pulverized Lignite Combustion Power Plant Pulverized Import Coal Combustion Power Plant Innovative Clean Coal Power Plant % % % The first four conventional technologies have been applied to generate conventional power in Turkey and their current installed capacities were stated in the methodology section. The last technology in table, innovative clean coal power plant, is not yet in the market. According to expert opinion, this power plant is expected to be available in Therefore, the expected capital cost is much higher than other technologies. The availability was commenced in 2035 in the software. Since the total emission of each scenario will be discussed and EU ETS was also applied in this study, emission characteristics of the fuels were integrated to the program. Fuel types and their emissions are shown in Table

69 Price ($/GJ) Table 5.3 : Emission Characteristics of the Fuels 75 Name of the Fuel Emission ($/kw) Natural Gas 56 Import Coal 96 Lignite 101 Fuel Oil 74 Asphaltite 96 As explained in the Section 2.4, lignite, is one of the main energy sources in Turkey and still has capacity expansion potential. Therefore, lignite price projections in this analysis are essential to calculate future market prices and to decide new investment opportunities. Since it is a domestic source and the prices would not depend on the extrinsic factors, the future lignite prices were counted with the same prices in allthescenarios. These projections were made on based on the current inflation rate of the country, shown in Figure Figure 5.1 : Lignite Price Projections, USD Base Year DEVAK, 2010, p:12 63

70 Turkey does not yet have any installed nuclear energy power plants. The first nuclear energy power plant, 4800 MW and four units, installation is going to start in Mersin Akkuyu. According to the experts in EMRA, the first unit will be expected to be commissioned in The second unit is going to generate power in 2020 and the third unit will follow itin Agreements have also been completed for the installation of asecond nuclear plant. It is going to be built up in Sinop and the capacity will be 4800 MW with four units. The same procedure is going to be performed. The first unit is going to be on the board since 2024 and the installation is going to be ended in Since the date information comes from the experts in the market and agreements have been already signed, these installation capacities and the dates were taken into account in the all scenarios. According to this, the expected installed capacity of nuclear in Turkey is summarized in Table 5.4. Table 5.4 : Installed Capacity of Nuclear Energy Years Installed Capacity (MW) The information regarding with the installation of the new thermal power plants was obtained from the experts in EMRA as well. As the licenses have been issued and construction has already started, the experts were able to provide information about the characteristics and date in which the new thermal plants will be commissioned. Since all the agreements were already made, these power plants were also added Turkey electricity installed capacity in all the scenarios according to their first generation date. The list of power plants and detailed information are shown in Table

71 The construction of the new big hydro dam power station is also about to be completed. The name of this dam is Deriner and the installed capacity is going to be 670 MW with the expected annual production is going to be 2.1 TWh. 76 According to the experts,generation will commence in Therefore, this power plant and its characteristics were integrated to the model as a constant parameter for all the scenarios. Table 5.5 : Short Term Thermal Power Plant Installation Type of the Plant InstalledCapacity First Generation Thermal (MW) Date Efficiency (%) Import Coal Power Plant % Import Coal Power Plant % Import Coal Power Plant % Lignite Power Plant % Lignite Power Plant % Lignite Power Plant % Lignite Power Plant % Lignite Power Plant % Lignite Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Natural Gas Power Plant % Turkey does not yet have any large solar PV systems installed, however, 2013 has been witnessed to important developments in the solar energy in Turkey. The new grid capacity 76 RetrievedSeptember 10,

