Exploring Low-Carbon Development Pathways for Bolivia

kth royal institute of technology

Doctoral Thesis in Energy and Environmental Systems Exploring low-carbon development pathways for Bolivia

A model-based analysis focused on the energy sector

JENNY GABRIELA PEÑA BALDERRAMA

Stockholm, Sweden 2020 Exploring low-carbon development pathways for Bolivia

A model-based analysis focused on the energy sector

JENNY GABRIELA PEÑA BALDERRAMA

Academic Dissertation which, with due permission of the KTH Royal Institute of Technology, is submitted for public defence for the Degree of Doctor of Philosophy on Friday the 18th December 2020, at 10:00 a.m. in E2, Lindstedsvägen 3, Stockholm.

Doctoral Thesis in Energy and Environmental Systems KTH Royal Institute of Technology Stockholm, Sweden 2020 © Jenny Gabriela Peña Balderrama

Cover page photo: Michael Tompsett

ISBN 978-91-7873-731-4 TRITA-ITM-AVL 2020:49

Printed by: Universitetsservice US-AB, Sweden 2020 ‘There is no failure, except in no longer trying’—

Elbert Hubbard

Photo credit by Todd Antony

Abstract

Global emissions have continued to rise steadily at levels exceeding the 1.5°C climate stabilization target. Therefore, the increase in the average global temperature and climate change will be determined by how we handle greenhouse gas (GHG) emissions over the next years. Decarbonizing economic growth and development add pressure to all countries in the world, but even more pressure to low and middle-income countries planning to use their fossil fuel resources as a ‘leading sector’ to achieve growth. Low and middle-income countries have limited financial resources and often have to prioritize short-term development goals with immediate local co-benefits over mitigation and adaptation strategies with long-term gains. Reaching the climate stabilization/decarbonization goal will require large investments to decarbonize the energy sector, together with investments and policy measures to ensure resilience and adaptation to climate change. Bolivia has signaled its intent to eradicate poverty and achieve economic growth while preserving environmental sustainability by adopting the seventeen Sustainable Development Goals (SDGs). In line with SDG7, Bolivia aims to achieve universal access to electricity by 2025. Although electricity access has improved significantly with large investments in grid-extension and decentralized systems, currently 61% of the grid generation capacity and 93% of the decentralized generation capacity is fossil-fueled. Other sectors, such as the transport sector, depend mostly on fossil fuels and largely contribute to Bolivia’s GHG emissions. Policies intended to increase energy security in Bolivia focus on the increased use of domestic natural gas, on investments in large-scale hydropower plants, and on first-generation (1G) biofuel production for the transport sector. In line with SDG 7 and SDG 13, this doctoral thesis examines low-carbon development pathways for the main policies addressing energy access and energy security in Bolivia. With methods deriving from systems analysis, the particulars of the Bolivian energy system were scrutinized and the effects of alternative energy planning decisions ‒ such as policies and investments ‒ displayed using scenario analysis. Five research articles answering four research questions form the main part of the thesis. The first research question examines the role of decentralized technologies (primarily micro-grids) and renewable energy for addressing universal

i electricity access targets. The cost-competitiveness of multi-source micro- grids is assessed using an innovative methodology developed to increase the technical accuracy of load simulation and microgrid system design optimization in an existing geospatial modelling tool. The results highlight the potential of decentralized electrification solutions and identify the location, size and investments required to meet electrification targets in 2025. The second research question focuses on evaluating alternative policies for decarbonizing the power generation sector using an energy system optimization model. The combined effects of inserting carbon taxes and modifying the weighted average cost of capital on the power generation emissions are measured in marginal abatement cost curves. Results from this conceptual and numerical analysis show that a deep decarbonization of the power generation system requires very high carbon prices if the costs of capital are high. Instead, moderate carbon prices combined with low costs of capital can lead to significant emissions reductions and comparably smaller increases in carbon abatement costs. The third research question examines Bolivia’s hydroelectricity export plans in the medium-term using a cost-optimization model of South America. The study also evaluates the fair distribution of benefits using a cooperative game-theory approach and the decarbonization achieved in a scenario of power systems integration in South America. Results of this study indicate that electricity from large-scale hydropower projects could be traded with Brazil and reduce Bolivia’s dependence on natural gas for power generation. The results also show that Bolivia has less bargaining power than its competitors have in the long-term and benefits less from emissions reductions in a scenario of trade with Brazil. Lastly, the fourth research question explores energy security in the transport sector by assessing Bolivia’s sugarcane-based ethanol production targets. Increased sustainability in the ethanol production chain is evaluated quantitatively by identifying opportunities for agricultural intensification and investments in advanced biorefineries in a least-cost optimization model. Results from this analysis demonstrate that Bolivia can cost-effectively reach its medium-term targeted volumes of ethanol production with a moderate expansion of sugarcane cropland and investments in agriculture intensification. The results further suggest that it is cost-optimal to invest in current technological advances (i.e. efficient co-generation plants) to maximize the renewable energy output and the economic benefits of sugarcane-derived ethanol. Finally, the study

ii identifies a range of biofuel-support incentives to promote investments in second-generation biofuel production. Keywords: decarbonization, sustainability, techno-economic analysis, Bolivia, energy access, energy security, systems analysis.

Sammanfattning

Globala utsläpp har fortsatt att stadigt öka till nivåer som överstiger klimatstabiliseringsmålet på 1.5°C. Därför kommer ökningen i den globala genomsnittliga temperaturen och klimatförändringen att avgöras av hur vi hanterar utsläppen av växthusgas under kommande år. Koldioxidutvecklingen ekonomisk tillväxt och utveckling sätter press på alla länder i världen. Men ännu större press på låg- och medelinkomstländer som planerar att använda sina fossila bränsleresurser som en ‘ledande sektor’ för att nå tillväxt. Låg- och medelinkomstländer har begränsade ekonomiska resurser och måste ofta prioritera kortsiktiga utvecklingsmål med omedelbara åtföljande lokala fördelar framför begränsningsstrategier med långsiktiga vinster. Att nå klimatstabiliserings - och koldioxidavvecklingsmålet kommer att kräva stora investeringar för att fasa ut fossila bränslen i energisektorn, tillsammans med investeringar och politiska åtgärder för att säkerställa uthållighet och anpassning till klimatförändringen. Bolivia har uttryckt ambitionen att utrota fattigdom och uppnå ekonomisk tillväxt och samtidigt bevara miljömässig hållbarhet genom att anta de sjutton globala målen. I linje med mål 7 strävar Bolivia att uppnå tillgång till elektricitet för alla till år 2025. Även om eltillgången har förbättrats avsevärt med stora investeringar i elnätet och decentraliserade el-system, så är fortfarande 61% av elproduktionskapaciteten och 93% av den decentraliserade produktionskapaciteten fossildriven. Andra sektorer, t.ex. transporter, domineras också av fossila bränslen och bidrar till stor del till växthusgasutsläppen. Politik som syftar till att öka energisäkerheten i Bolivia fokuserar på ökad användning av naturgas, på investeringar i storskaliga vattenkraftverk och på första generationens (1G) biobränsleproduktion för transportsektorn. I linje med dem globala målen 7 och 13 undersöker denna doktorsavhandling hållbara utvecklingsvägar med låga koldioxiduts läpp kring nyckelpolicyer som hanterar energitillgång och energisäkerhet i

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Bolivia. Med metoder som härrör från systemanalys av det bolivianska energisystemet och effekter av alternativa energiplaneringsbeslut – som strategier och investeringar – skärskådas vidare genom scenarioanalys. Fem forskningsartiklar presenteras i avhandlingen med vilka fyra forskningsfrågor besvaras. Den första forskningsfrågan undersöker rollen som decentraliserad teknik (främst mikronät) och förnybar energi för att möta modern energi till alla (mål 7). Konkurrenskraften för multidrivmedelmikronät bedöms med hjälp av en innovativ metod som utvecklats för att öka den tekniska noggrannheten för detaljerad belastningssimulering och optimering av system för systemdesign i ett geospatialt modelleringsverktyg. Resultaten belyser potentialen för decentraliserade elektrifieringslösningar och identifierar plats, storlek och investeringar som krävs för att uppnå elektrifieringsmål 2025. Den andra forskningsfrågan utvärderar alternativa strategier för en koldioxidfri kraftproduktionssektor med hjälp av en optimeringsmodell för energisystem. De kombinerade effekterna av att införa koldioxidskatter och ändra den viktade genomsnittliga kapitalkostnaden på kraftproduktionsutsläppen mäts i marginalkostnadskurvor. Resultat från denna konceptuella och numeriska analys visar att ett helt koldioxidfr itt kraftproduktionssystem kräver mycket höga koldioxidpriser om kapitalkostnaderna är höga. En kombination av måttliga koldioxidpriser med låga kapitalkostnader leder till inte bara betydande minskningar av utsläppen utan även mindre ökningar av koldioxidreduktionskostnaderna. Den tredje forskningsfrågan undersöker Bolivias exportplaner för vattenkraft på medellång sikt med hjälp av en kostnadsoptimeringsmodell i Sydamerika. Studien utvärderar också den rättvisa fördelningen av fördelar med hjälp av en kooperativ strategi för spelteori och de potentiella koldioxidutsläppen som uppnås i ett scenario för kraftsystemintegration i Sydamerika. Resultaten av denna studie tyder på att el från stora vattenkraftsprojekt kan handlas med Brasilien och minska Bolivias beroende av naturgas för kraftproduktion. Resultaten visar också att Bolivia har mindre förhandlingsstyrka än sina konkurrenter på lång sikt och drar mindre nytta av utsläppsminskningar i ett scenario med handel med Brasilien. Slutligen undersöker den fjärde forskningsfrågan energisäkerheten inom transportsektorn genom att utvärdera Bolivias mål för produktion av sockerrörsbaserad etanol. Ökad hållbarhet i etanolproduktionskedjan utvärderas kvantitativt genom att identifiera möjligheter för

iv jordbruksintensifiering och investeringar i avancerade bioraffinaderier i en kostnadsoptimeringsmodell. Resultaten från denna analys visar att Bolivia kostnadseffektivt kan nå sina medellångsiktiga volymer av etanolproduktion med en måttlig expansion av odling av sockerrör och investeringar i jordbruksintensifiering. Resultaten visar vidare att det är kostnadsoptimalt att investera i nuvarande tekniska framsteg (dvs. effektiva kraftproduktionsanläggningar) för att maximera den förnybara energiproduktionen och de ekonomiska fördelarna med etanol som härrör från sockerrör. Slutligen identifieras ett stödområde för biodrivmedel för att stimulera investeringar i 2G etanol på medellång sikt.

Nyckelord: Avkarbonisering, hållbarhet, teknisk-ekonomisk analys, Bolivia, energitillgång, energisäkerhet, systemanalys..

Resumen

Las emisiones globales han seguido aumentando de manera constante a niveles que superan el objetivo de estabilización climática de 1.5 ° C. Por lo tanto, el aumento en la temperatura global promedio y el cambio climático vendrá determinado por cómo manejemos las emisiones de gas de efecto invernadero (GEI) durante los próximos años. La descarbonización del desarrollo y crecimiento económico agrega presión a todos los países del mundo, pero aún más a los países de ingresos bajos y medianos que planean utilizar sus recursos fósiles como un "sector líder" para lograr crecimiento. Los países de ingresos bajos y medianos tienen recursos financieros limitados y, a menudo, deben priorizar objetivos de desarrollo a corto plazo con beneficios locales inmediatos sobre estrategias de mitigación y adaptación con beneficios a largo plazo. Alcanzar el objetivo de estabilización-climática/descarbonización requerirá grandes inversiones para descarbonizar el sector energético, junto con inversiones y medidas de política para garantizar la resiliencia y la adaptación al cambio climático. Bolivia ha manifestado su intención de erradicar la pobreza y lograr el crecimiento económico preservando la sostenibilidad ambiental mediante la adopción de los diecisiete Objetivos de Desarrollo Sostenible (ODS). De acuerdo con el ODS 7, Bolivia tiene el objetivo de lograr el acceso universal a la electricidad para 2025. Aunque el acceso a la electricidad ha mejorado significativamente con grandes inversiones en extensión de la red y sistemas descentralizados, actualmente el 61% de la capacidad de generación de la red y el 93% de la capacidad de generación descentralizada son alimentados con combustibles fósiles. Otros sectores, como el transporte, también están dominados por los combustibles fósiles y contribuyen en gran medida a las emisiones de GEI en Bolivia. Actualmente, las políticas destinadas a aumentar la seguridad energética en Bolivia se centran en un mayor uso de las reservas nacionales de gas natural, en inversiones en grandes centrales hidroeléctricas y en la producción de biocombustibles de primera generación (1G) para el sector del transporte. En línea con el ODS 7 y el ODS 13, esta tesis doctoral examina vías de desarrollo alternativas con bajas emisiones de carbono para las principales políticas que abordan el acceso a la energía y la seguridad energética en Bolivia. Con métodos derivados del análisis de sistemas, las características del sistema energético boliviano se analizaron a detalle y se mostraron los

vi efectos de decisiones alternativas de planificación energética, como políticas e inversiones, mediante el análisis de escenarios. Cinco artículos científicos que responden a cuatro preguntas de investigación forman la parte principal de la tesis. La primera pregunta de investigación examina el rol de las tecnologías descentralizadas (principalmente micro-redes) y la energía renovable para abordar los objetivos de acceso universal a la electricidad. La competitividad de costos de las micro-redes se evaluó utilizando una metodología innovadora, desarrollada para incrementar la precisión de la optimización del diseño del sistema de micro-redes y modelización de la demanda en una herramienta especializada de modelado geoespacial. Los resultados destacan el potencial de las soluciones de electrificación descentralizada e identifican la ubicación, el tamaño y las inversiones necesarias para cumplir los objetivos de electrificación al 2025. La segunda pregunta de investigación se centra en evaluar políticas alternativas para descarbonizar el sector eléctrico utilizando un modelo de optimización del sistema energético. Los efectos de combinados de impuestos sobre el carbono y de los costos del capital se midieron en curvas de costos marginales de reducción de emisiones. Los resultados de este análisis conceptual y numérico muestran que una descarbonización profunda del sistema eléctrico requiere precios de carbono muy altos si los costos de capital son sustanciales. Una combinación de precios moderados del carbono con bajos costos de capital conduce a importantes reducciones de emisiones y aumentos comparativamente menores en los costos de decarbonización. La tercera pregunta de investigación examina los planes a mediano plazo de exportación de hidroelectricidad utilizando un modelo de optimización de costos del continente Sudamericano. El estudio también evalúa la distribución justa de beneficios utilizando un enfoque de teoría de juegos cooperativos. Así mismo el estudio estima la descarbonización lograda en un escenario de integración de sistemas eléctricos en América del Sur. Los resultados de este estudio indican que la electricidad de proyectos hidroeléctricos a gran escala podría comercializarse con Brasil y reducir la dependencia del gas natural para la generación eléctrica en Bolivia. Los resultados también muestran que en el largo plazo Bolivia tendría un menor poder de negociación que sus competidores y se beneficiaría menos de la reducción de emisiones en un escenario de comercio con Brasil. Por último, la cuarta pregunta de investigación explora la seguridad energética en el sector del transporte mediante la evaluación de los

vii objetivos de producción de etanol a base de caña de azúcar en Bolivia. El aumento de la sostenibilidad en la cadena de producción de etanol se evalúa cuantitativamente mediante la identificación de oportunidades para la intensificación agrícola e inversiones en bio-refinerías avanzadas en un modelo de optimización. Los resultados de este análisis demuestran que Bolivia puede alcanzar sus objeticos de producción de etanol de manera costo-efectiva con una expansión moderada de la superficie agrícola de caña de azúcar e inversiones para la intensificación agrícola. Los resultados sugieren además que es costo-efectivo invertir en los avances tecnológicos actuales (es decir, plantas de cogeneración eficientes) para maximizar la producción de energía renovable y los beneficios económicos del etanol derivado de la caña de azúcar. Finalmente, el estudio identifica una gama de incentivos de apoyo para promover las inversiones en la producción de biocombustibles de segunda generación.

Palabras clave: descarbonización, sostenibilidad, análisis tecno- económico, Bolivia, acceso a la energía, seguridad energética, análisis de sistemas

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Preface

This doctoral thesis is the outcome of research conducted at the Energy Systems (ES) unit at KTH Royal Institute of Technology under the supervision of Professor Mark Howells (former supervisor) and Professor Viktoria Martin, head of the ES unit. Research at Energy Systems division has an interdisciplinary character with a broad system perspective, where energy technology, innovation, and policy are linked to sustainable development. This thesis adds to the field related to energy for sustainable development (SD) in developing and emerging economies and provides empirical findings to contribute to SDG 7 and SDG 13 from the development agenda of the United Nations. Although the focus of this thesis is on Bolivia, the conclusions and insights provided address a number of challenges of sustainable development that are currently relevant to energy planners in developing and emerging economies around the world. The research for this doctoral thesis was funded by the Swedish International Development Cooperation Agency and the International Science Programme under a Research Partnership Program with Bolivia (Contribution 75000554-06, Swedish Research Council). Part of the author’s research for this thesis was developed in collaboration with the United Nations Department of Economic and Social Affairs and the United Nations Development Programme in support of the projects: “Integrated modelling tools to support evidence-based sustainable development policies in Mexico, Bolivia and Costa Rica” and “Supporting the design of sustainable development policies in Bolivia with the use of modelling tools”.

