BachelorofScienceThesisKTHSchoolofIndustrialEngineeringandManagement

EnergyTechnologyEGI-2017SE-10044STOCKHOLMTRITA-ITM-EX2018:440

Modelling the power system of Bolivia in order to support the achievement

of SDG7

Saga Carle Viktor Vifell Nilsson

Picture 1: Cochabamba city (S.Carle 2018)

TRITA-ITM-EX 2018:440

Bachelor of Science Thesis EGI-2018

Modelling the power system of Bolivia in order to support the achievement of SDG7

Saga Carle

Viktor Vifell Nilsson

Approved 1st of June 2018

Examiner Mark Howells

Supervisor Georgios Averinopoulos Caroline Sundin

Commissioner

Contact person Viktor Vifell Nilsson Saga Carle

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Abstract Electricity is becoming a vital part of today's society. With improving living standards in developing countries, the demand for electricity is increasing within the public as well as the private sector. Developing countries often lack a well-designed electricity grid and also utilize limited energy resources, like gas and oil, when generating electricity. These resources are not only limited but, when burnt for the reason of generating electricity, greenhouse gas emissions are emitted in the atmosphere, causing global warming. A shift from fossil fuels, like gas and oil, to renewable resources such as wind and solar, is therefore crucial to prevent global warming and achieve sustainable development. The Bolivian government has formally expressed an ambition to achieve social and environmental development by adopting UN's 17 sustainable development goals (SDG). SDG number 7, “Ensure access to affordable, reliable, sustainable and modern energy for all “ aims to provide energy that preserves environmental and socio-economic sustainability. Today, a majority of the Bolivian population have access to electricity, but the usage in kWh per capita is low, and the primary source for electricity generation is natural gas. The aim of this paper is to analyse which economic and technical factors are needed for Bolivia as a country to achieve UN’s SDG7. A literature review of Bolivia as a country and its power system is completed to understand the general state of Bolivia and the current electricity situation. Furthermore, a field study in Cochabamba is conducted to gather information and data of current and projected power plants as well as demand and future expansion plans of power plants, in which together with previous studies provides a foundation for a model of Bolivia's power system. The modelling will be done using the long-term energy planning tool OSeMOSYS and its interface MoManI. With the collected data and information, different scenarios are modelled to investigate how the share of renewable energy sources can be increased. The final results show that Bolivia has high potential in achieving SD7 and that hydroelectric power will presumably be the most contributing factor if the goal is met. The amount of CO2 will therefore decrease, however, the results also showed that large capital investments must be made in order to achieve SDG 7. Keywords: OSeMOSYS, MoManI, Bolivia, Energy, Electricity, CLEWs, SDG7, Sustainable Development

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Sammanfattning Elektricitet blir allt viktigare i dagens samhälle. Med växande levnadsstandard i utvecklingsländer har efterfrågan på elektricitet ökat inom både den offentliga och privata sektorn. Utvecklingsländer saknar ofta fullt fungerande elnät och använder oftast ändliga energikällor som gas och olja, för att generera elektricitet. Förutom det faktum att dessa energikällor är ändliga så bidrar dessa också till klimatförändringen genom sina växthusgasutsläpp då de förbränns i syfte att generera el. Ett skifte till förnyelsebara energikällor är därför avgörande för att motverka klimatförändringen och bidra till en hållbar utveckling i samhället. Bolivia är ett av de länder som formellt utryckt en önskan om att uppnå en hållbarutveckling genom att anta FN:s 17 Globala mål för hållbar utveckling. Ett av dessa mål är hållbarhetsmålet nummer sju, ” Säkerställa tillgång till ekonomiskt överkomlig. tillförlitlig. hållbar och modern energi för alla. Målet syftar till att tillgodose energi på ett sätt som är hållbart, både ekonomiskt, socialt och miljömässigt. I dagsläget har majoriteten av Bolivias befolkning tillgång till elektricitet, dock är användandet i kWh per invånare lågt och den elektricitet som produceras kommer i synnerhet från naturgas. Denna studie syftar till att se vilka ekonomiska och teknologiska faktorer som behövs implementeras för att Bolivia som land uppnår hållbarhetsmålet nummer sju. Detta görs först genom en litteraturstudie om Bolivia som land och dess elsystem för att få en uppfattning om landet i helhet och för att förstå nuvarande situationen för elsystemet. Vidare utförs en fältstudie i Cochabamba för att samla information och data om nuvarande och planerade kraftverk, efterfrågan av elektricitet och framtida expansionsplaner för kraftverk, vilket senare ligger till grund för att bygga upp en modell för Bolivias elsystem. Modellerandet sker i långsiktiga energiplanerings modellen OSeMOSYS och dess användargränssnitt MoManI och med hjälp av data och information som insamlats modelleras olika scenarier för att analysera hur andelen förnyelsebar energi på bästa sätt kan öka i landet. Det slutgiltiga resultatet visar att Bolivia har en stor potential i att nå SDG 7och att vattenkraft troligtvis kommer vara den högst bidragande faktorn om detta skall lyckas. Mängden CO2 kommer därmed att minska. dock visade resultaten även att stora kapitalinvestering kommer att behövas för att uppnå SDG 7.

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Acknowledgements This thesis is carried out at the division of Energy System Analysis at the Royal Institute of Technology (KTH-dESA) as a Minor Field Study in Cochabamba, Bolivia. First of all, we would like to thank all the people who have given their time, thoughts and knowledge to us during this project. We also want to send out a special thanks to the following:

Professor Mark Howells for introducing us to the subject of energy modelling and giving us a chance to carry out this thesis under his supervision.

The team at KTH-dESA and especially Caroline Sundin and Georgios Avgerinopoulos for all the endless help and long skype-calls. Your ideas, supervision and patience have meant the world to us and we would not have made it without your help.

All of the staff and students Universidad Mayor de San Simón that welcomed us and made us feel like home in Cochabamba. Extra thanks to Gabriela Peña Balderrama, Erlan Duarte and and Carlos Fernandez for providing us data, information and inspiration for the thesis.

Sida for providing us with the scholarship Minor Field Study and ÅForsk for the travel grant, your economical support made the trip to Bolivia possible.

Last but not least we want to thank our families for always being there with love and support.

Disclaimer: This document presents the views of the author and may therefore not reflect views from or be supported by the parties who are related to the project that this report is supporting.

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Table of Contents Abstract ....................................................................................................................................... 1Sammanfattning .......................................................................................................................... 2Acknowledgements ..................................................................................................................... 4List of Tables .............................................................................................................................. 7List of Figures ............................................................................................................................. 8List of Abbreviations ................................................................................................................ 101 Introduction ....................................................................................................................... 12

1.1 Context ..................................................................................................................... 121.2 Previous work and motivation of study ................................................................... 131.3 Research question and objectives ............................................................................ 131.4 Outline of the report ................................................................................................. 14

2 Methodology ...................................................................................................................... 142.1 Results from literature review .................................................................................. 14

2.1.1 Bolivia – Country overview ............................................................................. 142.1.2 Electricity in Bolivia ........................................................................................ 152.1.3 Exporting Electricity ........................................................................................ 162.1.4 National policies .............................................................................................. 172.1.5 CLEWs ............................................................................................................. 18

2.2 Data collection ......................................................................................................... 192.3 Modelling ................................................................................................................. 20

2.3.1 Open Source Energy Modelling System (OSeMOSYS) ................................. 202.3.2 Model Management Infrastructure for OSeMOSYS (MoManI) ..................... 202.3.3 Reference Energy System ................................................................................ 202.3.4 Model set-up .................................................................................................... 21

2.4 Scenarios for Bolivia ................................................................................................ 242.4.1 Business As Usual ........................................................................................... 252.4.2 Scenario A - Increased Solar PV Capacity ...................................................... 252.4.3 Scenario B-D - Reaching INDC’s .................................................................... 252.4.4 Scenario E - Electricity hub in South America – Export ................................. 252.4.5 Scenario F-I - Learning curves renewables ..................................................... 252.4.6 Scenario J-M - Emission Cost .......................................................................... 262.4.7 Scenario N-Q - Combination of scenarios ....................................................... 262.4.8 Scenario R-U - Constrains in Natural gas ........................................................ 26

3 Results ............................................................................................................................... 273.1 Total discounted cost ............................................................................................... 273.2 Total capital investments ......................................................................................... 27

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3.3 Production by Technology Annual .......................................................................... 333.4 Annual Emissions .................................................................................................... 393.5 Annual Exports ........................................................................................................ 41

4 Discussion .......................................................................................................................... 414.1 Data-uncertainties .................................................................................................... 424.2 Results analysis ........................................................................................................ 424.3 Summary .................................................................................................................. 46

5 Conclusion ......................................................................................................................... 476 Suggestions for future work .............................................................................................. 487 References ......................................................................................................................... 498 Appendices ........................................................................................................................ 54

8.1 Appendix I ............................................................................................................... 548.2 Appendix II .............................................................................................................. 798.3 Appendix III ........................................................................................................... 1018.4 Appendix IIII ......................................................................................................... 113

Desired specific emission limit for electricity production according to the INDC’s : 0.04 ton/Mwh .................................................................................................................................. 113

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List of Tables Table 1: Time slices by hour .................................................................................................... 22Table 2: Matrix of all chosen scenarios ................................................................................... 24Table 3: Results for Accumulated total discounted cost .......................................................... 27Table 4: Lack of capacity by end year ..................................................................................... 45

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List of Figures Figure 1: Electricity production by energy source ................................................................... 15Figure 2: Electricity demand by sector. ................................................................................... 16Figure 3: Energy Reference System for Bolivia. ..................................................................... 21Figure 4: Capital cost for solar PV for scenarios F-I ............................................................... 26Figure 5: Total capital investments by year for BAU .............................................................. 28Figure 6: Total capital investments for scenario A .................................................................. 28Figure 7: Total capital investments for scenario B .................................................................. 28Figure 8: Total capital investments for scenario C .................................................................. 28Figure 9: Total capital investments for scenario D .................................................................. 29Figure 10: Total capital investmenst for Scenario E ................................................................ 29Figure 11: Total capital investments for scenario F ................................................................. 29Figure 12: Total capital investments for scenario G ................................................................ 29Figure 13: Total capital investments for scenario H ................................................................ 30Figure 14: Total capital investments for scenario I ................................................................. 30Figure 15: Total capital investments for scenario J ................................................................. 30Figure 16: Total capital investments for scenario K ................................................................ 30Figure 17: Total capital investments for scenario L ................................................................ 31Figure 18: Total capital investments for scenario M ............................................................... 31Figure 19: Total capital investments for scenario N ................................................................ 31Figure 20: Total capital investments for scenario O ................................................................ 31Figure 21: Total capital investments for scenario P ................................................................. 32Figure 22: Total capital investments for scenario Q ................................................................ 32Figure 23: Total capital investments for scenario R ................................................................ 32Figure 24: Total capital investments for scenario S ................................................................. 32Figure 25: Total capital investments for scenario T ................................................................ 33Figure 26: Total capital investments for scenario U ................................................................ 33Figure 27: Production by technology annual for scenario BAU .............................................. 33Figure 28: Production by technology annual for scenario A ................................................... 34Figure 29: Production by technology annual for scenario B ................................................... 34Figure 30: Production by technology annual for scenario C ................................................... 34Figure 31: Production by technology annual for scenario D ................................................... 34Figure 32: Production by technology annual for scenario E .................................................... 35Figure 33: Production by technology annual for scenario F .................................................... 35Figure 34: Production by technology annual for scenario G ................................................... 35Figure 35: Production by technology annual for scenario H ................................................... 35Figure 36: Production by technology annual for scenario I ..................................................... 36Figure 37: Production by technology annual for scenario J .................................................... 36Figure 38: Production by technology annual for scenario K ................................................... 36Figure 39: Production by technology annual for scenario L .................................................... 36Figure 40: Production by technology annual for scenario M .................................................. 37Figure 41: Production by technology annual for scenario N ................................................... 37Figure 42: Production by technology annual for scenario O ................................................... 37Figure 43: Production by technology annual for scenario P .................................................... 37Figure 44: Production by technology annual for scenario Q ................................................... 38Figure 45: Production by technology annual for scenario R ................................................... 38Figure 46: Production by technology annual for scenario S .................................................... 38Figure 47: Production by technology annual for scenario T .................................................... 38Figure 48: Production by technology annual for scenario U ................................................... 39Figure 49: Annual Emission for BAU and scenario A ............................................................ 39

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Figure 50: Annual Emission for BAU and scenario B-D ........................................................ 39Figure 51: Annual Emission for BAU and scenario E ............................................................. 40Figure 52: Annual Emission for BAU and scenario F-I .......................................................... 40Figure 53: Annual Emission for BAU and scenario J-M ......................................................... 40Figure 54: Annual Emission for BAU and scenario N-Q ........................................................ 41Figure 55: Annual Emission for BAU and scenario R-U ........................................................ 41Figure 56: Annual Exports for Scenario E ............................................................................... 41

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List of Abbreviations

AE Autoridad de Fiscalización y Control Social de Electricidad BAU Business As Usual C Celsius CEO Chief Executive Officer CIA Central Intelligence Agency CLEW Climate. Land-Use. Energy and Water System CNDC Comercial de Nacional Departomento CO2 Carbon dioxide COBEE Compañia Bolivian de Energía CRE Cooperativa de Rural Electrificación dESA division of Energy System Analysis at KTH EIA U.S. Energy Information Administration ENDE Empresa Nacional de Electricidad EPA U.S. Environmental Protection Agency FAO Food and Agriculture Organization GDP Gross Domestic Product GHG Greenhouse Gas GLPK GNU Linear Programming Kit GW Giga Watt HB Hidro Electrica Boliviana IEA International Energy Agency INDC Intended Nationally Determined Contribution k kilo km kilometer KTH Royal Institute of Technology in Stockholm M Million m meter ME Ministry of Energy MEM Mercado Eléctrico Mayorista mm millimeter MoManI Model Management Infrastructure for OSeMOSYS MW Mega Watt MWh Mega Watt hour NOX Nitrogen Oxide OEC Observatory of Economic Complexity

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OECD Organization for Economic Cooperation and Development OSeMOSYS Open Source energy Modeling System PDES The Economic and Social Development plan PEEP Plan Eléctrico del Estado Plurinacional de Boliva PJ Peta Joule POES Plan Óptimo de Expansión del Sistema Interconectado Nacional PP Power Plant PV Photovoltaic R/P Reserves-to-Production Ratio RES Reference Energy System SDG Sustainable Development Goal SEAP Sustainable Energy for All Program SPI Strategic Policies and Investments T&D Transmission & Distribution UDAPE Unidad de Análisis de Políticas Sociales y Económicas UN United Nations UNDESA United Nations Department of Economics and Social Affairs USD United States Dollars WEO World Energy Outlook WHO World Health Organization YPFB Yacimientos Petrolíferos Fiscales Bolivianos

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1 Introduction This section of the report will introduce the subject and the study in terms of previous work, motivation of research question, research question and objectives. The section will then end with an outline of the report.

1.1 Context Between year 2017 and 2050, the population in the world is estimated to increase from 7.6 billion to 9.8 billion (UN, 2017). An increase in population will subsequently lead to an increase in energy demand, especially electricity, which today represents 35% of the primary energy consumption in the world (EIA, 2018). Fossil fuels such as coal and natural gas are at present the main primary energy sources used for electricity generation. When natural gas and oil is combusted to generate electricity, greenhouse gases (GHG) like carbon dioxide CO2 and nitrogen oxides NOx are released into the atmosphere. This contributes to a rise in the global temperature, causing climate change which sequentially may cause natural catastrophes like flooding, droughts, storms etc. A higher electricity demand would thus lead to an increase in GHG emissions which would create an escalated amount of natural disasters. Electricity is, however, an essential resource for a country, and vital in the process of development. The Global Goals for Sustainable Development is a collection of seventeen Sustainable Development Goals (SDG) appointed by the United Nations (UN) (UN, 2018). It came in to force in 2015 and aims to "end all forms of poverty, fight inequalities and tackle climate change" by the end of 2030. The SDGs demand actions from all countries where they are expected to set national agendas for achieving the 17 goals. One of the agendas is the so-called Intended Nationally Determined Contribution (INDC), which is a set of national targets that aim to contribute to accomplishing the Paris Agreement that requires countries to limit global warming to 2°C (UNFCC, 2016a). Syria and Nicaragua are currently the only two countries that have not signed the Paris Agreement and withholds therefore no INDC’s. One of the seventeen goals is SDG 7 which aims it "Ensure access to affordable, reliable, sustainable and modern energy for all" (UN, 2018). By accomplishing SDG 7, other goals are also likely to be achieved such as improved health (SDG 3), education (SDG 4) and infrastructure (SDG 9). For instance, to guarantee safe and dependable hospitals, specific medical equipment is needed which often runs on electricity. No electricity for lights or computers makes it also problematic for students who need to study at night or gather information, which affects the quality of education. Public lightning such as road lighting is crucial for safe driving and can prohibit many driving accidents. Ensuring sustainable energy, like renewable and fossil-free energy, will also support the achievement of SDG 15 "Take urgent action to combat climate change and its impacts" with its reduction of GHG emissions. Bolivia is one of South America’s poorest countries, yet, in recent years the country has managed to double its gross domestic product (GDP), mainly because of the state’s natural gas reserves (The World Bank, 2018). In 2006, the newly elected president Evo Morales nationalised all hydrocarbons and established contracts with neighbouring countries such as Brazil and Argentina, making natural gas one of Bolivia's major exports and resource of income. However, it has recently been discovered that the amount of natural gas left was miscalculated and that there are fewer reserves then what was thought (Chavez-Rodriguez et al., 2015). Even though this issue still stands, Bolivia is yet set on becoming the energy hub of South America (J.Wilson, 2015). Thus, Bolivia will need to explore other alternative fossil-free

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energy resources in order to meet their future demand, the new demand for export and reduce GHG emissions.

1.2 Previous work and motivation of study The division of Energy System Analysis at the Swedish Royal Institute of Technology (KTH - dESA) which focus on research within the development of energy systems (Department of Energy Technology, 2015). In addition to core focus on energy, the division focuses on four key themes: The Climate, Land-Use, Energy and Water strategies (CLEW's), the Open Source energy Modelling System (OSeMOSYS), the Sustainable Energy for All Program (SEAP) and Strategic Policies and Investments (SPI). Previous research examining Bolivia's energy system with the assistance of CLEW's and OSeMOSYS has been done before. A study concerning the climate, land-use, energy and water (CLEW’s) nexus of Bolivia done by Arderne (2016) focused on finding critical points of interactions between the mentioned sectors using CLEW's analysis and proposed recommendations for policy actions. Additionally, a techno-economic demand projection for the energy system in Bolivia was examined by Balderrama et al (2017). This study was the first national energy demand model for Bolivia and focused on alternatives for energy savings, energy mix diversification and air quality. Furthermore, an OSeMOSYS model including 11 countries in South America was developed in a collaboration between KTH dESA and the University of Rio de Janeiro (G. Moura et al., 2015). The model goes under the acronym SAMBA and focuses on hydropower within the power sector, due to the high amount of electricity productions from and dependency on hydropower plants in South America, particularly in Brazil. This current study will take insights and knowledge from the previously mentioned studies but will focus on the achievement of SDG 7 in Bolivia using the long-term energy planning tool OSeMOSYS.

1.3 Research question and objectives The main aim of this study is to assist Bolivia in achieving SDG 7. The study will focus on investment outlooks for renewable energy sources in Bolivia’s power sector by using the long-term energy planning tool OSeMOSYS. This will give a projected outlook on what investments, both economic and technical, that must be made to achieve a higher participation of renewable energy sources. Different scenarios based on national policies and expected events such as reducing costs for renewable energy resources, emission penalties and reducing the use of fossil fuel power plants will be studied and analysed. The objectives of the study can be summarized as:

- Use the modelling tool OSeMOSYS to develop a long-term outlook on what technical and economic investments must be done to achieve a more sustainable power sector in Bolivia.

- Adopt relevant scenarios for Bolivia with the aim of increasing renewable participation.

- Evaluate the economic feasibility of the modelling results.

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1.4 Outline of the report The outline of this report is structured as following. Section 2 will present the methodology used in this study, which contains a literature review and an explanation of the data collection and modelling. Section 3 presents the results from the modelling and is followed by a discussion of these results in section 4. In section 5 the conclusion and policy recommendations from the discussions will be presented. Lastly, section 6 will identify the gaps of the study and propose improvements and future research for similar studies.

2 Methodology This section describes the methodology of this study and will be completed in three different parts: the first part includes a literature review; the second part will consist of data collection for the model and the last (third) part will shed light on the building of the model and formulation of different scenarios.

2.1 Results from literature review The study will commence by undertaking a literature review to get an overview of Bolivia as a country and its current electricity situation and the state’s national policies to understand what developments Bolivia might face. A brief climate, land-use, energy and water system (CLEW’s) analyse within in the country will also be made.

2.1.1 Bolivia – Country overview Bolivia is located in the middle of South America and is divided into three different geographical zones: Altiplano, Yungas and the tropical lowlands (Unicef, 2013). The Altiplano region in the west, which covers 28% of the national territory, is the coldest and driest region in the country due to its high altitude amounting to an average level of 3,750 meters above the sea level (m.a.s.l). The Yungas valley corresponds to the centre of Bolivia and covers 13% of the country and is the most humid, cloudiest and rainiest zone in the country. The tropical lowland consists of almost two thirds of the national territory and is mainly located in the north and east. This is the warmest and most tropical area of the country, with an average annual temperature of 25°C. This unique and varied geography gives each zone different potentials and challenges when it comes to farming, energy and water resources. For example, harvesting in the Yungas is done more easily than in The Altiplano due to the difference in climate and amount of rain. Bolivia has a population of almost 10.9 million out of which 31% are living in rural areas (The World Bank, 2016). It is one of the poorest countries in South America and in 2016 the GDP per capita was 3,105 USD (The World Bank, 2018). During the period of 2004-2014, the economy grew annually by 4.9% due to high commodity prices, increased export of natural gas to Argentina and a farsighted microeconomic strategy. During this decade, the population living in poverty decreased from 59% to 39%; however, since 2014, the poverty rate has remained the same. Bolivia's unique topography (mountainous) is one of the primary reasons for poor living conditions in the rural areas. Far distance between cities which creates difficulties for improving infrastructure such as roads and expanding of the power grid (Sida, 2017).

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For many centuries, the country suffered from postcolonial condition where dictatorship, corruption and racism against Bolivia’s indigenous population were major issues. The country has been rich in natural resources such as tin, silver and natural gas but the colonial powers, governments and private investors did not invest the money back into the country, prohibiting the country from development (Ranta M.E., 2014). In 2006, the same year Evo Morales was elected president, all hydrocarbons became nationalized1. The nationalization meant higher taxes and that the state energy company Yacimientos Petrolíferos Fiscales Bolivianos (YPFB) became in charge of all hydrocarbons, forcing foreign energy firms to convert their Bolivian operations into minor partnership with YPFB. In 2016, the CEO of YPFB announced that the nationalization had generated 31.5 billion USD for the public coffers (EFE, 2016).

2.1.2 Electricity in Bolivia The two main energy sources of electricity in Bolivia are natural gas and hydropower, constituting almost 67% and 29% of its electricity production respectively (IEA 2015) shown in Figure 1. The share of hydropower is, however, expected to grow from approximately 500 MW (2018) to 6,171 (2025) MW, where 5,552 MW constitute of three large scale hydropower projects; El Bala (1,680 MW), Cachuela Esperanza (990 MW) and Complejo hidroeléctrica Río Grande (2,882 MW) which are expected to participate in electricity exports (PEEP, 2014). As shown in Figure 1, solar power currently represents less than 1% of the current electricity production; nevertheless, Bolivia has great potential for increasing its amount of solar power plants as the country is positioned within the Sun Belt region with highest solar radiation where the Atacama Desert has a solar irradiation of 2,770 kWh/(m2a) (L. de Souza Noel Simas Barbosa et.al., 2017).

Figure 1: Electricity production by energy source (IEA 2015)

The national access to electricity in Bolivia is 92% which is high compared to the world’s electrification rate standing at 86% (IEA, 2017). In rural areas the electrification rate is 66% and in urban areas the rate is 98%. However, the per capita usage of electricity is low compared to neighbouring countries, 753 kWh per capita in Bolivia and 2,601 respectively 3,052 kWh in Brazil and Argentina (The World Bank, 2014a). As mentioned before, the low electrification rate in rural areas is due to vast distances between cities and power plants. The main demand sectors in Bolivia are residential, industrial and commercial sector, together representing almost 84 % of the total demand as seen in Figure 2 (AE, 2016). 1 The state became in complete control and ownership of the hydrocarbons in the country.

Solar PV0%

Oil1%

Bio3%

Gas67%

Hydro29%

Wind0%

ELECTRICITY PRODUCTION

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Figure 2: Electricity demand by sector (AE, 2016).

The power sector in Bolivia is mainly managed by four institutes: The Ministry of Energy (ME), the Autoridad de Fiscalización y Control Social de Electricidad (AE), the Comercial de Nacional Departomento (CNDC) and Empresa de Electricidad de Nacional (ENDE). The ME is in charge of policymaking and regulations for the power sector in Bolivia (ME, 2018). The ME is then supported by the AE who controls and regulates the electricity sector (AE, 2018). The AE holds information on matters such as generation, distribution and transmission information for the national grid. Sistema Interconectado Nacional (SIN), and other off-grid, smaller, isolated electricity systems. The CNDC is responsible of expanding and operating the SIN (CNDC, 2018) and is also in charge of the Mercado Eléctrico Mayorista (MEM) which is the national electricity market. In 2010 the government of Bolivia started re-nationalizing electricity companies and during the same year the three biggest electricity generation companies Guarachi, Corani, and Valle Hermoso where bought by the government (ENDE, 2018a). They were then put into the hands of Empresa Nacional de Electricidad (ENDE) whom manages transmission, distribution and generation within Bolivia (ENDE, 2018b). Other major private electricity companies are Hidro Electrica Boliviana (HB), Compañia Bolivian de Energía Eléctrica (COBEE) (both in charge of generation), ISA BOLIVIA (transmission) and Cooperativa de Rural Electrificación (CRE) (distribution) (P.Villarroel, 2018).

2.1.3 Exporting Electricity As mentioned earlier in the report, the consumption of electricity per Bolivian is low compared to the average consumption in the region. Despite the low electricity consumption, the country has a big potential in export of electricity. Today, Bolivia exports a significant amount of natural gas, 80% of the domestic extraction, but the export of electricity is on the other hand non- existent; in fact, Bolivia neither exports, nor imports, electricity (IEA, 2015). Bolivia is located in the middle of South America and shares borders with five countries which creates the potential of being an important electricity hub within the continent (G.N. Pinto de Moura et al., 2017). The government has acknowledged this potential and plans to start exporting electricity, This plan includes investments in three hydropower plants up to a value of USD 8.8 Billion as well as investments in transmission lines in order to connect to international destinations, measuring between 1,500-2,500 km (Plan Eléctrico del Estado Plurinacional de Bolivia 2025, 2016).

Residential39%

Commercial19%

Industrial26%

Mining9%

Public5%

Others 2%

ELECTRICITY DEMAND BY SECTOR

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The process of exporting electricity is however complicated and contracts with neighbouring countries are in need of negotiations regarding both prices and capacities (O.Bolivar & D.Bilbao, 2018). Bolivia is currently in negotiation with Argentina, Brazil and Peru in which the first two are the countries in South America with the highest electricity prices. Bolivia does not have the capacity required for exporting a considerable amount of electricity today but as mentioned above, investments in hydropower are projected to meet the target of exporting. Rio Bionacional Madera is one of the planned hydropower plants planned for mainly exporting electricity with a projected capacity of 1.5-3.0 GW in 2026.

2.1.4 National policies The Patriotic Agenda 2025 – 13 Pillars of Sovereign and Dignified Bolivia In 2013. President Evo Morales established the Patriotic Agenda 2025. The latter holds 13 development goals (Agenda Patriótica 2025, 2013). Each development goal is described as a pillar that aims to build and develop Bolivia into a more sustainable, democratic and non-discriminatory country. Each respective pillar takes on a different issue, such as poverty, discrimination, use of natural resources and health. The Economic and Social Development plan 2016-2020 The Economic and Social Development plan 2016-2020 (PDSE) was created in 2016 as a plan towards achieving the Patriotic Agenda 2025 and established numerous goals for each pillar (PDSE, 2016a). The goals are more direct actions on what will be done in order to achieve the aim of the Patriotic Agenda 2025. Goal number three for Pillar 2, universalization of basic services, intends to bring up the national electrification rate to 97% by 2025, and further the urban electrification to 100% and the rural electrification rate to 90%. In order to reach this goal, Bolivia will have to expand and densify networks in both rural and urban areas but also implement additional energy sources, such as solar and wind power, in secluded rural regions.

Goal number two for pillar 7, Sovereignty over natural resources, aims to increase efficient power generation up to 4,878 MW in order to guarantee supply of domestic demand and additional for export (PDSE, 2017b). The exported electricity is aimed to increase to 2,954 MW in order for Bolivia to become an energy hub of the region. The increase in power generation is to be achieved by implementing hydroelectric, thermoelectric and other electricity sources capable of generating the 2,954 MW. Goal number three for the same pillar also intends to boost and expand transmission lines and start research and potential development within the nuclear power sector.

In addition to the PDSE there are two governmental documents called the Plan Eléctrico del Estado Plurinacional de Boliva (PEEP) and the Plan Óptimo de Expansión del Sistema Interconectado Nacional (POES). Both of the plans are more detailed in the sense of how to achieve the goals for development within the power sector.