72 from different regions opened to install 600 MW solar PV systems in total and data measurement has been completed by potential investors. Now, EMRA is analyzing these measurements to decide the most feasible power plant options. According to experts in EMRA, the installation of this capacity will have been completed until the end of 2016 and there will be extra solar PV system installations due to unlicensed regulations until Therefore, 100 MW of solar installed capacity in 2014, 400 MW in 2015 and 300 MW in 2016 were also added to the Turkey electricity system in all the scenarios. According to all these parameters, four different scenario analyses were made with PLEXOS. 5.1 Analyses of Scenario 1-CLEAN TRANSITION Description of CLEAN TRANSITION Clean Transition depicts a world and Turkey with strong growth of renewable energy technologies and environmental awareness. Economic growth would takes place with higher acceleration and GDP higher than 5000$ at purchasing power parity,more than quadruple of today s value. These economic developments and population growth would also bring higher electricity demand in the country. The electricity demand would increase dramatically until the The electricity demand in the country has risen with 6% average per year since 1997.Based on this growth, in this scenario it is assumed that the demand would increase between 3-4% per year until 2025 and would develop between 1-3% until After this period of high economic growth, the economy stabilize.the effects of this stabilization would be seen on the electricity demand. With energy efficiency applications, the demand would be more stable after these years. The yearly expected demand of Turkey according to different years in this scenario is shown in Figure

73 Electricity Demand (TWh) Figure 5.2 : Electricity Demand of Clean Transition In this scenario, the main issue would be the environmental awareness not only by policy makers but also by public in the world and Turkey. The world would be experiencing strong natural disastrous such as destructive rains, tropical storms and serious droughts. These disastrous would be the proof that global environment is seriously in danger. Therefore, robust movements would take place for climate mitigation, environmental protection all around the world. These disasters would bring the idea that there must be fast technological developments to have environmental friendly policies. By the time, Turkey would be experiencing serious environmental problems with the economic growth as well. The new world trend would be to fight with these environmental problems. Turkey as a part of this new trend and would have emission reduction target. Green pricing schemes would also start to be applied. In 2025, Turkey would enter European Union and European Union Emission Trading System would be also applied in the country. The prices would be based on current prices. The carbon prices, which were applied in this scenario, can be seen in the Figure 5.3 below. By 2035, most of the countries would have adapted to emission control mechanisms and would have their own targets as well in this scenario. 67

74 Price $/ton Figure 5.3 : CO 2 prices 77, USD Base Year 2009 The world would become more transparent place with the improved communication services. The education level of people would rise and this would bring more globalized world. The trend would be dematerialization and less consumerism especially inthe Organisation for Economic Co-operation and Development (OECD) countries. Therefore; the manufacturing activities would be more labor intensive. Recycling would be more popular. The gap between developed & developing countries would shrink. The new partnerships would be developed between countries. These enhancements would make Middle East as a new investment area for developed countries and the role of Turkey would become more important due to its strategic location and growing economy. Turkey would be a bridge for the transmission of technological developments, energy transition and cultural interaction. These values would be the drivers of economic developments in the new world. By the time, because of the environmental changes, the electricity consumer profile in Turkey would be more aware of generation source of electricity and its effects on the environment Developed countries would take the action ofdeveloping new technologies to fight climate changeand environmental disasters. The countries with strong economy & companies with the knowledge would invest to renewable energy module developments such as solar and wind. Since knowledge transfer would be also fast in the world, the learning curve sdf estimate would prove accurately and solar PV module & wind turbine prices would go down as 77 Fraunhofer IWES, 2012, p:51 68

75 Price ($/kw) projected by the the IEA s Scenario NP 4 0. In the world, 376 TWh electricity would have been generated in 2020 and 1371 TWh in 2035 from solar energy and 1442 TWh in 2020 and 4281 TWh in 2035 from wind energy 78. According to the report, the estimated cost of solar PV systems& wind energy can be seen in Figure 5.4. These developments would make it appealing to invest in these technologies. Renewable energy incentives would be more popular all around the world until the technology prices are competitive with conventional sources. With the decreasing solar PV module, wind turbine prices and developing technology of battery storages, decentralized renewable energy systems would be a cost effective solution. This would help to complete energy access all around the world Solar PV System Cost Wind System Cost 1200 Figure 5.4 : Solar PV System&Wind Energy Capital Cost As described at the beginning of this section, the worldwide trend would be to invest green and smart energies. Since the manufacturers of these technologies would also need new markets to sell their products, they would also start to introduce the concept in Turkey after 2015s. They would make this introduction not only to the government but also to the public. As a bridge to the Middle East with historical connections, long standing business relations and shared culture, Turkey s location would be more strategic. Throught the period, like all around the world, Turkey would also experience some big natural disasters. Green gas emission would also rise. While the value was 3.39 ton/ capita in 1990, in 2010 it increased 78 IEA, 2012, p: 69