Stockholm, November 2020 J. Gabriela Peña Balderrama

Acknowledgements

This doctoral thesis is a milestone that was supported by several people to whom I would like to express my sincere appreciation. Firstly, I would like to express my genuine gratitude to Prof. Mark Howells and to Prof. Viktoria Martin who have been my principal PhD advisors at different stages of my doctoral studies. Thank you, Mark, for your inspirational guidance, constant motivation and for encouraging me to pursue my intellectual interests with independence. This has made me grow as a researcher as well as at a personal level. Thank you, Viktoria, for your trust, for being care about me and for your support during the final steps of my PhD. Besides my principal advisors, I would like to thank to Dr. Francesco Gardumi and Associate Prof. Dilip Khatiwada who have taken the role of being my assistant PhD advisors. Thank you, Francesco and Dilip, for being such fantastic and supportive advisors. You have been the primary source of insightful discussions and your advice was instrumental in helping me to sharpen and crank out this thesis. My thanks also go to Dr. Brijesh Mainali for his thorough questions during the final seminar and for providing insightful comments on the initial draft of this thesis. I would also like to thank Associate Professor Hatef Madani for his constructive and dedicated internal review of this thesis, which I believe helped to increase its clarity and highlight its contribution. A special gratitude goes to the bright and extraordinary researchers I have collaborated across the years and with whom I have written the articles appended to this dissertation. My thanks go to Prof. Gustavo Pinto de Moura, Prof. Mohammad Reza Hesamzadeh, Prof. Sylvain Quoilin, Prof. Emanuela Colombo and (soon to be Doctors) Sergio Balderrama, Francesco Lombardi and Nicolò Stevanato. Thank you all for sharing your knowledge and expertise during the years we have worked together. I have learned so much from each of you. A big thank you to Thomas Alfstad, adviser at UNDESA, to whom I owe so much for such a fruitful collaboration that profoundly transformed me as a researcher. Thank you, Tom, you are a professional role model to me. A very special gratitude to Silvia Ulloa and Joanna Felix, for being such enthusiastic, hard-working, caring and best team partners I have worked with. My sincerest gratitude extends to all of the experts and stakeholders in Bolivia whom I have talked with through the years collaborating with

UNDESA – particularly to Pamela Durán from CNDC, to Carlos Sevillano and Osmar Bolivar from UDAPE, to Marcelo Velazquez and Shirley Canedo from the Ministry of Energy, to Gustavo Ayala from the Ministry of Water and Environment and my deepest gratitude to Jorge Leitón Quiroga and Eduardo Zepeda whom I worked in the first missions in Bolivia and from whom I’ve learnt very much. During my time at KTH, I have been extremely lucky to meet such bright people and wonderful new friends. My thanks go to Dimitris, Costas, Rebecka, Oliver, Jorge, Mauri, Mohammad, Marco, Sotiris, Francescos, Alex, Viggie, Camilo, Youssef, Emir, Hauke, Nandi, Giannis, Georgios, Eunice, Maryna, Agnese, Adhemar, Luis, Jhonny, Saman, and all my other amazing colleagues and friends at the Energy Department. These lines remain short to mention all the wonderful people I’m grateful to have crossed paths. Thank you all for making KTH such a special place ☼. To my dearest friends who made my days so happy in many ways during this long journey. A heartfelt thank you to Pablo, Panchito, Susana, Alba, Eftychia, Gustavo, Majda, Salvatore, Uric and Pao for being such wonderful friends during all these years, for the laughs, complicity, understanding and for adding that extra spice to my life ☼. To my wonderful family that I miss so much, I am deeply thankful, especially to the three women of my life. To my mother Jenny who taught me the value of hard work and persistence, to my grandmother Teresa who taught me to do everything in life with love and to my sister Susan who taught me to turn over every rock in life to find a solution. Gracias por apoyar mis sueños y ambiciones, por llenar de alegrías los buenos momentos y por su amor que perdura sobre el tiempo y la distancia ♥. And last, but absolutely not the least, I dedicate this thesis to my partner Luca who brought love to my life in the most deep and sincere way. You have made our years together the most wonderful ♥. Grazie di cuore a te e alla tua splendida famiglia.

J. Gabriela Peña Balderrama Stockholm, 20th November 2020 ⸙

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List of Publications

This thesis is based on the following scientific papers:

Paper I: J. G. Peña Balderrama, S. Balderrama-Subieta, F. Lombardi, N. Stevanato, A. Sahlberg, E. Colombo, M. Howells and S. Quoilin, “Incorporating high-resolution demand and techno-economic optimization to evaluate micro-grids into the Open Source Spatial Electrification Tool (OnSSET)”, Energy for Sustainable Development, vol.56, pp. 98–118, May 2020. Paper II: J. G. Peña Balderrama, T. Alfstad, C. Taliotis, M. R. Hesamzadeh and M. Howells, “A Sketch of Bolivia’s Potential Low-Carbon Power System Configurations. The Case of Applying Carbon Taxation and Lowering Financing Costs,” Energies, vol. 11, no. 10, p. 2738, Oct. 2018. Paper III: J. G. Peña Balderrama, O. Broad, R. C. Sevillano, L. Alejo, and M. Howells, “Techno-economic demand projections and scenarios for the Bolivian energy system,” Energy Strategy Reviews, vol. 16, pp. 96–109, Apr. 2017. Paper IV: G. N. Pinto de Moura, L. F. Loureiro Legey, J.G. Peña Balderrama and M. Howells, “South America power integration, Bolivian electricity export potential and bargaining power: An OSeMOSYS SAMBA approach,” Energy Strategy Reviews, vol. 17, pp. 27–36, Sep. 2017. Paper V: J. G. Peña Balderrama, D. Khatiwada, F. Gardumi, T. Alfstad, S. Ulloa-Jimenez and M. Howells, “Integrated analysis of land-use, energy and water systems for ethanol production from sugarcane in Bolivia”. Submitted manuscript, Nov. 2020.

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Author contribution to appended papers

Paper I: The thesis author developed the methodology together with S.B., F.L., N.S., A.S., and S.Q. The thesis author collected the geospatial data, carried out the synthesis work to develop the overall research objective, carried out the modelling work, analysis and interpretation of results, wrote the article and produced the figures and tables. F.L. and N.S. carried out the simulation of the load demand profiles. S.B. carried out the mini-grid sizing optimization simulations. S.Q. supervised the overall work. All co- authors contributed revisions of the article. Paper II: The thesis author collected the data, carried out the synthesis work to develop the overall research objective, developed the energy model, carried out the analysis and interpretation of results, wrote the article and produced the figures and tables. All co-authors contributed revisions of the article. M.H. supervised the overall work and supported the interpretation of results. Paper III: The thesis author collected the data, carried out the synthesis work to develop the overall research objective, developed the energy model, carried out the analysis and interpretation of results, wrote the article and produced the figures and tables. C.S. performed the simulation in the Computable General Equilibrium model for Bolivia. O.B. and L.A. contributed revisions of the article. M.H. supervised the research work. Paper IV: The thesis author collected data and contributed to the design of the Bolivian power system model, contributed to the synthesis work to develop the overall research objective, supported the analysis and interpretation of results and contributed revisions of the article. The lead author of the article, G.N.P.M., developed the South America Model Base (SAMBA), carried out the modeling work, analyzed the results and wrote the article. L.F.L.L and M.H. supervised the work and contributed revisions of the article.

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Paper V: The thesis author (J.G.P.B.) carried out the data gathering, model simulations, analysis, and interpretation of results, wrote the article and produced the figures and tables of the manuscript. J.G.P.B., T.A. and S.U.J contributed the conceptualization of the methods. S.U.J supported the thesis author on data gathering and processing. D.K., F.G. and M.H. were involved in supervising the work and interpreting results. All co-authors contributed revisions of the article.

Additional reports and publications by the author

Research articles I. S. Balderrama Subieta, F. Lombardi, N. Stevanato, J.G. Peña- Balderrama, E. Colombo and S. Quoilin, “Surrogate models for energy planning: Application to the isolated communities in the lowlands of Bolivia.” Energy, November 2020 (in review).

II. M. Drews, M.A. Dahl-Larsen, and J. G. Peña Balderrama, “Biomass for energy production: Projected water usage and emissions from land use change (2015–2050).” Energy Strategy Reviews, vol 29, 100487, May 2020.

III. S. Balderrama Subieta, J.G. Peña Balderrama, F. Lombardi, N. Stevanato, A. Sahlberg, M. Howells, E. Colombo and S. Quoilin. “Model-based cost evaluation of micro-grid systems for rural electrification and energy planning purposes.” International Solar Energy Society – Solar World Congress, June 2019. IV. R. Rojas Candia, S. Balderrama Subieta, J. G. Peña Balderrama, J.A. Araoz Ramos, V. Senosiain Miquélez, H. Jaldín Florero, and S. Quoilin, “Techno-economic assessment of high variable renewable energy penetration in the Bolivian interconnected electric system.” Journal of Sustainable Energy Planning and Management, vol. 22, pp. 1738, May 2019. V. S. Balderrama Subieta, F. Lombardi, N. Stevanato, J.G. Peña Balderrama, E. Colombo and S. Quoilin, “Automated evaluation of levelized cost of energy of isolated micro-grids for energy planning purposes in developing countries.” In Proceedings of the

32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, pp. 2971–2984, ECOS Association, January 2019. VI. F. Gardumi, A. Shivakumar, R. Morrison, C. Taliotis, A. Beltramo, V. Sridharan, M. Howells, J. Hörsch, T. Niet, Y. Almulla, E. Ramos, T. Burandt, J.G. Peña Balderrama, G. N. Pinto de Moura, E. Zepeda, T. Alfstad, “From the development of an open-source energy modelling tool to its application and the creation of communities of practice: The example of OSeMOSYS.” Energy Strategy Reviews, vol. 20, pp. 209–228, April, 2018.

Energy Briefs

I. J. G. Peña Balderrama, E. Zepeda, J. Leitón Quiroga. “Prospectivas de Inversiones en el sector eléctrico y Exploración de hidrocarburos en Bolivia rumbo al PDES-2025.” Energy policy brief prepared for the project. Supporting the design of sustainable development policies in Bolivia with the use of modelling tools. Project 2937 ROA 2468. UNDP. 2015. II. J. G. Peña Balderrama, E. Zepeda, J. Leitón Quiroga. “Potencialidades de Integración Energética del Sector Eléctrico en Bolivia.” Energy policy brief prepared for the project. Supporting the design of sustainable development policies in Bolivia with the use of modelling tools. Project 2937 ROA 2468. UNDP. 2015. III. J. G. Peña Balderrama, E. Zepeda, J. Leitón Quiroga. “Diversificación de la Matriz Energética Boliviana rumbo al 2035.” Energy policy brief prepared for the project. Supporting the design of sustainable development policies in Bolivia with the use of modelling tools. Project 2937 ROA 2468. UNDP. 2015. IV. J. G. Peña Balderrama, E. Zepeda, J. Leitón Quiroga. Ahorro y “Eficiencia Energética en Bolivia rumbo al 2035.” Energy policy brief prepared for the project. Supporting the design of sustainable development policies in Bolivia with the use of modelling tools. Project 2937 ROA 2468. UNDP. 2015.

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Table of Contents

Abstract...... i Sammanfattning ...... iii Resumen ...... vi Preface ...... ix Acknowledgements ...... xi List of Publications ...... xiii 1 Introduction ...... 1 1.1 Low-carbon development pathways for the energy sector.....1 1.1.1 Low-carbon development pathways for achieving energy access and energy security considering climate stabilization goals...... 3 1.1.2 Opportunities for low-carbon development in Bolivia...5 1.2 Scope of the thesis ...... 9 1.3 Thesis structure and layout...... 11 2 Identified literature gaps and thesis contribution ...... 13 2.1 Decentralized systems for achieving low-carbon universal access to electricity ...... 13 2.1.1 Modelling tools for large-scale electrification planning ...... 13 2.1.2 Universal access to electricity in Bolivia ...... 14 2.1.3 Thesis contribution ...... 15 2.2 The impact of carbon pricing and financing costs on power systems decarbonization ...... 16 2.2.1 Studies worldwide ...... 16 2.2.2 Studies for Bolivia ...... 17 2.2.3 Thesis contribution ...... 18 2.3 The cross-border electricity trade in South America ...... 19 2.3.1 Bolivia’s hydropower export potential...... 19 2.3.2 Thesis contribution ...... 20

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2.4 Integrated nexus assessments of land-use, energy systems, and water systems for sustainable biofuel production...... 20 2.4.1 Nexus assessments of climate, land-use, and water and energy systems (CLEWs) ...... 20 2.4.2 Integrated CLEWs assessments for Bolivia...... 21 2.4.3 Thesis contribution ...... 22 3 Context...... 23 3.1 On the country of Bolivia...... 23 3.2 Economy and energy ...... 24 3.2.1 Historical economic development ...... 24 3.2.2 Natural gas in the economy ...... 25 3.2.3 A new development plan...... 27 3.3 The energy sector ...... 29 3.3.1 Overview of energy production and consumption ... 29 3.3.2 Electrical power systems...... 32 3.3.3 Access to electricity...... 35 3.3.4 Renewable energy potential ...... 37 3.3.5 Ethanol blending mandates, opportunities for increased sugarcane yield, and use of sugarcane biomass residues ...... 40 3.4 Bolivia’s INDC ...... 43 4 Methodological approach and modelling tools...... 47 4.1 An overview of the modelling frameworks and modelling tools 47 4.1.1 Accounting models ...... 47 4.1.2 Optimization models...... 49 4.1.3 Geospatial electrification models ...... 52 4.2 Development and applications of methods and models...... 54 4.2.1 Modelling technically-detailed micro-grids for high- level geo-spatial electrification applications ...... 54 4.2.2 Exploring Bolivia’s low-carbon power system configurations by applying carbon taxation and lowering financing costs ...... 59 4.2.3 Exploring Bolivia’s potential cross-border electricity trade 61 4.2.4 Increasing sugarcane agricultural yield and use of its biomass residues for electricity and 2G biofuel production.. 66 4.2.5 Energy demand projections ...... 76

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5 Results and discussion ...... 79 5.1 The role of decentralized systems and renewable energy for achieving universal access to electricity ...... 79 5.1.1 Influence of diesel subsidies and capital costs of micro- grid components in the cost-competitiveness of micro-grids 82 5.1.2 Surrogate models validation...... 84 5.2 The role of financing costs and carbon pricing in power systems decarbonization ...... 85 5.2.1 Effects on the optimal generation mix ...... 87 5.2.2 Effects on the power system carbon intensity ...... 89 5.2.3 Carbon abatement cost curves ...... 91 5.2.4 Effects on natural gas reserves ...... 93 5.3 The role of the cross-border electricity trade in South America ...... 94 5.3.1 Bolivia’s hydropower export potential...... 94 5.3.2 Bolivia’s bargaining power...... 98 5.3.3 Impacts of trade on emissions from the power sector .. 99 5.4 The role of increasing agricultural productivity and use of biomass residues for sustainable biofuel production...... 100 5.4.1 Agricultural production ...... 100 5.4.2 Industrial production ...... 104 5.4.3 Biofuel support ...... 108 6 Conclusions and scope for future research ...... 109 6.1 Research questions and insights...... 109 6.2 Thesis contributions...... 114 6.3 Limitations and recommendations for future work ...... 116 6.4 Thesis impact ...... 118 7 References ...... 121

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List of Abbreviations

BSPT Backpressure steam turbines CCGT Combined cycle gas turbine CER Certified Emissions Reductions CEST Condensing-extraction steam turbines CHP Combined heat and power CNG Compressed Natural Gas ET Evapotranspiration EtOH Ethanol FAO Food Administration Organization GAEZ Global Agro-Ecological Zones GCAM Global Change Assessment Model GDP Gross Domestic Product GW Giga Watt GVA Gross value added Hi High inputs IAEA International Atomic Energy Agency Ii Intermediate inputs IMF International Monetary Fund INDC Intended Nationally Determined Contribution LAC Latin American Countries LCA Life Cycle Assessment LCOE Levelized Cost Of Electricity LDC Least Developed Countries

LEAP Long-range Energy Alternatives Planning LHV low heating value Li Low inputs LLP Loss of load probability LMIC Low and Middle Income Countries LNG Liquefied Natural Gas O&M Operation and Maintenance MACC Marginal Abatement Cost Curves NPV Net Present Value OECD Organization for Economic Co-operation and Development OnSSET Open Source Spatial Electrification Tool OSeMOSYS Open Source Energy Modelling Systems PE Electric Plan (Plan Eléctrico in Spanish acronym) RAMP Remote Areas Multi-energy system load Profiles RES Reference Energy System RET Renewable Energy Technology RQ Research Question SA South America SAMBA South America Model Base SDG Sustainable Development Goal tc Tone of cane TWh Tera Watt hour WACC Weighted Average Cost of Capital WBB Water balance of Bolivia WEAP Water Evaluation and Planning System WEO World Energy Outlook 1G-2G First generation, Second generation

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List of Figures

Figure 1. Thesis layout ...... 12 Figure 2. Historical GDP annual growth 1960–2018...... 24 Figure 3. Contribution of natural gas exports to the Bolivian economy. .27 Figure 4. Economy vulnerability indicators...... 29 Figure 5. Bolivia in the world, comparisson of energy indicators...... 30 Figure 6. Energy consumption by fuel in Bolivia...... 31 Figure 7. Final energy consumption per fuel type...... 32 Figure 8 Generation units and trasmission lines...... 33 Figure 9. Installed capacity and electricity demand per node (SIN) ...... 33 Figure 10. Energy mix in the SIN and SA...... 35 Figure 11. Energy mix in the power sector...... 35 Figure 12 Historical and projected electrification rate...... 36 Figure 13. Renewable energy potential in Bolivia ...... 39 Figure 14. Tree structure in an energy demand model...... 49 Figure 15. Example of a simple Reference Energy System (RES)...... 50 Figure 16. Three-step core components of the modelling framework. ....56 Figure 17. Sequence of steps to generate the surrogate models...... 57 Figure 18. Hydropower export scenarios ...... 64 Figure 19. Harmonized zonation schemes...... 68 Figure 20. Comparisson of gasoline and ethanol demand projections for baseline and alternative scenarios...... 69 Figure 21. Scheme of the processes modelled in the agricultural production unit...... 69