Intended Nationally Determined Contribution from the Plurinational State of Bolivia According to Bolivia’s INDC, the country intends to increase the share of renewable energy from 39% in 2010 to 79% by 2030 and decrease CO2 emissions, caused by electricity generation, from 0.38 ton/MWh (2010) to 0.04 ton/MWh by 2030 (UNFCC, 2016b). They also intend to increase the power generation from 1,625 MW (2010) to 13,387 MW and develop the potential of exporting electricity, mainly generated by renewable energy, to reach a goal of exporting up to 8,930 MW by 2030.

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2.1.5 CLEWs The Climate, Land-Use, Energy and Water system (CLEW’s) is an integrated approach that acknowledges that resources are deeply interlinked and aims to identify complex relations amongst these. CLEWs is used in order to analyse where synergies and trade-offs occur within an integrated system and where utilization of resources can be more efficient (UN, 2016). CLEW’s combine both quantitative and qualitative measures in order to create feedback loops and identify interlinkages between the various resources and activities (Department of Energy Technology, 2017a). Climate impacts and causes Climate change poses a threat to both the urban and rural population of Bolivia. A further rise in temperature could exacerbate the scarcity of water in the dry and semiarid valleys as well as decrease the availability of water in the highlands. Reports state that rain periods will be shortened, which will increase the risk for droughts (Winters. C, 2012). As a result of shorter rain periods, the rain might become intensified and concentrated over a few weeks and therefore causing serious floods. The reduction of glaciers due to global warming can exaggerate seasonal runoff in the short term causing floods and in the longer run increase the dependence of rain for provision of water. Flooding and droughts will have an adverse impact on agriculture as well as the livelihoods of the poor that have a limited ability to adapt because of a higher economic vulnerability. A decrease in Bolivia’s GHG emissions, as well as for the rest of the world, is vital to reduce climate change. Bolivia’s energy supply is dominated by fossil fuels but the CO2 emissions in metric tons per capita in Bolivia (1.9) are less than half of the average CO2 emissions in the world (4.97) (The World Bank, 2014c). Even though the CO2 emissions per capita are relatively low, the emissions in Bolivia are growing linearly due to growing industries that are emitting CO2. Land use and agriculture Bolivia’s land use is divided into agricultural land (34.3%), forests (52.5 %) and other (13.2%) which include roads, built-up areas, barren lands and wastelands (CIA, 2014). Six of the most harvested crops are: soybeans, coffee, cocoa, rice, nuts and potatoes. The agriculture stands for 13.7% of the country’s GDP and 30% of the employment in Bolivia is within agriculture (The World Bank, 2014d). The amount of labours in agriculture is decreasing and the biggest challenges are lack of adequate technologies, limited rural infrastructure and a poor natural resource management (FAO, 2014). In Bolivia, 94% of the agriculture units are smallholders that mostly produce staple crops and most of these farmers are living around the Altiplano and Yungas valleys (J. McDowell et al., 2012). In recent years’ water shortages and rise in temperatures have forced farmers to change water and cropping use strategies and if the water shortage should become worse, then the rain-fed agriculture could be in danger. Because many farms are located in rural areas where electricity is scarce, it is also difficult to expand the business from the current small scale and family owned practice. Energy system Natural gas and oil represent 75% of the energy supply while biomass represents 14% and electricity 10%. The transport sector is the one with the highest energy demand followed by industry and residential. Bolivia exports 65% of their total energy production in which natural

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gas is the most exported energy resource. The majority of the natural gas is extracted in the south of Bolivia and 80% of the natural gas is latter exported, primarily to Brazil and Argentina (YPFB, 2017). The export of natural gas represented 41.8% of the total export in the country in 2015, natural gas is consequently one of the most important export goods (OEC, 2016). The natural gas is a big contributor to Bolivia’s economy due to both export and internal use. The complexity of Bolivia’s usage of natural gas is that, on the one hand, they are deeply dependent on it, both economically, in terms of export, but also for the reason of national energy use. On the other hand, natural gas emits CO2 which contributes to climate change. Water resources The three main drainage basins in Bolivia are The Amazon, La Plata and endorheic Basins in the surroundings of Altiplano (World atlas, 2014). The Amazon basin makes up for 44% of the total territory of South America and 66% of the Bolivian territory. It covers the vast majority of the population as well as it is the regions with the most rainfall. La Plata is located in the South and East of Bolivia and experiences less rain than the Bolivian average. The endorheic basins are located in the west and drain into a series of shallow lakes which is expected to shrink because of a reduction of precipitation due to the impact of climate change (Yapiyev. V, 2017). The average rainfall is annually 1,150 mm but this differs from one region to another. In Altiplano, the average rainfall is merely 254 mm, while in the Amazon it amounts to as much as 2,500 mm. Most of the rainfall occurs between December to January, which entails serious flooding during that period and risk for severe droughts during the long dry season (Vicente-Serrano S.M. et al., 2015). In Bolivia, hydropower is the power source with most variations in production throughout the year because of the various amounts of precipitation in dry and wet seasons (C. Arderne, 2016). As mentioned above, climate change may shorten the rain period, which furthermore will impact the electricity generation from hydropower. If the rain is concentrated during a shorter period, there is a risk that hydropower generation will reduce during the dry seasons even more dramatically than it does today. In 2015, 93% of Bolivia’s population had access to safe drinking water. In rural and urban areas, the access to safe drinking water was 79.9% and 99.3% respectively (WHO, 2016). However, problems regarding access to water have been on the agenda as late as 2015 when 172 of the 339 countries municipalities declared emergency related to drought in which was described as the worst drought in 25 years, caused by El Niño, poor water management and climate change (Reuters, 2015).

2.2 Data collection The data used in the model will be gathered through examination of governmental archives, interviews and previous works. The data from the previous study “A climate, land-use, energy and water nexus assessment of Bolivia” made by Arderne (2016) is going to be the foundation for the model and is provided by KTH-dESA. Data concerning the power plants will be obtained from previous studies done by KTH and Universidad Mayor de San Simón (C. Arderne, 2017) (G. Balderrama. et al., 2017). This data will be verified and updated with data that will be gathered from both governmental archives and interviews. Interviews with the governmental CNDC and non-governmental organization Energetica will be done to get more information and an understanding of the subject matter to choose relevant

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scenarios for the modelling as well as building and verifying the Business As Usual (BAU) scenario (CNDC, 2018) (Energetica, 2018).

2.3 Modelling This study mainly focuses on modelling the electricity system in Bolivia using a model that uses linear optimization to find the least cost solution while meeting the demand. In this study the following modelling tools will be used.

2.3.1 Open Source Energy Modelling System (OSeMOSYS) Open Source Energy Modelling System, OSeMOSYS, is an energy planning tool that uses linear optimization to find the least cost solution of the system. The optimization is done by considering a number of sets and parameters that can be summarized under the categories: natural, technical and economical. Sets are constant throughout the whole model period and parameters are functions of the sets that can be changed during the model period. Given the constraints, it finds the least cost solution to a given electricity demand and provides the cost-optimal electricity mix, OSeMOSYS is an open source piece of software that uses GNU linear programming kit (GLPK) solver (Howells et al., 2011).

2.3.2 Model Management Infrastructure for OSeMOSYS (MoManI) The OSeMOSYS code can run with different interfaces and programs and in this study, the interface Model Management Infrastructure for, MoManI, will be used. Similarly, to OSeMOSYS, MoManI is open source tool and is supposed to be available to all types of users that are interested in energy planning, from policy makers to researchers. The tool provides a visual interface required to generate models, create scenarios and visualize the results from the OSeMOSYS code (Department of Energy Technology, 2017).

2.3.3 Reference Energy System A Reference Energy System (RES) defines the total amount of various technologies, from extraction of resources to generation, transmission and distribution. The RES for Bolivia can be viewed in Figure 3 and gives an overview of Bolivia’s electricity system. Note that it only considers the electricity connected to the grid. Technologies (fuel extraction, power plants, transmission lines etc.) are symbolized by boxes while energy carriers, also known as fuels in this case, are symbolized as lines. PP in the figure stands for power plant and the power plants are grouped by energy source in the figure. When building the model, the term fuel is a direct form of energy. Technologies on the other hand are units that comprise any fuel use and conversion and is divided into sources (extraction and imports), generation (power plants) and transmission & distribution (T&D). In the analysis part of this study, the first of the two will be of most interest. All fuels and technologies can be found in Appendix I. The sources in the model will consist of diesel imports, diesel extraction, natural gas extraction and biomass extraction. The generation technologies are natural gas single cycle, natural gas closed cycle, biomass single cycle, diesel single cycle, geothermal, wind, solar PV and hydropower. The “dummy” technology Backstop is used in the model and is only used when the demand is higher than the capacity. Backstop has an unlimited capacity but has the highest cost possible and is therefore illustrating the lack of installed capacity in the model. A

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simplification of the model is shown in the RES in Figure 3 and in Appendix I the complete list of fuels, technologies and output-/input activity ratios can be found.

Figure 3: Energy Reference System for Bolivia. Primary energy appears natural in environment and can be used directly while secondary energy derives from the transformation of primary energy. The primary energy source Natural gas is found in the nature and is through a power plant (PP) transformed into the secondary energy source Electricity.

2.3.4 Model set-up In the model. the following assumptions have been made in regards to fuel, technologies, storage and emissions:

- Natural gas will not be imported due to Bolivia’s large reserves (Chavez-Rodriguez et al., 2015).

- Diesel can be imported or refined from domestic oil production. - The renewable sources are modelled without input fuel (sun, wind and water are

assumed to be unlimited) and will therefore be manually restricted by the capacity in the power plants.

- Biomass is available from the domestic shares. - The model does not consider any storage for technologies. - CO2 is the only GHG emission considered in the model.

Sets As mentioned before, OSeMOSYS is built on set and parameters. This part will explain the set values and the part that follows will clarify the parameter values. All input data for sets and parameters can be found in Appendix I. The Fuels and Technologies set in this model have been explained in the previous section and are shown in the RES, see Figure 3. The selected technologies and fuels in the model will be based on data from a previous OSeMOSYS model (C. Arderne, 2016). The modelling period is set to run between 2012 and 2040 and is divided into six Seasons. Each season is assumed to be two months long to capture the variations of the precipitation and is determined by analysing the generation curves for the hydropower plants connected to the

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national grid. Hydropower is the power source with the highest variation of capacity through the year due to dry seasons (C. Arderne, 2016). There is only one type of day during the year, the demand for weekends and weekdays differ with less than 10% from each other and will therefore not be taken into account when setting up the model. However, the day is split into four different Time slices where each time slice has a diverse length. The time slices are shown in Table 1.

Time slice Hours Morning 2.00 – 9.00

Day 10.00 – 18.00 Evening 19.00 – 22.00

Night 23.00 – 1.00 Table 1: Time slices by hour

Different electricity demand during the day as well as the fact that the solar power only can be used during daytime was taken into consideration when deciding each time slice. Parameters The demand sectors taken into consideration will be residential, commercial, industrial, mining, public (public lightning etc.), others2 and exportation. The Specified Annual Demand for each sector except for the export sector, which will be determined by the export price in the relevant scenario (not considered in the BAU), are values that are obtained from the AE (AE, 2016). However, this data only holds values up until 2016. A forecast in which states that the electricity demand in Bolivia will increase by 223% until 2035 compared to 2012 is used and the last five years are calculated using linear regression (G. Balderrama et al., 2017). The Specified Demand Profile for the residential sector will be calculated by taking the hourly electricity demand for previous years and then calculating the percentage of demand for each time slice for each season (C. Arderne, 2016). It will be assumed that The Specified Demand Profile will not experience any changes for coming years. The Emission Activity Ratio is set in kton/PJ and is only set for natural gas extraction, biomass extraction and diesel extraction and import, assuming these four are the only parameters that will emit CO2 emissions. Although the combustion takes place in the power plant it will not be taken into account for technologies in order to avoid double counting. The data is retrieved from the EIA for natural gas and diesel and from EPA for biomass (EIA, 2017a) (EPA, 2016). The Input Activity Ratio for all extraction and import technologies will be calculated by taking data for the efficiency for each technology. The value of one is then divided by the efficiency to obtain how many fuel units required to produce 1 PJ of electricity. This data is obtained from POES and EIA (EIA, 2017b) (POES, 2012). The distribution and transmission losses will be taken into account by giving transmission and distribution technologies an input of 1 divided by the distribution and transmission losses for Bolivia (World Bank Data, 2014b). It will also be assumed that there will be no improvements in heat rate. It is also assumed that there will not be any improvements in the transmission or distribution network. 2 Others is mainly commercial however different departments categorize facilities such as hospitals and schools differently and therefore others will cover facilities who have not been taken into account in the commercial sector.

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The Output Activity Ratio will be set as 1 for all technologies seeing how all losses are taken into account in the parameter Input Activity Ratio. Data concerning power plants is obtained from the data concerning the previous study from C. Aderene (2016). The Total Annual Max Capacity will be set by entering the installed capacity of the power plants and adding capacity for when new power plants are planned to be in operation. It is also assumed that there will be no more investments for already existing hydropower plants and natural gas single cycle. Power plants that are set to be in operation from 2014 to 2019 have been set to have a Total Min Capacity Investment which will be the same value as their installed capacity, forcing them to have a capacity, however, the model will not use these capacities if it is not optimal. Fixed, Variable and Capital Cost will have different assumptions for different technologies. For existing hydropower plants as well as for the extraction technologies an assumption will be made that the capital cost is zero. The assumption implies that functional generation systems for these are already in place so the only costs for these are variable and fixed costs. All the costs for electricity generation from hydropower (both existing and planned), wind, solar, biomass and natural gas will be taken from World Energy Outlook for Brazil (WEO, 2016). Brazil and Bolivia have similar costs when it comes to energy and since accurate values for Bolivia, are not available, an assumption is made that the costs for the various power plants in Brazil apply for Bolivia. The costs for diesel, biomass and diesel extraction as well as the electricity generations costs from diesel and geothermal have been gathered from the Ministry of Hydrocarbons and Energy (POES, 2012). The Discount Rate of 0.12 is used in the model and is collected from the medium term expansion of Bolivia (POES, 2012). Residual Capacity is the capacity left from a period prior to the modelling period. The capacity for each technology will overtime decrease due to that historic capacity will be retired. In the model, residual capacity will be used if there is any residual capacity in the year before the first modelling year, 2012 (CNDC, 2016c). The Availability Factor and capacity factor is correlated to when each technology generates electricity. While the capacity factor is exogenously determined (solar radiation determines when solar PV works), the availability factor is an upper limit on how much a technology can run in a given year. In the model natural gas, diesel and geothermal will have values for the availability factor that corresponds to historical data for these plants while the rest of the plants have a capacity factor (CNDC, 2016c). The Capacity Factor will be set for the fuels that does not have an availability factor, these are the renewable fuels: wind power, solar power, hydropower and biomass power plant. Capacity factor is the ratio of actual energy produced by a power plant divided by the installed capacity of the power plant. Data for capacity and generation is obtained from CNDC (CNDC, 2016c). For solar power, the assumptions will be made that the sun only shines during daytime while the wind blows evenly throughout the day. For both solar power and wind power, assumptions are made that the electricity production is likewise throughout the year. The generation of electricity from biomass takes places during the harvest season, April-October, which is applied in the model (Guabira, 2017). The capacity factor for the existing hydropower is calculated for each of the six seasons taking into consideration the various amounts of water for different seasons (C. Arderne, 2016).

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2.4 Scenarios for Bolivia The selected scenarios are based on material from the completed literature review and interviews. All the changed input data that has been changed for the scenarios can be found in Appendix II. A summary and explanation of all scenarios can be found in Table 2.

Scenario family Scenario Explanation

BAU BAU This scenario will be the reference scenario also known as the Business As Usual scenario. This scenario will have the same values as in the model set up.

Increased Solar PV Capacity

A An unlimited installed max capacity of solar power is set.

Reaching INDC’s

B A rate of 79% renewables in 2030.

C A rate of 79% renewables in 2030 and a CO2 emission cap of 0.04 tonnes/MWh by 2030.

D A rate of 79% renewables in 2030 and a CO2 emission cap of 0.04 tonnes/MWh by 2030 with an unlimited amount of installed max capacity of solar power.

Export Hub E Setting a negative variable cost for export to investigate the export potential

Renewable Learning Curves

F The price of renewables will linearly reduce and by 2040 it will be 20% cheaper than 2012.

G The price of renewables will linearly reduce and by 2040 it will be 40% cheaper than 2012.

H The price of renewables will linearly reduce and by 2040 it will be 60% cheaper than 2012.

I The price of renewables will linearly reduce and by 2040 it will be 80% cheaper than 2012.

Emission Penalty

J Emission penalty cost will be 0.02 M USD/kton of CO2. K Emission penalty cost will be 0.05 M USD/kton of CO2. L Emission penalty cost will be 0.075 M USD/kton of CO2. M Emission penalty cost will be 0.1 M USD/kton of CO2.

Combination of Scenarios – Renewable

Learing curves (RL) & Emission Penalty (EP)

N Combination of scenario F & J. 20% cheaper renewables in 2040 and an emission penalty of 0.02 M USD/kton of CO2

O Combination of scenario F & K. 20% cheaper renewables in 2040 and an emission penalty of 0.05 M USD/kton of CO2

P Combination of scenario G & J. 40% cheaper renewables in 2040 and an emission penalty of 0.02 M USD/kton of CO2

Q Combination of scenario G & K. 40% cheaper renewables in 2040 and an emission penalty of 0.05 M USD/kton of CO2

Constraints in Natural Gas

R No natural gas by 2025 S No natural gas by 2030 T No Natural gas by 2035 U No Natural gas by 2040

Table 2: Matrix of all chosen scenarios

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2.4.1 Business As Usual The first scenario created will be the Business As Usual (BAU) scenario which will hold the same values as in the model set up. From the BAU scenario, further scenarios will be created to see how the power system of Bolivia reacts to various circumstances. The scenarios will also serve as a kind of sensitivity analysis to see how different input values impact the model behavior.

2.4.2 Scenario A - Increased Solar PV Capacity As previously mentioned, Bolivia is located within a strip of land that receives the greatest radiation in the world. Low cloudiness and small differences in radiation rates between summer and winter makes solar PV an excellent resource of electricity for Bolivia (Energética, 2018). For example: in the dessert of Uyuni a square of 9×10km has the potential of generating 13.5 GW by 2025 but due to unavailability at night, it has been considered an undependable power source by the technical personal of the government’s energy sector, therefore, the current solar PV plans are very small (M. Fernandez F., 2018). Scenario A will study the potential of increased solar PV capacity by not putting any investment constraints on the technology.

2.4.3 Scenario B-D - Reaching INDC’s

The INDCs contextualize what the state in its self can do to contribute to the Paris Agreement, that was entered into force in November 2016 (UNFCCa, 2016). According to Bolivia’s INDCs, one of the country aspirations is to increase the share of renewable energy, mostly by installing hydropower, from generating 39% of the electricity in 2010 up to generating 79 % by 2030 and reducing carbon emissions to 0.04 ton/MWh by 2030 (UNFCCb, 2016). Scenario B will investigate an increase in renewables to 79 % of the total electricity production, scenario C will also add respectively an emission limit of 0.04 ton/MWh by 2030 (see Appendix IIII for calculations of annual emission limit for OSeMOSYS input data). Scenario D will inspect what role solar power could have if there was an unlimited amount of solar power while keeping the emission limit and the generation from 79% renewables, when trying to achieve the INDC’s.

2.4.4 Scenario E - Electricity hub in South America – Export

In this scenario different negative values for the variable costs for exporting will be examined until the model starts to exporting, which in the model will be illustrated with a difference in produced electricity and a lower model period cost in comparison to the BAU-scenario. The average electricity price in Brazil during 2017 was 0.14 USD/kWh in which the price must be cheaper than 0.14 USD/kWh for Brazil to want to import electricity from Bolivia (Statista, 2018). In the scenario an additional capacity of 1.5 GW extra will be available in 2026 to symbolize the Rio Madera project but the model will be able to export electricity from the first modelling year. An amended code will be used for this scenario; this code can be found in Appendix III.

2.4.5 Scenario F-I - Learning curves renewables Scenario F-I will investigate how much the prices for renewables have to reduce to make them cost-effective for the majority of the electricity to derive from renewables. Within this scenario, capital cost, fixed cost and variable cost for renewables will decrease linearly until 2040 where each scenario will decrease by a different percentage. The percentage will vary from 20-80% to see how the model reacts and to detect how sensitive the model is for price changes in

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renewables. Figure 4 demonstrates how the capital cost for Solar PV decreases in the different scenarios.

Figure 4: Capital cost for solar PV for scenarios F-I

2.4.6 Scenario J-M - Emission Cost Although, the Bolivian government will presumably not introduce an emission penalty cost in the near future, an emission penalty is one way to stimulate the use of renewables (P.Villarroel, 2018). In this scenario an investigation will be done to see what impact different values of emission penalty have on the power system in Bolivia. The four different values for the emission costs is chosen to somewhat correlate with the scenarios in the Learning Curves Renewables and the years when the majority of the generated electricity is from renewables.

2.4.7 Scenario N-Q - Combination of scenarios In these scenarios four combinations of different emission penalties and reduced cost for renewables will be examined.

2.4.8 Scenario R-U - Constrains in Natural gas While Bolivia has large gas reserves, they might be smaller than believed a couple of years ago. The R/P ratio, which is the remaining amount of gas resources expressed in time (amount of known resource)/(amount used per year), declined from 22 to 14 years from 2009 to 2014 (Chavez-Rodríguez M.F. et al., 2016b). If this prediction is correct and Bolivia continues to extract at the same pace as today, the natural gas reserves will be depleted before 2030. However, Bolivia’s government have several ongoing exploration projects to find additional gas reserves. Locations with a potential of new natural gas reserves have been found in the Amazon region but this could imply problems due to both nearby villages and environmental destructions when exploring (YPFB, 2018).

With these circumstances taken into consideration, scenarios R-U will put a constraint on the production of electricity from natural gas. Different scenarios will test how the system reacts to constraints on natural gas by different year ends. The constraint will linearly reduce the extraction of natural gas five years before the indicated year in each scenario. For example, in

0

500

1000

1500

2000

2500

2012 2017 2022 2027 2032 2037 2042

Capitalcost,SolarPV(MUSD/GW)

ScenarioA ScenarioB ScenarioC ScenarioD

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Scenario R the production of electricity from natural gas will start to linearly reduce by 2025 and in 2030 none will be produced.

3 Results This section presents the results from the modelling of the selected scenarios.

3.1 Total discounted cost The total discounted cost for all the scenarios is represented in Table 3 and is shown for the year 2025-2040 with five-year interval and therefore the values for 2040 shows the total cost for the entire model period. The cost is in million USD Dollars.

Scenario family Scenario 2025 2030 2035 2040

BAU BAU 4488.1 5242.1 5844.8 6249.6

Increased Solar PV Capacity

A 4488.1 5242.1 5844.8 6249.6

Reaching INDC’s

B 4482.8 6024.0 6422.3 6687.5 C 6011.7 6970.4 7607.4 7800.6 D 5996.9 6956.6 7613.1 7828.9

Export Hub E 4613.5 5261.4 5813.0 6190.0

Renewable Learning Curves

F 4485.9 5265.0 5806.0 6181.7 G 4527.2 5191.8 5738.3 6078.5 H 4474.2 5130.3 5680.4 5967.7 I 4421.1 5080.1 5621.0 5813.3

Emission Penalty

J 4791.1 5582.6 6204.0 6661.7 K 5124.1 5993.4 6731.5 7224.3 L 5321.8 6296.4 7131.3 7654.6 M 5703.9 6767.3 7529.6 7990.0

Combination of Scenarios

N 4764.5 5520.3 6164.0 6568.6 O 5068.7 5918.3 6663.7 7110.3 P 4701.3 5468.8 6093.6 6451.2 Q 4996.7 5990.6 6613.0 6947.3

Constraints in Natural Gas

R 6749.0 7681.6 8072.4 8222.9 S 4576.2 6597.9 6988.8 7139.5 T 4482.9 5262.6 6449.7 6600.3 U 4482.1 5230.7 5861.5 6327.4

Table 3: Results for Accumulated total discounted cost

3.2 Total capital investments Figure 5-26 illustrates how big investments the model does every year in M USD Dollars ands since the investment cost is represented for each year the costs in the graphs are undiscounted.

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Figure 5: Total capital investments by year for BAU

Figure 6: Total capital investments for scenario A

Figure 7: Total capital investments for scenario B

Figure 8: Total capital investments for scenario C

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Figure 9: Total capital investments for scenario D

Figure 10: Total capital investmenst for Scenario E

Figure 11: Total capital investments for scenario F

Figure 12: Total capital investments for scenario G

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Figure 13: Total capital investments for scenario H

Figure 14: Total capital investments for scenario I

Figure 15: Total capital investments for scenario J

Figure 16: Total capital investments for scenario K

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Figure 17: Total capital investments for scenario L

Figure 18: Total capital investments for scenario M

Figure 19: Total capital investments for scenario N

Figure 20: Total capital investments for scenario O

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Figure 21: Total capital investments for scenario P

Figure 22: Total capital investments for scenario Q

Figure 23: Total capital investments for scenario R

Figure 24: Total capital investments for scenario S

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Figure 25: Total capital investments for scenario T

Figure 26: Total capital investments for scenario U

3.3 Production by Technology Annual Figure 27-48 illustrates how much electricity each technology generates each year in PJ to meet the given demand of the scenario.

Figure 27: Production by technology annual for scenario BAU

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Figure 28: Production by technology annual for scenario A

Figure 29: Production by technology annual for scenario B

Figure 30: Production by technology annual for scenario C

Figure 31: Production by technology annual for scenario D

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Figure 32: Production by technology annual for scenario E

Figure 33: Production by technology annual for scenario F

Figure 34: Production by technology annual for scenario G

Figure 35: Production by technology annual for scenario H

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Figure 36: Production by technology annual for scenario I

Figure 37: Production by technology annual for scenario J

Figure 38: Production by technology annual for scenario K

Figure 39: Production by technology annual for scenario L

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Figure 40: Production by technology annual for scenario M

Figure 41: Production by technology annual for scenario N

Figure 42: Production by technology annual for scenario O

Figure 43: Production by technology annual for scenario P

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Figure 44: Production by technology annual for scenario Q

Figure 45: Production by technology annual for scenario R

Figure 46: Production by technology annual for scenario S

Figure 47: Production by technology annual for scenario T

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Figure 48: Production by technology annual for scenario U

3.4 Annual Emissions Figure 49-55 illustrates how much emissions each scenario emits each year compared to the BAU-scenario, the emission rate is set in kton of CO2.

Figure 49: Annual Emission for BAU and scenario A

Figure 50: Annual Emission for BAU and scenario B-D

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Figure 51: Annual Emission for BAU and scenario E

Figure 52: Annual Emission for BAU and scenario F-I

Figure 53: Annual Emission for BAU and scenario J-M

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Figure 54: Annual Emission for BAU and scenario N-Q

Figure 55: Annual Emission for BAU and scenario R-U

3.5 Annual Exports Figure 56 shows how much electricity the model exports each year in PJ in Scenario E.

Figure 56: Annual Exports for Scenario E

4 Discussion This section will discuss the results for each scenario family presented in the previous section as well as uncertainties of the data. The section will end with a summarized discussion regarding all scenarios.

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4.1 Data-uncertainties The energy modelling that lays the foundation of this thesis is based on previous data as well as future plans and forecasts. The difference between a theoretical model and the reality is in that aspect interesting to discuss. Bolivia’s INDC’s are very ambitious and so are their planned expansions of hydropower in the country. The hydropower projects have also a tendency of taking longer time to come into force than planned. An example of this is the hydropower Misicuni, in which was planned to be in operation by 2015 but was delayed by two years and was finished in the middle of 2017. Another factor that can cause delay, is the size of the hydropower plant. Bigger projects need more consultancies and negotiations which take time. It is also more difficult to estimate the installed capacity of the power plants. In 2030, several big hydropower projects are estimated to be in operation, with an installed capacity of 500 MW or more. As shown in the results, many scenarios rely on these big hydropower plants in order to meet the demand, however, because of their size these projects might not be in operation by the estimated time. Also, many of the major projected power plants will mainly be used for exportation, which the model has not taken into consideration. Further uncertainties are that capital, variable and fixed cost share Brazilian values. At present there does not exist accurate costs for Bolivia that use similar kind of levelized costs of electricity for the entire energy sector, derived from the same source. The costs in Bolivia and Brazil are however likewise and the results should therefore not differ too much. The model was based on previous works where some assumptions have been made that one has not been informed of. This unawareness will therefore lead to additional uncertainties.

4.2 Results analysis Increased Solar PV Capacity Between the BAU scenario and Scenario A there were not any difference at all regarding costs, production by technology or emissions. The result demonstrates that the other resources in the model are more feasible than Solar PV and therefore will not take advantage of the possible increase in capacity. The fact that the country is located in a region with a great amount of solar radiation gives Solar PV a great potential however, the cost of the technology must be reduced to be applicable in the electricity generation of Bolivia. Reaching INDC’s The results from Scenario B showed that a participation of 79% in 2030 renewables is possible, in which hydropower plants are the dominant electricity producer. But, when a carbon dioxide emission limit of 0.04 ton/MWh by 2030 was implemented in the model, it showed that a share of more than 79% of renewables would be needed to accomplish such an emission limit. When examining the production by technology it was discovered that the dummy technology Backstop had started to produce electricity, in which a conclusion could be drawn that under the current constraint of having an emission limit 0.04 ton/MWh, the demand can not be met with the available technologies.