76 to 5.51 ton/ capita. 79 The average of the world was calculated as 4.6 ton/ capita in The country would have to have emission target and reduce its emission because of its commitmentsand its strategic relations between the countries. Since the biggest share of the emission belongs to the energy sector, the reduction would have to start with the energy sector. Environmentalists would also be active, insisting on a clean environment. They would also work to make the public more conscious about the environment. At the beginning of 2020, Turkey would be hosting solar PV modules manufacturer companies,which would be building their manufacturer facilities in Turkey to produce their solar systems with the advanced technology in the country. This would accelerate solar investments in the country due to the domestic renewable energy incentives and would create another productto trade with the Middle East. In addition, big manufacturers in the country would seek away to generate their own electricity from renewable energies, especially from solar energy to benift from the incentives of unlicensed electricity generation. There would be specific renewable energy installation targets. The appliance of EU ETS would also contribute to increase in renewable energy installation due to zero emission. Old and inefficient coal power plants would retire at the end of their life. They would be replaced by gas turbine combined cycles or renewables. Until 2030, the installation of two nuclear power plant (4800 MW+4800 MW) would have been completed. Full geothermal utilization is reachedat 0.6 GW. Hydro-utilization, 36 GW, would also be completed in the period. The utilization of hydro power stations would be more environmentally friendly due to meeting EU regulations. The price of fossil fuels such as natural gas, import coal & fuel oil would be at the middle level since the demand would be lower compared to the last decades due to increased renewable energy use. Moreover, there would not be any extra exploration researches. However, natural gas power plants would be still vital because of their availability and their flexibility. Turkey would also try to step back from natural gas. This would require longer period; therefore, installation of natural gas plants would start to decrease in The expected price of natural gas and import coal over the period are shown in Figure Retrieved 10 September, Retrieved 10 September,

77 Price ($/GJ) Natural Gas Import Coal Figure 5.5 : Natural Gas and Import Coal Prices 81,USD Base Year 2009 Electricity market liberalization would continue making Turkey more attractive for foreign financiers.especially, after 2030s energy efficiency applications would also be more applied in the country. Energy efficient domestic appliances, boilers, electric devices, and solar thermal heaters would be more preferred and this would help to keep the demand stable. The usage of would increase around the country Results of the Scenario-CLEAN TRANSITION In Clean Transition scenario, the main priority would be to a create clean and green Turkey. Therefore, expansion of installed capacity was shaped according to this trend with the effect of low cost renewable energy technologies and application of the EU ETS mechanism. According to the parameters in this scenario the installed capacity of Turkey until 2050 was shaped as shown in Figure 5.6 below. Detailed numbers of installed capacity growth of Clean Transition scenario can be found in Appendix C1. 81 Fraunhofer IWES, 2012, p:51 71

78 Installed Capacity (MW) 200, , , , , , , , , Wind Hydro Nuclear Geo Biomass Solar Lignite NaturalGas ImportCoal Hard Coal Oil Products 20, Figure 5.6 : Installed Capacity Growth in Clean Transition Scenario In this scenario, it was projected that Turkey would be part of European Union in 2025 and EU ETS would be applied after this date. The effect of this development can be seen in the installed capacity growth. Since solar and wind energy do not emit any CO2, the installed capacity of these sources improved rapidly after The capacity of natural gas and coal power plants stayed more stable. Some reductions were also observed in the lignite installed capacity. In line with the installed capacity growth, CO2 prices, fuel prices and expected electricity demand of the country, wholesale market prices were calculated until These prices are shown below in Figure