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Figure 22. Agroclimatic yields for rainfed and irrigated conditions ...... 71 Figure 23. Biorefinery technological options...... 73 Figure 24. Schematic of the system boundaries for the lifecycle assesment...... 74 Figure 25. Simplified representation of the systems modelled and the nexus interactions...... 76 Figure 26. Radial representation of the energy demand model...... 78 Figure 27. LCOE results for the scenarios with/without diesel subsidy ..80 Figure 28. Geographical representation of the least-cost electrification technology at community level...... 82 Figure 29: Summary of the sensitivity analysis results for selected communities...... 84 Figure 30: Comparisson prodicted and computed values for each microgrid configuration ...... 85 Figure 31. Numerical representation...... 87 Figure 32. Accumulated new power generation capacity in the period 2013-2040 for all WACC scenarios when no carbon tax is included...... 88 Figure 33. Accumulated new transmission capacity 2013–2040 for all WACC scenarios...... 89 Figure 34. Average emission intensity and total accumulated emissions 90 Figure 35. Generation mix for selected carbon taxes for 2030 and 2040.91 Figure 36. Abatement costs and abated emissions for each scenario of carbon tax and WACC...... 92 Figure 37. Natural gas reserves required to supply domestic demand and exports and average natural gas production cost...... 94 Figure 38. Trade model results 2014-2058...... 97 Figure 39. Differences of the total installed capacity in Scenario 2 compared to the base scenario in which Bolivia does not export hydroelectricity ...... 99

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Figure 40. Comparison of the lifecycle production costs, emissions and energy use of sugarcane cultivated under high, intermediate and low input levels...... 101 Figure 41. Comparisson of agriculture area and national averaged yield between government projections and the optimization results ...... 102 Figure 42. Optimization results for the agriculture production model... 103 Figure 43. Optimization results for the agriculture production model by input level...... 104 Figure 44. Total biorefinery capacity and activity in million liters of ethanol (EtOH) ...... 105 Figure 45. Biorefinery capacity in million liters of ethanol (EtOH) per year in each region ...... 105 Figure 46. Comparison of the lifecycle energy, production costs and emissions...... 106 Figure 47. Emissions from ethanol production and avoided emissions from gasoline and electricity consumption...... 107 Figure 48. Total investments in biorefineries for each biofuel support scenario ...... 108

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List of Tables

Table 1. The relation among the thesis objectives, research questions and appended research papers...... 11 Table 2. Cost scenario components and values used...... 58 Table 3. Strategic large hydropower export scenarios planned by the government of Bolivia...... 64 Table 4. Agriculture management input levels...... 70 Table 5. Formulas for each energy balance indicator, ...... 74 Table 6. Bolivia’s potential electricity surplus trade in 2025...... 97 Table 7. Accumulated exports and bargaining power for all scenarios ...... 98 Table 8. Accumulated exports and theoretical bargaining power in Scenario II...... 98 Table 9. Energy balance indicators for ethanol production...... 107 Table 10. Thesis contributions...... 114

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Chapter 1. Introduction

1 Introduction

Green growth is necessary and critical for achieving sustainable development. But what is the role of low and middle-income countries, in the global pursuit of a low-carbon development? In this introductory chapter, the general context of decarbonizing development is presented along with the scope of the thesis, research questions, and an overview of the thesis structure.

1.1 Low-carbon development pathways for the energy sector

Climate change is the defining challenge of the 21st century, creating new obstacles to existing threats to development (OECD, 2019). Greenhouse gas emissions (GHG) from human activities are estimated to have caused between 0.8 to 1.2°C of global warming above pre-industrial levels (IPCC, 2018). On current trends of increasing GHG emissions, the global temperature is likely to increase an additional 2.5–7.8°C by the end of this century if no actions are taken (UNFCCC, 2015; IPCC, 2018). Climate change is a consequence of the unprecedented increase in GHG in the atmosphere and it encompasses the broader range of physical changes that are happening to our planet (Hansen et al., 1981). The latest scientific literature suggests that climate change will jeopardize the ability of society to reduce poverty, to satisfy basic human needs, and to achieve sustainable growth and development (Olsson et al., 2014; Hallegatte et al., 2018; OECD, 2019). To reduce future GHG emissions and limit global warming to less than 2°C1, a number of countries have collectively pledged to the

1 The Paris Agreement has the objective to “hold the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels.”

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Paris Agreement with short-term and long-term decarbonization commitments. Decarbonizing development puts pressure on all countries in the world, but even more pressure to low and middle-income countries (LMIC) planning to use fossil fuels as a ‘leading sector’ to achieve growth (Fay et al., 2015b; Bradley, Lahn and Pye, 2018). LMIC have limited financial resources and often have to prioritize short-term developmental goals with immediate local co-benefits over mitigation strategies with long-term gains (The World Bank, 2012; Fay et al., 2017). However, the fact that much of their infrastructure is yet to be built means opportunities exist, at present, for technological leapfrogging into cleaner and more efficient technologies (Sharif, 1989; Szabó et al., 2013). Achieving climate stabilization will require large investments to decarbonize the energy sector, together with investments and policies to increase resilience and adaptation to climate change (IPCC, 2018). Although climate change is a global issue, there are few generalizations about the capabilities of high-income and LMIC to mitigate climate change (Fay et al., 2015a). High-income countries have the financial capacity to lead technological innovation and to scale-up low-carbon investments to lower global costs (OECD et al., 2015). There has been significant progress in cost-reductions in renewable energy technologies, achieved by increasing economies of scale in manufacturing and by research and innovation in technology and new materials (Gross, Leach and Bauen, 2003). Yet, such progress has been enabled by effective policy implementation to stimulate investments in renewable energy in grid-connected systems (with e.g. feed-in tariff programs) as well as for off-grid rural applications (with e.g. long-term financing schemes) (Hoffmann, 2006). At the same time, LMIC have the capacity to identify early abatement actions consistent with immediate poverty alleviation and long-term actions in order to adapt to climate change-driven impacts that can exacerbate poverty (Olsson et al., 2014). In the short-term, climate mitigation strategies can focus on mobilizing finance towards renewable energy adoption and energy efficiency standards to avoid the risk of facing costly stranded assets in the future (Rozenberg, Vogt-Schilb and Hallegatte, 2014, 2017; Bos and Gupta, 2019; Ansari and Holz, 2020). Furthermore, LMIC can contribute to preserving forests and reducing agriculture emissions associated with deforestation by increasing agriculture productivity (The World Bank, 2012). For the medium and long-term, some examples of policies on climate change adaptation include

Chapter 1. Introduction

insurance schemes, social protections, and disaster risk-reduction programs intended to enhance the resilience of the poor (Olsson et al., 2014). All countries (regardless of income level) should work to avoid being locked into carbon-intensive pathways that will be costly to reverse in the future (Shalizi and Lecocq, 2009; Seto et al., 2016; IPCC, 2018). For example, it has been estimated that worldwide conventional coal capacity committed for 2011–2030 will become stranded by 2050 with a carbon price consistent with the goal of limiting global warming to 2°C (Johnson et al., 2015). It has also been estimated that a third of worldwide oil reserves, half the gas reserves, and more than 80% of coal reserves must be considered “un-burnable” by 2050 if climate stabilization goals are to be met (McGlade and Ekins, 2015). To limit global warming to 1.5°C or well below 2°C, all pathways from the latest report on the Intergovernmental Panel on Climate Change (IPCC) require increased energy efficiency, a rapid decarbonization of the energy supply, electrifying energy end-use, deep reductions in land-use emissions (mainly in agriculture and forestry), protection of natural carbon sinks (such as forest and oceans), and the use of negative emissions technologies (IPCC, 2018). Further commitments and actions are needed to plan, finance, deliver and maintain the pledged commitments called for by the Paris Agreement (OECD, 2019).

1.1.1 Low-carbon development pathways for achieving energy access and energy security considering climate stabilization goals Ensuring access to affordable, reliable, sustainable and modern energy for all is a central element acknowledged in the Sustainable Development Goals (SDGs), specifically SDG 7. Energy has a fundamental role in reducing poverty and is closely connected with progress in human development, including improved health, education, productivity and gender equality (Karekezi et al., 2012). The latest available report tracking progress on SDG 7 estimates that 789 million people (approximately 10.3% of the global population) lack access to electricity and 2.8 billion people (approximately 37% of the global population) do not have access to clean cooking fuels and similar clean technologies (IEA et al., 2020).

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In its latest report of the World Energy Outlook (WEO), the International Energy Agency (IEA) estimated that about 1 billion people should gain access to electricity by 2030 to achieve SDG 7 (IEA, 2019). In the same report, it was estimated that renewable energy and decentralized solutions have the potential to provide energy access to more than 500 million people by 2030. Expected cost reductions of renewable energy generation and batteries, super-efficient end-use appliances and innovative virtual financial services are current and future factors that should enable the rapid spread of low-carbon decentralized systems (Alstone, Gershenson and Kammen, 2015). According to the IEA (2019), investments in renewable energy and energy efficiency “remain among the most cost-effective ways to enhance security of energy supply, reduce carbon dioxide (CO₂) emissions and improve local air quality”. To meet the desired climate stabilization goals, the IEA (2019) estimates that up to 60% of global electricity production should come from low-carbon sources (renewable energy and nuclear) by 2030, and that number should increase to 78% by 2040 and 94% by 20502 (up from current levels of 36%). Additionally, the WEO report estimates that all coal-fired generation must be phased out by 2030 in advanced economies, and by 2045 in emerging economies, to limit global warming to 2°C. To this end, pricing reform schemes reflecting a price on emissions, feed-in tariffs, and emissions trading schemes are critical for incentivizing low-carbon technologies and finally achieving cost-effective decarbonization (Rogelj et al., 2018). However, implementing these schemes is particularly challenging for LMIC with prevailing fossil-fuel subsidies and undeveloped financial systems (which constrains access to affordable capital) (Fay et al., 2015b). Nonetheless, worldwide efforts in the form of subsidies for renewable energy continue to grow, along with meaningful increases in carbon pricing (International Energy Agency, 2019). In the latest IPCC report, transitions limiting global warming to 1.5°C include large-scale land-related measures (specifically, the carbon dioxide removal measures or CDR). Land-related measures such as afforestation, reforestation, land restoration and bioenergy with carbon capture and storage (BECCS) are found in all pathways (IPCC, 2018). Agriculture, forestry and other land-use (AFOLU) related CDR measures are projected to remove 0–5, 1–11, and 1–5 GtCO2 per year worldwide in 2030, 2050

2 Sustainable Development Scenario, World Energy Outlook 2019.

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and 2100 respectively (IPCC, 2018). Mitigation options limiting the demand for land include sustainable intensification of land-use practices (agriculture and forestry), ecosystem restoration, and changes towards less resource-intensive diets (IPCC, 2018). Increased use of bioenergy is also found across the scenario pathways as a complementary measure to the electrification of energy use. Importantly, advanced biofuels and bio- methane are required in the transport sector decarbonization pathways to reduce oil demand (IEA, 2019).

1.1.2 Opportunities for low-carbon development in Bolivia By adopting the 2030 agenda for sustainable development and the global goals (including seventeen SDGs), Bolivia has signaled its intent to eradicate poverty and achieve economic growth while preserving environmental sustainability. In line with SDG 7, Bolivia aims to make electricity universally accessible by 2025 (from 95.6% electrification rate today) (Ministry of Hydrocarbons and Energy, 2014; IEA et al., 2020). Although electricity access has increased significantly with large investments in grid-extension and decentralized systems, currently, 61% of the grid generation capacity and 93% of the off-grid generation capacity is fossil-fueled. Carefully selected policies and early planning could guide future investments to close the electrification gap with clean energy. Although energy access is a government priority acknowledged in several national strategic plans, Bolivia does not have a clear implementation plan for achieving universal access to electricity. In its latest National Electric Plan, national-level electrification targets and a budget for reaching those targets have been quantified (Ministry of Hydrocarbons and Energy, 2014). Yet, to date, many relevant questions remain unanswered in government plans and academic literature (for more on these questions, see Section 2.1). These include, which locations are (economically) out of reach of the national grid? Where can decentralized energy networks and renewable energy be the least-cost alternative for providing access? What are the technical characteristics of the electrification technologies for each location? While energy access can be largely achieved by extending the grid, decentralized energy networks can play an important role in the timely electrification of dispersed and isolated rural populations. In fact, for many countries, a mix of on and off-grid systems is necessary for achieving universal electrification by 2030 (Mentis et al., 2015; Szabó et al., 2016;

Chapter 1. Introduction

Moner Girona et al., 2018). Identifying optimal configurations/locations in which decentralized renewable energy is economically advantageous is an essential first step for facilitating the mobilization of financial resources and for speeding up electrification. To this end, modelling frameworks can be used as complementary tools to support decision making and provide useful insights for the implementation of electrification plans. This provides the scope and leads to the first research question (RQ) of this thesis: Research Question 1. From the perspective of least-cost electrification, to what extent can decentralized generation technologies (including renewable and non-renewable energy) help Bolivia achieve low-carbon universal access to electricity by 2025?

Stabilizing climate change requires achieving near-zero net emissions in the long-run, which requires prevention of the accumulation of fossil- fueled capital (Rozenberg, Vogt-Schilb and Hallegatte, 2014). To fulfil this objective, many challenges lie ahead for developing coherent policies for decarbonizing Bolivia’s power system in a manner consistent with its socio-economic development plan. Pricing reforms in the form of carbon pricing and access to affordable capital are two examples of policies that enable achieving decarbonization in LMIC (Painuly, 2001). Although the literature is rich in country-scale studies evaluating the impact of carbon taxation on the decarbonization of the energy sector (for further discussion, see Section 2); little attention has been given to small economies with high costs of capital such as exemplified by Bolivia.

The cost of capital is a determining factor influencing the cost of electricity. In capital budgeting, the weighted average cost of capital (WACC)3 is used to determine the discount rate of a given project. The WACC represents the opportunity costs for the capital invested adjusted to the investor’s degree of risk aversion. A high WACC implies planning with high-risk aversion (which is typical for LMIC countries), which is less favorable to capital intensive technologies such as renewable energy (Hirth and Steckel, 2016).

3 The WACC is used as the discount rate when budgeting for a new project according to “Guidelines on the Assessments of Investment Analysis” from the Clean Development Mechanism’s executive board. A higher WACC requires faster amortization or higher returns for an investment (Geiger, 2011).

Chapter 1. Introduction

According to the Bolivian Electricity Law and by Ministerial Resolution 02/2000, the discount rate applicable to the electricity sector was set to 12% in 2000 with no subsequent updates. The discount rate4 affects power system development, and it may be influenced/changed for strategic reasons (such as to move to low carbon development). This aspect has been often overlooked in global assessments, neglecting important differences in the cost of capital between high-income and LMI countries (Schmidt, Born and Schneider, 2012; Ondraczek, Komendantova and Patt, 2015). Countries with high costs of capital require larger efforts (in terms of, for example, renewables support schemes) to achieve the same degree of decarbonization compared to countries with low costs of capital (Hirth and Steckel, 2016). There have not been many studies looking into the combined effects of WACC and carbon taxes in the cost-optimal expansion of the power sector (for a review, see Section 2.2). Furthermore, there are no studies exploring this possible route for decarbonization in LMIC such as Bolivia. These observations lead to the second research question of this thesis.

Research Question 2. What is the potential impact of inserting carbon taxes and lowering financing costs on the transition to a low- carbon electricity generation system in Bolivia?

Current policies intended to increase energy security in Bolivia are focused on the increased use of natural gas, on first-generation (1G) biofuel production, and on investments in large multipurpose hydropower plants. Regarding energy security in the long-run, recent estimates indicate that Bolivia would require more than its current proven, probable and possible gas reserves to supply its domestic and international markets by 2030 (Chávez-Rodríguez et al., 2016). Limited investments in the exploration of natural gas reserves and uncertainties in the long-term continuation of export contracts (to Brazil and Argentina) add vulnerabilities to the Bolivian economy and to its energy security (Endegnanew and Tessema, 2019a).

For these reasons, current national policies redirected focus from gas exports to hydroelectricity exports. In the last decade, Bolivia has entered in ongoing discussions of bilateral agreements about the execution of large-scale hydropower projects and the future implementation of new

4 The terms discount rate, WACC, cost of capital and financing costs are used interchangeably in this thesis because they refer to the same concept.

Chapter 1. Introduction

transmission interconnections with other countries (Ministry of Hydrocarbons and Energy, 2014; IRENA, 2016). Three large-scale hydropower export-oriented projects are contemplated as strategic investments in the medium-term and included on its power expansion plan (Ministry of Hydrocarbons and Energy, 2014). Each of these is a cascade system of multiple hydropower units. The government does not plan to invest in the three identified projects at the same time, but to invest in a combination of these that can benefit the country with the highest energy exports (Ministry of Hydrocarbons and Energy, 2014). Currently, many of the electricity exchanges in South America are based on ‘ad hoc’ arrangements limited to scarcity conditions (IRENA, 2016). Since all countries have abundant renewable and non-renewable energy resources for potential cross-border electricity exports, future trade opportunities should be evaluated in a continent-scale to understand their economic viability. Such evaluations mandate the development of modelling frameworks to serve as test beds for identifying opportunities to support policymakers in international negotiations. At present, there are a limited number of studies assessing electricity trade opportunities in South America (CIER, 2018; Pinto de Moura, Legey and Howells, 2018; Moksnes et al., 2019). In particular, schemes for the fair distribution of benefits and the potential decarbonization achieved by power systems integration have not been scrutinized. This deficiency leads to the third research question of this thesis.