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Since solar power is a technology that has a potential (M. Fernandez, 2018) in becoming a main source of power, a new scenario was run in which there was set an unlimited max installed capacity in solar power, similar to Scenario A, however this time taking the INDC’s into consideration. The results were inspected and a reduction in electricity production by Backstop had been recorded but not eliminated. An examination of what technologies were producing electricity during daytime was made and one could note that solar power was one of the main electricity producers and that no generation from Backstop was needed during daytime. From this discovery it could be concluded that solar power will be invested in, however, since it can only produce during a limited number of hours when the sun shines, it still not has the capacity of producing the electricity needed to fully eliminate Backstop since an additional amount of electricity will be needed during hours when the sun does not shine. Compared to the BAU scenario, hydropower is invested in much earlier and there is a noticeable decrease within annual emissions from year 2020 which was expected since an emission limit had been set. In comparison to the BAU-scenario, the total accumulated discounted costs for scenarios B-D do not differ greatly, however, the costs for the lack of capacity has not been included and for scenario B and D and will therefore be an additional cost that will presumably indicate an even greater total cost for scenarios B and D. The conclusion of this scenario will therefore be that 79% renewables is achievable, in which hydropower will be the main electricity producer, whereas an emission limit of 0.04 ton/MWh by 2030 is not achievable with the amount of renewable energy plants planned to be installed and the physical constraints set. But as mentioned previously in the report, this study does not take into consideration storage for any kind of technology. If this parameter had been used, then it might be able to have met the need seeing how the water being kept in storage could be used during the other hours of the day when the sun is not able to produce electricity. When reducing the amount of CO2 emissions, the model decides to constrain the CO2 emitting technologies, constraining therefore capacity that otherwise could be used to meet the demand. But there are other ways of reducing CO2 emissions and thus meet the demand. What is not taken into consideration is if Bolivia has any plans on introducing Carbon Capture Storage technologies, which will help to reduce CO2 emissions or implementing more effective gas turbines which need fewer amounts of natural gas to produce the same amount of MWh and therefore fossil technologies may be more efficient in the future emitting less CO2. Renewable Learning Curve The reduction within accumulated total discount cost in scenarios F-I are expected due to the model set up and because prices for renewable electricity are lower. However, how much the cost differs and what technologies that are used in each scenario is more interesting. When comparing the BAU-scenario with the learning curve of 20% the actual difference in total cost for the total model period is low, it differs by 67.9 M USD, a cost reduction by only 1.1%. In scenario F the renewables are 80% cheaper in 2040 compared to 2012 which leads to a total accumulated discounted cost of 5.813.3 M USD. The difference in costs are immense, 436.3 M USD, nevertheless the cost reduction is only 7.0% compared to the BAU scenario. In this scenario family, scenario F uses a majority of renewable energy from the year of 2033. In scenario E it took until 2036 for the generation to be mainly from renewables and for scenarios C and D it took until the year of 2040. However, when examining the entire model

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period. scenario F is the only scenario where renewables dominate the total model generation of electricity. The renewable energy source with the highest capacity potential is hydropower, due to the low cost and lower level of intermittency as well as Bolivia’s policies and planned projects. With an increasing electricity demand and a lower cost for renewables the model decides to invest in hydropower plants and in scenario F, almost all of the renewable energy technologies are used in 2040, however, the amount of capacity being used seems a bit too optimistic. One of the assumptions made in this scenario, is that all renewable technologies have the same learning curve, this is nevertheless, incorrect. In the BAU scenario the learning curves for renewables were taken from WEO and differ from each other depending on the type of technology (WEO, 2016). But in these scenarios the aim is to see how much cheaper renewables have to become in able for it to be more profitable than natural gas. Furthermore, the chance of hydropower plants being 80% cheaper in 2040 compared to 2012 is not likely and according to WEO they will probably not be cheaper at all seeing how they are already quite far in their learning curve. The capital cost of solar PV on the other hand is forecasted to be almost 50% cheaper in 2040 since it is in an early stage of its learning curve. Emission Penalty The economic incentive for Bolivia to stop using natural gas, may not be enough at present, considering its available reserves and the cost. The price of natural gas may however change because of fluctuations in projected availability, demand or emission costs, In this scenario family, different emission penalties for CO2 were tested to see how much it would affect Bolivia’s electricity system. Because of the higher cost for fossil fuels, due to the emission penalty, the total accumulated cost will therefore be higher for each scenario. With an emission penalty of 0.02 M USD/kton the total cost for the whole period was 412.0 M USD higher than in the BAU-scenario, a rise of 6.6%. For the highest emission penalty of 0.1 M USD/kton the cost for Bolivia’s electricity system was 1740.4 M USD more compared to the BAU scenario, a growth by 27.8%. Scenario K was the only scenario which managed to give an outcome of 50% renewables during the entire period. With the emission cost of 0.1 M USD/kton. the model finds it feasible to have the majority of the electricity generated from renewable energy sources from the year of 2022. For the emission costs of 0.02, 0.05 and 0.075 M USD/kton the year that the model starts generating the majority of electricity from renewable energy sources by year 2040, 2039 and 2038 respectively. The variations of CO2 emissions between the different scenarios in this scenario family are significant. An emission cost of 0.02 M USD/kton reduced the total CO2 emissions by 10.4% and with a cost of 0.1 M USD/kton the emission dropped by 42.9% compared to the BAU. Even though a reduction in CO2 emission is beneficial for the environment and is necessary to reduce the intensity of climate change one must ask themselves if an emission penalty for a developing country is the appropriate way to proceed. Bolivia is not a wealthy country and could use the funds to improve their infrastructure and expand their national grid instead of paying emission penalties. An emission penalty would also deter international investments, which is a source of income Bolivia cannot afford to lose. As mentioned earlier, Bolivia has very low emissions per capita and one way to look at the rising CO2 emission situation is that Bolivia can afford to emit more, considering that their

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emission per capita is relatively low. Another perspective is that Bolivia has a chance to create a society that does not emit as much CO2 as the rest of the world and therefore should prioritize carbon free electricity and energy. It is also an interesting moral question if developed countries that were allowed to build up their societies using fossil fuels now can prevent poor countries from doing the same. In this aspect it depends on the point of view of what is correct and the ideal solution differs between these different agendas. To prioritize sustainable development for the Bolivian electricity system, it is important for the future and the environment, but the way to progress will probably not be by forcing Bolivia to establish an emission penalty. Combination of RL and EP In this scenario family, a combination between renewable learning curves and emission penalties were made to see how the combination of the renewables learning curves of 20% and 40% and the emission penalty of 0.02 and 0.05 M USD/kton would affect the model in four different combinations. When examining the accumulated total discounted cost for the whole model period it was discovered that none of the combinations were cheaper than the BAU-scenario. The combination of a renewable learning curve of 40% and an emission penalty of 0.02 M USD/kton was the cheapest alternative and was 3.2% more expensive than the BAU. If an emission penalty of 0.02 M USD/kton would come into force in Bolivia, the cost of renewables would therefore need to reduce by more than 40% by 2040 to become more profitable than the BAU scenario. Referring to the discussion about renewable learning curves, the total cost for renewables will probably not reduce by 40% by 2040 and in addition, an emission penalty is consequently threatening to Bolivia’s economy if the electricity mix looks like it does today. Besides the negative effects of an emission penalty in Bolivia’s economy, it does favour the environment and use of renewable energy. In all of four scenarios there was a decrease in CO2 emissions. The combination of 20% renewable learning curve and emission penalty of 0.02 M USD/kton led to a decrease of 9.4% in emissions. The scenario with 40% renewable learning curve and 0.05 M USD/kton declines the CO2 emissions with 33.1% when analysing the entire model period compared to BAU. For scenario N, O, P and Q the year that the majority of electricity came from renewables was 2039, 2038, 2038 and 2028. Constraints in Natural Gas The importance of natural gas in Bolivia cannot be overstated in regard to the national electricity situation. In the BAU-scenario 75.1% of the total electricity generation during the complete period comes from natural gas, therefore the forecast stating that natural gas reserves are coming to an end is most alarming. Whenever the natural gas terminates in this scenario family the planned capacity in Bolivia is not enough to cover the loss of natural gas. The lack of capacity for each scenario is shown in Table 4 and in figure 44-47 the lack of capacity is illustrated with the penetration of the dummy technology Backstop.

No Natural gas by: 2025 2030 2035 2040 Lack of capacity (PJ) 215.6 192.0 156.8 53.4

Table 4: Lack of capacity by end year

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With non-existing natural gas reserves big capital investments are needed, both to finish building all of the planned power plants, but also to plan and build power plants to cover the lack of electricity that is shown in Table 4. The BAU scenario indicates that a capital investment of 16,696.9 M USD was needed to generate electricity to meet the increasing demand. However, if the natural gas reserves decline an additional capital investment of 4,207.8 M USD will be needed to cover the loss of natural gas. Furthermore, the system lacks a capacity of 53.4 PJ, which would mean even more additional costs. In other words, if the reserves fade, Bolivia will need to build even more power plants than those planned today. Natural gas is not going to vanish overnight, and Bolivia’s government have several ongoing projects in order to explore more reserves. It is yet not certain if they will find new gas reserves and if they do, it is a most likely that they would discover reserves in the Amazon where villages and forests are located. This would thus lead to deforestation and additional cost of moving entire villages and have social implications. Exports The model experienced a significant difference in electricity production and total accumulated cost when the export price was 0.09 USD/kWh which is the same as 15 M USD/PJ. In this scenario the total cost for the entire period was 59.6 M USD less than the BAU-scenario which is a decrease by 1.0% while the production rose by 297.1 PJ, an increase by 12.8%. In this scenario natural gas was limited to not generate more than in the BAU-scenario to prevent the model to exporting electricity from fossil fuels and mainly export electricity produced by hydropower. In reality this is not possible since one will never be able to know exactly where an exact amount of electricity will be derived from. This constraint was however done in order prevent the model from generating electricity from fossil fuels since exporting electricity is stated to only be generated from the large scale hydropower projects (PEEP, 2014). An interesting aspect is that the model already the first year starts to export electricity even though the extra capacity of 1.5 GW is not yet introduced until 2026, proving that extra capacity exists as long as natural gas does not vanish. In this scenario, more hydropower plants were needed, and the capital investment was therefore, 622.6 M USD more in this scenario compared to BAU. Bolivia’s desire to start exporting electricity can be an effort to increase the state treasury and later investing the profits back into the power system. The potential of exporting electricity is big due to low domestic consumption and because of increasing amounts of hydropower plants, which can create a gap between the demand and possible production. Argentina and Brazil have the highest electricity prices in South America whilst Bolivia has amongst the lowest, consequently Bolivia can export electricity at a price that is higher than in the domestic market but cheaper than in the importing countries. When Bolivia can sell electricity to a price that is lower than in the other countries, the demand for Bolivia’s electricity will outcompete competitors while stile making a profit.

4.3 Summary The aim of this thesis is to investigate how to increase the share of renewable electricity in Bolivia and examine how the country could act to achieve SDG 7 with clean and affordable energy for all. The scenario families Renewable Learning Curves and Emission Penalties are both different ways to give economic incentive to use more renewable energy. Both of the scenarios correlate with the year that the system uses 50% renewables or more in the generation

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of electricity but the cost between the scenarios differs. The accumulated total cost for the full model period differs from 480 to 2,177 M USD between these scenarios, in which scenarios F-I are the least cost alternative. Taken into account that Bolivia is a developing country, it is as mentioned earlier not a realistic alternative for the country to introduce emission penalties at this stage, but there is a risk that costs for fossil fuels in the future will increase. The cost for renewable energy technologies will on the other hand most likely decrease in the future due to learning curves. To make significant cuts in emissions, using economic incitements, renewables have to become 60% cheaper, which is not a likely reduction for hydro power technology. The scenario families Constraints in Natural Gas and Reaching INDC’s are the scenarios with the lowest amount of emissions, then again, they also have the highest total accumulated cost as well as an insufficient capacity in the last years due to a growing demand and with todays planned power plants the capacity for renewables is not enough to achieve their goal.

5 Conclusion By the end of 2040 or earlier, all of the scenarios, including the BAU-scenario, produce a majority of electricity from renewable energy sources. What is common to all scenarios, is that hydropower is the dominating electricity producer when different scenarios reach 50% renewables. Bolivia has a great potential of hydropower and the country has many planned and ongoing projects concerning the expansion of hydropower plants. The result that hydropower would be the dominant renewable technology is consequently expected. However, the projects tend to take longer time than first planned and accordingly there is a risk that the hydropower projects adopted in the model might do so as well. The forecast that natural gas reserves are running out is alarming and according to the scenarios Bolivia will not have enough built or planned power plants to manage the growing demand without the natural gas. Furthermore, the expansive hydro power sector is not enough, and the country needs to either build more hydropower or look at other alternatives. Bolivia is receiving a great amount of solar radiation so one complementary technology could be solar power if the cost for the technology reduces. However, big capital investments are needed to build the planned hydropower plants and additional solar power, and Bolivia is a low-income country. The model does not consider Bolivia’s actual economy, it only calculates the least cost solution, furthermore the analysis of this report does not cover how or if Bolivia can afford such investments. Even though building hydropower plants is expensive, Bolivia can benefit from them by exporting electricity to neighbouring countries. The model has however not taken into account the environmental issues of building a lot of hydropower, for example it disrupts the flow of rivers and causes erosion and deforestation. As mentioned above, the planned capacity is not enough if natural gas depletes and furthermore exports would not be possible either if natural gas vanishes. If Bolivia discovered new natural gas reserves or the forecast that reserves are running out is incorrect then natural gas would be the cheapest alternative for the electricity sector and is today the dominant source of electricity. Bolivia’s INDC are ambitious and according to the model the country does not have enough renewable potential to fully achieve the desired goals. Bolivia has a great potential to achieve SDG, clean and affordable energy for all, but they are also facing several challenges. Cities are spread out throughout Bolivia and the distances between them can be big; consequently, there is a large cost for expanding the grid. The government has identified the potential of hydropower and there are many big projects for

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expanding this sector but the capital cost for these power plants is big and the construction of them tend to take longer time than first planned. Climate change affects Bolivia in many ways and in the future the rain period could be shorter which would affect the electricity generation from hydropower. In order to achieve SDG 7, one would recommend Bolivia to invest in hydropower and also ensure that projected plans are in operation when decided to be and minimize delays as much as possible. Except for large investments in hydropower, alternative renewables should be investigated in too, in case rain periods shorten, and droughts appear more often and also because of the fact that the building of hydropower can be an environmental concern. Solar power has a great potential due to the high radiation in the country but is today in the beginning of its learning curve and are consequently expensive. Until then natural gas is a low-cost solution that is important for Bolivia’s electricity generation, but it is emitting carbon dioxide and to achieve SDG 7 it must be phased out.

6 Suggestions for future work This study concentrated on examining the possibilities of an increase in renewable energy penetration in the power system connected to the national grid, in order to gain clean and affordable energy. The study did therefore not take into consideration the minor, off-grid, isolated systems, nor did it do an expansion of the grid. It would therefore be interesting for future work to see what kind of investments would be needed, and how such systems can assist, in order to achieve a 100% electrification rate and also reduce the dependency on natural gas seeing how this too is a part of SDG 7. The study has, as mentioned, only concentrated on electricity in Bolivia, however, it would be interesting to examine other forms of energy seeing how natural gas is used in more areas such as transport and industry than just electricity, and a decrease in natural gas reserves could therefore have a greater impact than already mentioned. In addition, other greenhouse gases could also be examined since the use of energy sources does not only emit CO2 emissions. As seen in the results and mentioned in the conclusion, hydropower will be the main electricity producer in order to achieve a higher share of renewables. But, what was not taken into account in the study is the GHG emissions from both the construction of hydropower plants but also the dams emitting methane. A suggestion for future work could therefore also be to study if hydropower in fact is as clean as assumed in this study.

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7 References AE (2016) Anuario Estadístico 2016 p. 28-29 Retrived 4 April 2018 from https://www.ae.gob.bo/aewebmobile/main?mid=1&cid=161 AE (2018) Misión y Visión Retrieved 27 March 2018 from https://www.ae.gob.bo/aewebmobile/main?mid=1&cid=33 Agenda Patriótica 2025 (2013, January 22) Retrieved 28 March, 2018 from https://theredddesk.org/sites/default/files/13_pillars_of_a_sovereign_and_dignified_bolivia_2013.pdf Arderne, C. (2016, June) A climate, land-use, energy and water nexus assessment of Bolivia. KTH, Royal Institute of Technology 2016. Retrieved 14 March 2018, from http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A946272&dswid=-2124 Balderrama, G., Broad, O., Sevillano, C., Alejo, L., Howells, M. (2017, 21 April). Techno-economic demand projections and scenarios for the Bolivian energy system. Energy Strategy Reviews Volume 16, June 2017, Pages 96-109. Retrieved 30 April 2018, from https://doi.org/10.1016/j.esr.2017.03.001 Chávez-Rodríguez.M. F, Szklo.A, Lucena.A. (2015). Forecasting natural gas supply and demand in Bolivia, 5th Latin American Energy Economics Meeting, 2015. Retrieved April 2, 2018, from https://www.iaee.org/en/publications/proceedingsabstractpdf.aspx?id=12637 Chavez-Rodríguez M.F., Garaffa R., Andrade G., Cardenas G., Szkloa A., Lucena F.P. (2016, June). Can Bolivia keep its role as a major natural gas exporter in South America? Journal of Natural Gas Science and Engineering 33, 717-730. Retrieved 3 April 2018, from https://doi.org/10.1016/j.jngse.2016.06.008 CIA. (2014). The World Fact Book. Retrieved 4 April 2018, from https://www.cia.gov/library/publications/the-world-factbook/fields/2097.html CIA (2018) The World Fact Book https://www.cia.gov/library/publications/resources/the-world-factbook/geos/bl.html (Retrieved March 27, 2018) CNDC. (2016a). Principales Sistemas Eléctricos Retrieved 4 April 2018, from https://www.cndc.bo/sin/index.php CNDC. (2016b). Bolivia’s Comite Nacional de Despacho de Carga. Retrieved April 2, 2018, from https://www.cndc.bo/estadisticas/anual.php CNDC. (2016c). Diagrama unifilar del sin. Retrieved 12 April 2018, from https://www.cndc.bo/sin/u_sin.php CNDC (2018) Presentación

50

https://www.cndc.bo/empresa/presentacion.php (Retrieved March 27, 2018) Department of Energy Technology (2015, April 17) Welcome to the Unit of Energy Systems Analysis (KTH-dESA) Retrieved April 29, 2018 from https://www.kth.se/en/itm/inst/energiteknik/forskning/desa/welcome-to-the-unit-of-energy-systems-analysis-kth-desa-1.197296 Department of Energy Technology. (2017a, July 14). CLEW’s - Climate, Land, Energy and Water strategies to navigate the nexus. Retrieved March 28, 2018, from https://www.kth.se/en/itm/inst/energiteknik/forskning/desa/researchareas/clews-climate-land-energyand-water-strategies-to-navigate-the-nexus-1.432255 Department of Technology. (2017b, July 14). Development of the Model Management Infrastructure MoManI Interface. Retrieved March 28, 2018, from https://www.kth.se/en/itm/inst/energiteknik/forskning/desa/projects/development-of-the-model-management-infrastructure-momani-interface-1.663641 EFE (2016, April 16). Bolivia Hydrocarbons. Retrieved March 26, 2018, from https://www.efe.com/efe/english/business/ypfb-bolivia-s-oil-and-gas-nationalization-generated-31-5-bn-over-a-decade/50000265-2898302 EIA (2018) Annual Energy Outlook 2018. Retrieved April 14, 2018 from https://www.eia.gov/outlooks/aeo/ EIA (2017a, June 8) How much carbon dioxide is produced when different fuels are burned? Retrieved April 5, 2018 from https://www.eia.gov/tools/faqs/faq.php?id=73&t=11 EIA (2017b, May 10) What is the efficiency of different types pf power plants? Retrieved 5th of April 2018 from https://www.eia.gov/tools/faqs/faq.php?id=107&t=3 ENDE (2018a) Nuestra Historia Retrived 17 April 2018 from http://www.ende.bo/historia ENDE (2018b) Plan Estratégico. Retrieved 27 March 2018 from http://www.ende.bo/plan-estrategico Energetica (2018) Mapa Solar de Bolivia Retrieved 26 April 2018 from http://www.energetica.org.bo/energetica/publicacionesd.asp?d=278 EPA (2016, January) Direct Emissions from Stationary Combustion Sources Retrived 5 April 2018 from https://www.epa.gov/sites/production/files/2016-03/documents/stationaryemissions_3_2016.pdf FAO. (2014). Bolivia Country Profile. Retrieved 4 April 2018, from http://www.fao.org/countryprofiles/index/en/?iso3=BOL Wilson. J (2015, October 26) Bolivia wants to become the energy heart of South America Financial Times. Retrived 14 April 2018, from https://www.ft.com/content/771ea816-50d3-11e5-b029-b9d50a74fd14

51

G. Moura and M. Howells, “SAMBA, The open source South American Model Base. A Brazilian Perspective on Long Term Power Systems Investment and Integration.,” Royal Institute of Technology (KTH), Stockholm, Work. Pap. Ser., no. DESA /15/8/11, DOI: 10.13140/RG.2.1.3038.7042. Aug. 2015. Retrieved 17 April 2018, from http://www.osemosys.org/samba.html Guabira. (2017). Guabirá Energia S.A. Retrieved 12 April 2018, form http://www.guabira.com/guabira/guabira-energia-sa.html Howells, M., Rogner, H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S., … Roehr, A. (2011, October). OSeMOSYS: TheOpenSourceEnergyModelingSystem An introduction to its ethos, structure and development, Energy Policy, vol. 39 (10): 5850-5870. Retrieved March 28, 2018, from http://www.osemosys.org/about.html Howells, M., Herman, S., Welsch, M., Bazilian, M., Segerström, R., Alfstad, T., … Ramma, I. (2013, June 25). Integrated analysis of climate change, land-use, energy and water strategies : Nature Climate Change, vol 3, no 7, pp.621-626. Retrieved March 28, 2018, from http://www.nature.com/nclimate/journal/v3/n7/full/nclimate1789.html IEA. (2015). Bolivia: Electricity and Heat 2015. Retrieved March 28, 2018, from https://www.iea.org/statistics/statisticssearch/report/?country=Bolivia&product=electricityandheat IEA (2017) Energy Access Outlook 2017. Retrived 4 April 2018 from https://www.iea.org/energyaccess/database/ J.C.J.M. van den Bergh, W.J.W Botzen (2015, March) Monetary valuation of the social cost of CO2 emissions: A critical survey p. 33. Retrieved 14 March 2018, from https://19january2017snapshot.epa.gov/climatechange/social-cost-carbon_.html

J. McDowell., J. Hess., (2012, January). Accessing adaptation: Multiple stressors on livelihoods in the Bolivian highlands under a changing climate. In: Global Environmental Change 22.2 (2012), pp. 342–352. doi: 10.1016/j.gloenvcha.2011.11.002. L. de Souza Noel Simas Barbosa et.al, (March 22, 2017) Hydro, wind and solar power as a base for a 100% renewable energy supply for South and Central America p.2 doi.org/10.1371/journal.pone.0173820

Miguel Fernández F.; CEO of Energética; Interview on the 18th of April 2018, Cochabamba

Ministerio de Energía (2017, January 22) Información Institucional https://www.minenergias.gob.bo/informacion-institucional (Retrieved March 27, 2018) OEC. (2016). The Observatory of Economic Complexity. Retrieved 3 April 2018, from https://atlas.media.mit.edu/en/ Osmar Bolivar & Daniela Bilbalo, Economist, Governmental Institute, Interview 2nd of May 2018, Cochabamba Pablo Villarroel, CNDC, Interview on the 13th of April 2018. Cochabamba

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Pinto de Moura G.N., Loureiro Legey L.F, Pena Balderrama G., Howells M. (2017, June 29). South America power integration, Bolivian electricity export potential and bargaining power: An OSeMOSYS SAMBA approach. Retrieved 3 April 2018, from https://www.sciencedirect.com/science/article/pii/S2211467X17300299 PDSE (2016a) Pilar 2: Universalización de los servicios básicos p.78-79 Retrived 4 April 2018 from http://vpc.planificacion.gob.bo/pdes/ PDSE (2016b) Pilar 7: Sobrenía sobre nuestros recursos naturales p.148- 154 Retrived 4 April 2018 from http://vpc.planificacion.gob.bo/pdes/ POES (2012) Ministerio de Hidrocarburos & Energia Retrived 5 of April 2018 from https://www.cndc.bo Plan Eléctrico del Estado Plurinacional de Bolivia 2025. (2014, January) Viceministerio de Electricidad y Energías Alternativas. Retrieved 3 April, 2018, from http://hubenergetico.com/ciner/plan-electrico-del-estado-plurinacional-de-bolivia-2025/ Ranta M.E. (2014, October 31). In the Name of Vivir Bien, p. 11-12. Retrieved April 4, 2028, from https://helda.helsinki.fi/bitstream/handle/10138/136101/inthenam.pdf Reuters. (2016, 21 November). Bolivia declares state of emergency due to drought, water shortage. Retrieved 4 April, 2018, from https://www.reuters.com/article/us-bolivia-drought/bolivia-declares-state-of-emergency-due-to-drought-water-shortage-idUSKBN13G1P4 S. Carle. (2018). Photo of Cochabamba city Sida. (2017, January 17). Vårt arbete i Bolivia. Retrieved March 26, 2018, from https://www.sida.se/Svenska/Har-arbetar-vi/Latinamerika/Bolivia/Vart-arbete-i-Bolivia/ Statista. (2018). Global electricity prices by select countries in 2017 (in U.S. dollars per kilowatt hour). Retrieved 3 May, 2018, from https://www.statista.com/statistics/263492/electricity-prices-in-selected-countries/ The Economic and Social Development plan 2016-2020 (2016a) p.70-71 http://www.planificacion.gob.bo/uploads/PDES_INGLES.pdf (Retrieved March 28, 2018) The Economic and Social Development plan 2016-2020 (2016b) p.131-135 http://www.planificacion.gob.bo/uploads/PDES_INGLES.pdf (Retrieved March 28, 2018) The World Bank. (2014a). Electric power consumption (kWh per capita). Retrieved 4 April 2018) https://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC The World Bank (2014b) Electric power transmission and distribution losses (% of output) Retrieved 7 April 2018 from https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS The World Bank. (2014c). CO2 emissions (metric tons per capita). Retrieved 5 April 2018, from https://data.worldbank.org/indicator/EN.ATM.CO2E.PC

53

The World Bank (2014d). Agriculture, value added (% of GDP). Retrieved 4 April 2018, from https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS?locations=BO The World banks. (2016a). GDP per Capita. Retrieved March 26, 2018, from https://data.worldbank.org/indicator/NY.GDP.PCAP.CD The World Bank. (2016b, February 23). Bolivia: Gender and Ethnicity. Retrieved 26 March 26, 2018, from http://www.worldbank.org/en/news/infographic/2016/02/23/bolivia-gender-and-ethnicity The World Bank. (2018a, February 26). The World Bank in Bolivia. Retrieved March 26, 2018, from http://www.worldbank.org/en/country/bolivia/overview UN (2016). Global Climate, Land, Energy & Water Strategies. Retrieved 29 April, 2018, from https://unite.un.org/sites/unite.un.org/files/app-globalclews-v-1-0/landingpage.html UN (2018). Sustainable Development, Energy. Retrieved March 28, 2018, from http://www.un.org/sustainabledevelopment/energy/ UN (2017, June 21) World Population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100) https://www.un.org/development/desa/en/news/population/world-population-prospects-2017.html ( Retrived April 14, 2018)

UNFCC (2016b, October 5) Intended Nationally Determined Contribution from the Plurinational State of Bolivia http://www4.unfccc.int/ndcregistry/PublishedDocuments/Bolivia%20(Plurinational%20State%20of)%20First/INDC-Bolivia-english.pdf (Retrieved March 27, 2018)

UNFCC (2016a, November 4) The Paris Agreement http://unfccc.int/paris_agreement/items/9485.php (Retrieved March 28, 2018) Unicef (2015). Geography and society Boliva. Retrieved March 26, 2018, from https://www.unicef.org/bolivia/resources_2334.htm Vicente-Serrano S.M., Kenawy A. El, Azorin-Molina C., Chura O., Trujillo F., Aguilar E., Martín-Hernández N., López-Moreno J.I.,. Sanchez-Lorenzo A, Moran-Tejeda E., Revuelto J., Ycaza P. & Friend F. (2016) Average monthly and annual climate maps for Bolivia, Journal of Maps, 12:2, 295-310, DOI: 10.1080/17445647.2015.1014940 World Health Organization. (2016). Data for Bolivia. Retreieved 2 May 2018, from https://washdata.org/data#!/table?geo0=country&geo1=BOL Winters. C. (2012, June). Impact of Climate Change on the Poor in Bolivia, Global Majority E-Journal, Vol. 3, No. 1 (June 2012), pp. 33-43. Retrieved 5 April 2018, from https://pdfs.semanticscholar.org/946e/1fe0d9b9fbddcc0c753664802fb4b6dd8dc9.pdf World Atlas. (2014). Bolivia Geography. Retrieved 4 April 2018, from https://www.worldatlas.com/webimage/countrys/samerica/bolivia/boland.htm