79 Prices ($/MW) Figure 5.7 : Electricity Market Prices in Clean Transition Scenario The commisioning of first nuclear power plant in 2019 resulted in some market pricereductions according to the software results. These reductions were more evident between 2020 and The effect of the EU ETS was also clear in the price fluctuations. Since the EU ETS would be applied in 2025 in this scenario,a sharp escalation in the market price was seen at this date and the increase continues until the end of the period with the growing CO2 prices. Another key result of this scenario is the CO 2 emission. Since Clean Transition was based on a green Turkey and increased renewable energy usage, certain emission reductions were achieved throughout the period. National emission production rate is shown in Figure

80 Emmision Production (tonne) 140,000, ,000, ,000,000 80,000,000 60,000,000 40,000,000 20,000,000 0 Figure 5.8 : Emission Production in Clean Transition Scenario With the application of EU ETS, installation of nuclear power plants and increasing share of renewable energy, certain reduction in CO2 emission was declined to half of the value in 2013 according to PLEXOS results. In this scenario, nearly all the economical wind and hydro power were installedand solar installed capacity reached 33 MW by Natural gas was still relevent with high flexibility and availability of power plants. However, with participation in the EU ETS, investments were reduced in this field. Moreover, installation of other fossil fuel plants were not deemed economical. Therefore, investments in clean energy were higher. In addition, dependency on fossil fuels decreased with renewable energy growth. 5.2 Analyses of Scenario 2-CONVENTIONAL DEVELOPMENT Description of Scenario-CONVENTIONAL DEVELOPMENT Conventional Development is characterized by materialism and consumerism. In this scenario, Turkey would be experiencing high economic growth and increasing electricity demand until 2040s. Electricity demand would increase between 4%-6% until 2030 and it would rise between 3-4% until After 2040s, the electricity demand would be more stable due to the 74

81 Electricity Demand (TWh) saturation point of the economic growth and electricity demand. The expected electricity demand of Turkey in this scenario according to different years is shown in Figure 5.9 below Figure 5.9 : Electricity Demand of Conventional Development The character of this scenario would be economic growth & profitable investments inthe world and in Turkey. Countries especially in Europe would be facing serious economic problems; therefore, they would not give the priority to environmental problems and climate mitigation. There would be increasing migration from poor rural to urban areas, areas all around the world. Environmental quality would be poor overall as well. The carbon mechanisms such as EU ETS would fail and the countries would not be committed to their reduction targets. There would be strong economic growths especially in Middle East and South America; however, these growths would be at the expense of environment and social equality. Turkey would be one of the countries growing faster than average andenvironmental concerns, green and smart energy generation would not be the priority. As a result of these developments, protecting the environment and giving the value to it would not be on the main stage. The main issue would be to keep energy prices as low as possible and to have maximum economic growth. 75

82 Price ($/GJ) As a result of materialistic approach, progress would be faster in fossil fuel technologies. Natural gas and oil products would be the main actor in energy field. Due to the extensive exploration and research in the field, new reserves for the such as shale gas would also be found. However, because of the high demand of the fossil fuels, the natural gas prices and import coal prices would be quite high within the period. The price of natural gas& import coal prices over the period are shown in Figure In this scenario, Turkey would also develop more aggressive policies to have steady supply of natural gas Natural Gas Import Coal Figure 5.10 : Natural Gas and Import Coal Prices 82,USD Base Year 2009 Nuclear gas would also have more important role in this materialistic and consumerist world and there would be new technologies to rise the usage of nuclear energy. Nuclear energy would be also attractive with its high capacity possibility for countries which would experience uncontrolled growth and have high electricity demand. The energy technology developments in this scenario would be mostly determined by their cost and their availability for the large scale. Solar energy would not be the first option and the expected cost reduction for solar PV module would prove accurate. According to IEA, the capital cost of solar module was 2850 $/kw in 2012 and in this scenario, over the period it would go down only to 2000 $/kw in The same case would be also valid in wind energy. The cost was 1690 $/kw 82 Fraunhofer IWES, 2012, p:51 76