Research Question 3. What is the theoretical bargaining power of Bolivia and the potential decarbonization effect that can be achieved in a scenario of hydroelectricity trade with neighboring countries?

Although Bolivia has modest oil reserves and refining capacities, 46% (in energy) of its total consumption is of imported diesel and gasoline (Ministerio de Hidrocarburos, 2019). The prevailing subsidized fossil- fuels for domestic consumption are environmentally harmful and a financial burden for public funds (Laan and Beaton, 2012). Several general attempts to reform fossil-fuel consumption subsidies have not succeeded in Bolivia (GIZ, 2011), and so current policies focus on encouraging the transport sector to substitute diesel and gasoline for natural gas and 1G biofuels (mainly ethanol). Other more capital-intensive alternatives, such electrification of the transport sector, have not been evaluated yet.

Chapter 1. Introduction

The objective of recent mandates on biofuel blending is to positively influence the energy security, to substitute highly subsidized gasoline imports, and to develop the agro-industrial sector (Nolte, 2018). To achieve sustainable ethanol production, several aspects of the supply chain must be addressed. It is of particular importance to evaluate strategies for increasing agricultural production sustainably, which will then limit the demand for land and not compromise forests and other natural carbon sinks. In addition, it is important to explore alternative technological options which can help utilize surplus bioenergy resources (e.g. sugarcane biomass) for second-generation (2G) ethanol and bioelectricity production in biorefineries. To date, enhancements to the entire ethanol production chain (agricultural and industrial production in biorefineries) have not been evaluated. (For more on integrated energy production and land use, see Section 2.4.) This deficiency leads to the fourth research question of this thesis.

Research Question 4. How can improvements in agricultural yield and advanced/efficient conversion technologies supply increasing biofuel production targets, thereby mitigating GHG emissions?

The four thesis questions converge into the general objective of this thesis.

Thesis Research Objective:

To examine low-carbon development pathways for selected policies intended to improve energy security and energy access in Bolivia.

1.2 Scope of the thesis

This doctoral thesis examines low-carbon development pathways for improving energy access and energy security policies in Bolivia. The empirical findings of this thesis aim to contribute to SDG 7 and SDG 13 from the development agenda 2030 of the United Nations, in particular to the following targets:

- SDG 7: Affordable and clean energy. Target 7.1: Ensure universal access to affordable, reliable and modern energy services. Target

Chapter 1. Introduction

7.2: Increase substantially the share of renewable energy in the global energy mix.

- SDG 13: Take urgent action to combat climate change and its impacts. Target 13.2: Integrate climate change measures into national policies, strategies and planning.

With methods deriving from systems analysis, the particulars of the Bolivian energy system were scrutinized, and the effects of alternative energy planning decisions (such as policies and investments) were demonstrated in scenarios pertinent to Bolivia’s plans/policies and investment needs. The doctoral thesis is based on research presented in five research articles. Paper I examines the potential of decentralized technologies (primarily micro-grids) and renewable energy to address universal electricity access targets while reducing carbon intensity. The cost-competitiveness of multi- source micro-grids is assessed over a range of techno-economic parameters including diesel costs, technology capital costs and renewable resource availability, among others. This paper specifically addresses the problem of access to energy. Specifically to the problem of energy security, Papers II-V provide insights on the integration of instruments for GHG emissions reductions into national policies. Paper II offers insights on the effects of carbon taxes and reducing the cost of capital on emission reductions and cost- optimal expansion of the power system of Bolivia. Paper III expands with energy demand projections in the residential, commercial, industrial, transport and agro-industrial sectors, and estimates potential avoided emissions after implementing energy-saving and fuel-switch targets. Paper IV assesses the potential contribution of Bolivia to decarbonizing electricity generation in South America by evaluating its hydroelectricity export potential and its trade relationship with neighboring countries. Finally, Paper V provides insights on the cost-optimal upgrades in agriculture systems (e.g. agricultural inputs and irrigation) and conversion technologies (e.g. thermochemical processes, biochemical processes) to for meeting stipulated ethanol blend mandates sustainably. Each research question is addressed by one or more of the appended articles as detailed in Table 1.

Chapter 1. Introduction

Table 1. The relation among the thesis objectiv es, research questions and appended research papers.

Thesis Research Articles Research questions objective I II III IV V i. From the perspectiv e of least-cost electrif ication, to what extent can decentralized generation technologies (including renewable x and non-renewable energy ) help Boliv ia achiev e low-carbon univ ersal access to electricity by To examine 2025? low-carbon dev elopment ii. What is the potential impact of inserting pathway s f or carbon taxes and lowering f inancing costs on the x x selected transition to a low-carbon electricity generation policies sy stem in Boliv ia? addressing energy iii. What is the theoretical bargaining power of Boliv ia and the potential decarbonization ef f ect security and x energy access that can be achiev ed in a scenario of in Boliv ia hy droelectricity trade with neighboring countries?

iv . How can improv ements in agricultural y ield and adv anced/ef f icient conv ersion technologies x x supply increasing biof uel production targets, thereby mitigating GHG emissions?

1.3 Thesis structure and layout

Figure 1 illustrates the layout of this thesis. The thesis is organized into four blocks. The first block consists of background and context information, and it is separated into three chapters. Chapter 1 begins with introductory information, the thesis motivation and scope, the research objective and research questions. Chapter 2 presents the state of the art in selected energy policies (rural electrification, power systems decarbonization, cross-border power systems integration and sustainable biofuel production). Chapter 3 presents background information about Bolivia with regards to its economy, energy and electricity sectors. The second block consists of Chapter 4, which introduces the modelling frameworks, modelling tools and the methods used in the thesis. The third block consists of Chapter 5, which describes, discusses, and interprets the results for each research question in four sub-sections. Lastly, the fourth block of Chapter 6 offers concluding remarks, including contributions, limitations, and future work.

Chapter 1. Introduction

Figure 1. Thesis layout

Chapter 2. Identif ied literature gaps and thesis contribution

2 Identified literature gaps and thesis contribution

This chapter reviews the current literature deliberately related to this dissertation’s research questions and identifies underlying knowledge gaps that the thesis is intended to close.

2.1 Decentralized systems for achieving low- carbon universal access to electricity

2.1.1 Modelling tools for large-scale electrification planning Electrification planning models associated with Geographic Information Systems (GIS) and remote sensing data have emerged fast in recent years as independent software packages (Moner Girona et al., 2018). These modelling tools address large-scale electrification plans using techno- economic and GIS data to match energy resources with potential energy demands at a minimum cost. The levelized cost of electricity (LCOE) is often used to compare grid-connected and off-grid solutions that best serve a territory in a specified time horizon. A handful of analytical tools for high-level electrification modelling are available and in continuous development (Moner Girona et al., 2018). The best examples of these first-generation GIS-based modelling tools are the Re2nAF, Network Planner (NP), the Reference Energy Model (REM) and the Open Source Spatial Electrification Tool (OnSSET) (Szabó et al., 2011, 2013; Modi et al., 2013; Ellman, 2015; Mentis et al., 2017a). While the methods among these modelling tools vary in complexity, common caveats are found in modelling technically-detailed operations of micro-grids and temporally-detailed demands. For example, the Re2nAF size mono-source micro-grids (diesel or solar with battery) assume a

Chapter 2. Identif ied literature gaps and thesis contribution

simple daily load demand profile, and varying the PV array and battery size (or diesel generator capacity) geographically to satisfy a demand proportional to population size (Szabó et al., 2011). Network Planner considers only diesel micro-grids for its analysis and estimates the size of the micro-grid components using a simple relation of generation capacity and peak demand data in every node (Ohiare, 2015). REM designs optimal configurations of multi-source micro-grids for a number of representative combinations of consumers, and these are later used to approximate micro- grid designs in real settlements (Ciller et al., 2019). Similar to Re2nAF, OnSSET size mono-source micro-grids use a simple energy balance to meet an average power peak demand in every settlement (Mentis et al., 2015; Korkovelos et al., 2019a). Adequate micro-grid sizing requires matching intermittent energy sources with load demands while accounting for uncertainty and optimizing for reliability and cost (Mandelli et al., 2016). Evaluating every micro-grid candidate (one-by-one optimization) in a high-level geographical space is computationally impractical. Therefore, a modelling framework bridging the “computational gap” between technically-detailed micro-grid systems analyses and high-level electrification modelling is required and, at present, nonexistent in the literature.

2.1.2 Universal access to electricity in Bolivia Current knowledge about rural electrification in Bolivia includes analyses on adoption and diffusion of renewable energy technologies (RET), characterization of energy needs in rural populations, and techno-economic evaluations of decentralized renewable technologies. However, there are no detailed spatial-explicit electrification studies at the national scale. Pansera (2012) provided an analytical framework for studying the network of stakeholders for the adoption, absorption and diffusion of RET for rural electrification in Bolivia. Similarly, Buch and Filho (2012) examined current barriers for the diffusion of RET for rural electrification in Bolivia based on interviews with experts. The latest National Power Plan of Bolivia dedicated a chapter to the investments required to achieve universal access to electricity by 2025 (Ministry of Hydrocarbons and Energy, 2014). In this document, electrification is expected to increase in urban areas by extending and densifying the existing distribution network. In rural areas, 70% of new users are expected to be electrified by grid extension, 20% by new

Chapter 2. Identif ied literature gaps and thesis contribution

distribution lines and 10% by stand-alone home systems. Yet, the master plan does not provide information regarding the spatial distribution of the electrification solutions, or about the technical characteristics of the electrification technologies required to meet the government electrification targets. A number of field-based studies have investigated the energy demands and technology prospects for rural electrification. Hallberg and Hallme (2015) collected primary data on energy demand for cooking, heating, cooling and other electrical uses to characterize three rural villages located in different topographical zones of Bolivia. Ålund (2017) collected primary data on energy demand for residential and public services from a rural village in the Bolivian Altiplano and investigated technological solutions for meeting future demands. Lombardi et al. (2019) used interview-based data about energy demand from El Espino village (in Bolivia’s lowlands) to validate a load profile generation model and generate different stochastic daily profiles for identified user types (household types and public services). Lindblad (2017) examined the techno-economic potential of decentralized hybrid generation systems to increase access to electricity using hypothetical demand scenarios for rural villages in Bolivia. Balderrama- Subieta et al. (2018) evaluated the operation of a solar-diesel hybrid micro- grid located in the village El Espino (in lowland Bolivia) and optimized the dispatch using a mixed-integer linear programming model. Similarly, Benavente et al. (2019) developed a model to assess the reliability of hybrid decentralized systems using hypothetical demand scenarios for rural villages in Bolivia. Despite the insights of the aforementioned studies about the economics, reliability, and environmental benefits of decentralized energy networks, key questions about the electrification problem at the national level (such as those required to guide investment needs for high-level electrification planning) remain unanswered.

2.1.3 Thesis contribution Despite recent advances in tools modelling geospatial electrification, there is no electrification roadmap for Bolivia that evaluates spatio-temporal components of electricity access, the contributions of Paper I are twofold. The first significant contribution of Paper I is its new applications and new data, because this is the first Bolivia-specific country-wide geospatial analysis in which an optimal electrification mix between grid-connected and off-grid solutions is studied in coordination. The second contribution

Chapter 2. Identif ied literature gaps and thesis contribution

is to new methodologies. This study combines existing knowledge about the generation of load demand profiles and knowledge about the operation of decentralized technologies to improve the way that micro-grids are modelled in a specialized high-level geospatial electrification tool, OnSSET (see also Chapter 4). An innovative three-step framework was designed to capture high-resolution peculiarities of electricity demand and to evaluate cost-optimal micro-grid performance in the OnSSET modelling tool. The methodology includes bottom-up stochastic load profiles generation, micro-grid system optimization under uncertainty, and the generation of surrogate models. Paper I therefore provides insights into the role of renewable energy and the adoption of hybrid mini-grids to electrify rural communities of Bolivia.

2.2 The impact of carbon pricing and financing costs on power systems decarbonization

2.2.1 Studies worldwide Carbon taxation is a price-based mechanism that imposes surcharges on fossil fuels proportional to the carbon dioxide emitted when burned. Several studies have reported the effects of carbon taxes on the cost- optimal energy system expansion. For example, Zachariadis (2016) and Zhou et al. (2011) assessed changes in fuel consumption, public revenues and carbon emissions in the energy system after the implementation of a carbon tax in Cyprus and China, respectively. There are several country-scale studies that evaluate the influence of carbon taxation, specifically on the cost-optimal investments in the power generation sector. For example, Lucena et al. (2016) investigated the ability of carbon taxes to reduce emissions from Brazil’s power sector by 2050. They found that carbon taxes on the order of US$50/tCO2 were required by 2020 and taxes of US$162/tCO2 by 2050 to induce average emission reductions of about 60% compared to the current conditions. Similarly, Calderón et al. (2014) evaluated the effect of carbon taxes (ranging from US$50/tCO2 to US$160/tCO2) on the cost-effectiveness of renewable energy and energy efficiency improvements in Colombia , expanding of previous studies by assessing the economy-wide implications in GDP growth and consumption.

Chapter 2. Identif ied literature gaps and thesis contribution

Because the power sector is capital-intensive, the weighted average cost of capital (WACC) is one of the cost-variables with the greatest impact on the LCOE and subsequently on the least-cost energy system mix (Simshauser, 2014). The selection of WACC depends, among other things, on country- specific macroeconomic drivers and perceptions of inherent systemic risk (Ondraczek et al., 2015). The WACC used for power expansion planning differs widely between high income and lower-income countries. For example, the WACC for solar photovoltaics in Japan is 3.7% compared to 29% in Madagascar (Ondraczek et al. 2015). There are only a few studies looking at the combined effects of WACC and carbon taxes on the cost-optimal expansion of the power sector. Hirth and Steckel (2016) performed a numerical exercise estimating carbon emission reductions when carbon taxes (in the range of 0–100 US$/tCO2) and WACC (in the range of 0–25%) were applied to the European power system. Their estimates suggest that WACC needs to be reduced to less than 5% to transform energy systems in line with the 1.5°C climate stabilization goal. Ondraczek et al (2015) studied the effects of solar resources and financing costs on the levelized cost of solar PV at a global scale concluding that differences in finance costs have a more important influence in the LCOE than variations in solar irradiation. , estimated a curve to relate carbon tax and wind energy deployment in Central West Europe. Investments in wind power were found to be more cost-effective than a fuel switch (coal to gas) when carbon taxes were higher than 35 €/tCO2.

2.2.2 Studies for Bolivia The National Power Plan of Bolivia details investments required within 10 years for generation and transmission capacity to supply the national demand in 2025 (Ministry of Hydrocarbons and Energy, 2014). The master plan also identifies potential large-scale hydropower projects for exporting electricity. To supply the domestic demand, 2.89 GW of additional capacity of a mix of hydropower (55.3%), gas-fired thermal (38.4%) and other renewable (6.3%) projects are planned in the period 2015–2025. In the master plan, the inclusion of hydropower and other smaller renewable projects are the only national strategy for reducing the emissions of the power sector. Currently, there are no complementary studies assessing carbon mitigation strategies. With regards to the modelling tools, the institutions developing the power sector capacity expansion plans use

Chapter 2. Identif ied literature gaps and thesis contribution

commercial optimization tools such as OptGen and SDDP, thus restricting the reproducibility, transparency and re-usability of this work. Current efforts using open-source tools include a few research articles and a master thesis study investigating decarbonization for the Bolivian electricity system. Rojas Candia et al. (2019) used an open-source unit- commitment model to investigate flexibility issues for scenarios assuming large wind and solar power implementation in the Bolivian power system. The results showed that configurations with up to 30% solar and wind power are possible without further grid reinforcements. In this analysis, however, the renewable generation capacities were exogenously defined and did not derive from a cost-optimization model. Arderne (2016) studied long-term scenarios using a uni-nodal representation of the Bolivian power system. In this study, exogenously defined targets for RET deployment, capital cost reductions for RET, and electricity demand scenarios were assessed together with two climate change scenarios. The results estimated the required additional investments in power generation to meet two demand and climate change scenarios.

2.2.3 Thesis contribution In view that long term power system expansion studies in Bolivia fail to evaluate decarbonization pathways in line with global climate goals, the contribution of Paper II is twofold. The first one relates to new applications and the second to the generation of new data. The model developed in Paper II is the first open-source energy model of Bolivia studying how investments in low-carbon energy technologies are triggered by the implementation of carbon taxes and lower costs of capital. To do so, an energy model was developed using the most up-to-date data on generation capacity, committed investments in the medium-term, energy demand, and investments in natural gas wells (see also Sections 3.3.2 and 3.3.4, and Chapter 4). Because energy demand is exogenously calculated before being introduced into the cost-optimization model, Paper III focus on demand projections using an accounting model. In this model, a combination of bottom-up and top-down accounting representations is used to generate energy demand projections using the Low Emission Analysis Platform (LEAP) modelling tool (C. Heaps, 2016). Results of Paper II delineate ranges of carbon taxes that should lead to emissions reductions under different WACC scenarios. These results are important for designing climate change mitigation policy in Bolivia.