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YPFB. (2016) Misión y Visión http://www.ypfb.gob.bo/es/informacion-institucional/mision-y-vision.html (Retrieved March 27, 2018) YPFB. (Feb 2018). Estudios estiman 50 millones de barriles de petróleo en el área Nueva Esperanza. Retreieved 29 April, 2018, from http://www.ypfb.gob.bo/en/14-noticias/543-estudios-estiman-50-millones-de-barriles-de-petr%C3%B3leo-en-el-%C3%A1rea-nueva-esperanza.html YPFB. (2017). Tarija concentra la mayor producción de gas natural e hidrocarburos líquidos. Retrieved 4 April, 2018, from http://www1.ypfb.gob.bo/index.php?option=com_content&view=article&id=2623:tarija-concentra-la-mayor-produccion-de-gas-natural-e-hidrocarburos-liquidos-&catid=121:agencia-de-noticias&Itemid=196 Yapiyev, V. (2017, October 21) Essentials of Endorheic Basins and Lakes – MDPI. Retrieved 29 April, 2018, from http://www.mdpi.com/2073-4441/9/10/798/pdf

8 Appendices

8.1 Appendix I Technologies Technologies Code OperationalLife CapacityToActivityUnit RETag

SOURCES

Diesel imports DL00I00 50 1 0

Diesel extraction DL00X00 50 1 0

Natural gas extraction NG00X00 50 1 0

Biomass extraction BM00X00 50 1 0

GENERATION

Hydro power planned HYDPL07 50 31.536 1

Hydro power planned HYDPL09 50 31.536 1

Hydro power planned HYDPL04 50 31.536 1

Hydro power planned HYDPL15 50 31.536 1

Hydro power planned HYDPL13 50 31.536 1

Hydro power planned HYDPL17 50 31.536 1

Hydro power planned HYDPL12 50 31.536 1

Hydro power planned HYDPL19 50 31.536 1

Hydro power planned HYDPL06 50 31.536 1

Hydro power planned HYDPL03 50 31.536 1

Hydro power planned HYDPL08 50 31.536 1

Hydro power planned HYDPL01 50 31.536 1

Hydro power planned HYDPL05 50 31.536 1

55

Hydro power planned HYDPL18 50 31.536 1

Hydro power planned HYDPL16 50 31.536 1

Hydro power planned HYDPL14 50 31.536 1

Hydro power planned HYDPL10 50 31.536 1

Hydro power planned HYDPL02 50 31.536 1

Hydro power planned HYDPL11 50 31.536 1

Natural gas single cycle NGGCP00 20 31.536 0

Natural gas closed cycle NGCCP00 20 31.536 0

Biomass single cycle BMGCP00 30 31.536 1

Diesel single cycle DLSCP00 20 31.536 0

Geothermal GOCVP00 25 31.536 1

Wind with CF3 30 WI30P00 25 31.536 1

Solar PV SOU1P00 20 31.536 1

Hydropower existing HYDEX01 50 31.536 1

Hydropower existing HYDEX02 50 31.536 1

Hydropower existing HYDEX03 50 31.536 1

Hydropower existing HYDEX04 50 31.536 1

Hydropower existing HYDEX05 50 31.536 1

Hydropower existing HYDEX06 50 31.536 1

Backstop BACKSTOP 1 31.536 0

T&D

Transmission EL00T00 60 31.536 0

Distribution to residential EL00TDR 60 31.536 0

Distribution to commercial EL00TDC 60 31.536 0

Distribution to industrial EL00TDI 60 31.536 0

Distribution to mining EL00TDM 60 31.536 0

Distribution to public EL00TDP 60 31.536 0

Distribution to export EL00TDE 60 31.536 0

Distribution to others EL00TDO 60 31.536 0 Fuels FUEL Code RETag

Natural gas NG 0

Diesel DL 0

3 CF – Capacity Factor

56

Biomass BM 0

Electricity (from generation) EL1 0

Electricity (from transmission) EL2 0

Electricity (residential) EL3R 1

Electricity (commercial) EL3C 1

Electricity (industrial) EL3I 1

Electricity (mining) EL3M 1

Electricity(public) EL3P 1

Electricity (export) EL3E 1

Electricity (others) EL3O 1 Timeslice TIMESLICE 02:00 - 09:00 10:00 - 18:00 19:00 - 22:00 23:00 - 01:00

Jan & Feb S1M S1D S1E S1N

Mar & April S2M S2D S2E S2N

May & Jun S3M S3D S3E S3N

Jul & Aug S4M S4D S4E S4N

Sep & Oct S5M S5D S5E S5N

Nov & Dec S6M S6D S6E S6N Sets Region Bolivia

Model Start year 2012

Model end year 2040

EMISSION CO2

MODE_OF_OPERATION 1

SEASON 1 2 3 4 5 6

DAILYTIMEBRACKET 1 2 3 4

DAYTYPE 1

STORAGE No storage

DiscountRate 0.12 Default values AccumulatedAnnualDemand 0

AnnualEmissionLimit 99999

AnnualExogenousEmission 0

57

AvailabilityFactor 1

CapacityFactor 1

CapacityOfOneTechnologyUnit 0

CapacityToActivityUnit 1

CapitalCost 0

DaysInDayType 7

DepreciationMethod 1

EmissionActivityRatio 0

EmissionsPenalty 0

FixedCost 0

InputActivityRatio 0

ModelPeriodEmissionLimit 99999

OperationalLife 1

OperationalLifeStorage 99

OutputActivityRatio 0

REMinProductionTarget 0

ReserveMargin 1

ResidualCapacity 0

ResidualStorageCapacity 0

SpecifiedAnnualDemand 0

SpecifiedDemandProfile 1

StorageMaxChargeRate 99

TotalAnnualMaxCapacity 99999

TotalAnnualMaxCapacityInvestment 99999

TotalAnnualMinCapacity 0

TotalAnnualMinCapacityInvestment 0

TotalTechnologyAnnualActivityUpperLimit 99999

TotalTechnologyModelPeriodActivityUpperLimit 99999

VariableCost 0 Availability Factor (%) Technologies BACKSTOP 1 DL00I00 1 DL00X00 1 NG00X00 1 BM00X00 1 NGGCP00 0.909 NGCCP00 0.909 BMGCP00 1 DLSCP00 0.900

58

GOCVP00 0.914 WI30P00 1 SOU1P00 1 HYDEX01 1 HYDEX02 1 HYDEX03 1 HYDEX04 1 HYDEX05 1 HYDEX06 1 HYDPL01 1 HYDPL02 1 HYDPL03 1 HYDPL04 1 HYDPL05 1 HYDPL06 1 HYDPL07 1 HYDPL08 1 HYDPL09 1 HYDPL10 1 HYDPL11 1 HYDPL12 1 HYDPL13 1 HYDPL14 1 HYDPL15 1 HYDPL16 1 HYDPL17 1 HYDPL18 1 HYDPL19 1 EL00T00 1 EL00TDR 1 EL00TDC 1 EL00TDI 1 EL00TDM 1 EL00TDP 1 EL00TDO 1 EL00TDE 1

Capacity Factor for technologies for 2012-2040(%) BMGCP00 WI30P00 SOU1P00 HYDEX01 HYDEX02 HYDEX03 HYDEX04 S1M 0 0.30 0 0.2 0.7 0.8 0.8 S1D 0 0.30 1.0 0.2 0.7 0.8 0.8 S1E 0 0.30 0 0.2 0.7 0.8 0.8 S1N 0 0.30 0 0.2 0.7 0.8 0.8 S2M 0 0.30 0 0.2 0.6 0.8 0.8 S2D 0 0.30 1.0 0.2 0.6 0.8 0.8 S2E 0 0.30 0 0.2 0.6 0.8 0.8 S2N 0 0.30 0 0.2 0.6 0.8 0.8 S3M 0.40 0.30 0 0.2 0.3 0.3 0.5 S3D 0.40 0.30 1.0 0.2 0.3 0.3 0.5 S3E 0.40 0.30 0 0.2 0.3 0.3 0.5 S3N 0.40 0.30 0 0.2 0.3 0.3 0.5 S4M 0.65 0.30 0 0.2 0.2 0.2 0.3 S4D 0.65 0.30 1.0 0.2 0.2 0.2 0.3

59

S4E 0.65 0.30 0 0.2 0.2 0.2 0.3 S4N 0.65 0.30 0 0.2 0.2 0.2 0.3 S5M 0.80 0.30 0 0.3 0.2 0.2 0.4 S5D 0.80 0.30 1.0 0.3 0.2 0.2 0.4 S5E 0.80 0.30 0 0.3 0.2 0.2 0.4 S5N 0.80 0.30 0 0.3 0.2 0.2 0.4 S6M 0.25 0.30 0 0.3 0.4 0.5 0.8 S6D 0.25 0.30 1.0 0.3 0.4 0.5 0.8 S6E 0.25 0.30 0 0.3 0.4 0.5 0.8 S6N 0.25 0.30 0 0.3 0.4 0.5 0.8 HYDEX05 HYDEX06 HYDPL01 HYDPL02 HYDPL03 HYDPL04 HYDPL05 S1M 0.7 0.8 0.7 0.7 0.6 0.7 0.7 S1D 0.7 0.8 0.7 0.7 0.6 0.7 0.7 S1E 0.7 0.8 0.7 0.7 0.6 0.7 0.7 S1N 0.7 0.8 0.7 0.7 0.6 0.7 0.7 S2M 0.8 0.9 0.6 0.6 0.5 0.6 0.6 S2D 0.8 0.9 0.6 0.6 0.5 0.6 0.6 S2E 0.8 0.9 0.6 0.6 0.5 0.6 0.6 S2N 0.8 0.9 0.6 0.6 0.5 0.6 0.6 S3M 0.7 0.9 0.5 0.5 0.4 0.5 0.5 S3D 0.7 0.9 0.5 0.5 0.4 0.5 0.5 S3E 0.7 0.9 0.5 0.5 0.4 0.5 0.5 S3N 0.7 0.9 0.5 0.5 0.4 0.5 0.5 S4M 0.6 0.7 0.3 0.3 0.2 0.3 0.3 S4D 0.6 0.7 0.3 0.3 0.2 0.3 0.3 S4E 0.6 0.7 0.3 0.3 0.2 0.3 0.3 S4N 0.6 0.7 0.3 0.3 0.2 0.3 0.3 S5M 0.6 0.8 0.4 0.4 0.3 0.4 0.4 S5D 0.6 0.8 0.4 0.4 0.3 0.4 0.4 S5E 0.6 0.8 0.4 0.4 0.3 0.4 0.4 S5N 0.6 0.8 0.4 0.4 0.3 0.4 0.4 S6M 0.7 0.7 0.5 0.5 0.4 0.5 0.5 S6D 0.7 0.7 0.5 0.5 0.4 0.5 0.5 S6E 0.7 0.7 0.5 0.5 0.4 0.5 0.5 S6N 0.7 0.7 0.5 0.5 0.4 0.5 0.5

Capacity Factor for Technologies for 2012-2040 (%) HYDPL06 HYDPL07 HYDPL08 HYDPL09 HYDPL10 HYDPL11 HYDPL12 S1M 0.7 0.7 0.8 0.7 0.7 0.7 0.7 S1D 0.7 0.7 0.8 0.7 0.7 0.7 0.7 S1E 0.7 0.7 0.8 0.7 0.7 0.7 0.7 S1N 0.7 0.7 0.8 0.7 0.7 0.7 0.7 S2M 0.6 0.6 0.7 0.6 0.6 0.6 0.6 S2D 0.6 0.6 0.7 0.6 0.6 0.6 0.6 S2E 0.6 0.6 0.7 0.6 0.6 0.6 0.6 S2N 0.6 0.6 0.7 0.6 0.6 0.6 0.6 S3M 0.5 0.5 0.6 0.5 0.5 0.5 0.5 S3D 0.5 0.5 0.6 0.5 0.5 0.5 0.5 S3E 0.5 0.5 0.6 0.5 0.5 0.5 0.5 S3N 0.5 0.5 0.6 0.5 0.5 0.5 0.5 S4M 0.3 0.3 0.4 0.3 0.3 0.3 0.3 S4D 0.3 0.3 0.4 0.3 0.3 0.3 0.3 S4E 0.3 0.3 0.4 0.3 0.3 0.3 0.3

60

S4N 0.3 0.3 0.4 0.3 0.3 0.3 0.3 S5M 0.4 0.4 0.5 0.4 0.4 0.4 0.4 S5D 0.4 0.4 0.5 0.4 0.4 0.4 0.4 S5E 0.4 0.4 0.5 0.4 0.4 0.4 0.4 S5N 0.4 0.4 0.5 0.4 0.4 0.4 0.4 S6M 0.5 0.5 0.6 0.5 0.5 0.5 0.5 S6D 0.5 0.5 0.6 0.5 0.5 0.5 0.5 S6E 0.5 0.5 0.6 0.5 0.5 0.5 0.5 S6N 0.5 0.5 0.6 0.5 0.5 0.5 0.5 HYDPL13 HYDPL14 HYDPL15 HYDPL16 HYDPL17 HYDPL18 HYDPL19 S1M 0.7 0.9 0.7 0.7 0.7 0.9 0.6 S1D 0.7 0.9 0.7 0.7 0.7 0.9 0.6 S1E 0.7 0.9 0.7 0.7 0.7 0.9 0.6 S1N 0.7 0.9 0.7 0.7 0.7 0.9 0.6 S2M 0.6 0.9 0.6 0.6 0.6 0.9 0.5 S2D 0.6 0.9 0.6 0.6 0.6 0.9 0.5 S2E 0.6 0.9 0.6 0.6 0.6 0.9 0.5 S2N 0.6 0.9 0.6 0.6 0.6 0.9 0.5 S3M 0.5 0.7 0.5 0.5 0.5 0.7 0.4 S3D 0.5 0.7 0.5 0.5 0.5 0.7 0.4 S3E 0.5 0.7 0.5 0.5 0.5 0.7 0.4 S3N 0.5 0.7 0.5 0.5 0.5 0.7 0.4 S4M 0.3 0.6 0.3 0.3 0.3 0.6 0.2 S4D 0.3 0.6 0.3 0.3 0.3 0.6 0.2 S4E 0.3 0.6 0.3 0.3 0.3 0.6 0.2 S4N 0.3 0.6 0.3 0.3 0.3 0.6 0.2 S5M 0.4 0.8 0.4 0.4 0.4 0.8 0.3 S5D 0.4 0.8 0.4 0.4 0.4 0.8 0.3 S5E 0.4 0.8 0.4 0.4 0.4 0.8 0.3 S5N 0.4 0.8 0.4 0.4 0.4 0.8 0.3 S6M 0.5 0.9 0.5 0.5 0.5 0.9 0.4 S6D 0.5 0.9 0.5 0.5 0.5 0.9 0.4 S6E 0.5 0.9 0.5 0.5 0.5 0.9 0.4 S6N 0.5 0.9 0.5 0.5 0.5 0.9 0.4

Capital Cost 2012-2020 (M USD/ GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 0 0 0 0 0 0 0 0 0 BMGCP00 2250 2250 2250 2250 2250 2250 2250 2250 2200 DL00I00 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 DLSCP00 590 590 590 590 590 590 590 590 590 EL00T00 365 365 365 365 365 365 365 365 365 EL00TDC 1173 1173 1173 1173 1173 1173 1173 1173 1173 EL00TDE 0 0 0 0 0 0 0 0 0 EL00TDI 639 639 639 639 639 639 639 639 639 EL00TDM 622 622 622 622 622 622 622 622 622 EL00TDO 491 491 491 491 491 491 491 491 491 EL00TDP 993 993 993 993 993 993 993 993 993 EL00TDR 814 814 814 814 814 814 814 814 814 GOCVP00 5218 5218 5218 5218 5218 5218 5218 5218 5218 HYDEX01 0 0 0 0 0 0 0 0 0 HYDEX02 0 0 0 0 0 0 0 0 0

61

HYDEX03 0 0 0 0 0 0 0 0 0 HYDEX04 0 0 0 0 0 0 0 0 0 HYDEX05 0 0 0 0 0 0 0 0 0 HYDEX06 0 0 0 0 0 0 0 0 0 HYDPL01 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL02 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL03 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL04 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL05 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL06 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL07 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL08 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL09 3350 3350 3350 3350 3350 3350 3350 3350 3300 HYDPL10 3350 3350 3350 3350 3350 3350 3350 3350 3300 HYDPL11 3350 3350 3350 3350 3350 3350 3350 3350 3300 HYDPL12 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL13 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL14 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL15 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL16 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL17 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL18 2100 2100 2100 2100 2100 2100 2100 2100 2100 HYDPL19 2100 2100 2100 2100 2100 2100 2100 2100 2100 NG00X00 0 0 0 0 0 0 0 0 0 NGCCP00 700 700 700 700 700 700 700 700 700 NGGCP00 400 400 400 400 400 400 400 400 400 SOU1P00 1980 1980 1980 1980 1980 1980 1980 1980 1360 WI30P00 1380 1380 1380 1380 1380 1380 1380 1380 1320

Capital Cost 2021-2030 (M USD/ GW) 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 0 0 0 0 0 0 0 0 0 0 BMGCP00 2200 2200 2200 2200 2200 2200 2200 2200 2200 2150 DL00I00 0 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 0 DLSCP00 590 590 590 590 590 590 590 590 590 590 EL00T00 365 365 365 365 365 365 365 365 365 365 EL00TDC 1173 1173 1173 1173 1173 1173 1173 1173 1173 1173 EL00TDE 0 0 0 0 0 0 0 0 0 0 EL00TDI 639 639 639 639 639 639 639 639 639 639 EL00TDM 622 622 622 622 622 622 622 622 622 622 EL00TDO 491 491 491 491 491 491 491 491 491 491 EL00TDP 993 993 993 993 993 993 993 993 993 993 EL00TDR 814 814 814 814 814 814 814 814 814 814 GOCVP00 5218 5218 5218 5218 5218 5218 5218 5218 5218 5218 HYDEX01 0 0 0 0 0 0 0 0 0 0 HYDEX02 0 0 0 0 0 0 0 0 0 0 HYDEX03 0 0 0 0 0 0 0 0 0 0 HYDEX04 0 0 0 0 0 0 0 0 0 0 HYDEX05 0 0 0 0 0 0 0 0 0 0 HYDEX06 0 0 0 0 0 0 0 0 0 0 HYDPL01 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL02 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150

62

HYDPL03 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL04 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL05 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL06 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL07 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL08 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL09 3300 3300 3300 3300 3300 3300 3300 3300 3300 3350 HYDPL10 3300 3300 3300 3300 3300 3300 3300 3300 3300 3350 HYDPL11 3300 3300 3300 3300 3300 3300 3300 3300 3300 3350 HYDPL12 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL13 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL14 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL15 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL16 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL17 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL18 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 HYDPL19 2100 2100 2100 2100 2100 2100 2100 2100 2100 2150 NG00X00 0 0 0 0 0 0 0 0 0 0 NGCCP00 700 700 700 700 700 700 700 700 700 700 NGGCP00 400 400 400 400 400 400 400 400 400 400 SOU1P00 1360 1360 1360 1360 1360 1360 1360 1360 1360 1080 WI30P00 1320 1320 1320 1320 1320 1320 1320 1320 1320 1280

Capital Cost 2031-2040 (M USD/GW) 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 0 0 0 0 0 0 0 0 0 0 BMGCP00 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 DL00I00 0 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 0 DLSCP00 590 590 590 590 590 590 590 590 590 590 EL00T00 365 365 365 365 365 365 365 365 365 365 EL00TDC 1173 1173 1173 1173 1173 1173 1173 1173 1173 1173 EL00TDE 0 0 0 0 0 0 0 0 0 0 EL00TDI 639 639 639 639 639 639 639 639 639 639 EL00TDM 622 622 622 622 622 622 622 622 622 622 EL00TDO 491 491 491 491 491 491 491 491 491 491 EL00TDP 993 993 993 993 993 993 993 993 993 993 EL00TDR 814 814 814 814 814 814 814 814 814 814 GOCVP00 5218 5218 5218 5218 5218 5218 5218 5218 5218 5218 HYDEX01 0 0 0 0 0 0 0 0 0 0 HYDEX02 0 0 0 0 0 0 0 0 0 0 HYDEX03 0 0 0 0 0 0 0 0 0 0 HYDEX04 0 0 0 0 0 0 0 0 0 0 HYDEX05 0 0 0 0 0 0 0 0 0 0 HYDEX06 0 0 0 0 0 0 0 0 0 0 HYDPL01 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL02 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL03 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL04 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL05 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL06 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL07 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150

63

HYDPL08 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL09 3350 3350 3350 3350 3350 3350 3350 3350 3350 3350 HYDPL10 3350 3350 3350 3350 3350 3350 3350 3350 3350 3350 HYDPL11 3350 3350 3350 3350 3350 3350 3350 3350 3350 3350 HYDPL12 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL13 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL14 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL15 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL16 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL17 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL18 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 HYDPL19 2150 2150 2150 2150 2150 2150 2150 2150 2150 2150 NG00X00 0 0 0 0 0 0 0 0 0 0 NGCCP00 700 700 700 700 700 700 700 700 700 700 NGGCP00 400 400 400 400 400 400 400 400 400 400 SOU1P00 1080 1080 1080 1080 1080 1080 1080 1080 1080 1080 WI30P00 1280 1280 1280 1280 1280 1280 1280 1280 1280 1280

Emission Activity Ratio 2012-2040 (kton/PJ) kton/ PJ DL00I00 69.410 DL00X00 69.410 NG00X00 50.350 BM00X00 64.900

Fixed Cost 2012 – 2020 (M USD/GW) 0 2012 2013 2014 2015 2016 2017 2018 2019 2020 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 0 0 0 0 0 0 0 0 0 BMGCP00 0 0 0 0 0 0 0 0 0 DL00I00 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 DLSCP00 10 10 10 10 10 10 10 10 10 EL00T00 0 0 0 0 0 0 0 0 0 EL00TDC 0 0 0 0 0 0 0 0 0 EL00TDE 0 0 0 0 0 0 0 0 0 EL00TDI 0 0 0 0 0 0 0 0 0 EL00TDM 0 0 0 0 0 0 0 0 0 EL00TDO 0 0 0 0 0 0 0 0 0 EL00TDP 0 0 0 0 0 0 0 0 0 EL00TDR 0 0 0 0 0 0 0 0 0 GOCVP00 78 78 78 78 78 78 78 78 78 HYDEX01 55 55 55 55 55 55 55 55 55 HYDEX02 55 55 55 55 55 55 55 55 55 HYDEX03 55 55 55 55 55 55 55 55 55 HYDEX04 55 55 55 55 55 55 55 55 55 HYDEX05 55 55 55 55 55 55 55 55 55 HYDEX06 55 55 55 55 55 55 55 55 55 HYDPL01 55 55 55 55 55 55 55 55 55 HYDPL02 55 55 55 55 55 55 55 55 55 HYDPL03 55 55 55 55 55 55 55 55 55 HYDPL04 55 55 55 55 55 55 55 55 55 HYDPL05 55 55 55 55 55 55 55 55 55 HYDPL06 55 55 55 55 55 55 55 55 55

64

HYDPL07 55 55 55 55 55 55 55 55 55 HYDPL08 55 55 55 55 55 55 55 55 55 HYDPL09 70 70 70 70 70 70 70 70 70 HYDPL10 70 70 70 70 70 70 70 70 70 HYDPL11 70 70 70 70 70 70 70 70 70 HYDPL12 55 55 55 55 55 55 55 55 55 HYDPL13 55 55 55 55 55 55 55 55 55 HYDPL14 55 55 55 55 55 55 55 55 55 HYDPL15 55 55 55 55 55 55 55 55 55 HYDPL16 55 55 55 55 55 55 55 55 55 HYDPL17 55 55 55 55 55 55 55 55 55 HYDPL18 55 55 55 55 55 55 55 55 55 HYDPL19 55 55 55 55 55 55 55 55 55 NG00X00 0 0 0 0 0 0 0 0 0 NGCCP00 0 0 0 0 0 0 0 0 0 NGGCP00 7 7 7 7 7 7 7 7 7 SOU1P00 0 0 0 0 0 0 0 0 0 WI30P00 0 0 0 0 0 0 0 0 0

Fixed Cost 2021 – 2030 (M USD/GW) 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 0 0 0 0 0 0 0 0 0 0 BMGCP00 0 0 0 0 0 0 0 0 0 0 DL00I00 0 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 0 DLSCP00 10 10 10 10 10 10 10 10 10 10 EL00T00 0 0 0 0 0 0 0 0 0 0 EL00TDC 0 0 0 0 0 0 0 0 0 0 EL00TDE 0 0 0 0 0 0 0 0 0 0 EL00TDI 0 0 0 0 0 0 0 0 0 0 EL00TDM 0 0 0 0 0 0 0 0 0 0 EL00TDO 0 0 0 0 0 0 0 0 0 0 EL00TDP 0 0 0 0 0 0 0 0 0 0 EL00TDR 0 0 0 0 0 0 0 0 0 0 GOCVP00 78 78 78 78 78 78 78 78 78 78 HYDEX01 55 55 55 55 55 55 55 55 55 55 HYDEX02 55 55 55 55 55 55 55 55 55 55 HYDEX03 55 55 55 55 55 55 55 55 55 55 HYDEX04 55 55 55 55 55 55 55 55 55 55 HYDEX05 55 55 55 55 55 55 55 55 55 55 HYDEX06 55 55 55 55 55 55 55 55 55 55 HYDPL01 55 55 55 55 55 55 55 55 55 55 HYDPL02 55 55 55 55 55 55 55 55 55 55 HYDPL03 55 55 55 55 55 55 55 55 55 55 HYDPL04 55 55 55 55 55 55 55 55 55 55 HYDPL05 55 55 55 55 55 55 55 55 55 55 HYDPL06 55 55 55 55 55 55 55 55 55 55 HYDPL07 55 55 55 55 55 55 55 55 55 55 HYDPL08 55 55 55 55 55 55 55 55 55 55 HYDPL09 70 70 70 70 70 70 70 70 70 70 HYDPL10 70 70 70 70 70 70 70 70 70 70 HYDPL11 70 70 70 70 70 70 70 70 70 70 HYDPL12 55 55 55 55 55 55 55 55 55 55

65

HYDPL13 55 55 55 55 55 55 55 55 55 55 HYDPL14 55 55 55 55 55 55 55 55 55 55 HYDPL15 55 55 55 55 55 55 55 55 55 55 HYDPL16 55 55 55 55 55 55 55 55 55 55 HYDPL17 55 55 55 55 55 55 55 55 55 55 HYDPL18 55 55 55 55 55 55 55 55 55 55 HYDPL19 55 55 55 55 55 55 55 55 55 55 NG00X00 0 0 0 0 0 0 0 0 0 0 NGCCP00 0 0 0 0 0 0 0 0 0 0 NGGCP00 7 7 7 7 7 7 7 7 7 7 SOU1P00 0 0 0 0 0 0 0 0 0 0 WI30P00 0 0 0 0 0 0 0 0 0 0

Fixed Cost 2031 – 2040 (M USD/GW) 0 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 0 0 0 0 0 0 0 0 0 0 BMGCP00 0 0 0 0 0 0 0 0 0 0 DL00I00 0 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 0 DLSCP00 10 10 10 10 10 10 10 10 10 10 EL00T00 0 0 0 0 0 0 0 0 0 0 EL00TDC 0 0 0 0 0 0 0 0 0 0 EL00TDE 0 0 0 0 0 0 0 0 0 0 EL00TDI 0 0 0 0 0 0 0 0 0 0 EL00TDM 0 0 0 0 0 0 0 0 0 0 EL00TDO 0 0 0 0 0 0 0 0 0 0 EL00TDP 0 0 0 0 0 0 0 0 0 0 EL00TDR 0 0 0 0 0 0 0 0 0 0 GOCVP00 78 78 78 78 78 78 78 78 78 78 HYDEX01 55 55 55 55 55 55 55 55 55 55 HYDEX02 55 55 55 55 55 55 55 55 55 55 HYDEX03 55 55 55 55 55 55 55 55 55 55 HYDEX04 55 55 55 55 55 55 55 55 55 55 HYDEX05 55 55 55 55 55 55 55 55 55 55 HYDEX06 55 55 55 55 55 55 55 55 55 55 HYDPL01 55 55 55 55 55 55 55 55 55 55 HYDPL02 55 55 55 55 55 55 55 55 55 55 HYDPL03 55 55 55 55 55 55 55 55 55 55 HYDPL04 55 55 55 55 55 55 55 55 55 55 HYDPL05 55 55 55 55 55 55 55 55 55 55 HYDPL06 55 55 55 55 55 55 55 55 55 55 HYDPL07 55 55 55 55 55 55 55 55 55 55 HYDPL08 55 55 55 55 55 55 55 55 55 55 HYDPL09 70 70 70 70 70 70 70 70 70 70 HYDPL10 70 70 70 70 70 70 70 70 70 70 HYDPL11 70 70 70 70 70 70 70 70 70 70 HYDPL12 55 55 55 55 55 55 55 55 55 55 HYDPL13 55 55 55 55 55 55 55 55 55 55 HYDPL14 55 55 55 55 55 55 55 55 55 55 HYDPL15 55 55 55 55 55 55 55 55 55 55 HYDPL16 55 55 55 55 55 55 55 55 55 55 HYDPL17 55 55 55 55 55 55 55 55 55 55

66

HYDPL18 55 55 55 55 55 55 55 55 55 55 HYDPL19 55 55 55 55 55 55 55 55 55 55 NG00X00 0 0 0 0 0 0 0 0 0 0 NGCCP00 0 0 0 0 0 0 0 0 0 0 NGGCP00 7 7 7 7 7 7 7 7 7 7 SOU1P00 0 0 0 0 0 0 0 0 0 0 WI30P00 0 0 0 0 0 0 0 0 0 0

Input Activity Ratio 2012-2040 Technologies Fuel NGGCP00 NG 2.60 NGCCP00 NG 2.08 BMGCP00 BM 2.63 DLSCP00 DL 3.03 EL00T00 EL1 1.099 EL00TDR EL2 1.099 EL00TDC EL2 1.099 EL00TDI EL2 1.099 EL00TDM EL2 1.099 EL00TDP EL2 1.099 EL00TDO EL2 1.099