83 and it would be around1630 $/kw in Therefore, these sources would not bethe priority for investors. However, the unlicensed energy production regulation would still be valid during the period and renewables still would be preferred by big manufacturers or small investors with the current feed in tariff scheme to generate their own electricity. With the solar energy trend especially in 2013, solar energy investments would become appealing especially for foreign solar systems manufacturers. This trend would continue until Wind energy investments would also be affected and the installed capacity would grow in this short period. Security of energy supply would be a problem at the global level since the world trend would be more self-centered, consumerist and materialist. There would be international cartels affecting the trend in the world in energy field.the security of energy supply would be also a problem for Turkey with it s limited domestic fuel and higher share of natural gas plants in installed capacity. The installation of two nuclear power plants (4800 MW+4800 MW) would have been completed at the end of 2028 and the country would also decide to install athird. The installation of the third would have also been completed until the end of Furthermore, old coal power plants would be replaced withhigh efficiency plants or to new technology natural gas power plants. The maximum hydro utilization (36 GW) would have also been constructed until the end of It would be always an option with lower investment cost compared to other renewables and with the possibility to regulate electricity generation. Until 2020, the installed capacity from hydro sources would develop constantly as expected. It would reach around 30 GW and rest of the potential would be always an alternative. Geothermal utilization (0.6 GW) would have been completed in this period as well. Until 2020, it is expected to be installed quarter of this potential. It would assume that after 2020, the utilization of the potential would be achieved according to market system Results of the Scenario-CONVENTIONAL DEVELOPMENT Conventional Development was characterized by the increasing trend of consumerism along with continued usage of fossil fuels in Turkey and the world. As explained in the scenario description, fuel prices and renewable energy technology cost would be high and construction 77

84 Installed Capacity (MW) of a third nuclear power plant would be completed in According to these parameters, the national installed capacity growth was structured by PLEXOS as shown in Figure A Detailed table of the installed capacity growth can be found in Appendix C2. 180, , , , ,000 80,000 60,000 40,000 Wind Hydro Nuclear Geo Biomass Solar Lignite NaturalGas ImportCoal Hard Coal Oil Products 20, Figure 5.11 : Installed Capacity Growth in Conventional Development Scenario Along with the installed capacity growth, fuel prices and electricity demand of the country, wholesale market prices calculated by the software are presented in Figure Electrcity prices were calculated between $/MWh until 2022, After, they increased with a constant slope until the end of the period, due to increasing demand and fuel prices. The effect of commissioning the nuclear units were not observed clearly in this scenario due to the high growth rate in the electricity demand. 78

85 Emmision Production (tonne) Electrcity Prices ($/MW) Figure 5.12 : Electricity Market Prices in Conventional Development Scenario In this scenario, electricity was mainly generated from conventional sources. Along with these developments, according to PLEXOS results CO 2 emissions increased more than 200% during the period which can be seen in Figure ,000, ,000, ,000, ,000, ,000,000 50,000,000 0 Figure 5.13 : Emission Production in Conventional Development Scenario The installed capacity of natural gas almost tripled and import coal capacity increased more than four times current capacity by Since Turkey does not have natural gas sources and 79

86 import coal is purchased from other countries as well, it can be said that the dependency on fossil fuels would be growing in the next 40 years according to this scenario. On the other hand, since the market prices were projected higher than thelast three years average, investors could consider investing in Turkey with new power generation facilities to benefit from these high prices. In this scenario, almost all the economical hydro potential was installed until 2025 and wind energy installed capacity raised to 10 GWs around Since the trend would not be green energy or clean environment, the high cost of solar technology, prevents capacity from reaching more than 4 GW. 5.3 Analyses of Scenario 3-THE RESTLESS YEARS Description of Scenario-THE RESTLESS YEARS The Restless Years is a scenario of instability inturkey with fluctuating economic growth and constantly changing parameters such as electricity demand and GDP. In this scenario, the economy would grow constantly but gradually until Until this date, the country would be able to meet its economic and energy targets. The electricity demand would also continuously increase with the expected growth rate (3-4%/year). The construction of first nuclear power plant would have been completed and the second one would be near completion. After this date, because of the political fluctuations in the country and instability in the world economy, economic growth would stop; therefore, the demand would stay constant and may even see some declines. In the world, economic developments would not also take place as expected and public awareness would not increase enough for strong renewable energy usage and clean environment policies. There would be some research in the field of solar PV and wind. However, this research would not be enough to reachthe projected costs in the learning curve. Module prices would be around 2500$/kW in 2020 and 2300 $/kw in 2035, remaining uncompetitive. This option could not be affordable in large scales to generate electricity in the world and in Turkey. However, unlicensed production would be still valid and quite popular since the electricity would always be expensive. This option would be especially preferred by industrial organization with high electricity consumption. 80