Chapter 2. Identif ied literature gaps and thesis contribution

2.3 The cross-border electricity trade in South America

2.3.1 Bolivia’s hydropower export potential Bolivia has an estimated hydropower potential of 40 GW (equivalent to 173 TWh electricity per year), almost 80% of which is located in the Amazon basin (Ministry of Hydrocarbons and Energy, 2014). This total capacity is almost three times the installed capacity of the largest operational hydropower plant in South America (the Itaipú Dam located on the border between Paraguay and Brazil). In 2019, the hydropower installed capacity was 735 MW (31% of the total installed capacity), which represents less than 2% of Bolivia’s hydropower potential. Moreover, the power generation mix is still largely dependent on gas-fired generation with a share of 61%. The Electric Plan of Bolivia 2025 identified three strategic large-scale hydropower projects, along with their electricity export potential: Cachuela Esperanza (with 0.99 GW installed capacity), El Bala (with 1.68 GW), and the Río Grande hydropower complex (with 2.88 GW). These projects would require large capital investments, estimated at US$ 8.8 billion (Ministry of Hydrocarbons and Energy, 2014). The export potential of four scenarios consisting of different combinations of hydropower projects was assessed in the master plan. The scenarios were modelled with the computational tool OptGen (PSR, 2019). The study was, however, limited to estimating the electricity surplus and did not consider the hydropower availability nor other capacity expansion projects in neighboring countries to assess the electricity trade. In order to support the government’s hydropower export aspirations, several feasibility studies are being undertaken to assess investments in long- distance transmission lines5. Additional studies are required to inform Bolivia’s long-term cross-border hydroelectricity trade potential (Pinto de Moura et al., 2017).

5 To connect the hydropower projects to their main load destinations abroad, about 1,500 and 2,500 km of super high-voltage transmission lines will be necessary to reach the Brazilian Southeast subsystem and the north of Argentina respectively (Ministry of Hydrocarbons and Energy, 2014).

Chapter 2. Identif ied literature gaps and thesis contribution

2.3.2 Thesis contribution Currently, the only open-access initiative to model long-term, country- detailed power systems expansion in South America is the SAMBA model (South America Model BAse) developed by Pinto de Moura et al. (2018). The SAMBA model was initially conceived to assess energy security in Brazil through strategic interconnections with hydropower projects in neighboring countries. Building on the same initiative, Paper IV examines the potential hydroelectricity export of Bolivia with increased detail about the representation of its power system, contributing new applications and insights. Because hydroelectricity export plans do not consider multiple trade interactions and investment plans in neighboring countries, an additional contribution of Paper IV is the evaluation of Bolivia’s export potential in a trade scenario with perfect market conditions. This assessment is the first of its kind for Bolivia. The hydroelectricity export potential was estimated together with its bargaining power in a cross-border trade integration perspective. Determining the bargaining power of Bolivia is essential for future bilateral trade negotiations because Peru and Paraguay are also planning to invest in large-scale dams for exports and hope to become important export competitors in the long term. In addition to the analysis of the export potential and trade opportunities, the contribution to the overall system decarbonization (in Bolivia and South America) is assessed in this thesis for each hydropower export scenario compared to a base scenario in which Bolivia does not invest in large hydropower for exports.

2.4 Integrated nexus assessments of land-use, energy systems, and water systems for sustainable biofuel production

2.4.1 Nexus assessments of climate, land-use, and water and energy systems (CLEWs) Several methodologies with varied foci were developed based on the established Climate, Land, Energy and Water strategies (or CLEWs) framework for integrated resource assessment (Howells et al., 2013). In this framework, integration aspects are identified at a high level of

Chapter 2. Identif ied literature gaps and thesis contribution

aggregation, quantified and represented in a simple model structure (Bazilian et al., 2011; Howells et al., 2013; Welsch et al., 2014). CLEWs nexus studies focused on the energy sector and its interdependencies with water and land-use systems are presented in a growing number of research articles. For example, Howells et al. (2013) and Welsch et al. (2014) assessed the trade-offs of the development of a local biofuel industry in the island of Mauritius using an accounting framework with elements of a simulation model. Sridharan et al. (2019) assessed the vulnerability of planned hydropower plants in Africa to the effects of climate change. Lindblad (2018) made the first attempt to model agricultural land expansion using the OSeMOSYS modelling tool, assessing food security through the identification of necessary upgrades in the agriculture sector in Ghana using a cost-optimization approach. With regards to Latin-American and Caribbean (LAC) regions, (Muñoz Castillo et al. (2019) evaluated water security using a CLEWS nexus framework, with a focus on quantifying climate and socio-economic developments on water security and identifying water infrastructure needs. The results generated spatial indicators in water scarcity and availability for a number of socio-economic scenarios deriving from the Global Change Assessment Model, or GCAM. Effects of water on other sectors (such as the effect of irrigation on the productivity of agriculture) were not in the scope of the analysis. 2.4.2 Integrated CLEWs assessments for Bolivia Few studies are available assessing the complex interactions of land-use, energy and water systems in Bolivia. Multidimensional analyses assessing land-use change include the work from Andersen et al. (2017) and Tejada et al. (2016). Andersen et al. assessed the potential of simultaneously stemming deforestation and reducing poverty when international conservation payments (REDD mechanisms) and decentralized deforestation taxes are implemented. An optimizing-heterogeneous agent model was used to explore the socio-environmental trade-offs of applying such policies in the context of rural populations in the lowlands of Bolivia. Evaluating the applicability of this model to the national scale would be necessary for deriving policy indications. Other important interactions with water systems and energy are beyond the scope of this model. Tejada et al. (2016) developed a spatially-explicit, land-cover-change model for Bolivia in which the percentage of the deforested area is linearly correlated with a number of selected deforestation drivers at grid-cell

Chapter 2. Identif ied literature gaps and thesis contribution

resolution (Tejada et al., 2016). Three exogenously-defined, linearly- increasing deforestation scenarios to 2050 were proposed. The model determines the percentage of deforestation in each cell and provides a spatially-explicit illustration of the deforestation process for every 25 x 25 km2 of Bolivian territory. However, this analysis does not add to the understanding of the underlying issue of how to address the causes of deforestation. Arderne (2016) made the very first attempt to develop an integrated CLEWS study for Bolivia. Using the OSeMOSYS modelling tool, interactions between land-use, water and energy resources were quantified and represented in a single model. The scope of this study was limited to modelling interactions to identify resource pressure points sufficiently. The analysis focused on three basins (delineated by the author) that do not cover the entire territory of Bolivia, but only existing and planned hydropower plants. In this study, the land-use model had limited detail and was used to account for water requirements for agricultural irrigation. These limitations derived in part from the scope of the study and in part from the assumptions made to compensate for lack of data.

2.4.3 Thesis contribution Recent applications of the CLEWs framework has been used to make integrated resource assessments. Following these examples, Paper V investigates local biofuel production in Bolivia that is intended to help meet the mandated biofuel blending targets. In order to meet these production volumes in an environmentally responsible manner, several aspects of ethanol production must be assessed, ranging from the feedstock production to the industrial production of ethanol. Paper V examines potential improvements in sugarcane feedstock production using a tiered approach of agricultural management levels, and evaluates alternative uses of biomass residues for second-generation ethanol and bioelectricity production. The study performs techno-economic optimization to evaluate cost-optimized upgrades of agricultural production systems and to evaluate the location and size of different biorefinery configurations. In addition, an integrated multi-resource modelling approach allows the measurement of effects on water stress, effects on future investments in the power generation system, and quantification of emissions from feedstock- production to bioenergy generation.

Chapter 3. Context

3 Context

This chapter sets the context for this research and gives the background information necessary for understanding in four subsections. The specific research questions asked and papers presented in this thesis are highlighted in the context of the relevant background information.

3.1 On the country of Bolivia

Bolivia is the fifth largest country in South America, covering 1,098,581 km² (comparable to the size of Sweden and France combined). It is landlocked and located in the middle of the continent. Bolivia borders Brazil to the north and northeast, Paraguay to the southeast, Argentina to the south and Chile and Peru to the west. The economy of Bolivia is classified as lower-middle-income and it is listed as the 95th largest economy in the world in nominal terms (Endegnanew and Tessema, 2019a). With a Development Index of 0.693, Bolivia ranks as a medium human development country (UNDP, 2019). Currently, Bolivia has 11,350,000 inhabitants, of which 31% live in rural areas (Instituto Nacional de Estadísticas, 2018b), a percentage that has however been steadily decreasing due to migration to urban areas. Since post-colonial times, Bolivia has had a strong inclination to produce and export primary commodities (Morales, 2017): these exports included for example silver in the 19th century, tin and rubber in the 20th century, and now in the 21st century, natural gas (Morales, 2017). However, exporting abundances of natural resources did not prevent Bolivia from becoming one of the poorest countries in South America, nor has it been able to reverse the historical trend and become an industrialized economy (Kehoe, Machicado and Peres-Cajías, 2019).

Chapter 3. Context

3.2 Economy and energy

This section summarizes the modern economic history of Bolivia from 1960 to the present, then takes a closer look specifically at 2006–2018 to discuss current macro-economic reforms and drivers of economic growth. This section is not intended to expand on the analysis of the economic situation of Bolivia, but to describe some of the issues relevant to the analysis of other subjects that are themselves relevant to the energy sector (and presented in the following chapters). 3.2.1 Historical economic development Figure 2 illustrates annual changes in GDP growth from 1960 to 2018, with five distinctive periods named by Machicado (2018): 1) stabilization, 2) debt crisis, 3) recovery, 4) financial crisis, 5) nationalization.

GDP growth Stabilization Debt crisis Recovery Financial crisis Nationalization 9 6 3 0

-3

1968 1972 1976 1996 2000 1960 1964 1980 1984 1988 1992 2004 2008 2012 2016 annual annual growth, % -6 Figure 2. Historical GDP annual growth 1960–2018. Author’s elaboration using data f rom (Instituto Nacional de Estadísticas, 2018a).

The stabilization period (from 1960 to 1977) was characterized by favorable economic conditions. Sustained economic growth resulted from high commodity prices for the main export products (i.e. silver and tin) and increased access to international credit funds. In the same period, external finance was used for public investments, which turned into an increasing external debt and fiscal deficit during the 1970s (Kehoe, Machicado and Peres-Cajías, 2019) and consequently, a debt crisis period occurred between 1977 and 1986. In the 1980s, external credit was constrained by the global recession (Kehoe, Machicado and Peres-Cajías, 2019), and during this period, the external debt doubled, the international reserves dropped reaching negative values and hyperinflation followed with dollarization. In this period, Bolivia lost almost all the gains in GDP per capita it had achieved since 1960 (Morales 1988). Structural economic reforms took place starting in 1986, which subsequently initiated a period of economic recovery (Antelo 2000). The

Chapter 3. Context

structural reforms were written in a stabilization plan called the New Economic Policy or NPE (Nueva Política Económica, NPE, Supreme Decree 21060). The objective of the NPE was to stabilize the economy by reducing inflation and attracting foreign and private capital using mechanisms such as financial markets liberalization, privatization, and commercial policies instead of exports (Machicado, 2018). The NPE allowed a reduction of the fiscal deficit, and the economy started to grow again, slowing down only during the financial crisis from 1988–2002 originated by external flows (Machicado, 2018). The period 2002–2016 was characterized by favorable economic conditions with high natural gas market prices (Kehoe, Machicado and Peres-Cajías, 2019). In May 2016, Evo Morales nationalized previously privately-owned companies after a few months in the presidency. Before 2006, Bolivia received only tax income from natural gas exports. After the Nationalization, enterprises from strategic sectors such as hydrocarbons (oil and gas), electricity and telecommunications became state-owned. With nationalization in place, the state of Bolivia fully controls the extraction, sales, transportation, distribution and decisions about industrialization. The increased sovereignty over its natural resources reflected the increased ability to capture rents from exports and to negotiate export prices (Machicado, 2018). Together with policies to reduce social inequality, moderate poverty decreased (from 59% in 2005 to 39% in 2018), and the Gini coefficient of inequality lowered (from 0.6 to 0.47 in the same period) (Banco Central de Bolivia, 2019). This period marks the end of the neoliberal period in which market forces were left free to guide most of the economy's decisions (Remmer, 1979).

3.2.2 Natural gas in the economy

The extractive industries (mostly natural gas, but also minerals to a lesser extent) play a central role in the Bolivian economy. In the period 2006– 2017, revenues from the extractive sector accounted for an average of 70% of the total export revenues, 37% of government income and 12% of GDP (Figure 3.a) (Banco Central de Bolivia, 2019). With the discoveries of large natural gas reservoirs, gas production had a six-fold increase between 2000–2016 and the total extractive revenues increased eight-fold (Banco Central de Bolivia, 2019; Ministerio de Hidrocarburos, 2019). Bolivia has enjoyed significant progress in social and economic development and rapid

Chapter 3. Context

growth in public investment largely thanks to these natural gas export revenues (Endegnanew and Tessema, 2019a). The large contribution of the extractive sector to the economy, however, adds vulnerability to price volatility of international markets (IMF, 2018). Since natural gas export prices are indexed to international oil prices, the effects of price volatility are reflected in the exports revenues, which can be observed in Figure 3.a and b, in which a drop in revenues can be observed after a drop in oil prices between 2015 and 20166. In contrast to from revenues from natural gas exports, revenues from domestic gas consumption has steadily increased and were higher than export revenues when international prices were low (Figure 3.c). This pattern is because domestic gas prices are regulated by the government and not affected by international prices. Because of the importance of natural gas in Bolivia’s energy sector and economy, Paper II models in detail the natural gas production chain and takes into account the opportunity cost of natural gas consumed in electricity production (related to the economic return of exports). Despite a less favorable gas export market, the economy of Bolivia grew between 2015 and 2018 because of several fiscal buffers (Banco Central de Bolivia, 2019). On the demand side, growth was driven by increasing domestic demand for electricity and natural gas. Starting in 2015, revenues from agriculture exports had a strong output, which has helped to sustain growth. Industrial agriculture has remained as an important driver for economic growth. In 2016, the agro-industry contributed 13% of the GDP, and it accounted for 10% of the total export revenues between 2000 and 2016 (Figure 3.a). This sector has a multiplicative effect on other sectors of the economy, increasing the activity of machinery and agro-industrial inputs production, transport and financial services (Machicado, 2018). In 2019, the GDP was expected to grow 4.2% due to an expected positive impact in agriculture activity from the production and substitution of imported gasoline by ethanol (IMF, 2018). Because of the importance of ethanol production on the added-value of the agro-industrial sector, opportunities for Bolivia to become a low- carbon and sustainable ethanol producer are investigated in Paper V.

6 Although export volumes in 2017 have decreased only 13 % compared to exported volumes in 2014, the export revenues have dropped 71 % as export prices dropped from an average of 9.85 in 2014 to an avg. of 3.51 USD/thousand ft3 in 2017, see Figure 3.b.

Chapter 3. Context

Extractive Agroindustry Manufacture Others Export revenues 100% 16 000

75% 12 000

50% 8 000

25% 4 000 million USD % % Total Exports

0% -

2007 2009 2011 2016 2000 2008 2010 2012 2013 2014 2015

Domestic consumption % Gas revenue in gov. income Export volume Income domestic gas revenues Gas price to Argentina Income gas exports renevues Gas price to Brazil Total government revenue 25 16 100% 20 000

20 12 75% 15 000 15 8 50% 10 000

Mtoe 10 4 25% 5 000

5 % totalincome USDthoudsand/ f t3

0 0 0% - Income,million USD

2007 2009 2014 2016 2006 2008 2010 2011 2012 2013 2015 2017

2000 2008 2014 2004 2006 2010 2012 2016 2002 Figure 3. Contribution of natural gas exports to the Bolivian economy. a. Export rev enues by selected sector 2007–2016. b. Natural gas f or national consumption and exports in energy units. Gas prices f or Argentina and Brazil. c. Total gov ernment income, Income f rom natural gas exports and domestic sales. Author’s illustration using the latest data f rom INE, National Energy Balance and the Central Bank of Boliv ia.

3.2.3 A new development plan

Approved in 2016, the National Economic and Social Development Plan (Plan de Desarrollo Económico y Social, PDES) identifies key investments for the period 2016–2020 (Estado Plurinacional de Bolivia, 2015). The primary objectives of this plan are to maintain economic growth, to continue poverty reduction, and to increase access to basic services. The PDES includes large-scale public investments and strategies to support economic growth (Endegnanew and Tessema, 2019a). Investments in productive projects, road infrastructure, gas and oil exploration, the industrialization of natural gas and new electricity generation projects (and other smaller projects) are being contemplated. Financing the PDES is expected by a combination of fiscal buffers, international financing and Central Bank loans (Estado Plurinacional de Bolivia, 2015).

Chapter 3. Context

A recent analysis by the International Monetary Fund (IMF) lists several structural interventions necessary for implementing PDES. Among others, these include to increase the efficiency of public spending, to ensure sufficient return from large capital-intensive projects, and to join forces with the private sector to generate additional sources of income (IMF, 2018). The Bolivian economy is specifically vulnerable because of its continued reliance on natural resource extraction (being an important source of national income and foreign exchange) and the future performance of commodity prices can affect the entire Bolivian economy considerably (Toscani, 2017). Figure 4 compares four economic indicators in three years after Nationalization. While a positive decrease in the total debt (internal and external) has occurred, the international reserves and fiscal savings in the Central Bank have decreased. Public investments have increased by 45% compared to values in 2006.

According to Kehoe et al., current policies and the economic situation are reminiscent of the policies from the 1970s that led to a debt crisis (see Section 3.2) (Kehoe, Machicado and Peres-Cajías, 2019). Some of the similarities to the current economic policy are found in the decrease in the international reserves due to expansions of domestic credit and increasing fiscal deficits of public enterprises (Kehoe, Machicado and Peres-Cajías, 2019). Similarly, a recent analysis by the IMF revealed that sustained high rates of public investments together with declining gas revenues (due low international prices) and gas reserves could push the debt levels from current 33% to 100% of the GDP by 2030 (Endegnanew and Tessema, 2019a). With absent new discoveries of natural gas reserves, the proven reserves in 2018 could last roughly ten years at current extraction rates (Chávez-Rodríguez et al., 2016; Endegnanew and Tessema, 2019a). Apart from hydrocarbon extraction, other natural resource sectors remain unexplored and underexploited in Bolivia. These underutilized sectors include hydroelectricity and industrial agriculture, which are currently included in government plans to play an essential role to generate income and increase energy security.