Output Activity Ratio 2012-2040 Technologies Fuel BACKSTOP EL1 1 DL00I00 DL 1 DL00X00 DL 1 NG00X00 NG 1 BM00X00 BM 1 NGGCP00 EL1 1 NGCCP00 EL1 1 BMGCP00 EL1 1 DLSCP00 EL1 1 GOCVP00 EL1 1 WI30P00 EL1 1 SOU1P00 EL1 1 HYDEX01 EL1 1 HYDEX02 EL1 1 HYDEX03 EL1 1 HYDEX04 EL1 1 HYDEX05 EL1 1 HYDEX06 EL2 1 HYDPL01 EL1 1 HYDPL02 EL1 1 HYDPL03 EL1 1 HYDPL04 EL1 1 HYDPL05 EL1 1 HYDPL06 EL1 1 HYDPL07 EL1 1 HYDPL08 EL1 1 HYDPL09 EL1 1 HYDPL10 EL1 1 HYDPL11 EL1 1

67

HYDPL12 EL1 1 HYDPL13 EL1 1 HYDPL14 EL1 1 HYDPL15 EL1 1 HYDPL16 EL1 1 HYDPL17 EL1 1 HYDPL18 EL1 1 HYDPL19 EL1 1 EL00T00 EL2 1 EL00TDR EL3R 1 EL00TDC EL3C 1 EL00TDI EL3I 1 EL00TDM EL3M 1 EL00TDP EL3P 1 EL00TDO EL3O 1

Residual Capacity 2012-2020 (GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 BACKSTOP 0 0 0 0 0 0 0 0 0 BM00X00 0 0 0 0 0 0 0 0 0 BMGCP00 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 DL00I00 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 DLSCP00 0.0394 0.0394 0.0394 0.0394 0.03 0.03 0.03 0.03 0.03 EL00T00 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 EL00TDC 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 EL00TDE 0 0 0 0 0 0 0 0 0 EL00TDI 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDM 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDO 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDP 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDR 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 GOCVP00 0 0 0 0 0 0 0 0 0 HYDEX01 0.0514 0.0514 0.0514 0.0514 0.0514 0.0514 0.0514 0.0514 0.0514 HYDEX02 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 HYDEX03 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 HYDEX04 0.118 0.118 0.118 0.118 0.118 0.118 0.118 0.118 0.118 HYDEX05 0.1336 0.1336 0.1336 0.1336 0.1336 0.1336 0.1336 0.1336 0.1336 HYDEX06 0.0746 0.0746 0.0746 0.0746 0.0746 0.0746 0.0746 0.0746 0.0746 HYDPL01 0 0 0 0 0 0 0 0 0 HYDPL02 0 0 0 0 0 0 0 0 0 HYDPL03 0 0 0 0 0 0 0 0 0 HYDPL04 0 0 0 0 0 0 0 0 0 HYDPL05 0 0 0 0 0 0 0 0 0 HYDPL06 0 0 0 0 0 0 0 0 0 HYDPL07 0 0 0 0 0 0 0 0 0 HYDPL08 0 0 0 0 0 0 0 0 0 HYDPL09 0 0 0 0 0 0 0 0 0 HYDPL10 0 0 0 0 0 0 0 0 0 HYDPL11 0 0 0 0 0 0 0 0 0 HYDPL12 0 0 0 0 0 0 0 0 0 HYDPL13 0 0 0 0 0 0 0 0 0 HYDPL14 0 0 0 0 0 0 0 0 0

68

HYDPL15 0 0 0 0 0 0 0 0 0 HYDPL16 0 0 0 0 0 0 0 0 0 HYDPL17 0 0 0 0 0 0 0 0 0 HYDPL18 0 0 0 0 0 0 0 0 0 HYDPL19 0 0 0 0 0 0 0 0 0 NG00X00 0 0 0 0 0 0 0 0 0 NGCCP00 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 NGGCP00 1.0232 1.0232 1.0232 1.0232 0.9928 0.9928 0.9928 0.9928 0.9928 SOU1P00 0 0 0 0 0 0 0 0 0 WI30P00 0 0 0 0 0 0 0 0 0

Residual Capacity 2021-2030 (GW) 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 BACKSTOP 0 0 0 0 0 0 0 0 0 0 BM00X00 0 0 0 0 0 0 0 0 0 0 BMGCP00 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0 0 0 DL00I00 0 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 0 DLSCP00 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0 EL00T00 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 EL00TDC 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 EL00TDE 0 0 0 0 0 0 0 0 0 0 EL00TDI 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDM 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDO 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDP 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDR 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 GOCVP00 0 0 0 0 0 0 0 0 0 0 HYDEX01 0.0404 0.0404 0.0342 0.0342 0.0342 0.0342 0.0342 0.0342 0.0342 0.0342 HYDEX02 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 HYDEX03 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 HYDEX04 0.088 0.088 0.088 0.088 0.058 0.058 0.058 0.058 0.058 0.058 HYDEX05 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 HYDEX06 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 HYDPL01 0 0 0 0 0 0 0 0 0 0 HYDPL02 0 0 0 0 0 0 0 0 0 0 HYDPL03 0 0 0 0 0 0 0 0 0 0 HYDPL04 0 0 0 0 0 0 0 0 0 0 HYDPL05 0 0 0 0 0 0 0 0 0 0 HYDPL06 0 0 0 0 0 0 0 0 0 0 HYDPL07 0 0 0 0 0 0 0 0 0 0 HYDPL08 0 0 0 0 0 0 0 0 0 0 HYDPL09 0 0 0 0 0 0 0 0 0 0 HYDPL10 0 0 0 0 0 0 0 0 0 0 HYDPL11 0 0 0 0 0 0 0 0 0 0 HYDPL12 0 0 0 0 0 0 0 0 0 0 HYDPL13 0 0 0 0 0 0 0 0 0 0 HYDPL14 0 0 0 0 0 0 0 0 0 0 HYDPL15 0 0 0 0 0 0 0 0 0 0 HYDPL16 0 0 0 0 0 0 0 0 0 0 HYDPL17 0 0 0 0 0 0 0 0 0 0 HYDPL18 0 0 0 0 0 0 0 0 0 0 HYDPL19 0 0 0 0 0 0 0 0 0 0

69

NG00X00 0 0 0 0 0 0 0 0 0 0 NGCCP00 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 0.2348 NGGCP00 0.9718 0.9718 0.9718 0.9718 0.9718 0.9718 0.9718 0.9314 0.9314 0.9142 SOU1P00 0 0 0 0 0 0 0 0 0 0 WI30P00 0 0 0 0 0 0 0 0 0 0

Residual Capacity 2031-2040 (GW) 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 BACKSTOP 0 0 0 0 0 0 0 0 0 0 BM00X00 0 0 0 0 0 0 0 0 0 0 BMGCP00 0 0 0 0 0 0 0 0 0 0 DL00I00 0 0 0 0 0 0 0 0 0 0 DL00X00 0 0 0 0 0 0 0 0 0 0 DLSCP00 0 0 0 0 0 0 0 0 0 0 EL00T00 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 0.9029 EL00TDC 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 0.2091 EL00TDE 0 0 0 0 0 0 0 0 0 0 EL00TDI 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDM 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDO 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDP 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 0.2695 EL00TDR 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 0.2826 GOCVP00 0 0 0 0 0 0 0 0 0 0 HYDEX01 0.0342 0.0342 0.0342 0.0342 0.0282 0.0282 0.0282 0.0282 0.0282 0.0282 HYDEX02 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 HYDEX03 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 0.0531 HYDEX04 0.058 0.058 0.058 0.058 0.058 0.058 0.058 0.058 0.058 0.058 HYDEX05 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 0.0934 HYDEX06 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 HYDPL01 0 0 0 0 0 0 0 0 0 0 HYDPL02 0 0 0 0 0 0 0 0 0 0 HYDPL03 0 0 0 0 0 0 0 0 0 0 HYDPL04 0 0 0 0 0 0 0 0 0 0 HYDPL05 0 0 0 0 0 0 0 0 0 0 HYDPL06 0 0 0 0 0 0 0 0 0 0 HYDPL07 0 0 0 0 0 0 0 0 0 0 HYDPL08 0 0 0 0 0 0 0 0 0 0 HYDPL09 0 0 0 0 0 0 0 0 0 0 HYDPL10 0 0 0 0 0 0 0 0 0 0 HYDPL11 0 0 0 0 0 0 0 0 0 0 HYDPL12 0 0 0 0 0 0 0 0 0 0 HYDPL13 0 0 0 0 0 0 0 0 0 0 HYDPL14 0 0 0 0 0 0 0 0 0 0 HYDPL15 0 0 0 0 0 0 0 0 0 0 HYDPL16 0 0 0 0 0 0 0 0 0 0 HYDPL17 0 0 0 0 0 0 0 0 0 0 HYDPL18 0 0 0 0 0 0 0 0 0 0 HYDPL19 0 0 0 0 0 0 0 0 0 0 NG00X00 0 0 0 0 0 0 0 0 0 0 NGCCP00 0.2348 0.2348 0 0 0 0 0 0 0 0 NGGCP00 0.9142 0.9142 0.2304 0.2304 0.2304 0.2304 0.2304 0.2304 0.2304 0.2304 SOU1P00 0 0 0 0 0 0 0 0 0 0 WI30P00 0 0 0 0 0 0 0 0 0 0

70

Specified Annual Demand 2012-2040 (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BM 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

DL 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL3C 4.809 5.112 5.657 6.232 6.607 6.825 7.275 7.755 8.267 8.812

EL3E 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL3I 6.758 7.236 7.756 7.958 8.038 8.770 9.224 9.702 10.205 10.733

EL3M 1.078 1.160 1.212 1.192 1.295 1.363 1.434 1.509 1.588 1.672

EL3O 1.091 0.987 0.738 0.789 0.834 1.074 1.143 1.217 1.296 1.379

EL3P 1.185 1.286 1.462 1.529 1.603 1.681 1.762 1.848 1.937 2.031

EL3R 9.870 10.492 11.437 12.559 13.246 14.048 15.029 16.078 17.201 18.402

NG 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BM 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

DL 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL3C 9.394 10.014 10.674 11.379 12.130 12.930 13.783 14.693 15.662 16.696

EL3E 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL3I 11.289 11.874 12.489 13.136 13.816 14.532 15.285 16.076 16.909 17.785

EL3M 1.759 1.852 1.949 2.051 2.158 2.272 2.391 2.516 2.648 2.787

EL3O 1.469 1.563 1.664 1.772 1.886 2.008 2.138 2.276 2.423 2.580

EL3P 2.129 2.232 2.340 2.454 2.573 2.697 2.828 2.965 3.109 3.259

EL3R 19.687 21.062 22.532 24.106 25.789 27.590 29.517 31.578 33.783 36.142

NG 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

2032 2033 2034 2035 2036 2037 2038 2039 2040

BM 0.203 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

DL 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL3C 17.797 18.972 20.223 21.558 22.980 24.497 26.113 27.836 29.673

EL3E 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

EL3I 18.706 19.675 20.694 21.766 22.893 24.079 25.326 26.638 28.018

EL3M 2.933 3.086 3.248 3.419 3.598 3.786 3.985 4.194 4.414

EL3O 2.746 2.924 3.113 3.314 3.528 3.756 3.998 4.257 4.532

EL3P 3.417 3.582 3.756 3.938 4.129 4.329 4.538 4.758 4.989

71

EL3R 38.666 41.366 44.254 47.345 50.651 54.188 57.972 62.020 66.351

NG 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Specified Demand Profile 2012-2020 2012 2013 2014 2015 2016 2017 2018 2019 2020

S1D 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065

S1E 0.033 0.033 0.033 0.033 0.033 0.033 0.033 0.033 0.033

S1M 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044

S1N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

S2D 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066

S2E 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035

S2M 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045

S2N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

S3D 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062

S3E 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034

S3M 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043

S3N 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019

S4D 0.064 0.064 0.064 0.064 0.064 0.064 0.064 0.064 0.064

S4E 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035

S4M 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044

S4N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

S5D 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070

S5E 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037

S5M 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048

S5N 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022

S6D 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069

S6E 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036

S6M 0.047 0.047 0.047 0.047 0.047 0.047 0.047 0.047 0.047

S6N 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 Specified Demand Profile 2021-2030 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

S1D 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065

S1E 0.033 0.033 0.033 0.033 0.033 0.033 0.033 0.033 0.033 0.033

S1M 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044

S1N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

S2D 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066

S2E 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035

S2M 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045

S2N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

72

S3D 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062

S3E 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034

S3M 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043

S3N 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019

S4D 0.064 0.064 0.064 0.064 0.064 0.064 0.064 0.064 0.064 0.064

S4E 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035

S4M 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044

S4N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

S5D 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070

S5E 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037

S5M 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048

S5N 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022

S6D 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069

S6E 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036

S6M 0.047 0.047 0.047 0.047 0.047 0.047 0.047 0.047 0.047 0.047

S6N 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 Specified Demand Profile 2031-2040 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

S1D 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065

S1E 0.033 0.033 0.033 0.033 0.033 0.033 0.033 0.033 0.033 0.033

S1M 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044

S1N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

S2D 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066 0.066

S2E 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035

S2M 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.045

S2N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

S3D 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062

S3E 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034

S3M 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043 0.043

S3N 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019

S4D 0.064 0.064 0.064 0.064 0.064 0.064 0.064 0.064 0.064 0.064

S4E 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035 0.035

S4M 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044 0.044

S4N 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

S5D 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070

S5E 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037

S5M 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048

S5N 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022

S6D 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069 0.069

S6E 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036

73

S6M 0.047 0.047 0.047 0.047 0.047 0.047 0.047 0.047 0.047 0.047

S6N 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 Total Max Capacity 2012 – 2020 (GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 BMGCP00 0.022 0.022 0.0708 0.0858 0.0958 0.0958 0.0958 0.1358 0.1358 DL00I00 99999 99999 99999 99999 99999 99999 99999 99999 99999 DL00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 DLSCP00 0.0394 0.0394 0.0394 0.0394 0.03 0.03 0.03 0.03 0.03 EL00T00 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDC 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDE 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDI 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDM 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDO 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDP 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDR 99999 99999 99999 99999 99999 99999 99999 99999 99999 GOCVP00 0 0 0 0 0 0.005 0.005 0.005 0.055 HYDEX01 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX02 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX03 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX04 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX05 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX06 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDPL01 0 0 0 0.08 0.08 0.08 0.12 0.12 0.12 HYDPL02 0 0 0 0 0 0 0.124 0.124 0.124 HYDPL03 0 0 0 0 0 0 0 0 0.253 HYDPL04 0 0 0 0 0 0 0 0 0.093 HYDPL05 0 0 0 0 0 0 0 0 0 HYDPL06 0 0 0 0 0 0 0 0 0 HYDPL07 0 0 0 0 0 0 0 0 0 HYDPL08 0 0 0 0 0 0 0 0 0.2 HYDPL09 0 0 0 0 0 0 0 0.019 0.019 HYDPL10 0 0 0 0 0 0 0 0.017 0.017 HYDPL11 0 0 0 0 0 0 0 0.009 0.009 HYDPL12 0 0 0 0 0 0 0 0 0 HYDPL13 0 0 0 0 0 0 0 0 0 HYDPL14 0 0 0 0 0 0 0 0 0 HYDPL15 0 0 0 0 0 0 0 0 0 HYDPL16 0 0 0 0 0 0 0 0 0 HYDPL17 0 0 0 0 0 0 0 0 0 HYDPL18 0 0 0 0 0 0 0 0 0 HYDPL19 0 0 0 0 0 0 0 0 0 NG00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 NGCCP00 0.2348 0.2348 0.2578 0.2578 0.2578 0.4178 0.4178 0.9978 1.1778 NGGCP00 9999 9999 9999 9999 9999 9999 9999 9999 9999 SOU1P00 0 0 0 0 0 0.07 0.07 0.12 0.12 WI30P00 0 0.003 0.024 0.024 0.048 0.108 0.18 0.18 0.18

Total Max Capacity 2021-2030 (GW)

74

2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BMGCP00 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 DL00I00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 DL00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 DLSCP00 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 EL00T00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDC 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDE 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDI 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDM 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDO 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDR 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 GOCVP00 0.055 0.055 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 HYDEX01 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX02 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX03 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX04 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX05 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX06 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDPL01 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 HYDPL02 0.124 0.124 0.124 0.124 0.124 0.124 0.124 0.124 0.404 0.404 HYDPL03 0.253 0.253 0.253 0.253 0.253 0.253 0.253 0.253 0.253 0.253 HYDPL04 0.093 0.093 0.093 0.093 0.093 0.093 0.093 0.093 0.093 0.093 HYDPL05 0 0.132 0.132 0.132 0.132 0.132 0.132 0.132 0.132 0.132 HYDPL06 0 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 HYDPL07 0 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 HYDPL08 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 HYDPL09 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 HYDPL10 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 HYDPL11 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 HYDPL12 0 0 0 0 1.6 1.6 1.6 1.6 1.6 1.6 HYDPL13 0 0 0 0 0.99 0.99 0.99 0.99 0.99 0.99 HYDPL14 0 0 0 0.4 0.4 0.4 0.4 0.4 0.4 0.4 HYDPL15 0 99999 0 0 0 0 0 0 0 0 HYDPL16 0 0 0 0 0 0.13 0.55 1.05 1.22 1.542 HYDPL17 0.347 0.347 0.347 0.347 0.347 0.347 0.347 0.347 0.347 0.347 HYDPL18 0 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 HYDPL19 0 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 NG00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 NGCCP00 1.5417 1.7594 1.9771 2.1948 2.4125 2.6302 2.8479 3.0656 3.2833 3.501 NGGCP00 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 SOU1P00 0.12 0.12 0.12 0.12 0.12 0.12 0.17 0.22 0.22 0.22 WI30P00 0.18 0.18 0.18 0.18 0.18 0.21 0.24 0.27 0.27 0.27

Total Max Capacity 2031-2040 (GW) 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BMGCP00 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 0.1358 DL00I00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999

75

DL00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 DLSCP00 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 EL00T00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDC 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDE 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDI 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDM 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDO 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 EL00TDR 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 GOCVP00 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 HYDEX01 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX02 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX03 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX04 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX05 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDEX06 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 HYDPL01 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 HYDPL02 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404 HYDPL03 0.253 0.253 0.253 0.253 0.253 0.253 0.253 0.253 0.253 0.253 HYDPL04 0.093 0.093 0.093 0.093 0.093 0.093 0.093 0.093 0.093 0.093 HYDPL05 0.132 0.132 0.132 0.132 0.132 0.132 0.132 0.132 0.132 0.132 HYDPL06 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 HYDPL07 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 HYDPL08 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 HYDPL09 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019 HYDPL10 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 HYDPL11 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 HYDPL12 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 HYDPL13 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 HYDPL14 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 HYDPL15 0 0 0 0 0 0 0 0 0 0 HYDPL16 2.062 2.142 2.142 2.142 2.142 2.142 2.142 2.142 2.142 2.142 HYDPL17 0.347 0.347 0.347 0.347 0.347 0.347 0.347 0.347 0.347 0.347 HYDPL18 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 HYDPL19 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 NG00X00 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 NGCCP00 3.7187 3.9364 4.1541 4.3718 4.5895 4.8072 5.0249 5.2426 5.4603 5.678 NGGCP00 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 SOU1P00 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 WI30P00 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27

Variable Costs 2012–2020 (M USD/ PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 BMGCP00 2.54 2.54 2.54 2.54 2.54 2.54 2.54 2.54 2.38 DL00I00 25.95 25.95 25.95 25.95 25.95 25.95 25.95 25.95 25.95 DL00X00 4.67 4.67 4.67 4.67 4.67 4.67 4.67 4.67 4.67 DLSCP00 9 9 9 9 9 9 9 9 9 EL00T00 0 0 0 0 0 0 0 0 0 EL00TDC 0 0 0 0 0 0 0 0 0 EL00TDE 0 0 0 0 0 0 0 0 0

76

EL00TDI 0 0 0 0 0 0 0 0 0 EL00TDM 0 0 0 0 0 0 0 0 0 EL00TDO 0 0 0 0 0 0 0 0 0 EL00TDP 0 0 0 0 0 0 0 0 0 EL00TDR 0 0 0 0 0 0 0 0 0 GOCVP00 1 1 1 1 1 1 1 1 1 HYDEX01 0 0 0 0 0 0 0 0 0 HYDEX02 0 0 0 0 0 0 0 0 0 HYDEX03 0 0 0 0 0 0 0 0 0 HYDEX04 0 0 0 0 0 0 0 0 0 HYDEX05 0 0 0 0 0 0 0 0 0 HYDEX06 0 0 0 0 0 0 0 0 0 HYDPL01 0 0 0 0 0 0 0 0 0 HYDPL02 0 0 0 0 0 0 0 0 0 HYDPL03 0 0 0 0 0 0 0 0 0 HYDPL04 0 0 0 0 0 0 0 0 0 HYDPL05 0 0 0 0 0 0 0 0 0 HYDPL06 0 0 0 0 0 0 0 0 0 HYDPL07 0 0 0 0 0 0 0 0 0 HYDPL08 0 0 0 0 0 0 0 0 0 HYDPL09 0 0 0 0 0 0 0 0 0 HYDPL10 0 0 0 0 0 0 0 0 0 HYDPL11 0 0 0 0 0 0 0 0 0 HYDPL12 0 0 0 0 0 0 0 0 0 HYDPL13 0 0 0 0 0 0 0 0 0 HYDPL14 0 0 0 0 0 0 0 0 0 HYDPL15 0 0 0 0 0 0 0 0 0 HYDPL16 0 0 0 0 0 0 0 0 0 HYDPL17 0 0 0 0 0 0 0 0 0 HYDPL18 0 0 0 0 0 0 0 0 0 HYDPL19 0 0 0 0 0 0 0 0 0 NG00X00 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 NGCCP00 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 NGGCP00 0 0 0 0 0 0 0 0 0 SOU1P00 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.7 WI30P00 1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.21

Variable Costs 2021–2030 (M USD/PJ) 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 BMGCP00 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 DL00I00 25.95 25.95 25.95 25.95 25.95 25.95 25.95 25.95 25.95 25.95 DL00X00 4.67 4.67 4.67 4.67 4.67 4.67 4.67 4.67 4.67 4.67 DLSCP00 9 9 9 9 9 9 9 9 9 9 EL00T00 0 0 0 0 0 0 0 0 0 0 EL00TDC 0 0 0 0 0 0 0 0 0 0 EL00TDE 0 0 0 0 0 0 0 0 0 0 EL00TDI 0 0 0 0 0 0 0 0 0 0 EL00TDM 0 0 0 0 0 0 0 0 0 0 EL00TDO 0 0 0 0 0 0 0 0 0 0 EL00TDP 0 0 0 0 0 0 0 0 0 0 EL00TDR 0 0 0 0 0 0 0 0 0 0 GOCVP00 1 1 1 1 1 1 1 1 1 1 HYDEX01 0 0 0 0 0 0 0 0 0 0

77

HYDEX02 0 0 0 0 0 0 0 0 0 0 HYDEX03 0 0 0 0 0 0 0 0 0 0 HYDEX04 0 0 0 0 0 0 0 0 0 0 HYDEX05 0 0 0 0 0 0 0 0 0 0 HYDEX06 0 0 0 0 0 0 0 0 0 0 HYDPL01 0 0 0 0 0 0 0 0 0 0 HYDPL02 0 0 0 0 0 0 0 0 0 0 HYDPL03 0 0 0 0 0 0 0 0 0 0 HYDPL04 0 0 0 0 0 0 0 0 0 0 HYDPL05 0 0 0 0 0 0 0 0 0 0 HYDPL06 0 0 0 0 0 0 0 0 0 0 HYDPL07 0 0 0 0 0 0 0 0 0 0 HYDPL08 0 0 0 0 0 0 0 0 0 0 HYDPL09 0 0 0 0 0 0 0 0 0 0 HYDPL10 0 0 0 0 0 0 0 0 0 0 HYDPL11 0 0 0 0 0 0 0 0 0 0 HYDPL12 0 0 0 0 0 0 0 0 0 0 HYDPL13 0 0 0 0 0 0 0 0 0 0 HYDPL14 0 0 0 0 0 0 0 0 0 0 HYDPL15 0 0 0 0 0 0 0 0 0 0 HYDPL16 0 0 0 0 0 0 0 0 0 0 HYDPL17 0 0 0 0 0 0 0 0 0 0 HYDPL18 0 0 0 0 0 0 0 0 0 0 HYDPL19 0 0 0 0 0 0 0 0 0 0 NG00X00 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 NGCCP00 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 NGGCP00 0 0 0 0 0 0 0 0 0 0 SOU1P00 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.63 WI30P00 1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.14

Variable Costs 2031–2040 (M USD/PJ) 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 BACKSTOP 99999 99999 99999 99999 99999 99999 99999 99999 99999 99999 BM00X00 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6 BMGCP00 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 DL00I00 25.95 25.95 25.95 25.95 25.95 25.95 25.95 25.95 25.95 25.95 DL00X00 4.67 4.67 4.67 4.67 4.67 4.67 4.67 4.67 4.67 4.67 DLSCP00 9 9 9 9 9 9 9 9 9 9 EL00T00 0 0 0 0 0 0 0 0 0 0 EL00TDC 0 0 0 0 0 0 0 0 0 0 EL00TDE 0 0 0 0 0 0 0 0 0 0 EL00TDI 0 0 0 0 0 0 0 0 0 0 EL00TDM 0 0 0 0 0 0 0 0 0 0 EL00TDO 0 0 0 0 0 0 0 0 0 0 EL00TDP 0 0 0 0 0 0 0 0 0 0 EL00TDR 0 0 0 0 0 0 0 0 0 0 GOCVP00 1 1 1 1 1 1 1 1 1 1 HYDEX01 0 0 0 0 0 0 0 0 0 0 HYDEX02 0 0 0 0 0 0 0 0 0 0 HYDEX03 0 0 0 0 0 0 0 0 0 0 HYDEX04 0 0 0 0 0 0 0 0 0 0 HYDEX05 0 0 0 0 0 0 0 0 0 0 HYDEX06 0 0 0 0 0 0 0 0 0 0 HYDPL01 0 0 0 0 0 0 0 0 0 0 HYDPL02 0 0 0 0 0 0 0 0 0 0

78

HYDPL03 0 0 0 0 0 0 0 0 0 0 HYDPL04 0 0 0 0 0 0 0 0 0 0 HYDPL05 0 0 0 0 0 0 0 0 0 0 HYDPL06 0 0 0 0 0 0 0 0 0 0 HYDPL07 0 0 0 0 0 0 0 0 0 0 HYDPL08 0 0 0 0 0 0 0 0 0 0 HYDPL09 0 0 0 0 0 0 0 0 0 0 HYDPL10 0 0 0 0 0 0 0 0 0 0 HYDPL11 0 0 0 0 0 0 0 0 0 0 HYDPL12 0 0 0 0 0 0 0 0 0 0 HYDPL13 0 0 0 0 0 0 0 0 0 0 HYDPL14 0 0 0 0 0 0 0 0 0 0 HYDPL15 0 0 0 0 0 0 0 0 0 0 HYDPL16 0 0 0 0 0 0 0 0 0 0 HYDPL17 0 0 0 0 0 0 0 0 0 0 HYDPL18 0 0 0 0 0 0 0 0 0 0 HYDPL19 0 0 0 0 0 0 0 0 0 0 NG00X00 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 NGCCP00 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 0.79 NGGCP00 0 0 0 0 0 0 0 0 0 0 SOU1P00 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 WI30P00 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14 1.14

79

8.2 Appendix II Scenario A Total Annual Max Capacity (GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

SOU1P00 0 0 0 0 0 0.07 0.07 0.12 9999 9999

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

SOU1P00 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999

2032 2033 2034 2035 2036 2037 2038 2039 2040

SOU1P00 9999 9999 9999 9999 9999 9999 9999 9999 9999 Scenario B Total Technology Annual Activty Lower Limit (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

RESOP00 0 0 0 0 0 0 0 0 0 0

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

RESOP00 0 0 0 0 0 0 0 0 90.01 90.01

2032 2033 2034 2035 2036 2037 2038 2039 2040

RESOP00 90.01 90.01 90.01 90.01 90.01 90.01 90.01 90.01 90.01 Scenario C Total Technology Annual Activty Lower Limit (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

RESOP00 0 0 0 0 0 0 0 0 0 0

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

RESOP00 0 0 0 0 0 0 0 0 90.01 90.01

2032 2033 2034 2035 2036 2037 2038 2039 2040

RESOP00 90.01 90.01 90.01 90.01 90.01 90.01 90.01 90.01 90.01 Annual Emission Limit (kton) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

CO2 2173.68 2309.81

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

CO2 2454.62 2608.66 2772.53 920.90 978.86 1040.53 1106.16 1175.99 1000.24 1063.50

2032 2033 2034 2035 2036 2037 2038 2039 2040

CO2 1130.84 1202.50 1278.77 1359.97 1446.39 1538.39 1636.34 1740.61 1816.63 Scenario D

80

Total Technology Annual Activty Lower Limit (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

RESOP00 0 0 0 0 0 0 0 0 0 0

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

RESOP00 0 0 0 0 0 0 0 0 90.01 90.01

2032 2033 2034 2035 2036 2037 2038 2039 2040

RESOP00 90.01 90.01 90.01 90.01 90.01 90.01 90.01 90.01 90.01 Annual Emission Limit (kton) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