87 Price ($/GJ) Hydro utilization especially run of rivers would always be an option to supply energy in this scenario. Until 2020, installed capacity would grow constantly but also slower than expected. In 2020, the installed capacity would be more than 20 GW. Since demand would also not increase as expected, high amount of hydro utilization would not be urgent. Hydro would be an alternative not only with big dams but also run of rivers for small and big investors. Solar farm installation, 600 MW, would be completed until Opening new grid capacity would not be certain. However, unlicensed solar farm installation would become quite popular and this would increase the installed capacity of solar,though this increase would be slow. Until 2020, the quarter of geothermalwould be installed and installation would continue in the period. Alternative sources of natural gas would be found; natural gas prices would decline as it was expected from the new explorations and low demand in the world.the natural gas and oil products demand would be high in Middle East countries with high energy demand such as India and China compared to European countries. However, this demand would not seem high enough to raise fuel prices. These countries would make long term contracts with the natural gas provider countries. Therefore, the natural gas and import coal prices would be quite low. These prices can be seen in Figure Natural Gas Import Coal Figure 5.14 : Natural Gas and Import Coal Prices 83,USD Base Year Fraunhofer IWES, 2012, p:51 81

88 Electricity Demand (TWh) In this period, coal would be a default solution for Europe and for Turkey due to low initial investment cost and high availability. Since the security of energy supply would be a problem, coal would be the solution for this problem. These developments would bring more environmental problems after Public awareness for environmental problems would rise again. The economy would be inbetter shape and countries would start to look again for different energy sources. After 2040, Turkey catch again political and economic stability. This would lead rising electricity demand in the country. Foreign investors would be willing to invest inthe country again. The expected demand of this scenario is shown in Figure After 2040, the cost of Solar PV systems would start to go down. The price would be around 2000 $/kw in Since climate change & CO2 emissions would be central problems and prioritized, research would be accelerated in the clean energy field. The trend would start to change again since the cost of solar PV systems would decrease especially after It would again be an attractive option in Turkey Figure 5.15 : Electricity Demand of Restless Years 82

89 Installed Capacity (MW) Results of the Scenario- THE RESTLESS YEARS The Restless Years was the scenario which was structured according to fluctuations of electricity demand in the country and low fuel prices in the world. In this scenario, political instability was the leading driver. Therefore, electricity demand experienced unpredictable oscillations. The installed capacity growth which was obtained from PLEXOS is shown in Figure Detailed numbers can be found in Appendix C3. 120, ,000 80,000 60,000 40,000 20,000 Wind Hydro Nuclear Geo Biomass Solar Lignite NaturalGas ImportCoal Hard Coal Oil Products Figure 5.16 : Installed Capacity Growth in the Restless Years Scenario Since the demand would grow slowly, installation of the nuclear power plants and other thermal power plants already under construction at the beginning of the period were mostly enough to supply projected electricity demand in this scenario. Since the fuel prices were 83

90 Electrcity Prices ($/MW) projected low and renewable energy technology prices were foreseen higher, the software chose to build natural gas & coal power plants. In addition to thermal power plants, hydro power was another option to generate economical electricity in this scenario. Solar and wind energy were not priorized in this scenario. Therefore, their installed capacities remained quite low compared to other sources in Along with the installed capacity growth and fuel price projections, electricity market prices are shownshown in Figure Figure 5.17 : Electricity Market Prices in the Restless Years Scenario The effect of the nuclear power plants on the electricity market prices can be seen in the graph between and However, the commisioning of the nuclear power plants was not the only reason a reduction in the market prices was observed. Low fuel prices and low electricity demand were also other important drivers to these prices in the market. With low fuel prices and new thermal power plants, the usage of fossil fuels increased through the period in this scenario. Therefore, the emissions also rose as it can be seen in Figure