Hydroelectricity export potential and the development of the biofuel industry in Bolivia are evaluated in Paper IV and Paper V respectively.

Chapter 3. Context

Series1 Series2 Series3 Series4 60

v alues 20

% % GDP in nominal 0

1 2 3 4 5 6 7 8 9

10 11 12 13

Figure 4. Economy vulnerability indicators. Author’s illustration using data f rom Banco Central de Boliv ia (2019).

3.3 The energy sector

3.3.1 Overview of energy production and consumption Because of its low industrial development, Bolivia is not a large energy consumer (Pansera, 2012). Figure 5.a and 5.b compare Bolivian energy and electricity consumption per capita with the average from Latin America and the Caribbean (LAC), least developed countries (LDC), and the Organization for Economic Co-operation and Development (OECD) countries. Energy and electricity consumption per capita are smaller in Bolivia than average LAC and OECD countries, but larger than an average LDC. On average, a Bolivian citizen consumes nearly half the energy (and a third of the electricity) of a citizen from other LACs and one-tenth of energy (and one-tenth of electricity) of a citizen from others in OECD. Conversely, the carbon footprint related to the energy consumption of a Bolivian citizen is near half of a citizen from other LACs and one-fifth of the carbon footprint of a citizen from an OECD country. Currently, the electrification rate in Bolivia is 95.6%, which is lower than the average 98% in LAC but higher than the 41% of LDCs. With higher rates of electrification in urban areas (about 98%) and lower rates in rural areas (about 78%), Bolivia’s greatest challenge lies in reducing the existing gap between urban and rural areas. Section 3.3.3 expands on past and current efforts to reach universal electricity access in Bolivia.

Chapter 3. Context

Energy Electricity Energy use Fossil Renewable Energy emissions per capita Electrification 5 10 8000 100% 4 8 6000 75% 3 5 4000 50% 2 3 2000 25%

1 percapita kWh

toe per capita capita per toe

% electrification % tCO2 per capita per tCO2

0 0 0 0%

LAC

LDC

OECD

OECD Bolivia Bolivia

Figure 5. Bolivia in the world, comparisson of energy indicators. a. Comparison of f inal energy consumption and carbon dioxide emissions per unit of energy consumed in Boliv ia in LAC, LDC and OECD countries. b. Comparison of electricity consumption (with % f ossil and % renewable generation) and electrif ication rate in Boliv ia, in LAC, LDC and OECD countries. Author’s elaboration using data f rom Ministerio de Hidrocarburos (2019), PNUD (2019), and the World Bank (2019b).

The energy mix in Bolivia is highly dependent on fossil fuels. In 2019, fossil fuels made up 90% of the domestic primary energy production (see Figure 6). From the total primary energy produced, 59% of the total primary energy production was exported, 10% was transformed into electricity, 2% was transformed into LNG, 14% was refined into liquid fuels (oil) and 15% was used without transformation. Secondary energy is produced from the transformation of primary energy (power plants, refineries, gas treatment plants). In 2019, 31% of the secondary energy produced was gasoline, 24% electricity, 21% diesel, 13% LNG and 10 % other oil derivatives (Figure 6).

Total primary energy production Primary energy - domestic use

Biomass Biomass Petroleum 3% 7% Petroleum 14% 34% Wind and Solar Wind and 0.08% Solar 0.20% Hydropower 1% Hydropower 3% Natural gas Natural gas 82% 56%

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Secondary energy production Final energy consumption

Other oil Other oil derivates Natural derivates Electricity 3% gas 10% 24% Biomass 26% 4%

Diesel Electricity Gasoline LNG Oil 11% 31% 13% 26%

Diesel LNG Oil Gasoline 7% 21% 22%

Figure 6. Energy consumption by fuel in Bolivia. Total primary energy production includes domestic use and exports. Author’s illustration using data f rom (Ministerio de Hidrocarburos, 2019).

With subsidiary policies to introduce gas and LNG in the residential and transport sector, consumption of natural gas and LNG increased on average of 9% annually between 2001 and 2014. This growth slowed down the last five years to an average of 3.4% annually. By 2019, natural gas and LNG made up 33% of final energy consumption. Natural gas is mainly used in the industrial and transport sector (vehicular compressed gas), while LNG is used mainly in the residential sector (Figure 7). Diesel and gasoline consumption increased by an average of 7% annually and made up 48% of final energy consumption. In 2019, Bolivia imported 62% of the diesel and 29% of the gasoline it consumed. Gasoline is used in the transport sector only, while diesel splits mainly between transport (87%), industry, agriculture and mining (13%). Electricity consumption grew steadily at an annual rate of 57%, reaching 11% of the total final energy consumption in 2019. Electricity use is split mainly among the residential (39%), industrial (26%), commercial (25%) and agro-mining (10%) sectors (Figure 7). Biomass (charcoal, wood fuel and agriculture residue) is used for cooking in the residential sector and for heat and power in the industrial sector (for instance, in the sugar and Amazonian chestnut industry). In 2019, biomass made up 4% of the final energy consumption. In Paper III, historical data of the national energy balance is disaggregated into primary end-use activities, and projections are generated using a detailed accounting model introducing current growth trends and government targets for fuel-switch and energy savings in the medium-term (2035).

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Residential Commercial Industrial Natural Gas Diesel Oil Gasoline Transport Agro & mining Biomass Electricity LNG Other oil 2 000 4 500

1 500 3 000

1 000 ktoe

ktoe 1 500 500 0

LNG

Industrial

Transport

Other oil

Biomass

Gasoline

Residential

Diesel Oil Electricity

Commercial Natural Gas Agro&mining Figure 7. Final energy consumption per fuel type. Units are expressed in ktoe (thousand of tonne of oil equiv alent). Author’s illustration using data f rom (Ministerio de Hidrocarburos, 2019).

3.3.2 Electrical power systems

The Bolivian electrical system is divided into the ‘National Interconnected System’ and the ‘Isolated Systems’ (SIN and SA, respectively, according to their Spanish acronyms). The SIN provides electrical energy to the main cities of the country while the SA are mini-grids supplying to smaller populations that are far from the main grid. The SIN is composed of generation, transmission and distribution facilities, and it covers approximately 90% of the total electricity production. It is expected that most of the mini-grids will be connected to the national grid by 2030 and operate as peak-demand technologies (Ministry of Hydrocarbons and Energy, 2014). In 2019, the SIN was composed of 5364 km of high voltage transmission lines and 3150 MW of generation capacity. In the same year, the installed generation capacity in the SA was 0.18 GW. The annual generation in the SIN was 9.53 TWh and 0.95 TWh in the SA. Geographically, the SIN is divided into four main nodes: North (La Paz and Beni), East (Santa Cruz), Central (Oruro and Cochabamba) and South (Potosí and Chuquisaca) (Figure 8). Each node has different characteristics in demand and generation capacity (Figure 9). All hydropower capacity is installed in the North and Central nodes, while gas- fuelled generation is present in all four nodes. The central node has the largest installed capacity and exchanges electricity with all the other three nodes.

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Such peculiarities in energy demand, installed (and planned expansion) capacity, renewable energy potential and transmission interconnections have been introduced in a four-node power system model developed in Paper II. Further applications for electricity exports are evaluated in Paper IV.

NORTH

CENTRAL

EAST

SOUTH

Figure 8 Generation units and trasmission lines. Generation units f rom the SIN and SA are illustrated. SIN is div ided in 4 nodes. Author’s illustration based on data f rom GeoBoliv ia (2018).

Hydro Solar Wind Biomass Thermal Demand 1500 4000 3000 1000

2000 MW 500 MWh 1000 0 0 North Central East South Figure 9. Installed capacity and electricity demand per node (SIN) Author’s illustration based on data f rom Comite Nacional de Despacho de Carga (2019).

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In 2019, 61% of the installed generation capacity in the SIN consisted of gas-fired generation, 31% of hydropower and 8% of other renewable generation such as wind and solar PV (Figure 10.a). In the SA, these numbers were 30% gas, 18% diesel, 45% diesel-gas hybrid, 4% hydro and 3% solar (Figure 10.b). With an increasing gas-fired capacity over time, the overall conversion efficiency of the power generation system has declined from 48% in 2000 to 42% in 2019 (Figure 11.a). In 2019, hydropower generation made 34% of the total generation with average capacity factors ranging from a maximum of 0.64 in the rainy season (October-March) to a minimum of 0.40 in the dry season (April- September) (Figure 11.b.). With an extensive river network, Bolivia is endowed with a large hydropower potential, estimated to be more than 40 GW. However, Bolivia currently uses less than 2% of its potential capacity. Similarly, other renewable sources are still untapped. Because of subsidies for natural gas for electricity generation, electricity prices remain low compared to other countries in South America. Such subsidies are a fiscal burden for the country (6.77% of GDP post-tax in 2019) and an economic disincentive for investments in renewable energy. To supply the increasing national demand, an additional 2.9 GW of generation capacity was planned for the period 2015–2025 (Ministry of Hydrocarbons and Energy, 2014), which means that in 10 years, the installed generation capacity will increase two-fold. From this additional 2.9 GW, 55.4% is hydropower, 38.3% is gas-fired, and 6.3% is renewable energy (Ministry of Hydrocarbons and Energy, 2014). Additionally, three large hydropower projects (5.55 GW) are contemplated for exporting electricity in 2025 (Ministry of Hydrocarbons and Energy, 2014). RQ3 and Paper IV explore Bolivia’s hydropower export capacity in a scenario of cross-border trade in South America in the medium-term (2025), and further exports and integration are explored in the long term (2058).

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SIN generation SA generation Hydropower Hydro 23% 4% Wind Hydro 1% Solar Wind 3% Solar Solar Diesel 4% Diesel- Diesel Gas- Gas gas 18% fired Biomass Diesel-gas 45% 70% 2% Gas 30%

Figure 10. Energy mix in the SIN and SA. a. Power generation capacity in the SIN in 2018 (2.24 GW, 92.4% of total installed capacity ). b. Power generation capacity in the SA in 2018 (0.184 GW). Author’s illustration using data f rom Autoridad de Electricidad (2018)

Natural gas Hydropower Biomass Hydro Solar Diesel Oil Electricity Solar Wind Biomass Wind Efficiency Thermal Demand 2 000 60% 800

1 000 600 40% 0 400 -1 000 MW h 20% 200 -2 000

0 %conversion efficiency

ktoe (stackedcolumns) -3 000 0%

JUL

JAN

JUN FEB

SEP

APR

OCT DEC

NOV MAY AUG

MAR

2000 2006 2010 2014 2004 2008 2012 2016 2018 2002 Figure 11. Energy mix in the power sector. a. Energy sources f or electricity generation and av erage power sy stem ef ficiency 2000–2019 (SIN and SA). b. Monthly power generation and demand in 2019. Author’s illustration using data f rom Ministerio de Hidrocarburos (2019) and Autoridad de Electricidad (2018).

3.3.3 Access to electricity

Achieving universal access to electricity in Bolivia is a priority emphasized in several national strategic plans. In 2018, the electrification rate in Bolivia was 95.6% (World Bank, 2019a), and the government has set a goal of achieving universal access to electricity by 2025, requiring a national strategy to guide investment needs for grid-extension and off-grid solutions (Ministry of Hydrocarbons and Energy, 2014). In the past two decades, the electrification rate in Bolivia increased from 64% in 2000 to 93% in 2018. In the same period, the electrification rate in urban areas increased from 85% to 98% and, in rural areas, from 25% to 78% (Figure 12). During this period, Bolivia led the largest rural electrification program

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in South America by adopting an innovative off-the-grid model (Pansera 2012).

Urban Rural Bolivia

97% 100% 100% 100% 85% 87% 90% 87% 66% 53% 50% 33% 25%

0% 2000 2005 2010 2015 2020 2025 Figure 12 Historical and projected electrification rate. Author’s illustration using data f rom Ministry of Hy drocarbons and Energy (2014).

In 2018, mini-grids (from the SA) supplied electricity to nearly 10% of all electrified households (which is 211,000 households). Operational mini- grids have various sizes ranging from a few megawatts up to 20 MW per unit. In 2015, mini-grids consumed 7000 million cubic feet of natural gas and 39 million liters of diesel (Autoridad de Electricidad 2015), equivalent to 11% and 2% of 2015’s national demand for gas and diesel respectively. According to the National Electrification Plan, the capacity of the mini- grid systems is expected to double, increasing to 274 MW by 2025 from 180 MW today (Ministry of Hydrocarbons and Energy 2014). However, there is no information yet about whether the energy source will include renewable energy, nor the location or size of planned mini-grids.

In recent years, several mini-grid systems have been incorporated to the national grid, reducing their overall carbon footprint by being dispatched as peak technologies. In 2015, 75 MW of gas and diesel-fueled mini-grids were connected to the grid, and it was expected that another 37 MW would be incorporated with the grid by 2020 (Ministry of Hydrocarbons and Energy 2014). Smaller systems than mini-grids (such as micro-grids and stand-alone home systems) have been implemented extensively in rural areas. Decentralized energy networks play a pivotal role for electrifying isolated rural communities, if electrification targets are to be achieved timely.

RQ1 and Paper I are dedicated to assessing micro-grid systems using high-level electrification modelling and to identifying cost-optimal configurations and locations where renewable energy is economically advantageous.

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Looking at the policy context, the main enabler of a national strategy for rural electrification was the promulgation of the Law of Popular Participation (Ley de Participación Popular) in April 1994 (Pansera, 2012). This law established a decentralization program in which funds and administrative responsibilities are transferred from the central government to municipal governments. The decentralization process aims to increase administration efficiency (and capacity) and political accountability of municipalities (Kohl 2003). Building on this law, the program “Electricity for a life in dignity” (PEDV going by the Spanish acronym) was created by Supreme Decree 29635 in 2008 to execute electrification projects financed by credits, public funds and donations. The program coordinates actions with municipalities, service providers and customers to provide access to financial resources. Additionally, the program provides support for search financing (PEVD 2016).

The PEDV program has been pivotal for increasing energy access in Bolivia’s rural areas. A total of 109,000 households were electrified between 2011 and 2018, with a total investment of $125 million (PEVD, 2014, 2016). The largest share of newly electrified connections derived from grid extension and grid densification projects making up to 69% of the total of new connections. The remaining 31% was composed of hydropower micro-grids (1.5%), diesel-PV hybrid micro-grids (9.8%) and stand-alone PV (19.4%). Despite the success of the PEDV program in executing electrification projects, Bolivia has not yet been able to implement rural electrification projects without the commitment of foreign financial aid. International credits have financed 80.2% of the PEDV program, and international donations have financed 14.4% (PEVD, 2016). Only 5.4% has been financed by local public and private capital.

3.3.4 Renewable energy potential Bolivia is endowed with high solar irradiation levels across its territory, two-thirds of which are on some of the world’s highest solar radiation levels. Nearly 97% of the territory is suitable for using solar energy as a primary source for electricity generation. Only 3% of the territory, located in in the northwestern side, has a low rate of solar irradiation as a result of zones with dense cloud formation (Fernandez, 2012). The regions with the highest solar irradiation are located in the western highlands of the country, with average global horizontal irradiation, GHI, in the range of 5.1–6.9 kWh/m2 per day (see Figure 13), while the regions with the lowest solar

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irradiation (located in the eastern lowlands) still have GHI values in the range of 3.9–5.1 kWh/m2 per day. At Bolivia’s latitude, daylight hours do not vary much between the summer and winter seasons (less than 1 hour of variation). Bolivia’s average received radiation is around 2 times higher than the average solar radiation in Germany, which is a leading country in Europe in solar energy (GHI around 2.7–3.2 kWh/m2 per day). By 2019, only 115 MW of solar capacity were installed in Bolivia. Compared to its solar energy potential, the potential for using wind resources is more limited. The highest average wind speeds are found in the eastern lowlands (near Santa Cruz city), with average wind speeds (at 100m height) between 8.0–9.5 m/s2. The second highest wind speeds are found on the western side (bordering with Argentina and Chile) with average speeds between 5.0–8.0 m/s2 (see Figure 13). Wind energy projects have only recently been started in Bolivia, with the first wind project dating from 2014, when 3 MW of wind turbines were installed in the valleys of Cochabamba (2900 m altitude), which was expanded to its current installed capacity of 27 MW. A recent study assessing the effects on the turbulence intensity on the wind turbines in Bolivia shows that high altitudes induce important reductions in air density due to turbulence (air is about 27% less dense at the Qolpana wind farm), ultimately reducing the power output (Mamani, Hackenberg and Hendrick, 2018). The hydropower potential of Bolivia is estimated to be about 40 GW (equivalent to 173 TWh), of which 735 MW are currently used to supply 34% of the total electricity generation (3244 GWh in 2019) (Comité Nacional de Despacho de Carga, 2018). The largest hydropower potential is in the sub-Andean region, that is, the region going from the Andes to the valleys and plains in the south (Figure 13). The physical geography of the rest of the country and its extensive river network allows for potential small-scale hydropower generation across its territory (Ministry of Hydrocarbons and Energy, 2014). In the period 2015–2025, 1850 MW of medium-scale and 147 MW of small-scale hydropower are planned. In addition, three export-oriented large-scale hydropower dams are identified in Bolivia’s National Electricity Plan 2025 with a combined generation capacity of 5552 MW. Investments for these projects are estimated to up to US$ 8.8 billion (Ministry of Hydrocarbons and Energy, 2014).