CO2 2173.68 2309.81

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

CO2 2454.61 2608.65 2772.52 920.90 978.86 1040.53 1106.15 1175.98 1000.23 1063.50

2032 2033 2034 2035 2036 2037 2038 2039 2040

CO2 1130.83 1202.50 1278.77 1359.97 1446.39 1538.39 1636.34 1740.61 1816.63 Total Annual Max Capacity (GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

SOU1P00 0 0 0 0 0 0.07 0.07 0.12 9999 9999

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

SOU1P00 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999

2032 2033 2034 2035 2036 2037 2038 2039 2040

SOU1P00 9999 9999 9999 9999 9999 9999 9999 9999 9999 Scenario E Total Annual Actvity Upper Limit (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

NGCCP00 6.731 6.731 7.390 7.390 7.390 11.977 11.977 23.350 25.016 28.039

NGGCP00 14.197 15.955 17.500 18.647 20.081 16.735 15.962 6.191 7.404 9.207

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

NGCCP00 28.603 28.603 28.603 31.552 37.371 43.551 51.780 57.157 63.089 70.301

NGGCP00 11.900 15.411 19.097 20.592 18.949 17.211 13.727 13.377 12.814 12.657

2032 2033 2034 2035 2036 2037 2038 2039 2040

NGCCP00 76.524 90.230 95.880 97.313 96.517 102.773 102.647 101.778 74.255

NGGCP00 12.493 5.236 4.496 3.662 2.486 2.892 2.342 1.790 0.151 Total Annual Max Capacity (GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 HYDPL15 0 0 0 0 0 0 0 0 0 0

81

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 HYDPL15 0 0 0 0 1.5 1.5 1.5 1.5 1.5 1.5 2032 2033 2034 2035 2036 2037 2038 2039 2040 HYDPL15 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5

Variable Cost for years 2012-2040 M USD/PJ EL00TDE -15 Scenario F Capital Cost (M USD/GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BMGCP00 2250.00 2233.93 2217.86 2201.79 2185.71 2169.64 2153.57 2137.50 2121.43 2105.36

GOCVP00 5218.00 5180.73 5143.46 5106.19 5068.92 5031.64 4994.37 4957.10 4919.83 4882.56

HYDPL01 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL02 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL03 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL04 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL05 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL06 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL07 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL08 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL09 3350.00 3326.07 3302.14 3278.21 3254.29 3230.36 3206.43 3182.50 3158.57 3134.64

HYDPL10 3350.00 3326.07 3302.14 3278.21 3254.29 3230.36 3206.43 3182.50 3158.57 3134.64

HYDPL11 3350.00 3326.07 3302.14 3278.21 3254.29 3230.36 3206.43 3182.50 3158.57 3134.64

HYDPL12 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL13 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL14 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL15 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL16 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL17 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL18 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

HYDPL19 2100.00 2085.00 2070.00 2055.00 2040.00 2025.00 2010.00 1995.00 1980.00 1965.00

SOU1P00 1980.00 1966.00 1952.00 1938.00 1923.00 1909.00 1895.00 1881.00 1867.00 1853.00

WI30P00 1380.00 1370.19 1360.33 1350.47 1340.61 1330.76 1320.90 1311.04 1301.19 1291.33

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BMGCP00 2089.29 2073.22 2057.14 2041.07 2025.00 2008.93 1992.86 1976.79 1960.72 1944.65

GOCVP00 4845.29 4808.02 4770.75 4733.48 4696.20 4658.93 4621.66 4584.39 4547.12 4509.85

HYDPL01 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL02 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL03 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL04 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

82

HYDPL05 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL06 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL07 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL08 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL09 3110.72 3086.79 3062.86 3038.93 3015.00 2991.07 2967.15 2943.22 2919.29 2895.36

HYDPL10 3110.72 3086.79 3062.86 3038.93 3015.00 2991.07 2967.15 2943.22 2919.29 2895.36

HYDPL11 3110.72 3086.79 3062.86 3038.93 3015.00 2991.07 2967.15 2943.22 2919.29 2895.36

HYDPL12 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL13 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL14 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL15 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL16 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL17 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL18 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

HYDPL19 1950.00 1935.00 1920.00 1905.00 1890.00 1875.00 1860.00 1845.00 1830.00 1815.00

SOU1P00 1839.00 1824.00 1810.00 1796.00 1782.00 1768.00 1754.00 1740.00 1725.00 1711.00

WI30P00 1281.47 1271.61 1261.76 1251.90 1242.04 1232.19 1222.33 1212.47 1202.62 1192.76

2032 2033 2034 2035 2036 2037 2038 2039 2040

BMGCP00 1928.57 1912.50 1896.43 1880.36 1864.29 1848.22 1832.15 1816.07 1800.00

GOCVP00 4472.58 4435.31 4398.04 4360.76 4323.49 4286.22 4248.95 4211.68 4174.41

HYDPL01 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL02 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL03 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL04 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL05 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL06 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL07 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL08 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL09 2871.43 2847.50 2823.58 2799.65 2775.72 2751.79 2727.86 2703.93 2680.01

HYDPL10 2871.43 2847.50 2823.58 2799.65 2775.72 2751.79 2727.86 2703.93 2680.01

HYDPL11 2871.43 2847.50 2823.58 2799.65 2775.72 2751.79 2727.86 2703.93 2680.01

HYDPL12 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL13 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL14 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL15 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL16 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL17 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

HYDPL18 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

83

HYDPL19 1800.00 1785.00 1770.00 1755.00 1740.00 1725.00 1710.00 1695.00 1680.00

SOU1P00 1697.00 1683.00 1669.00 1655.00 1641.00 1626.00 1612.00 1598.00 1584.00

WI30P00 1182.90 1173.04 1163.19 1153.33 1143.47 1133.62 1123.76 1113.90 1104.00 Fixed Cost (M USD/GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

GOCVP00 78.000 77.443 76.886 76.329 75.771 75.214 74.657 74.100 73.543 72.986

HYDEX01 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDEX02 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDEX03 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDEX04 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDEX05 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDEX06 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL01 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL02 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL03 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL04 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL05 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL06 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL07 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL08 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL09 70.000 69.500 69.000 68.500 68.000 67.500 67.000 66.500 66.000 65.500

HYDPL10 70.000 69.500 69.000 68.500 68.000 67.500 67.000 66.500 66.000 65.500

HYDPL11 70.000 69.500 69.000 68.500 68.000 67.500 67.000 66.500 66.000 65.500

HYDPL12 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL13 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL14 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL15 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL16 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL17 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL18 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

HYDPL19 55.000 54.607 54.214 53.821 53.429 53.036 52.643 52.250 51.857 51.464

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

GOCVP00 72.429 71.871 71.314 70.757 70.200 69.643 69.086 68.529 67.972 67.414

HYDEX01 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDEX02 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDEX03 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDEX04 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDEX05 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDEX06 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL01 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

84

HYDPL02 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL03 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL04 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL05 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL06 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL07 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL08 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL09 65.000 64.500 64.000 63.500 63.000 62.500 62.000 61.500 61.000 60.500

HYDPL10 65.000 64.500 64.000 63.500 63.000 62.500 62.000 61.500 61.000 60.500

HYDPL11 65.000 64.500 64.000 63.500 63.000 62.500 62.000 61.500 61.000 60.500

HYDPL12 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL13 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL14 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL15 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL16 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL17 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL18 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

HYDPL19 51.071 50.679 50.286 49.893 49.500 49.107 48.714 48.321 47.929 47.536

2032 2033 2034 2035 2036 2037 2038 2039 2040

GOCVP00 66.857 66.300 65.743 65.186 64.629 64.072 63.514 62.957 62.400

HYDEX01 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDEX02 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDEX03 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDEX04 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDEX05 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDEX06 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL01 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL02 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL03 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL04 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL05 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL06 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL07 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL08 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL09 60.000 59.500 59.000 58.500 58.000 57.500 57.000 56.500 56.000

HYDPL10 60.000 59.500 59.000 58.500 58.000 57.500 57.000 56.500 56.000

HYDPL11 60.000 59.500 59.000 58.500 58.000 57.500 57.000 56.500 56.000

HYDPL12 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL13 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL14 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

85

HYDPL15 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL16 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL17 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL18 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000

HYDPL19 47.143 46.750 46.357 45.964 45.572 45.179 44.786 44.393 44.000 Variable Cost (M USD/PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BMGCP00 2.540 2.522 2.504 2.486 2.467 2.449 2.431 2.413 2.395 2.377

GOCVP00 1.000 0.993 0.986 0.979 0.971 0.964 0.957 0.950 0.943 0.936

SOU1P00 0.760 0.755 0.749 0.744 0.738 0.733 0.728 0.722 0.717 0.711

WI30P00 1.210 1.201 1.193 1.184 1.176 1.167 1.158 1.150 1.141 1.133

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BMGCP00 2.359 2.340 2.322 2.304 2.286 2.268 2.250 2.232 2.213 2.195

GOCVP00 0.929 0.921 0.914 0.907 0.900 0.893 0.886 0.879 0.871 0.864

SOU1P00 0.706 0.701 0.695 0.690 0.684 0.679 0.674 0.668 0.663 0.657

WI30P00 1.124 1.115 1.107 1.098 1.090 1.081 1.072 1.064 1.055 1.047

2032 2033 2034 2035 2036 2037 2038 2039 2040

BMGCP00 2.177 2.159 2.141 2.123 2.105 2.086 2.068 2.050 2.032

GOCVP00 0.857 0.850 0.843 0.836 0.829 0.821 0.814 0.807 0.800

SOU1P00 0.652 0.647 0.641 0.636 0.630 0.625 0.620 0.614 0.608

WI30P00 1.038 1.029 1.021 1.012 1.004 0.995 0.986 0.978 0.968 Scenario G Capital Cost (M USD/GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BMGCP00 2250.00 2217.86 2185.71 2153.57 2121.43 2089.29 2057.14 2025.00 1992.86 1960.71

GOCVP00 5218.00 5143.46 5068.91 4994.37 4919.83 4845.29 4770.74 4696.20 4621.66 4547.11

HYDPL01 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL02 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL03 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL04 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL05 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL06 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL07 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL08 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL09 3350.00 3302.14 3254.29 3206.43 3158.57 3110.71 3062.86 3015.00 2967.14 2919.29

HYDPL10 3350.00 3302.14 3254.29 3206.43 3158.57 3110.71 3062.86 3015.00 2967.14 2919.29

HYDPL11 3350.00 3302.14 3254.29 3206.43 3158.57 3110.71 3062.86 3015.00 2967.14 2919.29

HYDPL12 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

86

HYDPL13 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL14 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL15 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL16 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL17 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL18 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

HYDPL19 2100.00 2070.00 2040.00 2010.00 1980.00 1950.00 1920.00 1890.00 1860.00 1830.00

SOU1P00 1980.00 1952.00 1923.00 1895.00 1867.00 1839.00 1810.00 1782.00 1754.00 1725.00

WI30P00 1380.00 1360.27 1340.56 1320.84 1301.13 1281.42 1261.70 1241.99 1222.27 1202.56

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BMGCP00 1928.57 1896.43 1864.29 1832.14 1800.00 1767.86 1735.71 1703.57 1671.43 1639.29

GOCVP00 4472.57 4398.03 4323.49 4248.94 4174.40 4099.86 4025.31 3950.77 3876.23 3801.69

HYDPL01 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL02 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL03 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL04 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL05 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL06 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL07 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL08 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL09 2871.43 2823.57 2775.71 2727.86 2680.00 2632.14 2584.29 2536.43 2488.57 2440.71

HYDPL10 2871.43 2823.57 2775.71 2727.86 2680.00 2632.14 2584.29 2536.43 2488.57 2440.71

HYDPL11 2871.43 2823.57 2775.71 2727.86 2680.00 2632.14 2584.29 2536.43 2488.57 2440.71

HYDPL12 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL13 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL14 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL15 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL16 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL17 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL18 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

HYDPL19 1800.00 1770.00 1740.00 1710.00 1680.00 1650.00 1620.00 1590.00 1560.00 1530.00

SOU1P00 1697.00 1669.00 1641.00 1612.00 1584.00 1556.00 1527.00 1499.00 1471.00 1443.00

WI30P00 1182.85 1163.13 1143.42 1123.70 1103.99 1084.28 1064.56 1044.85 1025.13 1005.42

2032 2033 2034 2035 2036 2037 2038 2039 2040

BMGCP00 1607.14 1575.00 1542.86 1510.71 1478.57 1446.43 1414.29 1382.14 1350.00

GOCVP00 3727.14 3652.60 3578.06 3503.51 3428.97 3354.43 3279.89 3205.34 3130.80

HYDPL01 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL02 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL03 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL04 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

87

HYDPL05 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL06 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL07 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL08 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL09 2392.86 2345.00 2297.14 2249.29 2201.43 2153.57 2105.71 2057.86 2010.00

HYDPL10 2392.86 2345.00 2297.14 2249.29 2201.43 2153.57 2105.71 2057.86 2010.00

HYDPL11 2392.86 2345.00 2297.14 2249.29 2201.43 2153.57 2105.71 2057.86 2010.00

HYDPL12 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL13 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL14 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL15 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL16 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL17 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL18 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

HYDPL19 1500.00 1470.00 1440.00 1410.00 1380.00 1350.00 1320.00 1290.00 1260.00

SOU1P00 1414.00 1386.00 1358.00 1329.00 1301.00 1273.00 1245.00 1216.00 1188.00

WI30P00 985.71 965.99 946.28 926.56 906.85 887.14 867.42 847.71 828.00 Fixed Cost (M USD/GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

GOCVP00 78.000 76.886 75.771 74.657 73.543 72.429 71.314 70.200 69.086 67.971

HYDEX01 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDEX02 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDEX03 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDEX04 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDEX05 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDEX06 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL01 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL02 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL03 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL04 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL05 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL06 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL07 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL08 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL09 70.000 69.000 68.000 67.000 66.000 65.000 64.000 63.000 62.000 61.000

HYDPL10 70.000 69.000 68.000 67.000 66.000 65.000 64.000 63.000 62.000 61.000

HYDPL11 70.000 69.000 68.000 67.000 66.000 65.000 64.000 63.000 62.000 61.000

HYDPL12 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

88

HYDPL13 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL14 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL15 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL16 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL17 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL18 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

HYDPL19 55.000 54.214 53.429 52.643 51.857 51.071 50.286 49.500 48.714 47.929

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

GOCVP00 66.857 65.743 64.629 63.514 62.400 61.286 60.171 59.057 57.943 56.829

HYDEX01 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDEX02 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDEX03 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDEX04 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDEX05 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDEX06 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL01 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL02 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL03 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL04 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL05 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL06 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL07 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL08 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL09 60.000 59.000 58.000 57.000 56.000 55.000 54.000 53.000 52.000 51.000

HYDPL10 60.000 59.000 58.000 57.000 56.000 55.000 54.000 53.000 52.000 51.000

HYDPL11 60.000 59.000 58.000 57.000 56.000 55.000 54.000 53.000 52.000 51.000

HYDPL12 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL13 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL14 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL15 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL16 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL17 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL18 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

HYDPL19 47.143 46.357 45.571 44.786 44.000 43.214 42.429 41.643 40.857 40.071

2032 2033 2034 2035 2036 2037 2038 2039 2040

GOCVP00 55.714 54.600 53.486 52.371 51.257 50.143 49.029 47.914 46.800

HYDEX01 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDEX02 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDEX03 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDEX04 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

89

HYDEX05 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDEX06 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL01 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL02 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL03 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL04 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL05 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL06 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL07 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL08 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL09 50.000 49.000 48.000 47.000 46.000 45.000 44.000 43.000 42.000

HYDPL10 50.000 49.000 48.000 47.000 46.000 45.000 44.000 43.000 42.000

HYDPL11 50.000 49.000 48.000 47.000 46.000 45.000 44.000 43.000 42.000

HYDPL12 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL13 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL14 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL15 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL16 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL17 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL18 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000

HYDPL19 39.286 38.500 37.714 36.929 36.143 35.357 34.571 33.786 33.000 Variable Cost (M USD/PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BMGCP00 2.540 2.504 2.467 2.431 2.395 2.359 2.322 2.286 2.250 2.213

GOCVP00 1.000 0.986 0.971 0.957 0.943 0.929 0.914 0.900 0.886 0.871

SOU1P00 0.760 0.749 0.738 0.727 0.716 0.706 0.695 0.684 0.673 0.662

WI30P00 1.210 1.193 1.175 1.158 1.141 1.124 1.106 1.089 1.072 1.054

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BMGCP00 2.177 2.141 2.105 2.068 2.032 1.996 1.959 1.923 1.887 1.851

GOCVP00 0.857 0.843 0.829 0.814 0.800 0.786 0.771 0.757 0.743 0.729

SOU1P00 0.651 0.640 0.629 0.618 0.607 0.597 0.586 0.575 0.564 0.553

WI30P00 1.037 1.020 1.002 0.985 0.968 0.951 0.933 0.916 0.899 0.881

2032 2033 2034 2035 2036 2037 2038 2039 2040

BMGCP00 1.814 1.778 1.742 1.705 1.669 1.633 1.597 1.560 1.524

GOCVP00 0.714 0.700 0.686 0.671 0.657 0.643 0.629 0.614 0.600

SOU1P00 0.542 0.531 0.520 0.509 0.498 0.488 0.477 0.466 0.456

90

WI30P00 0.864 0.847 0.829 0.812 0.795 0.778 0.760 0.743 0.726 Scenario H Capital Cost (M USD/GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BMGCP00 2250.00 2201.79 2153.57 2105.36 2057.14 2008.93 1960.71 1912.50 1864.29 1816.07

GOCVP00 5218.00 5106.19 4994.37 4882.56 4770.74 4658.93 4547.11 4435.30 4323.49 4211.67

HYDPL01 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL02 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL03 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL04 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL05 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL06 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL07 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL08 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL09 3350.00 3278.21 3206.43 3134.64 3062.86 2991.07 2919.29 2847.50 2775.71 2703.93

HYDPL10 3350.00 3278.21 3206.43 3134.64 3062.86 2991.07 2919.29 2847.50 2775.71 2703.93

HYDPL11 3350.00 3278.21 3206.43 3134.64 3062.86 2991.07 2919.29 2847.50 2775.71 2703.93

HYDPL12 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL13 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL14 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL15 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL16 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL17 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL18 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

HYDPL19 2100.00 2055.00 2010.00 1965.00 1920.00 1875.00 1830.00 1785.00 1740.00 1695.00

SOU1P00 1980.00 1938.00 1895.00 1853.00 1810.00 1768.00 1725.00 1683.00 1641.00 1598.00

WI30P00 1380.00 1350.46 1320.89 1291.32 1261.75 1232.17 1202.60 1173.03 1143.46 1113.89

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BMGCP00 1767.86 1719.64 1671.43 1623.21 1575.00 1526.79 1478.57 1430.36 1382.14 1333.93

GOCVP00 4099.86 3988.04 3876.23 3764.41 3652.60 3540.79 3428.97 3317.16 3205.34 3093.53

HYDPL01 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL02 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL03 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL04 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL05 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

91

HYDPL06 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL07 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL08 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL09 2632.14 2560.36 2488.57 2416.79 2345.00 2273.21 2201.43 2129.64 2057.86 1986.07

HYDPL10 2632.14 2560.36 2488.57 2416.79 2345.00 2273.21 2201.43 2129.64 2057.86 1986.07

HYDPL11 2632.14 2560.36 2488.57 2416.79 2345.00 2273.21 2201.43 2129.64 2057.86 1986.07

HYDPL12 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL13 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL14 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL15 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL16 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL17 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL18 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

HYDPL19 1650.00 1605.00 1560.00 1515.00 1470.00 1425.00 1380.00 1335.00 1290.00 1245.00

SOU1P00 1556.00 1513.00 1471.00 1428.00 1386.00 1344.00 1301.00 1259.00 1216.00 1174.00

WI30P00 1084.32 1054.75 1025.18 995.61 966.04 936.46 906.89 877.32 847.75 818.18

2032 2033 2034 2035 2036 2037 2038 2039 2040

BMGCP00 1285.71 1237.50 1189.29 1141.07 1092.86 1044.64 996.43 948.21 900.00

GOCVP00 2981.71 2869.90 2758.09 2646.27 2534.46 2422.64 2310.83 2199.01 2087.20

HYDPL01 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL02 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL03 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL04 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL05 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL06 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL07 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL08 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL09 1914.29 1842.50 1770.71 1698.93 1627.14 1555.36 1483.57 1411.79 1340.00

HYDPL10 1914.29 1842.50 1770.71 1698.93 1627.14 1555.36 1483.57 1411.79 1340.00

HYDPL11 1914.29 1842.50 1770.71 1698.93 1627.14 1555.36 1483.57 1411.79 1340.00

HYDPL12 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL13 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL14 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL15 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL16 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL17 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

92

HYDPL18 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

HYDPL19 1200.00 1155.00 1110.00 1065.00 1020.00 975.00 930.00 885.00 840.00

SOU1P00 1131.00 1089.00 1047.00 1004.00 962.00 919.00 877.00 834.00 792.00

WI30P00 788.61 759.04 729.47 699.90 670.33 640.75 611.18 581.61 552.00 Fixed Cost (M USD/GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

GOCVP00 78.000 76.329 74.657 72.986 71.314 69.643 67.971 66.300 64.629 62.957

HYDEX01 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDEX02 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDEX03 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDEX04 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDEX05 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDEX06 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL01 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL02 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL03 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL04 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL05 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL06 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL07 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL08 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL09 70.000 68.500 67.000 65.500 64.000 62.500 61.000 59.500 58.000 56.500

HYDPL10 70.000 68.500 67.000 65.500 64.000 62.500 61.000 59.500 58.000 56.500

HYDPL11 70.000 68.500 67.000 65.500 64.000 62.500 61.000 59.500 58.000 56.500

HYDPL12 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL13 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL14 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL15 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL16 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL17 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL18 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

HYDPL19 55.000 53.821 52.643 51.464 50.286 49.107 47.929 46.750 45.571 44.393

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

GOCVP00 61.286 59.614 57.943 56.271 54.600 52.929 51.257 49.586 47.914 46.243

HYDEX01 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDEX02 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

93

HYDEX03 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDEX04 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDEX05 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDEX06 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL01 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL02 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL03 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL04 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL05 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL06 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL07 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL08 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL09 55.000 53.500 52.000 50.500 49.000 47.500 46.000 44.500 43.000 41.500

HYDPL10 55.000 53.500 52.000 50.500 49.000 47.500 46.000 44.500 43.000 41.500

HYDPL11 55.000 53.500 52.000 50.500 49.000 47.500 46.000 44.500 43.000 41.500

HYDPL12 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL13 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL14 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL15 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL16 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL17 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL18 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

HYDPL19 43.214 42.036 40.857 39.679 38.500 37.321 36.143 34.964 33.786 32.607

2032 2033 2034 2035 2036 2037 2038 2039 2040

GOCVP00 44.571 42.900 41.229 39.557 37.886 36.214 34.543 32.871 31.200

HYDEX01 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDEX02 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDEX03 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDEX04 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDEX05 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDEX06 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL01 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL02 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL03 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL04 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL05 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

94

HYDPL06 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL07 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL08 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL09 40.000 38.500 37.000 35.500 34.000 32.500 31.000 29.500 28.000

HYDPL10 40.000 38.500 37.000 35.500 34.000 32.500 31.000 29.500 28.000

HYDPL11 40.000 38.500 37.000 35.500 34.000 32.500 31.000 29.500 28.000

HYDPL12 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL13 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL14 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL15 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL16 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL17 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL18 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000

HYDPL19 31.429 30.250 29.071 27.893 26.714 25.536 24.357 23.179 22.000 Variable Cost (M USD/PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BMGCP00 2.540 2.486 2.431 2.377 2.322 2.268 2.213 2.159 2.105 2.050

GOCVP00 1.000 0.979 0.957 0.936 0.914 0.893 0.871 0.850 0.829 0.807

SOU1P00 0.760 0.744 0.727 0.711 0.695 0.679 0.662 0.646 0.630 0.613

WI30P00 1.210 1.184 1.158 1.132 1.106 1.081 1.055 1.029 1.003 0.977

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BMGCP00 1.996 1.941 1.887 1.832 1.778 1.724 1.669 1.615 1.560 1.506

GOCVP00 0.786 0.764 0.743 0.721 0.700 0.679 0.657 0.636 0.614 0.593

SOU1P00 0.597 0.581 0.564 0.548 0.532 0.516 0.499 0.483 0.467 0.450

WI30P00 0.951 0.925 0.899 0.873 0.847 0.822 0.796 0.770 0.744 0.718

2032 2033 2034 2035 2036 2037 2038 2039 2040

BMGCP00 1.451 1.397 1.343 1.288 1.234 1.179 1.125 1.070 1.016

GOCVP00 0.571 0.550 0.529 0.507 0.486 0.464 0.443 0.421 0.400

SOU1P00 0.434 0.418 0.401 0.385 0.369 0.353 0.336 0.320 0.304

WI30P00 0.692 0.666 0.640 0.614 0.588 0.563 0.537 0.511 0.484 Scenario I Capital Cost (M USD/GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BMGCP00 2250.00 2185.71 2121.43 2057.14 1992.86 1928.57 1864.29 1800.00 1735.71 1671.43

GOCVP00 5218.00 5068.91 4919.83 4770.74 4621.66 4472.57 4323.49 4174.40 4025.31 3876.23

95

HYDPL01 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL02 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL03 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL04 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL05 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL06 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL07 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL08 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL09 3350.00 3254.29 3158.57 3062.86 2967.14 2871.43 2775.71 2680.00 2584.29 2488.57

HYDPL10 3350.00 3254.29 3158.57 3062.86 2967.14 2871.43 2775.71 2680.00 2584.29 2488.57

HYDPL11 3350.00 3254.29 3158.57 3062.86 2967.14 2871.43 2775.71 2680.00 2584.29 2488.57

HYDPL12 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL13 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL14 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL15 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL16 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL17 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL18 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

HYDPL19 2100.00 2040.00 1980.00 1920.00 1860.00 1800.00 1740.00 1680.00 1620.00 1560.00

SOU1P00 1980.00 1923.00 1867.00 1810.00 1754.00 1697.00 1641.00 1584.00 1527.00 1471.00

WI30P00 1380.00 1340.54 1301.11 1261.68 1222.26 1182.83 1143.40 1103.97 1064.54 1025.11

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BMGCP00 1607.14 1542.86 1478.57 1414.29 1350.00 1285.71 1221.43 1157.14 1092.86 1028.57

GOCVP00 3727.14 3578.06 3428.97 3279.89 3130.80 2981.71 2832.63 2683.54 2534.46 2385.37

HYDPL01 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL02 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL03 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL04 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL05 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL06 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL07 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL08 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL09 2392.86 2297.14 2201.43 2105.71 2010.00 1914.29 1818.57 1722.86 1627.14 1531.43

HYDPL10 2392.86 2297.14 2201.43 2105.71 2010.00 1914.29 1818.57 1722.86 1627.14 1531.43

HYDPL11 2392.86 2297.14 2201.43 2105.71 2010.00 1914.29 1818.57 1722.86 1627.14 1531.43

HYDPL12 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

96

HYDPL13 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL14 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL15 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL16 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL17 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL18 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

HYDPL19 1500.00 1440.00 1380.00 1320.00 1260.00 1200.00 1140.00 1080.00 1020.00 960.00

SOU1P00 1414.00 1358.00 1301.00 1245.00 1188.00 1131.00 1075.00 1018.00 962.00 905.00

WI30P00 985.68 946.25 906.82 867.39 827.97 788.54 749.11 709.68 670.25 630.82

2032 2033 2034 2035 2036 2037 2038 2039 2040

BMGCP00 964.29 900.00 835.71 771.43 707.14 642.86 578.57 514.29 450.00

GOCVP00 2236.29 2087.20 1938.11 1789.03 1639.94 1490.86 1341.77 1192.69 1043.60

HYDPL01 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL02 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL03 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL04 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL05 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL06 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL07 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL08 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL09 1435.71 1340.00 1244.29 1148.57 1052.86 957.14 861.43 765.71 670.00

HYDPL10 1435.71 1340.00 1244.29 1148.57 1052.86 957.14 861.43 765.71 670.00

HYDPL11 1435.71 1340.00 1244.29 1148.57 1052.86 957.14 861.43 765.71 670.00

HYDPL12 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL13 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL14 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL15 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL16 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL17 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL18 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

HYDPL19 900.00 840.00 780.00 720.00 660.00 600.00 540.00 480.00 420.00

SOU1P00 849.00 792.00 735.00 679.00 622.00 566.00 509.00 453.00 396.00

WI30P00 591.39 551.96 512.53 473.10 433.68 394.25 354.82 315.39 276.00 Fixed Cost (M USD/GW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

GOCVP00 78.000 75.771 73.543 71.314 69.086 66.857 64.629 62.400 60.171 57.943

97

HYDEX01 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDEX02 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDEX03 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDEX04 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDEX05 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDEX06 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL01 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL02 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL03 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL04 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL05 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL06 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL07 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL08 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL09 70.000 68.000 66.000 64.000 62.000 60.000 58.000 56.000 54.000 52.000

HYDPL10 70.000 68.000 66.000 64.000 62.000 60.000 58.000 56.000 54.000 52.000

HYDPL11 70.000 68.000 66.000 64.000 62.000 60.000 58.000 56.000 54.000 52.000

HYDPL12 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL13 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL14 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL15 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL16 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL17 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL18 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

HYDPL19 55.000 53.429 51.857 50.286 48.714 47.143 45.571 44.000 42.429 40.857

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

GOCVP00 55.714 53.486 51.257 49.029 46.800 44.571 42.343 40.114 37.886 35.657

HYDEX01 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDEX02 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDEX03 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDEX04 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDEX05 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDEX06 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL01 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL02 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL03 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