91 Emmision Production (tonne) 160,000, ,000, ,000, ,000,000 80,000,000 60,000,000 40,000,000 20,000,000 0 Figure 5.18 : Emission Production in the Restless Years Scenario In this scenario,the main electricity generation sources were conventional and hydro. This case can be also obtained by increased emission production in the country. This means that in this scenario the dependency on fossil fuels were increased over the time in the country. 5.4 Analyses of Scenario 4-MODERATE DEVELOPMENT Description of Scenario-MODERATE DEVELOPMENT The Moderate Development scenario describes Turkey with gradual economic & population growth. economic growth would continue slowly but restrainedly until Therefore, the electricity demand would also grow constantly, between 3-4%, until then. Afterwards, energy efficiency applications would be more applied and economic growth would slow. Therefore; electricity demand shown in Figure 5.19 would be more stable. The economic growth would be similarin Middle East, China and India. These countries, like Turkey, would be attractive places for new investments due to Europes stability in energy field and its economic permanency. 85

92 Electricity Demand (TWh) Figure 5.19 : Electricity Demand of Moderate Development In this scenario, climate change and environmental problems would be an important issue during the whole period especially amongstdeveloped countries. In developing countries, environmental problems would not be the priority; however, public and governments would still be aware of it. Profitable investments would have higher value than environmentalfriendly investments. Hence, Turkey would not have any emission reduction target or be a part of EU ETS. Nevertheless, it would be still an issue and a topic of discussions in public. In the world, the exploration of new gas sources would continue and new sources such as shale gas would be developed which would affect the natural gas and oil prices investments in the world. The conventional energy trend would still continue; therefore, the prices would still be high because the demand for these sources would also increase dramatically, especially in the Middle East. Since the trend still continues, natural gas investments would be still a choice to generate electricity in Turkey as well. Domestic coal would be another option during the period with the developing technology in this field. Furthermore, old and inefficient coal power plants would get retired and replaced with new coal technologies or new natural gas technologies. In this scenario, natural gas and import coal would be in the same trend with scenario Clean Transition and these prices can be seen in Figure 5.4. Through the period, Asian & Latin American markets would also be growing dramatically. This would bring new investment opportunities between the countries and it would make 86

93 possiblelong distance electricity transportation as well. There would be European, Asian, and Latin American open electricity market systems. Turkey s location would gain more importance especially for the relationship between Asia and European countries. Since the developed countries would be more aware of the environment, they would invest inclean energy sources. These investments would help to reducethe initial cost of these technologies,following the expected learning curve in IEA NP450 scenario. This reduction would benefit new investments around the world. Having high renewable energy installed capacities would be the new trend to show the level of development between countries, leading Turkey to follow this new trend; therefore feed-in-tariff schemes would be valid during the whole period. The country would have specific targets in each period for renewable energy installation; however, these targets would not be very ambitious. Wind energy might be prominent since it is now almost competitive with conventional sources. The cost of these technologies would be same with Clean Transition Scenario. The costs can be found in Figure 5.3. The installation of hydro power plants would increase constantly until 2020 since there are already some signed contracts and constructions underway. After 2020, hydro would still be popular since the country economic potential is 36 GW and there would still be remaining potential. On the other hand, discussions would be rising regarding the environmental damages which they bring. Especially with the environmentally friendly trend in the world, investors and government would be more careful in this field but continue to invest. Geothermal economic installed capacity potential is 600 MW which would be constructed quite fast. Since one of the trends in the world would be recycling and energy efficiency applications, biomass power plants would alsobe popular especially in the countries which have considerable amount of organic waste and biomass potential as Turkey does Results of the Scenario-MODERATE DEVELOPMENT In Moderate Development scenario, the electricity market was characterized according to middle fuel prices and low renewable energy technology costs. As it was defined in the scenario description part, electricity demand was also in the middle level. According to these 87