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Figure 13. Renewable energy potential in Bolivia Solar irradiation (lef t), av erage wind speed at 100m (middle), and hy dropower potential (right) in Boliv ia. Author’s illustration using data f rom the World Bank (2016), DTU et al., (2018) and GeoBoliv ia (2018) Currently, there are no regulations for the use of forest biomass. However, several types of biomass (including firewood, biomass waste, charcoal and animal waste) are important sources of energy in rural areas and in the industrial sector of Bolivia. Biomass is used for cooking in the domestic sector and for heat production in industries producing bricks, plaster, pottery and rice. In addition, biomass residues such as sugarcane bagasse, chestnut shells and rice husks are currently used for generating heat and electricity in small-scale co-generation plants. Currently, 52.22 MW of surplus electricity (making 2.2% of the total generation capacity in the SIN) is exported to the grid from co-generation plants from sugarcane and chestnut industries (Comité Nacional de Despacho de Carga, 2018). This potential is expected to grow with the increasing demand for sugarcane for ethanol production. Most of the high-enthalpy geothermal resources in Bolivia have been discovered along the Southern Altiplano on the border with Chile and in the foothills of the Eastern and Western Cordilleras, areas characterized by their volcanic activity (Lahsen et al., 2015). Feasibility studies, including drilling of six wells, were made between 1990 and 2000 in the Laguna Colorada field located in the province of Sud Lípez from Potosí (Terceros, 2000). Reservoir engineering studies suggest a power potential of 350 MW for this project (Aguilera, 2013). The estimated exploitable geothermal power in Bolivia is between 510 and 1260 MW (Lahsen et al., 2015). Currently, a pilot project of 5 MW in Laguna Colorada is under construction, and construction of infrastructure for another 100 MW are expected to be in place by 2025 (ENDE, 2019). In this thesis, the renewable energy potential of Bolivia is included explicitly in a least-cost optimization model developed in Paper II and Paper IV, in which future investments in the power generation expansion

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are evaluated. Moreover, the role of financing costs and carbon pricing on increasing renewable energy cost competitiveness are evaluated by RQ2.

3.3.5 Ethanol blending mandates, opportunities for increased sugarcane yield, and use of sugarcane biomass residues In the past two decades, Bolivia transitioned from being a self-sufficient energy producer to be a large importer of liquid fuels. One of the underlying causes is the significant growth of its automobile fleet, which has more than doubled in the past decade, together with a significant and sustained economic growth (INE, 2019). Current subsidy schemes for liquid fuels have maintained domestic prices for gasoline and diesel at nearly constant levels for the last 15 years; these are, on average, about half of their international price levels (Laserna, 2018). This situation increases fiscal pressures because nearly 62% of the diesel and 29% of the gasoline are imported at international market prices (Ministerio de Hidrocarburos, 2019). To reduce dependence on liquid fuel imports, the government of Bolivia signed the Law for Vegetable Derived Additives with the goal of initiating large-scale liquid biofuel production and blending. The mandate required gasoline to be blended with at least 8% anhydrous ethanol (E10) by the start of 2018, which should have gradually increased to 25% (E25) by 2025. In an agreement with the private sector (sugar producers), the government targeted initial annual production of 80 million liters of anhydrous ethanol in 2018, scheduled to increase to 380 million liters by 2025 (Nolte, 2018). These target volumes of anhydrous ethanol aimed to introduce the ethanol-gasoline blend while gradually reducing pure gasoline use and availability. The government of Bolivia estimated to increase the sugarcane agricultural area by 18 thousand hectares per year, reaching a total expansion of additional 155 thousand hectares by 2025 (nearly doubling its current harvesting area) (Nolte, 2018; YPFB, 2018). Due to the increasing importance of biofuels in Bolivia’s economy and energy sector, RQ4 and Paper V explore opportunities for Bolivia to become a low-carbon and sustainable ethanol producer.

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Ethanol and the sugarcane industry In 2019, the total agricultural land for sugarcane production was 172.6 thousand hectares, producing over 9.5 million tons of sugarcane (Instituto Nacional de Estadísticas, 2019). The latest national census on agriculture indicates that 96.4% of the sugarcane cultivated is rainfall dependent, with yields and crop profitability particularly susceptible to hydroclimatic variability. The average national sugarcane yield is low (about 55.3 ton of sugarcane per hectare) with a substantial yield gap compared to other regions worldwide (nearly half of its potential yield7) (Fischer et al., 2012). Although large-scale producers have narrowed the country-wide average yield gap by advanced agricultural practices minimizing yield-limiting factors (nutrient deficiencies) and yield-reducing factors (weeds, pests and diseases), no research has investigated the cost efficiency of narrowing yield gaps. According to the latest census on agriculture, small properties (<10 ha), medium-scale properties (10–100 ha ) and large-scale properties (>100 ha) accounted for 14%, 39% and 47% respectively of the land dedicated to sugarcane cultivation (Instituto Nacional de Estadistica, 2015). Small producers use family labor while medium and large-scale producers combine mechanized harvesting with human labor for the harvest. There are seven sugar factories in the country, five of them located in Santa Cruz, one in Tarija and one in La Paz. Together, these have a milling capacity of 86,000 tons of sugarcane per day and a distillation capacity of 250 million liters per year in 2019. Before the promulgation of the Law for Vegetable Derived Additives (N°1098/2018), sugarcane was used for sugar production (as the main product), although hydrous ethanol was also made as a byproduct (AEMP, 2018). To meet the mandated biofuel production targets, existing sugar factories (which are also owners of most of the distillation units) have expanded their milling and distillation capacities and implemented molecular sieves to dehydrate hydrous ethanol in order to produce anhydrous ethanol (99.5°GL). Anhydrous ethanol can be blended with gasoline up to 25% (E25) and used in internal combustion engines of most modern light-duty automobiles without requiring any modification of the engine or fuel system; higher blends (e.g. E70, E75, E85, E95, E100) are standard fuels for flex-fuel vehicles only. Hydrous ethanol can be used directly in flex-

7 Considering a potential yield of 107 t/ha with irrigation and agriculture production with high-inputs.

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fuel vehicles (Cruz, Souza and Cortez, 2014); however, no subsidiary policies for increasing the fleet of flex-fuel cars have been implemented in Bolivia.

Opportunities of energy use of sugarcane biomass residues As briefly mentioned in Section 3.3.4, the sugar industry produces its electricity and heat using biomass fibrous residues (bagasse) in co- generation plants, and four of the seven plants export electricity surpluses to the national grid during the harvesting season (in the months between July and November). In 2019, 150 GWh of bioelectricity was exported to the grid, making 1.6% of the total electricity generation. Typically, sugar factories have been stand-alone units not connected to the grid. Because there is a surplus amount of bagasse, the factories can burn bagasse inefficiently in low-pressure boilers (30 bar, 340°C) and backpressure steam turbines (BPST) as means of biomass excess disposal rather than for efficient energy generation (Deshmukh et al., 2013). Excess bagasse is also often used to feed livestock, or it can be dried for use as litter for pigs and poultry. Today, advanced cogeneration systems are implemented with high-pressure direct combustion systems (80–105 bar, 480–525°C) in the form of condensing-extraction steam turbines (CEST). These systems provide higher electricity generation and export potential than low-pressure BSPT systems. If connected to the grid, traditional low-pressure systems can export up to 26 kWh per ton of sugarcane (tc) in sugar factories, while advanced cogeneration systems can export up to 150 kWh/tc (Khatiwada et al., 2012; Deshmukh et al., 2013). By upgrading inefficient co-generation systems, the sugarcane industry stands to earn revenues by selling surplus electricity. Moreover, investments in ‘bioelectricity’ from bagasse projects can provide energy capable of replacing higher-emission gas-fired projects. Other alternative technologies for increasing the electricity generation potential in the future include the biomass integrated gasifier combined cycle, BIGCC, with further surplus electricity potential of more than 250 kWh/tc (Mbohwa, 2003). Among sugarcane producing countries, bagasse availability varies from 23% to 37% of the sugarcane production (at 48%–50% moisture content) (Khatiwada et al., 2012). Apart from bagasse, the leaves and tops residues (commonly referred to as “cane trash”) are often left in the field and burned after the sugarcane harvesting. The amount of cane trash corresponds to about 140 kg of dry matter per ton of sugarcane production (Hassuani, Leal

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and Macedo, 2005). Trash recovery systems recover 50% of the total sugarcane trash available on the field at the moment of harvesting, and this matter is transported to the sugarcane mill and separated from the stalks. The other 50% is left at the fields to maintain the soil quality (Hassuani, Leal and Macedo, 2005; Cardoso et al., 2015). This biomass residue can be used together with bagasse as fuel in co-generation plants and reduce or eliminate polluting burning practices. Alternatively, the lignocellulosic materials of biomass residues (bagasse and trash) can be hydrolyzed into fermentable sugars and then converted to ethanol (second-generation ethanol, 2G). Several technological developments for 2G production processes are available, with 2G ethanol yields ranging from 20 to 40 liters/tc (Dias, Cunha, et al., 2011; Dias, Da Cunha, et al., 2011; Seabra et al., 2011; Dias, Junqueira, Cavalett, et al., 2012; World Energy Council- Bioenergy, 2017). Because a higher investment is required for advanced co-generation and 2G ethanol production, lifecycle economic analyses are required to determine the economic feasibility of using surplus biomass. In developing nations, an additional potential for revenue is the sale of Certified Emissions Reductions (CERs) under the Clean Development Mechanism (CDM) of the Kyoto Protocol (Mcnish et al., 2009; Flamos et al., 2010). A CER is a carbon credit (equal to one metric ton of CO2,eq) obtained in a project to reduce carbon emissions, and these can then be sold on the carbon market to entities in developed countries in order to help them achieve their own carbon reduction targets.

3.4 Bolivia’s INDC

In October 2015, Bolivia submitted for the first time its Intended Nationally Determined Contribution (INDC) to the United Nations. On its INDC, Bolivia presented its contribution with a holistic vision of development in harmony with nature – “Living Well”. In this vision, Bolivia aims to help solve the climate crisis with structural changes to its development, for which the rights of “Mother Earth”8 integrate with the rights of people to live free of poverty and with a full realization of their economic, social and cultural rights. Such commitments for “Integral

8 Law No.071 of the Rights of Mother Earth and Law No. 300 of Mother Earth and Integral Development to live well

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Development for Living Well” are guided by its 2025 Patriotic Bicentennial Agenda, in which government targets for social and economic development are driven by state-led industrialization and large-scale public investments in infrastructure (Endegnanew and Tessema, 2019b). In its INDC, Bolivia does not have specific, clear targets for reducing its emissions intensity, nor clearly-stated emission mitigation objectives. In its INDC, Bolivia proposes a “Climate Justice Index for a fair and equitable sharing of the global emissions budget” to comply with the principle of “Common but differentiated responsibility” of countries stated under article 4.7 of the UNFCCC. However, the country does not specify contributions to reducing global emissions, nor pursuit of a low-carbon transition integrated with its overall development agenda. (Note: at the time of this writing, Bolivia is on the way to updating its INDC and submitting its National Determined Contribution, NDC, to the UNFCCC). Bolivia has designed an unconditional INDC (uINDC) with efforts the country is committed to achieving for itself and a conditional INDC (cINDC) representing goals that the country could achieve with financial and technological support from international cooperation. For example, the uINDCs commit to achieving 100% drinking water coverage by 2025, to tripling agriculture irrigation surface to over 1 million hectares by 2030 (1.5 million ha with the cINDC), to tripling food production under irrigation by 2030 (quadruple with the cONDC), and to reducing vulnerability to water scarcity from 0.5 to 0.3 units of the National Index of Hybrid Vulnerability by 2030, among other commitments. With regards to energy, the country commits to reaching universal access to electricity by 2030, to increasing the efficiency of gas-fired generation by upgrading to gas combined cycles, to increasing renewable energy capacity to 79% by 2030 (81% with the cNDC) through renewable projects and hydropower, and to investing in large scale hydropower projects for exporting 8930 MW by 2030 (10489 MW with the cNDC), thereby increasing State revenues. Regarding forests, the country commits to achieving zero illegal deforestation by 2020, to increasing the forested and reforested areas to 4.5 million hectares by 2030 (6 million ha with the cNDC), and to increasing forest areas with sustainable community management approaches by 16.9 million hectares in 2030 (compared to 3.1 million hectares in 2010). However, in line with deep decarbonization pathways necessary to reach the 1.5°C climate stabilization goal, Bolivia’s INDC is not ambitious enough. To strengthen Bolivia’s NDC, long term strategies with committed

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decarbonization and mitigation targets, as well as sectoral goals for increasing adaptation and resilience to climate change, should be envisioned in the future submissions. In addition, shifting to more stringent targets should be followed by increased transparency in order to describe potential emission reductions as resulting from committed actions, along with cost estimates and a plan for implementation and monitoring of the NDCs. To date, no carbon pricing instruments nor emission reduction targets have been discussed in Bolivia’s climate action plans. In contrast, more than two-thirds of the NDCs of Latin American and Caribbean nations refer to the use of carbon pricing mechanisms to achieve the key objective of the Paris Agreement (UN Climate Press Release, 2016). Brazil has a federal fuel tax (Contribution of Intervention in the Economic Domain, CIDE tax) levied on specific fuels, and it is committed to reducing, by 2025, 6% of its 1990 GHG emission levels, and by 2030, reducing the 1990 levels by 16%. In 2014, Mexico imposed a tax on several fossil fuels averaging 3 US$/tCO2,e and has committed to reducing its GHG emissions a further 22% by 2030. In 2017, Chile’s carbon tax came into effect and targeted thermal power plants at 5 US$/tCO2, and Chile committed its INDC to cutting GHG emissions to 20% below 2007 levels by 2020. Research is required to provide related insights for how similar types of carbon pricing could work in Bolivia. The effect of carbon pricing on increasing renewable energy cost competitiveness is evaluated by RQ2 in Paper II.

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Chapter 4. Methodological approach and modelling tools

4 Methodological approach and modelling tools

This chapter is divided into two sections. The first gives an overview of the modelling frameworks used in the thesis, together with the selected modelling tools. The second summarizes the methodologies and models used in the research articles and is intended to be a complement to the methods described in detail in the appended research articles.

4.1 An overview of the modelling frameworks and modelling tools

4.1.1 Accounting models Energy accounting is one of the approaches used in energy systems analysis. Energy balances provide a simple representation of the energy system, and this accounting approach captures the physical flows of various energy carriers while obeying energy and mass balances (Bhattacharyya and Timilsina, 2010). These accounting models are characterized by their simplicity and transparency, and selected elements are represented by verifiable and exogenously defined interrelations (Welsch, 2013; Rogan et al., 2014). Socio-economic, technological, and demographic developments (among other possibilities) are used to account for future energy demands. Accounting frameworks used in this field include MAED (IAEA, 2006) and elements of LEAP (C. . Heaps, 2016). In Paper III, the LEAP modelling tool was selected to develop an energy demand model and projections for Bolivia.

Chapter 4. Methodological approach and modelling tools

LEAP

The Low Emissions Analysis Platform (LEAP) is an integrated, scenario- based modelling tool widely used for medium to long term energy systems planning. It can be used to model resource extraction, energy production and energy consumption with a scenario-based approach. LEAP is also used as an accounting tool for GHG mitigation assessments because it includes a technology and environmental database with technology- specific emission factors. The structure of LEAP allows comparison of future pathways to a user-defined reference scenarios, from which alternative scenarios inherit their main assumptions. Different methodologies are supported for modelling demand and capacity expansions for transformation and resource extraction. On the demand side, LEAP calculates future demands based on top-down macroeconomic assumptions or by bottom-up, detailed technological representations. On the supply side, LEAP allows accounting, simulation and optimization methodologies to model capacity expansion planning. The energy system is modelled in a tree structure with a hierarchical organization of data (Figure 14). In a demand analysis, two components named ‘category branches’ and ‘technology branches’ contain information about activity and energy intensities, respectively. The energy demand is calculated by multiplying the activity and energy intensity. Depending on the methodology used, the ‘activity’ information could be at top-down level (e.g. a macro-economic indicator such as GDP) or at bottom-up level (e.g. an end-use technology such as cars or cookstoves). Consequently, the energy intensity will represent the energy consumed per unit of the activity chosen. LEAP was created in the 1980s and is currently developed by the Stockholm Environmental Institute. By the end of 2019, it had been applied by over 36,000 users in more than 195 countries. Free LEAP licenses are available for non-commercial use and to modelers from developing countries. LEAP has been used by the Ministry of Hydrocarbons and Energy (MHE) in Bolivia for developing policy notes on energy security.

Chapter 4. Methodological approach and modelling tools

Figure 14. Tree structure in an energy demand model. Author’s elaboration.

4.1.2 Optimization models Energy systems optimization allows the assessment of optimal investments in alternative technologies, energy sources, and policy options given energy demands and specified constraints. These models are driven by an objective function which is usually cost-minimization or profit- maximization. Selected interactions of energy flows and different conversion technologies are the baseline of optimization models, and these are called the reference energy system (RES). Figure 15 exemplifies a RES for a simple energy system, in which the horizontal lines represent resources, demands, and flows of energy, and the boxes represent energy conversion technologies. The energy flow describing the RES begins at the energy resource level, then is converted to primary energy, secondary energy and subsequent auxiliary levels, all the way to the final energy consumption. Several long-term energy optimization models are widely used (including MARKAL, TIMES, MESSAGE, TEMOA and PLEXOS); the research in this thesis will use the OSeMOSYS model (see below). Limitations associated with cost-optimization models include assumptions that do not necessarily reflect reality, such as perfect foresight of the future (e.g. demands, fuel prices), inelastic demands, and perfect market competition (Welsch, 2013).

Chapter 4. Methodological approach and modelling tools

Figure 15. Example of a simple Reference Energy System (RES). Author's elaboration.