98

HYDPL04 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL05 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL06 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL07 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL08 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL09 50.000 48.000 46.000 44.000 42.000 40.000 38.000 36.000 34.000 32.000

HYDPL10 50.000 48.000 46.000 44.000 42.000 40.000 38.000 36.000 34.000 32.000

HYDPL11 50.000 48.000 46.000 44.000 42.000 40.000 38.000 36.000 34.000 32.000

HYDPL12 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL13 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL14 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL15 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL16 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL17 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL18 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

HYDPL19 39.286 37.714 36.143 34.571 33.000 31.429 29.857 28.286 26.714 25.143

2032 2033 2034 2035 2036 2037 2038 2039 2040

GOCVP00 33.429 31.200 28.971 26.743 24.514 22.286 20.057 17.829 15.600

HYDEX01 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDEX02 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDEX03 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDEX04 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDEX05 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDEX06 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL01 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL02 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL03 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL04 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL05 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL06 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL07 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL08 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL09 30.000 28.000 26.000 24.000 22.000 20.000 18.000 16.000 14.000

HYDPL10 30.000 28.000 26.000 24.000 22.000 20.000 18.000 16.000 14.000

HYDPL11 30.000 28.000 26.000 24.000 22.000 20.000 18.000 16.000 14.000

HYDPL12 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

99

HYDPL13 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL14 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL15 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL16 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL17 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL18 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000

HYDPL19 23.571 22.000 20.429 18.857 17.286 15.714 14.143 12.571 11.000 Variable Cost (M USD/PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

BMGCP00 2.540 2.467 2.395 2.322 2.250 2.177 2.105 2.032 1.959 1.887

GOCVP00 1.000 0.971 0.943 0.914 0.886 0.857 0.829 0.800 0.771 0.743

SOU1P00 0.760 0.738 0.717 0.695 0.673 0.652 0.630 0.608 0.586 0.565

WI30P00 1.210 1.175 1.141 1.106 1.072 1.037 1.002 0.968 0.933 0.899

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

BMGCP00 1.814 1.742 1.669 1.597 1.524 1.451 1.379 1.306 1.234 1.161

GOCVP00 0.714 0.686 0.657 0.629 0.600 0.571 0.543 0.514 0.486 0.457

SOU1P00 0.543 0.521 0.500 0.478 0.456 0.435 0.413 0.391 0.369 0.348

WI30P00 0.864 0.829 0.795 0.760 0.726 0.691 0.656 0.622 0.587 0.553

2032 2033 2034 2035 2036 2037 2038 2039 2040

BMGCP00 1.089 1.016 0.943 0.871 0.798 0.726 0.653 0.581 0.508

GOCVP00 0.429 0.400 0.371 0.343 0.314 0.286 0.257 0.229 0.200

SOU1P00 0.326 0.304 0.283 0.261 0.239 0.218 0.196 0.174 0.152

WI30P00 0.518 0.483 0.449 0.414 0.380 0.345 0.310 0.276 0.242 Scenario J Emission Penalty (M USD/kton) 2019-2040 CO2 0.02

Scenario K Emission Penalty (M USD/kton) 2019-2040 CO2 0.05

Scenario L Emission Penalty (M USD/kton) 2019-2040 CO2 0.075

100

Scenario M Emission Penalty (M USD/kton) 2019-2040 CO2 0.1

Scenario N Capital Cost. Variable Cost & Fixed Cost from Scenario F and Emission Penalty from Scenario J Scenario O Capital Cost. Variable Cost & Fixed Cost from Scenario F and Emission Penalty from Scenario K Scenario P Capital Cost. Variable Cost & Fixed Cost from Scenario G and Emission Penalty from Scenario J Scenario Q Capital Cost. Variable Cost & Fixed Cost from Scenario G and Emission Penalty from Scenario K Scenario R Total Technology Annual Activity Upper Limit (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

NG00X00 50.911 55.482 60.873 63.853 67.583 68.424 66.412 64.664 71.284 57.026

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

NG00X00 42.769 28.512 14.255 0.000 0.000 0.000 0.000 0.000 0.000 0.000

2032 2033 2034 2035 2036 2037 2038 2039 2040

NG00X00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Scenario S Total Technology Annual Activity Upper Limit (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

NG00X00 50.924 55.497 60.889 63.870 67.601 68.443 66.432 64.684 71.304 82.280

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

NG00X00 90.461 99.591 109.177 119.167 95.334 71.501 47.668 23.835 0.000 0.000

2032 2033 2034 2035 2036 2037 2038 2039 2040

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NG00X00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Scenario T Total Technology Annual Activity Upper Limit (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

NG00X00 50.911 55.482 60.873 63.853 67.583 68.424 66.412 64.664 71.284 82.259

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

NG00X00 90.435 99.563 109.148 119.167 126.999 135.337 143.393 153.666 164.541 131.634

2032 2033 2034 2035 2036 2037 2038 2039 2040

NG00X00 98.726 65.818 32.910 0.000 0.000 0.000 0.000 0.000 0.000 Scenario U Total Technology Annual Activity Upper Limit (PJ) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

NG00X00 50.911 55.482 60.873 63.853 67.583 68.424 66.412 64.664 71.284 82.259

2022 2023 2024 2025 2026 2027 2028 2029 2030 2031

NG00X00 90.435 99.563 109.148 119.167 126.998 135.336 143.392 153.666 164.541 179.134

2032 2033 2034 2035 2036 2037 2038 2039 2040

NG00X00 191.651 201.290 211.110 211.931 169.548 127.162 84.776 42.390 0.000

8.3 Appendix III Amended code for scenario E ############### # Sets # ############### # set DAILYTIMEBRACKET; set DAYTYPE; set EMISSION; set FUEL; set MODE_OF_OPERATION; set REGION; set SEASON; set STORAGE; set TECHNOLOGY; set TIMESLICE; set YEAR; # ##################### # Parameters # ##################### # param AccumulatedAnnualDemand{r in REGION, f in FUEL, y in YEAR}; param AnnualEmissionLimit{r in REGION, e in EMISSION, y in YEAR};

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param AnnualExogenousEmission{r in REGION, e in EMISSION, y in YEAR}; param AvailabilityFactor{r in REGION, t in TECHNOLOGY, y in YEAR}; param CapacityFactor{r in REGION, t in TECHNOLOGY, l in TIMESLICE, y in YEAR}; param CapacityOfOneTechnologyUnit{r in REGION, t in TECHNOLOGY, y in YEAR}; param CapacityToActivityUnit{r in REGION, t in TECHNOLOGY}; param CapitalCost{r in REGION, t in TECHNOLOGY, y in YEAR}; param CapitalCostStorage{r in REGION, s in STORAGE, y in YEAR}; param Conversionld{l in TIMESLICE, ld in DAYTYPE}; param Conversionlh{l in TIMESLICE, lh in DAILYTIMEBRACKET}; param Conversionls{l in TIMESLICE, ls in SEASON}; param DaySplit{lh in DAILYTIMEBRACKET, y in YEAR}; param DaysInDayType{ls in SEASON, ld in DAYTYPE, y in YEAR}; param DepreciationMethod{r in REGION}; param DiscountRate{r in REGION}; param EmissionActivityRatio{r in REGION, t in TECHNOLOGY, e in EMISSION, m in MODE_OF_OPERATION, y in YEAR}; param EmissionsPenalty{r in REGION, e in EMISSION, y in YEAR}; param FixedCost{r in REGION, t in TECHNOLOGY, y in YEAR}; param InputActivityRatio{r in REGION, t in TECHNOLOGY, f in FUEL, m in MODE_OF_OPERATION, y in YEAR}; param MinStorageCharge{r in REGION, s in STORAGE, y in YEAR}; param ModelPeriodEmissionLimit{r in REGION, e in EMISSION}; param ModelPeriodExogenousEmission{r in REGION, e in EMISSION}; param OperationalLife{r in REGION, t in TECHNOLOGY}; param OperationalLifeStorage{r in REGION, s in STORAGE}; param OutputActivityRatio{r in REGION, t in TECHNOLOGY, f in FUEL, m in MODE_OF_OPERATION, y in YEAR}; param REMinProductionTarget{r in REGION, y in YEAR}; param RETagFuel{r in REGION, f in FUEL, y in YEAR}; param RETagTechnology{r in REGION, t in TECHNOLOGY, y in YEAR}; param ReserveMargin{r in REGION, y in YEAR}; param ReserveMarginTagFuel{r in REGION, f in FUEL, y in YEAR}; param ReserveMarginTagTechnology{r in REGION, t in TECHNOLOGY, y in YEAR}; param ResidualCapacity{r in REGION, t in TECHNOLOGY, y in YEAR}; param ResidualStorageCapacity{r in REGION, s in STORAGE, y in YEAR}; param SpecifiedAnnualDemand{r in REGION, f in FUEL, y in YEAR}; param SpecifiedDemandProfile{r in REGION, f in FUEL, l in TIMESLICE, y in YEAR}; param StorageLevelStart{r in REGION, s in STORAGE}; param StorageMaxChargeRate{r in REGION, s in STORAGE}; param StorageMaxDischargeRate{r in REGION, s in STORAGE}; param TechnologyFromStorage{r in REGION, t in TECHNOLOGY, s in STORAGE, m in MODE_OF_OPERATION}; param TechnologyToStorage{r in REGION, t in TECHNOLOGY, s in STORAGE, m in MODE_OF_OPERATION}; param TotalAnnualMaxCapacity{r in REGION, t in TECHNOLOGY, y in YEAR}; param TotalAnnualMaxCapacityInvestment{r in REGION, t in TECHNOLOGY, y in YEAR}; param TotalAnnualMinCapacity{r in REGION, t in TECHNOLOGY, y in YEAR}; param TotalAnnualMinCapacityInvestment{r in REGION, t in TECHNOLOGY, y in YEAR}; param TotalTechnologyAnnualActivityLowerLimit{r in REGION, t in TECHNOLOGY, y in YEAR}; param TotalTechnologyAnnualActivityUpperLimit{r in REGION, t in TECHNOLOGY, y in YEAR}; param TotalTechnologyModelPeriodActivityLowerLimit{r in REGION, t in TECHNOLOGY}; param TotalTechnologyModelPeriodActivityUpperLimit{r in REGION, t in TECHNOLOGY}; param TradeRoute{r in REGION, rr in REGION, f in FUEL, y in YEAR}; param VariableCost{r in REGION, t in TECHNOLOGY, m in MODE_OF_OPERATION, y in YEAR};

103

param YearSplit{l in TIMESLICE, y in YEAR}; # ########################## # Model Variables # ########################## # var AccumulatedNewCapacity{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var AccumulatedNewStorageCapacity{r in REGION, s in STORAGE, y in YEAR} >= 0; var AnnualEmissions{r in REGION, e in EMISSION, y in YEAR} >= 0; var AnnualFixedOperatingCost{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var AnnualTechnologyEmission{r in REGION, t in TECHNOLOGY, e in EMISSION, y in YEAR} >= 0; var AnnualTechnologyEmissionByMode{r in REGION, t in TECHNOLOGY, e in EMISSION, m in MODE_OF_OPERATION, y in YEAR} >= 0; var AnnualTechnologyEmissionPenaltyByEmission{r in REGION, t in TECHNOLOGY, e in EMISSION, y in YEAR} >= 0; var AnnualTechnologyEmissionsPenalty{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var AnnualVariableOperatingCost{r in REGION, t in TECHNOLOGY, y in YEAR}; var CapitalInvestment{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var CapitalInvestmentStorage{r in REGION, s in STORAGE, y in YEAR} >= 0; var Demand{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} >= 0; var DemandNeedingReserveMargin{r in REGION, l in TIMESLICE, y in YEAR} >= 0; var DiscountedCapitalInvestment{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var DiscountedCapitalInvestmentStorage{r in REGION, s in STORAGE, y in YEAR} >= 0; var DiscountedOperatingCost{r in REGION, t in TECHNOLOGY, y in YEAR}; var DiscountedSalvageValue{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var DiscountedSalvageValueStorage{r in REGION, s in STORAGE, y in YEAR} >= 0; var DiscountedTechnologyEmissionsPenalty{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var ModelPeriodCostByRegion{r in REGION} >= 0; var ModelPeriodEmissions{r in REGION, e in EMISSION} >= 0; var NetChargeWithinDay{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR}; var NetChargeWithinYear{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR}; var NewCapacity{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var NewStorageCapacity{r in REGION, s in STORAGE, y in YEAR} >= 0; var NumberOfNewTechnologyUnits{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0, integer; var OperatingCost{r in REGION, t in TECHNOLOGY, y in YEAR}; var Production{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} >= 0; var ProductionAnnual{r in REGION, f in FUEL, y in YEAR} >= 0; var ProductionByTechnology{r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR} >= 0; var ProductionByTechnologyAnnual{r in REGION, t in TECHNOLOGY, f in FUEL, y in YEAR} >= 0; var RETotalProductionOfTargetFuelAnnual{r in REGION, y in YEAR}; var RateOfActivity{r in REGION, l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION, y in YEAR} >= 0; var RateOfDemand{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} >= 0; var RateOfProduction{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} >= 0; var RateOfProductionByTechnology{r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR} >= 0; var RateOfProductionByTechnologyByMode{r in REGION, l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION, f in FUEL, y in YEAR} >= 0;

104

var RateOfStorageCharge{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR}; var RateOfStorageDischarge{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR}; var RateOfTotalActivity{r in REGION, t in TECHNOLOGY, l in TIMESLICE, y in YEAR} >= 0; var RateOfUse{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} >= 0; var RateOfUseByTechnology{r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR} >= 0; var RateOfUseByTechnologyByMode{r in REGION, l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION, f in FUEL, y in YEAR} >= 0; var SalvageValue{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var SalvageValueStorage{r in REGION, s in STORAGE, y in YEAR} >= 0; var StorageLevelDayTypeFinish{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, y in YEAR} >= 0; var StorageLevelDayTypeStart{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, y in YEAR} >= 0; var StorageLevelSeasonStart{r in REGION, s in STORAGE, ls in SEASON, y in YEAR} >= 0; var StorageLevelYearFinish{r in REGION, s in STORAGE, y in YEAR} >= 0; var StorageLevelYearStart{r in REGION, s in STORAGE, y in YEAR} >= 0; var StorageLowerLimit{r in REGION, s in STORAGE, y in YEAR} >= 0; var StorageUpperLimit{r in REGION, s in STORAGE, y in YEAR} >= 0; var TotalAnnualTechnologyActivityByMode{r in REGION, t in TECHNOLOGY, m in MODE_OF_OPERATION, y in YEAR} >= 0; var TotalCapacityAnnual{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var TotalCapacityInReserveMargin{r in REGION, y in YEAR} >= 0; var TotalDiscountedCost{r in REGION, y in YEAR} >= 0; var TotalDiscountedCostByTechnology{r in REGION, t in TECHNOLOGY, y in YEAR}; var TotalDiscountedStorageCost{r in REGION, s in STORAGE, y in YEAR} >= 0; var TotalREProductionAnnual{r in REGION, y in YEAR}; var TotalTechnologyAnnualActivity{r in REGION, t in TECHNOLOGY, y in YEAR} >= 0; var TotalTechnologyModelPeriodActivity{r in REGION, t in TECHNOLOGY}; var Trade{r in REGION, rr in REGION, l in TIMESLICE, f in FUEL, y in YEAR}; var TradeAnnual{r in REGION, rr in REGION, f in FUEL, y in YEAR}; var Use{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} >= 0; var UseAnnual{r in REGION, f in FUEL, y in YEAR} >= 0; var UseByTechnology{r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR} >= 0; var UseByTechnologyAnnual{r in REGION, t in TECHNOLOGY, f in FUEL, y in YEAR} >= 0; # ###################### # Objective Function # ###################### # minimize OFL_Cost: sum{r in REGION, y in YEAR} TotalDiscountedCost [r, y]; # ##################### # Constraints # ##################### # # Common_Equations s.t. Acc1_FuelProductionByTechnology{r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR}: RateOfProductionByTechnology [r, l, t, f, y] * YearSplit [l, y] = ProductionByTechnology [r, l, t, f, y]; s.t. Acc2_FuelUseByTechnology{r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR}: RateOfUseByTechnology [r, l, t, f, y] * YearSplit [l, y] = UseByTechnology [r, l, t, f, y];

105

s.t. Acc3_AverageAnnualRateOfActivity{r in REGION, t in TECHNOLOGY, m in MODE_OF_OPERATION, y in YEAR}: sum{l in TIMESLICE} RateOfActivity [r, l, t, m, y] * YearSplit [l, y] = TotalAnnualTechnologyActivityByMode [r, t, m, y]; s.t. CAa1_TotalNewCapacity{r in REGION, t in TECHNOLOGY, y in YEAR}: AccumulatedNewCapacity [r, t, y] = sum{yy in YEAR: y - yy < OperationalLife [r, t] && y - yy >= 0} NewCapacity [r, t, yy]; s.t. CAa2_TotalAnnualCapacity{r in REGION, t in TECHNOLOGY, y in YEAR}: AccumulatedNewCapacity [r, t, y] + ResidualCapacity [r, t, y] = TotalCapacityAnnual [r, t, y]; s.t. CAa5_TotalNewCapacity{r in REGION, t in TECHNOLOGY, y in YEAR: CapacityOfOneTechnologyUnit [r, t, y] <> 0}: CapacityOfOneTechnologyUnit [r, t, y] * NumberOfNewTechnologyUnits [r, t, y] = NewCapacity [r, t, y]; s.t. CC1_UndiscountedCapitalInvestment{r in REGION, t in TECHNOLOGY, y in YEAR}: CapitalCost [r, t, y] * NewCapacity [r, t, y] = CapitalInvestment [r, t, y]; s.t. E2_AnnualEmissionProduction{r in REGION, t in TECHNOLOGY, e in EMISSION, y in YEAR}: sum{m in MODE_OF_OPERATION} AnnualTechnologyEmissionByMode [r, t, e, m, y] = AnnualTechnologyEmission [r, t, e, y]; s.t. EBa10_EnergyBalanceEachTS4{r in REGION, rr in REGION, l in TIMESLICE, f in FUEL, y in YEAR}: Trade [r, rr, l, f, y] = -1 * Trade [r, rr, l, f, y]; s.t. EBa1_RateOfFuelProduction1{r in REGION, l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION, y in YEAR, f in FUEL: OutputActivityRatio [r, t, f, m, y] <> 0}: RateOfActivity [r, l, t, m, y] * OutputActivityRatio [r, t, f, m, y] = RateOfProductionByTechnologyByMode [r, l, t, m, f, y]; s.t. EBa2_RateOfFuelProduction2{r in REGION, t in TECHNOLOGY, f in FUEL, y in YEAR, l in TIMESLICE}: sum{m in MODE_OF_OPERATION: OutputActivityRatio [r, t, f, m, y] <> 0} RateOfProductionByTechnologyByMode [r, l, t, m, f, y] = RateOfProductionByTechnology [r, l, t, f, y]; s.t. EBa4_RateOfFuelUse1{r in REGION, l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION, y in YEAR, f in FUEL: InputActivityRatio [r, t, f, m, y] <> 0}: RateOfActivity [r, l, t, m, y] * InputActivityRatio [r, t, f, m, y] = RateOfUseByTechnologyByMode [r, l, t, m, f, y]; s.t. EBa5_RateOfFuelUse2{r in REGION, t in TECHNOLOGY, f in FUEL, y in YEAR, l in TIMESLICE}: sum{m in MODE_OF_OPERATION: InputActivityRatio [r, t, f, m, y] <> 0} RateOfUseByTechnologyByMode [r, l, t, m, f, y] = RateOfUseByTechnology [r, l, t, f, y]; s.t. NCC1_TotalAnnualMaxNewCapacityConstraint{r in REGION, t in TECHNOLOGY, y in YEAR}: NewCapacity [r, t, y] <= TotalAnnualMaxCapacityInvestment [r, t, y]; s.t. NCC2_TotalAnnualMinNewCapacityConstraint{r in REGION, t in TECHNOLOGY, y in YEAR: TotalAnnualMinCapacityInvestment [r, t, y] > 0}: NewCapacity [r, t, y] >= TotalAnnualMinCapacityInvestment [r, t, y]; s.t. OC1_OperatingCostsVariable{r in REGION, t in TECHNOLOGY, y in YEAR}: sum{m in MODE_OF_OPERATION} TotalAnnualTechnologyActivityByMode [r, t, m, y] * VariableCost [r, t, m, y] = AnnualVariableOperatingCost [r, t, y]; s.t. OC2_OperatingCostsFixedAnnual{r in REGION, t in TECHNOLOGY, y in YEAR}: TotalCapacityAnnual [r, t, y] * FixedCost [r, t, y] = AnnualFixedOperatingCost [r, t, y]; s.t. SI6_SalvageValueStorageAtEndOfPeriod1{r in REGION, s in STORAGE, y in YEAR: (y + OperationalLifeStorage [r, s] - 1) <= (max{yy in YEAR} max(yy))}: 0 = SalvageValueStorage [r, s, y]; s.t. SV3_SalvageValueAtEndOfPeriod3{r in REGION, t in TECHNOLOGY, y in YEAR: (y + OperationalLife [r, t] - 1) <= (max{yy in YEAR} max(yy))}: SalvageValue [r, t, y] = 0; s.t. SV4_SalvageValueDiscountedToStartYear{r in REGION, t in TECHNOLOGY, y in YEAR}: DiscountedSalvageValue [r, t, y] = SalvageValue [r, t, y] / ((1 + DiscountRate [r]) ^ (1 + max{yy in YEAR} max(yy) - min{yy in YEAR} min(yy))); s.t. TAC1_TotalModelHorizonTechnologyActivity{r in REGION, t in TECHNOLOGY}: sum{y in YEAR} TotalTechnologyAnnualActivity [r, t, y] = TotalTechnologyModelPeriodActivity [r, t]; # # Long_Code_Equations

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s.t. AAC1_TotalAnnualTechnologyActivity{r in REGION, t in TECHNOLOGY, y in YEAR}: sum{l in TIMESLICE} RateOfTotalActivity [r, t, l, y] * YearSplit [l, y] = TotalTechnologyAnnualActivity [r, t, y]; s.t. AAC2_TotalAnnualTechnologyActivityUpperLimit{r in REGION, t in TECHNOLOGY, y in YEAR}: TotalTechnologyAnnualActivity [r, t, y] <= TotalTechnologyAnnualActivityUpperLimit [r, t, y]; s.t. AAC3_TotalAnnualTechnologyActivityLowerLimit{r in REGION, t in TECHNOLOGY, y in YEAR: TotalTechnologyAnnualActivityLowerLimit [r, t, y] > 0}: TotalTechnologyAnnualActivity [r, t, y] >= TotalTechnologyAnnualActivityLowerLimit [r, t, y]; s.t. Acc4_ModelPeriodCostByRegion{r in REGION}: sum{y in YEAR} TotalDiscountedCost [r, y] = ModelPeriodCostByRegion [r]; s.t. CAa3_TotalActivityOfEachTechnology{r in REGION, l in TIMESLICE, t in TECHNOLOGY, y in YEAR}: sum{m in MODE_OF_OPERATION} RateOfActivity [r, l, t, m, y] = RateOfTotalActivity [r, t, l, y]; s.t. CAa4_Constraint_Capacity{r in REGION, t in TECHNOLOGY, l in TIMESLICE, y in YEAR}: RateOfTotalActivity [r, t, l, y] <= TotalCapacityAnnual [r, t, y] * CapacityFactor [r, t, l, y] * CapacityToActivityUnit [r, t]; s.t. CAb1_PlannedMaintenance{r in REGION, t in TECHNOLOGY, y in YEAR}: sum{l in TIMESLICE} RateOfTotalActivity [r, t, l, y] * YearSplit [l, y] <= sum{l in TIMESLICE} (TotalCapacityAnnual [r, t, y] * CapacityFactor [r, t, l, y] * YearSplit [l, y]) * AvailabilityFactor [r, t, y] * CapacityToActivityUnit [r, t]; s.t. CC2_DiscountingCapitalInvestment{r in REGION, t in TECHNOLOGY, y in YEAR}: CapitalInvestment [r, t, y] / ((1 + DiscountRate [r]) ^ (y - min{yy in YEAR} min(yy))) = DiscountedCapitalInvestment [r, t, y]; s.t. E1_AnnualEmissionProductionByMode{r in REGION, t in TECHNOLOGY, e in EMISSION, m in MODE_OF_OPERATION, y in YEAR}: EmissionActivityRatio [r, t, e, m, y] * TotalAnnualTechnologyActivityByMode [r, t, m, y] = AnnualTechnologyEmissionByMode [r, t, e, m, y]; s.t. E3_EmissionsPenaltyByTechAndEmission{r in REGION, t in TECHNOLOGY, e in EMISSION, y in YEAR}: AnnualTechnologyEmission [r, t, e, y] * EmissionsPenalty [r, e, y] = AnnualTechnologyEmissionPenaltyByEmission [r, t, e, y]; s.t. E4_EmissionsPenaltyByTechnology{r in REGION, t in TECHNOLOGY, y in YEAR}: sum{e in EMISSION} AnnualTechnologyEmissionPenaltyByEmission [r, t, e, y] = AnnualTechnologyEmissionsPenalty [r, t, y]; s.t. E5_DiscountedEmissionsPenaltyByTechnology{r in REGION, t in TECHNOLOGY, y in YEAR}: AnnualTechnologyEmissionsPenalty [r, t, y] / ((1 + DiscountRate [r]) ^ (y - min{yy in YEAR} min(yy) + 0.5)) = DiscountedTechnologyEmissionsPenalty [r, t, y]; s.t. E6_EmissionsAccounting1{r in REGION, e in EMISSION, y in YEAR}: sum{t in TECHNOLOGY} AnnualTechnologyEmission [r, t, e, y] = AnnualEmissions [r, e, y]; s.t. E7_EmissionsAccounting2{r in REGION, e in EMISSION}: sum{y in YEAR} AnnualEmissions [r, e, y] = ModelPeriodEmissions [r, e] - ModelPeriodExogenousEmission [r, e]; s.t. E8_AnnualEmissionsLimit{r in REGION, e in EMISSION, y in YEAR}: AnnualEmissions [r, e, y] + AnnualExogenousEmission [r, e, y] <= AnnualEmissionLimit [r, e, y]; s.t. E9_ModelPeriodEmissionsLimit{r in REGION, e in EMISSION}: ModelPeriodEmissions [r, e] <= ModelPeriodEmissionLimit [r, e]; s.t. EBa11_EnergyBalanceEachTS5{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR}: Production [r, l, f, y] >= Demand [r, l, f, y] + Use [r, l, f, y] + sum{rr in REGION} Trade [r, rr, l, f, y] * TradeRoute [r, rr, f, y]; s.t. EBa3_RateOfFuelProduction3{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR}: sum{t in TECHNOLOGY} RateOfProductionByTechnology [r, l, t, f, y] = RateOfProduction [r, l, f, y]; s.t. EBa6_RateOfFuelUse3{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR}: sum{t in TECHNOLOGY} RateOfUseByTechnology [r, l, t, f, y] = RateOfUse [r, l, f, y];