94 Installed Capacity (MW) parameters, the installed capacity growth as modeled by PLEXOS is shown in Figure Detailed numbers are shown in Appendix C Wind Hydro Nuclear Geo Ren.+Waste Solar Lignite NaturalGas ImportCoal Hard Coal Oil Products Figure 5.20 : Installed Capacity Growth in Moderate Development Scenario Since the fuel prices were at the middle level and renewable energy technology cost was quite competitive with the traditional energy sources, the software employed a range of power plants to generate electricity. In this scenario, installed capacity of solar reached 10 MWs and wind power capacity raised to 20 MWs. Installation of hydro potential, which was 36 GW economically, were completed in the 2030s. Natural gas power plants with their high availability and coal power with low investment cost were chosen to generate electricity. According to these installations, calculated market prices can be seen in Figure

95 Emmision Production (tonne) Electrcity Prices ($/MW) Figure 5.21 : Electricity Market Prices in Moderate Development Scenario The effect of nuclear power plants on the electricity market prices can be seen between 2013 & Afterwards, the prices were rising with increasing fuel prices. Along with the growing usage of fossil fuels, the emissions increased as well. National Emission production in this scenario is shown in Figure ,000, ,000, ,000, ,000,000 50,000,000 0 Figure 5.22 : Emission Production in the Restless Years Scenario 89

96 Electricity Demand (TWh) 6 Discussion 6.1 Long Term Analysis The aim of this study is to project long term installed capacity expansion and calculate long term electricity market prices according to different scenarios. To model the long term Turkish electricity market, first the market structure was built in the software, PLEXOS, according to data from 2010, 2011 and This process was analyzed and discussed in Section 4. Historical data was compared with the model results to verify the reliability of the model. While the generation of electricity according to the different sources calculated in the model was consistent with the historical data, electricity market prices made some fluctuations between model results and historical data. The possible reasons for these fluctuations were discussed in the section 4.4. Along with the back testing results, the long term structure of the Turkish electricity market according to different scenarios was modeled.as stated in the scenario analyses, 4 different scenarios were structured and their key drivers were uploaded to the software as input parameters. The key drivers were determined as electricity demand of the country, price of the fossil fuels, renewable energy technology cost in the next 38 years and environmental policies. Electricity demand comparison in Figure 6.1 and fuel price in Figure 6.2& 6.3 can be seen according to different scenarios and installed capacity of Turkey in 2050 according to different sources along with key drivers of 4 different scenarios is shown in Figure Clean Transition Conventional Development The Restless Years Moderate Development Figure 6.1 : Electricity Demand Comparison 90

97 Price ($/GJ) Price ($/GJ) Conventional Development Clean Transition & Moderate Development The Restless Years Figure 6.2 : Natural Gas Price Comparison,USD Base Year Conventional Development Clean Transition & Moderate Development The Restless Years Figure 6.3 : Import Coal Price Comparison,USD Base Year

98 Instaleld Capacıty (MW) 200, , , , Wind Hydro Nuclear 120, , , Biomass & Geothermal Solar NaturalGas 60, Import Coal 40, , Lignite & Hard Coal Oil Products 0.00 Clean Transition Conventional Development The Restless Years Moderate Development Figure 6.4 : Installed Capacity of Turkey According to Different Scenarios in 2050 Clean Transition has the highest installed capacity of the 4 different scenarios; although, it does not have the highest demand. This occasion is likely due tothe lower capacity factors of renewable energy power plants. Since the renewable energy is not stored to, the installed capacity has to be higher than conventional power plants. On the other hand, in this study battery technology and their possible effects on the market prices were not taken into account. With the pace of developments in the renewable energy field, these numbers could be quite different than projected. As mentioned in the scenario descriptions, Clean Transition & Moderate Development Scenarios have the same fuel prices and renewable energy technology cost reduction through the period. However, installed capacity of renewables was higher in Clean Transition Scenario than Moderate Development. In Clean Transition, the software built clean 92

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