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s.t. EBa7_EnergyBalanceEachTS1{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR}: RateOfProduction [r, l, f, y] * YearSplit [l, y] = Production [r, l, f, y]; s.t. EBa8_EnergyBalanceEachTS2{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR}: RateOfUse [r, l, f, y] * YearSplit [l, y] = Use [r, l, f, y]; s.t. EBa9_EnergyBalanceEachTS3{r in REGION, l in TIMESLICE, f in FUEL, y in YEAR}: RateOfDemand [r, l, f, y] * YearSplit [l, y] = Demand [r, l, f, y]; s.t. EBb1_EnergyBalanceEachYear1{r in REGION, f in FUEL, y in YEAR}: sum{l in TIMESLICE} Production [r, l, f, y] = ProductionAnnual [r, f, y]; s.t. EBb2_EnergyBalanceEachYear2{r in REGION, f in FUEL, y in YEAR}: sum{l in TIMESLICE} Use [r, l, f, y] = UseAnnual [r, f, y]; s.t. EBb3_EnergyBalanceEachYear3{r in REGION, rr in REGION, f in FUEL, y in YEAR}: sum{l in TIMESLICE} Trade [r, rr, l, f, y] = TradeAnnual [r, rr, f, y]; s.t. EBb4_EnergyBalanceEachYear4{r in REGION, f in FUEL, y in YEAR}: ProductionAnnual [r, f, y] >= UseAnnual [r, f, y] + sum{rr in REGION} TradeAnnual [r, rr, f, y] * TradeRoute [r, rr, f, y] + AccumulatedAnnualDemand [r, f, y]; s.t. EQ_SpecifiedDemand{r in REGION, f in FUEL, y in YEAR, l in TIMESLICE}: SpecifiedAnnualDemand [r, f, y] * SpecifiedDemandProfile [r, f, l, y] / YearSplit [l, y] = RateOfDemand [r, l, f, y]; s.t. OC3_OperatingCostsTotalAnnual{r in REGION, t in TECHNOLOGY, y in YEAR}: AnnualFixedOperatingCost [r, t, y] + AnnualVariableOperatingCost [r, t, y] = OperatingCost [r, t, y]; s.t. OC4_DiscountedOperatingCostsTotalAnnual{r in REGION, t in TECHNOLOGY, y in YEAR}: OperatingCost [r, t, y] / ((1 + DiscountRate [r]) ^ (y - min{yy in YEAR} min(yy) + 0.5)) = DiscountedOperatingCost [r, t, y]; s.t. RE1_FuelProductionByTechnologyAnnual{r in REGION, t in TECHNOLOGY, f in FUEL, y in YEAR}: sum{l in TIMESLICE} ProductionByTechnology [r, l, t, f, y] = ProductionByTechnologyAnnual [r, t, f, y]; s.t. RE2_TechIncluded{r in REGION, y in YEAR}: sum{t in TECHNOLOGY, f in FUEL} ProductionByTechnologyAnnual [r, t, f, y] * RETagTechnology [r, t, y] = TotalREProductionAnnual [r, y]; s.t. RE3_FuelIncluded{r in REGION, y in YEAR}: sum{l in TIMESLICE, f in FUEL} RateOfProduction [r, l, f, y] * RETagFuel [r, f, y] * YearSplit [l, y] = RETotalProductionOfTargetFuelAnnual [r, y]; s.t. RE4_EnergyConstraint{r in REGION, y in YEAR}: REMinProductionTarget [r, y] * RETotalProductionOfTargetFuelAnnual [r, y] <= TotalREProductionAnnual [r, y]; s.t. RE5_FuelUseByTechnologyAnnual{r in REGION, t in TECHNOLOGY, f in FUEL, y in YEAR}: sum{l in TIMESLICE} RateOfUseByTechnology [r, l, t, f, y] * YearSplit [l, y] = UseByTechnologyAnnual [r, t, f, y]; s.t. RM1_ReserveMargin_TechnologiesIncluded_In_Activity_Units{r in REGION, y in YEAR}: sum{t in TECHNOLOGY} TotalCapacityAnnual [r, t, y] * ReserveMarginTagTechnology [r, t, y] * CapacityToActivityUnit [r, t] = TotalCapacityInReserveMargin [r, y]; s.t. RM2_ReserveMargin_FuelsIncluded{r in REGION, l in TIMESLICE, y in YEAR}: sum{f in FUEL} RateOfProduction [r, l, f, y] * ReserveMarginTagFuel [r, f, y] = DemandNeedingReserveMargin [r, l, y]; s.t. RM3_ReserveMargin_Constraint{r in REGION, l in TIMESLICE, y in YEAR}: DemandNeedingReserveMargin [r, l, y] * ReserveMargin [r, y] <= TotalCapacityInReserveMargin [r, y]; s.t. S11_and_S12_StorageLevelDayTypeStart{ld in DAYTYPE, r in REGION, s in STORAGE, ls in SEASON, y in YEAR}: if ld = min{ldld in DAYTYPE} min(ldld) then StorageLevelSeasonStart [r, s, ls, y] else StorageLevelDayTypeStart [r, s, ls, ld-1, y] + sum{lh in DAILYTIMEBRACKET} NetChargeWithinDay [r, s, ls, ld-1, lh, y] * DaysInDayType [ls, ld-1, y] = StorageLevelDayTypeStart [r, s, ls, ld, y]; s.t. S13_and_S14_and_S15_StorageLevelDayTypeFinish{ls in SEASON, ld in DAYTYPE, r in REGION, s in STORAGE, y in YEAR}: if ls = max{lsls in SEASON} max(lsls) && ld = max{ldld in DAYTYPE} max(ldld) then StorageLevelYearFinish [r, s, y] else if ld = max{ldld in DAYTYPE} max(ldld) then

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StorageLevelSeasonStart [r, s, ls+1, y] else StorageLevelDayTypeFinish [r, s, ls, ld+1, y] - sum{lh in DAILYTIMEBRACKET} NetChargeWithinDay [r, s, ls, ld+1, lh, y] * DaysInDayType [ls, ld+1, y] = StorageLevelDayTypeFinish [r, s, ls, ld, y]; s.t. S1_RateOfStorageCharge{r in REGION, s in STORAGE, y in YEAR, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET}: sum{l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION: TechnologyToStorage [r, t, s, m] > 0} RateOfActivity [r, l, t, m, y] * TechnologyToStorage [r, t, s, m] * Conversionls [l, ls] * Conversionld [l, ld] * Conversionlh [l, lh] = RateOfStorageCharge [r, s, ls, ld, lh, y]; s.t. S2_RateOfStorageDischarge{r in REGION, s in STORAGE, y in YEAR, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET}: sum{l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION: TechnologyFromStorage [r, t, s, m] > 0} RateOfActivity [r, l, t, m, y] * TechnologyFromStorage [r, t, s, m] * Conversionls [l, ls] * Conversionld [l, ld] * Conversionlh [l, lh] = RateOfStorageDischarge [r, s, ls, ld, lh, y]; s.t. S3_NetChargeWithinYear{ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, r in REGION, s in STORAGE, y in YEAR}: sum{l in TIMESLICE: Conversionls [l, ls] > 0 && Conversionld [l, ld] > 0 && Conversionlh [l, lh] > 0} (RateOfStorageCharge [r, s, ls, ld, lh, y] - RateOfStorageDischarge [r, s, ls, ld, lh, y]) * YearSplit [l, y] * Conversionls [l, ls] * Conversionld [l, ld] * Conversionlh [l, lh] = NetChargeWithinYear [r, s, ls, ld, lh, y]; s.t. S4_NetChargeWithinDay{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR}: (RateOfStorageCharge [r, s, ls, ld, lh, y] - RateOfStorageDischarge [r, s, ls, ld, lh, y]) * DaySplit [lh, y] = NetChargeWithinDay [r, s, ls, ld, lh, y]; s.t. S5_and_S6_StorageLevelYearStart{y in YEAR, r in REGION, s in STORAGE}: if y = min{yy in YEAR} min(yy) then StorageLevelStart [r, s] else StorageLevelYearStart [r, s, y-1] + sum{ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET} NetChargeWithinYear [r, s, ls, ld, lh, y-1] = StorageLevelYearStart [r, s, y]; s.t. S7_and_S8_StorageLevelYearFinish{y in YEAR, r in REGION, s in STORAGE}: if y < max{yy in YEAR} max(yy) then StorageLevelYearStart [r, s, y+1] else StorageLevelYearStart [r, s, y] + sum{ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET} NetChargeWithinYear [r, s, ls, ld, lh, y] = StorageLevelYearFinish [r, s, y]; s.t. S9_and_S10_StorageLevelSeasonStart{ls in SEASON, r in REGION, s in STORAGE, y in YEAR}: if ls = min{lsls in SEASON} min(lsls) then StorageLevelYearStart [r, s, y] else StorageLevelSeasonStart [r, s, ls-1, y] + sum{ld in DAYTYPE, lh in DAILYTIMEBRACKET} NetChargeWithinYear [r, s, ls-1, ld, lh, y] = StorageLevelSeasonStart [r, s, ls, y]; s.t. SC1_LowerLimit_BeginningOfDailyTimeBracketOfFirstInstanceOfDayTypeInFirstWeekConstraint{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, y in YEAR, lh in DAILYTIMEBRACKET}: 0 <= (StorageLevelDayTypeStart [r, s, ls, ld, y] + sum{lhlh in DAILYTIMEBRACKET: lh - lhlh > 0} NetChargeWithinDay [r, s, ls, ld, lhlh, y]) - StorageLowerLimit [r, s, y]; s.t. SC1_UpperLimit_BeginningOfDailyTimeBracketOfFirstInstanceOfDayTypeInFirstWeekConstraint{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, y in YEAR, lh in DAILYTIMEBRACKET}: (StorageLevelDayTypeStart [r, s, ls, ld, y] + sum{lhlh in DAILYTIMEBRACKET: lh - lhlh > 0} NetChargeWithinDay [r, s, ls, ld, lhlh, y]) - StorageUpperLimit [r, s, y] <= 0; s.t. SC2_LowerLimit_EndOfDailyTimeBracketOfLastInstanceOfDayTypeInFirstWeekConstraint{ld in DAYTYPE, r in REGION, s in STORAGE, ls in SEASON, y in YEAR, lh in DAILYTIMEBRACKET}: 0 <= if ld > min{ldld in DAYTYPE} min(ldld) then (StorageLevelDayTypeStart [r, s, ls, ld, y] - sum{lhlh in DAILYTIMEBRACKET: lh - lhlh < 0} NetChargeWithinDay [r, s, ls, ld-1, lhlh, y]) - StorageLowerLimit [r, s, y]; s.t. SC2_UpperLimit_EndOfDailyTimeBracketOfLastInstanceOfDayTypeInFirstWeekConst

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raint{ld in DAYTYPE, r in REGION, s in STORAGE, ls in SEASON, y in YEAR, lh in DAILYTIMEBRACKET}: if ld > min{ldld in DAYTYPE} min(ldld) then (StorageLevelDayTypeStart [r, s, ls, ld, y] - sum{lhlh in DAILYTIMEBRACKET: lh - lhlh < 0} NetChargeWithinDay [r, s, ls, ld-1, lhlh, y]) - StorageUpperLimit [r, s, y] <= 0; s.t. SC3_LowerLimit_EndOfDailyTimeBracketOfLastInstanceOfDayTypeInLastWeekConstraint{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, y in YEAR, lh in DAILYTIMEBRACKET}: 0 <= (StorageLevelDayTypeFinish [r, s, ls, ld, y] - sum{lhlh in DAILYTIMEBRACKET: lh - lhlh < 0} NetChargeWithinDay [r, s, ls, ld, lhlh, y]) - StorageLowerLimit [r, s, y]; s.t. SC3_UpperLimit_EndOfDailyTimeBracketOfLastInstanceOfDayTypeInLastWeekConstraint{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, y in YEAR, lh in DAILYTIMEBRACKET}: (StorageLevelDayTypeFinish [r, s, ls, ld, y] - sum{lhlh in DAILYTIMEBRACKET: lh - lhlh < 0} NetChargeWithinDay [r, s, ls, ld, lhlh, y]) - StorageUpperLimit [r, s, y] <= 0; s.t. SC4_LowerLimit_BeginningOfDailyTimeBracketOfFirstInstanceOfDayTypeInLastWeekConstraint{ld in DAYTYPE, r in REGION, s in STORAGE, ls in SEASON, y in YEAR, lh in DAILYTIMEBRACKET}: 0 <= if ld > min{ldld in DAYTYPE} min(ldld) then (StorageLevelDayTypeFinish [r, s, ls, ld-1, y] + sum{lhlh in DAILYTIMEBRACKET: lh - lhlh > 0} NetChargeWithinDay [r, s, ls, ld, lhlh, y]) - StorageLowerLimit [r, s, y]; s.t. SC4_UpperLimit_BeginningOfDailyTimeBracketOfFirstInstanceOfDayTypeInLastWeekConstraint{ld in DAYTYPE, r in REGION, s in STORAGE, ls in SEASON, y in YEAR, lh in DAILYTIMEBRACKET}: if ld > min{ldld in DAYTYPE} min(ldld) then (StorageLevelDayTypeFinish [r, s, ls, ld-1, y] + sum{lhlh in DAILYTIMEBRACKET: lh - lhlh > 0} NetChargeWithinDay [r, s, ls, ld, lhlh, y]) - StorageUpperLimit [r, s, y] <= 0; s.t. SC5_MaxChargeConstraint{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR}: RateOfStorageCharge [r, s, ls, ld, lh, y] <= StorageMaxChargeRate [r, s]; s.t. SC6_MaxDischargeConstraint{r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR}: RateOfStorageDischarge [r, s, ls, ld, lh, y] <= StorageMaxDischargeRate [r, s]; s.t. SI10_TotalDiscountedCostByStorage{r in REGION, s in STORAGE, y in YEAR}: DiscountedCapitalInvestmentStorage [r, s, y] - DiscountedSalvageValueStorage [r, s, y] = TotalDiscountedStorageCost [r, s, y]; s.t. SI1_StorageUpperLimit{r in REGION, s in STORAGE, y in YEAR}: AccumulatedNewStorageCapacity [r, s, y] + ResidualStorageCapacity [r, s, y] = StorageUpperLimit [r, s, y]; s.t. SI2_StorageLowerLimit{r in REGION, s in STORAGE, y in YEAR}: MinStorageCharge [r, s, y] * StorageUpperLimit [r, s, y] = StorageLowerLimit [r, s, y]; s.t. SI3_TotalNewStorage{y in YEAR, r in REGION, s in STORAGE}: sum{yy in YEAR: y - yy < OperationalLifeStorage [r, s] && y - yy > 0} NewStorageCapacity [r, s, yy] = AccumulatedNewStorageCapacity [r, s, y]; s.t. SI4_UndiscountedCapitalInvestmentStorage{r in REGION, s in STORAGE, y in YEAR}: CapitalCostStorage [r, s, y] * NewStorageCapacity [r, s, y] = CapitalInvestmentStorage [r, s, y]; s.t. SI5_DiscountingCapitalInvestmentStorage{r in REGION, s in STORAGE, y in YEAR}: CapitalInvestmentStorage [r, s, y] / ((1 + DiscountRate [r]) ^ (y - min{yy in YEAR} min(yy))) = DiscountedCapitalInvestmentStorage [r, s, y]; s.t. SI7_SalvageValueStorageAtEndOfPeriod2{r in REGION, s in STORAGE, y in YEAR: (DepreciationMethod [r] = 1 && y + OperationalLifeStorage [r, s] - 1 > max{yy in YEAR} max(yy) && DiscountRate [r] = 0) || (DepreciationMethod [r] = 2 && (y + OperationalLifeStorage [r, s] - 1) > (max{yy in YEAR} max(yy)))}: CapitalInvestmentStorage [r, s, y] * (1 - (max{yy in YEAR} max(yy) - y+1) / OperationalLifeStorage [r, s]) = SalvageValueStorage [r, s, y];

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s.t. SI8_SalvageValueStorageAtEndOfPeriod3{r in REGION, s in STORAGE, y in YEAR: DepreciationMethod [r] = 1 && (y + OperationalLifeStorage [r, s] - 1) > (max{yy in YEAR} max(yy)) && DiscountRate [r] > 0}: CapitalInvestmentStorage [r, s, y] * (1 - ((((1 + DiscountRate [r]) ^ (max{yy in YEAR} max(yy) - y+1)) - 1) / ((1 + DiscountRate [r]) ^ OperationalLifeStorage [r, s] - 1))) = SalvageValueStorage [r, s, y]; s.t. SI9_SalvageValueStorageDiscountedToStartYear{r in REGION, s in STORAGE, y in YEAR}: SalvageValueStorage [r, s, y] / ((1 + DiscountRate [r]) ^ (max{yy in YEAR} max(yy) - min{yy in YEAR} min(yy) + 1)) = DiscountedSalvageValueStorage [r, s, y]; s.t. SV1_SalvageValueAtEndOfPeriod1{r in REGION, t in TECHNOLOGY, y in YEAR: DepreciationMethod [r] = 1 && (y + OperationalLife [r, t] - 1) > (max{yy in YEAR} max(yy)) && DiscountRate [r] > 0}: SalvageValue [r, t, y] = CapitalCost [r, t, y] * NewCapacity [r, t, y] * (1 - (((1 + DiscountRate [r]) ^ (max{yy in YEAR} max(yy) - y+1) - 1) / ((1 + DiscountRate [r]) ^ OperationalLife [r, t] - 1))); s.t. SV2_SalvageValueAtEndOfPeriod2{r in REGION, t in TECHNOLOGY, y in YEAR: (DepreciationMethod [r] = 1 && (y + OperationalLife [r, t] - 1) > (max{yy in YEAR} max(yy)) && DiscountRate [r] = 0) || (DepreciationMethod [r] = 2 && (y + OperationalLife [r, t] - 1) > (max{yy in YEAR} max(yy)))}: SalvageValue [r, t, y] = CapitalCost [r, t, y] * NewCapacity [r, t, y] * (1 - (max{yy in YEAR} max(yy) - y+1) / OperationalLife [r, t]); s.t. TAC2_TotalModelHorizonTechnologyActivityUpperLimit{r in REGION, t in TECHNOLOGY: TotalTechnologyModelPeriodActivityUpperLimit [r, t] > 0}: TotalTechnologyModelPeriodActivity [r, t] <= TotalTechnologyModelPeriodActivityUpperLimit [r, t]; s.t. TAC3_TotalModelHorizenTechnologyActivityLowerLimit{r in REGION, t in TECHNOLOGY: TotalTechnologyModelPeriodActivityLowerLimit [r, t] > 0}: TotalTechnologyModelPeriodActivity [r, t] >= TotalTechnologyModelPeriodActivityLowerLimit [r, t]; s.t. TCC1_TotalAnnualMaxCapacityConstraint{r in REGION, t in TECHNOLOGY, y in YEAR}: TotalCapacityAnnual [r, t, y] <= TotalAnnualMaxCapacity [r, t, y]; s.t. TCC2_TotalAnnualMinCapacityConstraint{r in REGION, t in TECHNOLOGY, y in YEAR: TotalAnnualMinCapacity [r, t, y] > 0}: TotalCapacityAnnual [r, t, y] >= TotalAnnualMinCapacity [r, t, y]; s.t. TDC1_TotalDiscountedCostByTechnology{r in REGION, t in TECHNOLOGY, y in YEAR}: DiscountedOperatingCost [r, t, y] + DiscountedCapitalInvestment [r, t, y] + DiscountedTechnologyEmissionsPenalty [r, t, y] - DiscountedSalvageValue [r, t, y] = TotalDiscountedCostByTechnology [r, t, y]; s.t. TDC2_TotalDiscountedCost{r in REGION, y in YEAR}: sum{t in TECHNOLOGY} TotalDiscountedCostByTechnology [r, t, y] + sum{s in STORAGE} TotalDiscountedStorageCost [r, s, y] = TotalDiscountedCost [r, y]; # ##################### # solve; # ##################### # ################ # Output # ################ # table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/AccumulatedNewCapacity.csv" : r, t, y, AccumulatedNewCapacity[r, t, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/AccumulatedNewStorageCapacity.csv" : r, s, y, AccumulatedNewStorageCapacity[r, s, y]; table tout {r in REGION, e in EMISSION, y in YEAR} OUT "CSV" "res/csv/AnnualEmissions.csv" : r, e, y, AnnualEmissions[r, e, y];

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table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/AnnualFixedOperatingCost.csv" : r, t, y, AnnualFixedOperatingCost[r, t, y]; table tout {r in REGION, t in TECHNOLOGY, e in EMISSION, y in YEAR} OUT "CSV" "res/csv/AnnualTechnologyEmission.csv" : r, t, e, y, AnnualTechnologyEmission[r, t, e, y]; table tout {r in REGION, t in TECHNOLOGY, e in EMISSION, m in MODE_OF_OPERATION, y in YEAR} OUT "CSV" "res/csv/AnnualTechnologyEmissionByMode.csv" : r, t, e, m, y, AnnualTechnologyEmissionByMode[r, t, e, m, y]; table tout {r in REGION, t in TECHNOLOGY, e in EMISSION, y in YEAR} OUT "CSV" "res/csv/AnnualTechnologyEmissionPenaltyByEmission.csv" : r, t, e, y, AnnualTechnologyEmissionPenaltyByEmission[r, t, e, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/AnnualTechnologyEmissionsPenalty.csv" : r, t, y, AnnualTechnologyEmissionsPenalty[r, t, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/AnnualVariableOperatingCost.csv" : r, t, y, AnnualVariableOperatingCost[r, t, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/CapitalInvestment.csv" : r, t, y, CapitalInvestment[r, t, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/CapitalInvestmentStorage.csv" : r, s, y, CapitalInvestmentStorage[r, s, y]; table tout {r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} OUT "CSV" "res/csv/Demand.csv" : r, l, f, y, Demand[r, l, f, y]; table tout {r in REGION, l in TIMESLICE, y in YEAR} OUT "CSV" "res/csv/DemandNeedingReserveMargin.csv" : r, l, y, DemandNeedingReserveMargin[r, l, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/DiscountedCapitalInvestment.csv" : r, t, y, DiscountedCapitalInvestment[r, t, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/DiscountedCapitalInvestmentStorage.csv" : r, s, y, DiscountedCapitalInvestmentStorage[r, s, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/DiscountedOperatingCost.csv" : r, t, y, DiscountedOperatingCost[r, t, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/DiscountedSalvageValue.csv" : r, t, y, DiscountedSalvageValue[r, t, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/DiscountedSalvageValueStorage.csv" : r, s, y, DiscountedSalvageValueStorage[r, s, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/DiscountedTechnologyEmissionsPenalty.csv" : r, t, y, DiscountedTechnologyEmissionsPenalty[r, t, y]; table tout {r in REGION} OUT "CSV" "res/csv/ModelPeriodCostByRegion.csv" : r, ModelPeriodCostByRegion[r]; table tout {r in REGION, e in EMISSION} OUT "CSV" "res/csv/ModelPeriodEmissions.csv" : r, e, ModelPeriodEmissions[r, e]; table tout {r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR} OUT "CSV" "res/csv/NetChargeWithinDay.csv" : r, s, ls, ld, lh, y, NetChargeWithinDay[r, s, ls, ld, lh, y]; table tout {r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR} OUT "CSV" "res/csv/NetChargeWithinYear.csv" : r, s, ls, ld, lh, y, NetChargeWithinYear[r, s, ls, ld, lh, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/NewCapacity.csv" : r, t, y, NewCapacity[r, t, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/NewStorageCapacity.csv" : r, s, y, NewStorageCapacity[r, s, y];

112

table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/NumberOfNewTechnologyUnits.csv" : r, t, y, NumberOfNewTechnologyUnits[r, t, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/OperatingCost.csv" : r, t, y, OperatingCost[r, t, y]; table tout {r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} OUT "CSV" "res/csv/Production.csv" : r, l, f, y, Production[r, l, f, y]; table tout {r in REGION, f in FUEL, y in YEAR} OUT "CSV" "res/csv/ProductionAnnual.csv" : r, f, y, ProductionAnnual[r, f, y]; table tout {r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR} OUT "CSV" "res/csv/ProductionByTechnology.csv" : r, l, t, f, y, ProductionByTechnology[r, l, t, f, y]; table tout {r in REGION, t in TECHNOLOGY, f in FUEL, y in YEAR} OUT "CSV" "res/csv/ProductionByTechnologyAnnual.csv" : r, t, f, y, ProductionByTechnologyAnnual[r, t, f, y]; table tout {r in REGION, y in YEAR} OUT "CSV" "res/csv/RETotalProductionOfTargetFuelAnnual.csv" : r, y, RETotalProductionOfTargetFuelAnnual[r, y]; table tout {r in REGION, l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION, y in YEAR} OUT "CSV" "res/csv/RateOfActivity.csv" : r, l, t, m, y, RateOfActivity[r, l, t, m, y]; table tout {r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} OUT "CSV" "res/csv/RateOfDemand.csv" : r, l, f, y, RateOfDemand[r, l, f, y]; table tout {r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} OUT "CSV" "res/csv/RateOfProduction.csv" : r, l, f, y, RateOfProduction[r, l, f, y]; table tout {r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR} OUT "CSV" "res/csv/RateOfProductionByTechnology.csv" : r, l, t, f, y, RateOfProductionByTechnology[r, l, t, f, y]; table tout {r in REGION, l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION, f in FUEL, y in YEAR} OUT "CSV" "res/csv/RateOfProductionByTechnologyByMode.csv" : r, l, t, m, f, y, RateOfProductionByTechnologyByMode[r, l, t, m, f, y]; table tout {r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR} OUT "CSV" "res/csv/RateOfStorageCharge.csv" : r, s, ls, ld, lh, y, RateOfStorageCharge[r, s, ls, ld, lh, y]; table tout {r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, lh in DAILYTIMEBRACKET, y in YEAR} OUT "CSV" "res/csv/RateOfStorageDischarge.csv" : r, s, ls, ld, lh, y, RateOfStorageDischarge[r, s, ls, ld, lh, y]; table tout {r in REGION, t in TECHNOLOGY, l in TIMESLICE, y in YEAR} OUT "CSV" "res/csv/RateOfTotalActivity.csv" : r, t, l, y, RateOfTotalActivity[r, t, l, y]; table tout {r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} OUT "CSV" "res/csv/RateOfUse.csv" : r, l, f, y, RateOfUse[r, l, f, y]; table tout {r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR} OUT "CSV" "res/csv/RateOfUseByTechnology.csv" : r, l, t, f, y, RateOfUseByTechnology[r, l, t, f, y]; table tout {r in REGION, l in TIMESLICE, t in TECHNOLOGY, m in MODE_OF_OPERATION, f in FUEL, y in YEAR} OUT "CSV" "res/csv/RateOfUseByTechnologyByMode.csv" : r, l, t, m, f, y, RateOfUseByTechnologyByMode[r, l, t, m, f, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/SalvageValue.csv" : r, t, y, SalvageValue[r, t, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/SalvageValueStorage.csv" : r, s, y, SalvageValueStorage[r, s, y]; table tout {r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, y in YEAR} OUT "CSV" "res/csv/StorageLevelDayTypeFinish.csv" : r, s, ls, ld, y, StorageLevelDayTypeFinish[r, s, ls, ld, y]; table tout {r in REGION, s in STORAGE, ls in SEASON, ld in DAYTYPE, y in YEAR} OUT "CSV" "res/csv/StorageLevelDayTypeStart.csv" : r, s, ls, ld, y, StorageLevelDayTypeStart[r, s, ls, ld, y];

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table tout {r in REGION, s in STORAGE, ls in SEASON, y in YEAR} OUT "CSV" "res/csv/StorageLevelSeasonStart.csv" : r, s, ls, y, StorageLevelSeasonStart[r, s, ls, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/StorageLevelYearFinish.csv" : r, s, y, StorageLevelYearFinish[r, s, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/StorageLevelYearStart.csv" : r, s, y, StorageLevelYearStart[r, s, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/StorageLowerLimit.csv" : r, s, y, StorageLowerLimit[r, s, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/StorageUpperLimit.csv" : r, s, y, StorageUpperLimit[r, s, y]; table tout {r in REGION, t in TECHNOLOGY, m in MODE_OF_OPERATION, y in YEAR} OUT "CSV" "res/csv/TotalAnnualTechnologyActivityByMode.csv" : r, t, m, y, TotalAnnualTechnologyActivityByMode[r, t, m, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/TotalCapacityAnnual.csv" : r, t, y, TotalCapacityAnnual[r, t, y]; table tout {r in REGION, y in YEAR} OUT "CSV" "res/csv/TotalCapacityInReserveMargin.csv" : r, y, TotalCapacityInReserveMargin[r, y]; table tout {r in REGION, y in YEAR} OUT "CSV" "res/csv/TotalDiscountedCost.csv" : r, y, TotalDiscountedCost[r, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/TotalDiscountedCostByTechnology.csv" : r, t, y, TotalDiscountedCostByTechnology[r, t, y]; table tout {r in REGION, s in STORAGE, y in YEAR} OUT "CSV" "res/csv/TotalDiscountedStorageCost.csv" : r, s, y, TotalDiscountedStorageCost[r, s, y]; table tout {r in REGION, y in YEAR} OUT "CSV" "res/csv/TotalREProductionAnnual.csv" : r, y, TotalREProductionAnnual[r, y]; table tout {r in REGION, t in TECHNOLOGY, y in YEAR} OUT "CSV" "res/csv/TotalTechnologyAnnualActivity.csv" : r, t, y, TotalTechnologyAnnualActivity[r, t, y]; table tout {r in REGION, t in TECHNOLOGY} OUT "CSV" "res/csv/TotalTechnologyModelPeriodActivity.csv" : r, t, TotalTechnologyModelPeriodActivity[r, t]; table tout {r in REGION, rr in REGION, l in TIMESLICE, f in FUEL, y in YEAR} OUT "CSV" "res/csv/Trade.csv" : r, rr, l, f, y, Trade[r, rr, l, f, y]; table tout {r in REGION, rr in REGION, f in FUEL, y in YEAR} OUT "CSV" "res/csv/TradeAnnual.csv" : r, rr, f, y, TradeAnnual[r, rr, f, y]; table tout {r in REGION, l in TIMESLICE, f in FUEL, y in YEAR} OUT "CSV" "res/csv/Use.csv" : r, l, f, y, Use[r, l, f, y]; table tout {r in REGION, f in FUEL, y in YEAR} OUT "CSV" "res/csv/UseAnnual.csv" : r, f, y, UseAnnual[r, f, y]; table tout {r in REGION, l in TIMESLICE, t in TECHNOLOGY, f in FUEL, y in YEAR} OUT "CSV" "res/csv/UseByTechnology.csv" : r, l, t, f, y, UseByTechnology[r, l, t, f, y]; table tout {r in REGION, t in TECHNOLOGY, f in FUEL, y in YEAR} OUT "CSV" "res/csv/UseByTechnologyAnnual.csv" : r, t, f, y, UseByTechnologyAnnual[r, t, f, y]; # end;

8.4 Appendix IIII Calculating annual emission limit for OSeMOSYS input data Desired specific emission limit for electricity production according to the INDC’s : 0.04 ton/Mwh

114

Conversing into ton/PJ:

0.04𝑡𝑜𝑛𝑀𝑤ℎ

∗ 277777.78𝑀𝑊ℎ𝑃𝐽

= 11111.1112𝑡𝑜𝑛𝑃𝐽

Conversion into kton/PJ:

11111.1112 𝑡𝑜𝑛𝑃𝐽

1000 = 11.111112 𝑘𝑡𝑜𝑛𝑃𝐽

Calculation of annual emission limit for year XXXX:

𝑇𝑜𝑡𝑎𝑙𝑎𝑚𝑜𝑢𝑛𝑡𝑜𝑓𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑡𝑦𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑𝑓𝑜𝑟𝑦𝑒𝑎𝑟𝑋𝑋𝑋𝑋𝑖𝑛𝑃𝐽 ∗ 11.111112𝑘𝑡𝑜𝑛𝑃𝐽

= 𝑋𝑘𝑡𝑜𝑛