OSeMOSYS

The Open Source Energy Modelling Systems (OSeMOSYS) is an open- source, bottom-up and multi-regional energy system modelling framework that uses linear and mixed-integer linear programming (LP and MILP) methods. The objective function is to minimize the present value of expanding and operating the energy system to meet an exogenously defined demand (Howells et al., 2011). The optimization is subject to constraints that are aimed to represent real-world restrictions such as energy resources availability, load demand profiles, environmental regulations, investment limitations, activity or capacity of processes, availability and price of fuels, market penetration of new technologies, among others. Although OSeMOSYS was conceived as a modelling tool for energy systems analysis, its structure allows for modelling any type of system. OSeMOSYS has been widely used in studies of multi-resource systems (that is, CLEWs studies) in which links between energy systems, water systems, land-use systems and effects of climate are studied in conjunction (Pereira-Ramos et al., 2020). While simplifying some of the dynamics underlying energy systems, the linear structure of OSeMOSYS allows analysis of complex scenarios with reduced computational efforts. In contrast to abstractions commonly used in programming tools, OSeMOSYS uses long and self-explanatory parameters and variable naming, which allows for rapid familiarization with the code. In addition to its convenient name conventions, the

Chapter 4. Methodological approach and modelling tools

OSeMOSYS code is well documented and clearly structured, allowing further enhancements and modifications. OSeMOSYS is characterized by its flexible systems representation in which technologies can represent any type of system. The model is driven by exogenously defined demands for commodities/services (e.g. electricity, water for domestic use, agricultural products). These demands can be met through a range of conversion technologies which draw on a set of resources defined by their potential and costs (Figure 15). The model formulation is characterized by a flexible technology definition. Each technology is characterized by economic, technical and environmental parameters, for example, capital investment and operational costs, conversion efficiencies, and emissions intensities. Further constraints can be imposed on technology’s operation and investments to simulate, for example, technical constraints, economic realities, and environmental targets. The code is written in several programming languages such as GNU- Mathprog, GAMS and Python, and optimization carried out with the open- source GNU linear programming kit (GLPK). The first available detailed description of the OSeMOSYS algebraic formulation was published by Howells et al. (2011). Several expansions of this code are available at www.osemosys.org. The objective function of the model is presented in Equation 1. The net present value (NPV) of the system comprises the discounted costs incurred by each technology, in each year and for each region modelled. The costs associated with technologies include operating costs (fixed and variable), investment costs, emission costs and salvage value costs. Each cost is discounted to its present value and given a discount rate. Emission penalties are subject to an exogenously defined emission price. The salvage value is the cost of a technology invested during the model period, which still has operational life at the end of the modelling period (for the complete model, see Howells et al., 2011). 푅 푇 푌 퐷𝑖푠푐표푢푛푡푒푑 퐷𝑖푠푐표푢푛푡푒푑 푀𝑖푛𝑖푚𝑖푧푒 ∑ ∑∑ [퐼푛푣푒푠푡푚푒푛푡 ] + [ 푂푝푒푟푎푡𝑖푛푔 ] Equation 1 푟 푡 푦 퐶표푠푡 푦,푡,푟 푉푎푟𝑖푎푏푙푒 퐶표푠푡 푦,푡 ,푟 퐷𝑖푠푐표푢푛푡푒푑 퐷𝑖푠푐표푢푛푡푒푑 퐸푚𝑖푠푠푠𝑖표푛푠 [ ] + 푂푝푒푟푎푡𝑖푛푔 + [ 푃푒푛푎푙푡푦 푏푦 ] 퐹𝑖푥푒푑 퐶표푠푡 푦,푡 ,푟 푇푒푐ℎ푛표푙표푔푦 푦,푡,푟 퐷𝑖푠푐표푢푛푡푒푑 퐸푚𝑖푠푠푠𝑖표푛푠 퐷𝑖푠푐표푢푛푡푒푑 [ ] + [ 푃푒푛푎푙푡푦 푏푦 ] − 푆푎푙푣푎푔푒 푉푎푙푢푒 푦,푡,푟 푇푒푐ℎ푛표푙표푔푦 푦,푡,푟

Chapter 4. Methodological approach and modelling tools

Subject to linear energy balances, linear activity and capacity constraints with the form given in Equation 2:

푃푟표푑푢푐푡𝑖표푛푟,푡,푦,푙,푚 = 퐷푒푚푎푛푑푟,푦,푙,푚 Equation 2 푓(푥 ) ≥ 0 푟,푡,푦,푙,푚 where r is each region of the total R regions of the model; t is each technology in the energy system with a total of T technologies; y is each year of the model.

4.1.3 Geospatial electrification models The increasing abundance of geospatial data has allowed electrification models and approaches associated with Geographic Information Systems (GIS) to be developed (Moner-Girona et al., 2018; Ciller et al., 2019). In principle, the first-generation modelling tools addressing high-level electrification plans (regional, country or continent level) use techno- economic and GIS data to match energy resources with potential energy demands to identify the least-cost electricity service. The levelized cost of electricity (LCOE) has been often used to compare electrification technologies (grid-connection and decentralized) over their lifetime to identify the solution that can serve as the least-cost option in a given territory. Few geospatial tools are being developed to specifically aid electrification modelling. Some examples of these first-generation GIS-based modelling tools are the Re2nAF, Network Planner (NP), the Reference Energy Model (REM) and the Open Source Spatial Electrification Tool (OnSSET) (Szabó et al., 2011; Ohiare, 2015; Mentis et al., 2017b; Amatya et al., 2019). Other noteworthy works include independent electrification studies for Uganda (Kaijuka, 2007), Kenya (Zeyringer et al., 2015), Nigeria (Bertheau, Cader and Blechinger, 2016) and the West African continent (Auth et al., 2014). To ensure accessibility and transparency (two important features of high- quality scientific research, Pfenninger et al., 2017), OnSSET was selected among the other electrification modelling tools for being fully open- source.

Chapter 4. Methodological approach and modelling tools

OnSSET The Open Source Spatial Electrification Toolkit, OnSSET, is a GIS-based tool developed to help identify the least-cost technology mix required to achieve universal electrification in a given country (Mentis et al. 2017). The tool is written in Python and is fully open-source. The technology options include grid-extension, mini-grids and stand-alone systems. Results from electrification analyses indicate the technology mix, new capacity and investments required to fully electrify a country. Currently, OnSSET accounts only for residential electricity demands. OnSSET uses GIS to extract location-explicit data for energy resource availability, power infrastructure, population, economic activities and other demand-related parameters using a large number of spatial datasets (Mentis, 2017). GIS analysis is performed to process the spatial data into a table database each settlement location held in rows and the processed data specific to each settlement held in columns. This data is later used by OnSSET to calculate techno-economic inputs to assess the performance of the various technology options. For each settlement in the database, the LCOE is calculated for each of the decentralized technology configurations following Equation 3. Settlements that cannot be cost-effectively connected to the grid because of distance restrictions will get access to electricity through mini-grid or stand-alone systems. This decision is based on a cost-comparison in which the technology with the lowest LCOE is selected for each settlement. (For a more detailed description of the model, the reader is referred to Mentis, 2017; Mentis et al., 2017a; Korkovelos et al., 2019b). The LCOE for all technologies is calculated following Equation 3:

푇 −푡 ∑푡=1(퐶푐,푡 + 퐶푓,푡 + 퐶푣,푡) ∙ (1 + 푟) 퐿퐶푂퐸 = Equation 3 ∑푇 −푡 푡=1(퐺푡 ) ∙ (1 + 푟)

where Cc,t is the investment expenditures in year t; Cf,t is the fixed operational and maintenance costs in year t; Cv,t is the fuel costs in year t; r is the discount rate; T is the lifetime of the system. To estimate the capacity of each electrification option, geospatial data (e.g. electrical infrastructure and topology), technical data (e.g. conversion efficiency) and renewable resource data (e.g. global horizontal irradiation, wind speed) are all used.

Chapter 4. Methodological approach and modelling tools

4.2 Development and applications of methods and models

4.2.1 Modelling technically-detailed micro-grids for high-level geo-spatial electrification applications Hybrid micro-grids are constituted by a renewable component (the main energy supply), a combustion engine (used as backup when renewable energy is not available) and a battery bank (which stores renewable energy surplus). Adequate hybrid micro-grids sizing aims to use the available renewable energy cost-effectively without compromising the reliability of the energy supply and the cost to the final user. Therefore, sizing micro- grids requires a high-temporal representation that incorporates the stochastic nature of load demand and renewable energy. In contrast to mono-sourced micro-grids, sizing hybrid micro-grids by means of a simplified representation would exacerbate the effects of the low technical accuracy, giving inaccurate results. In Paper I, a modelling framework was developed to bridge the “computational gap” between technically detailed micro-grid systems analyses and high-level geospatial electrification modelling. This framework expands the capabilities of the previously described OnSSET, which is a mature geospatial electrification tool. Two specialized open- source modelling tools were used in a sequence of three steps to generate surrogate models to evaluate the LCOE of micro-grids in OnSSET. Typical general features and typical energy needs of populations in rural areas of Bolivia were introduced to the model. The open-source RAMP model (Remote Areas Multi-energy system load Profiles) was used to generate representative load demand profiles using interview-based information and a stochastic bottom-up approach with specific correlations between main load-profile parameters (i.e. load factor and coincidence factor) for a number of settlement archetypes (Lombardi et al., 2019). The open-source Micro-GridsPy model was used to optimize the size of the micro-grid system components using a two-stage stochastic optimization approach (Balderrama et al., 2019). Surrogate models were then derived from a multivariate regression of micro-grid optimization results as a function of influencing parameters. Additionally, a “Solution Pool” dataset containing optimized solutions for a wide range of possible settlement

Chapter 4. Methodological approach and modelling tools

archetypes and other techno-economic parameters (diesel cost, lost load and capital costs) was used to obtain an adequate size for every micro-grid candidate. Figure 16 illustrates a simplified representation of the 3-step methodology designed with the purpose of generating surrogate models that are then hard-linked into the OnSSET model. As a result of the coupling, the LCOE of different configurations of micro-grids is compared to the LCOE of stand-alone generation systems and grid extension possibilities to determine the cost-optimal solution. The reader is referred to Paper I for the complete methodological description. To alleviate the uncertainty influencing techno-economic parameters to the optimal size (and therefore, the LCOE) of micro-grids, several scenario instances were designed before the optimization. A scenario instance consists of a combination of the load demand of a settlement archetype with a number of mutable techno-economic parameters influencing the LCOE. The selected three parameters were diesel cost, loss of load and capital investment costs (for the PV module, diesel generator and battery). These parameters were permutated using equally spaced steps over a specified range. A set containing all the scenario instances was then optimized using the MicroGridsPy modelling tool, with the LCOE and the size of each micro-grid component as the main outputs. Figure 17 illustrates the sequence of steps used to generate the model instances and surrogate models. The surrogate model is presented in Equation 4:

푓푢푒푙 퐿퐶푂퐸푡 = 푐표 + 푐1 ∙ 퐿퐿푃 ∙ 100 + 푐2 ∙ 푐 + 푐3 ∙ 1/퐻 + Equation 4 푔푒 푅 푏푎푡 푐4 ∙ 퐺퐻퐼 + 푐5 ∙ 푐 + 푐6 ∙ 푐 + 푐7 ∙ 푐 where t is the micro-grid technology (solar-battery, diesel or hybrid); 퐿퐿푃 is the loss of load probability in percentage; 푐푓푢푒푙 is the diesel cost in US$/liter; 퐻 is the number of households; 퐺퐻퐼 is the global horizontal irradiation; 푐푔푒, 푐푅 and 푐푏푎푡 are the capital costs in US$/kW of the generator, PV unit and battery respectively; 푐0 to 푐7 are the coefficients of the regression. Equation 4 and its adjusted parameters are then incorporated into OnSSET to calculate the LCOE of each micro-grid possibility for every community i.

Chapter 4. Methodological approach and modelling tools

Figure 16. Three-step core components of the modelling framework. For micro-grid LCOE and inv estment cost calculations in each settlement, low-v oltage distribution network costs and household connection costs were added in Step 3 using results f rom OnSSET mini-grid algorithms. Figure reprinted f rom Paper I. Note that surrogate models are based on a multivariate regression of datasets resolved with an hourly time resolution; thus, the surrogate models inherit the critical information associated with the time component despite not explicitly carrying it. To approximate the size of each micro-grid component in a given community i, a look-up algorithm consisting of the following steps was designed (Figure 17). First, a “solution pool” dataset containing the entry information for each scenario instance modelled and its solution output (battery, generator and PV nominal capacities) was set

Chapter 4. Methodological approach and modelling tools

up. For each settlement i, the size of the micro-grid components was selected from the most similar scenario available in the solution pool dataset. For both datasets, for the ith settlement in the census data and the solution pool, a one-dimensional similarity index was calculated to set up a nearest-key merge function. To this end, principal component analysis was used to reduce the dimensionality of the features into a smaller set of principal components containing most of the variance, then Euclidean distance (‘distance measure’) was calculated to unify these principal components into a unidimensional index.

Figure 17. Sequence of steps to generate the surrogate models. Figure reprinted f rom Paper I

Key model assumptions, limitations and delimitations Geo-referenced data from the latest National Census on Population and Households was available for this study, including the number of households, electricity access, electricity source and exact geographic location of 19,280 communities (Instituto Nacional de Estadística and Viceministerio de Electricidad y Energías Alternativas, 2016). A preliminary assessment of the census database revealed that communities with more than 550 non-electrified households have a percentage of households connected to the grid, while communities with fewer than 50 households have high shares of low-income households. These characteristics have direct implications for the least cost electrification solution. An initial connection to the grid and an accumulated high demand is anticipated to make grid extension the most cost-effective alternative. In

Chapter 4. Methodological approach and modelling tools

contrast, low demand and a large distance from the high-voltage grid make stand-alone systems the most cost-effective solution. Therefore, the micro- grid systems analysis for Bolivia focuses specifically on communities with a minimum of 50 households (sufficient demand) and a maximum of 550 households. In this study, micro-grids are defined as centralized generation units with individual distribution networks operating in islanded mode. Three micro- grids types are modelled, but future expansions of this methodology could include a broader portfolio of micro-grid technologies and fuels. Similarly, the methodology could be expanded to assess larger systems such as mini- grids, which require additional information on appliance ownership and usage habits, among other things. Since investments decisions at grid-level affect the grid-electricity price directly and, therefore, the LCOE for grid expansion, decisions at grid-level could affect the selection over off-grid technologies. Such interactions were out of the scope of the present study but could be incorporated in the future to expand the analysis. Costs scenarios To account for steady improvements in renewable technology costs and uncertainty about whether fossil fuels subsidies will continue, cost scenarios (fuel and capital costs) were performed. Two existing subsidy schemes in the Bolivian electricity sector were included as scenarios together with international diesel prices. Table 2 illustrates the scenarios modelled and the cost values used in each of them.

Table 2. Cost scenario components and v alues used.

Scenarios Diesel price, Capital costs Capital costs Capital costs US$/liter battery, PV panel, generator, US$/Wh US$/Wh US$/Wh Ref erence International Ref erence: 0.6 Ref erence: 1.5 Ref erence: 1.48 price: 0.8 Scenario 1: Low International Low: 0.3 Low: 1 Low: 1 capital costs price: 0.8 Scenario 2: High International High: 0.8 High: 2 High: 2 capital costs price: 0.8 Scenario 3: Diesel Subsidy : 0.53 Ref erence: 0.6 Ref erence: 1.5 Ref erence: 1.48 subsidy Scenario 4: Diesel Subsidy high: Ref erence: 0.6 Ref erence: 1.5 Ref erence: 1.48 subsidy high 0.18

Chapter 4. Methodological approach and modelling tools

4.2.2 Exploring Bolivia’s low-carbon power system configurations by applying carbon taxation and lowering financing costs

Due to the capital-intensive characteristics of the power sector, the weighted average cost of capital (WACC) is one of the cost-variables that has the greatest impact on LCOE, and thus on the least-cost energy system mix (Simshauser, 2014). Several barriers (economic, financial, market or technical) make the costs of capital vary across countries. In a structured review of multidimensional barriers impeding the penetration of RET, Painuly (2001) identified major economic and financial barriers for developed and developing countries. He found that high capital costs were triggered by high interest rates, scarcity or lack of access to affordable capital, governmental policies on the cost of capital, risk perception by financial institutions, and macro-economic parameters such as inflation rate. Another group found that WACC is influenced by inherent systemic risk in the market and risk perception by investors (Schmidt, Born and Schneider, 2012; Schmidt, 2014).

The WACC used for power expansion planning differs widely between high-income and lower-income countries. For example, the WACC for solar photovoltaics in Japan is 3.7%, much lower than Madagascar’s 29% (Ondraczek, Komendantova and Patt, 2015). Paper II addresses selected cost-variables that influence increased shares of renewable energy. Additionally, the effects of carbon taxes on long term investments are analyzed in conjunction with WACC scenarios. Results of the analysis delineate ranges of carbon taxes which lead to emissions reductions under different WACC scenarios. These results are important for designing climate change mitigation policy in Bolivia. Key model assumptions, limitations and delimitations A sub-regional representation of the power sector in four main nodes (see Figure 8 in Section 3.4) was used to capture regional differences in demand, installed capacity, and energy resources (illustrated Paper II). Additionally, existing mini-grids were added as two separate sub-systems (all mini-grids in the north separated from the mini-grids in the south). Capacity expansion in decentralized systems was not evaluated in this study. However, the plan to connect existing mini-grids to the main grid was introduced in the model. No dynamic trade interactions with other countries were included. Currently, Bolivia is self-sufficient in electricity

Chapter 4. Methodological approach and modelling tools

production. Despite several planned investments to export electricity, such projects were not included in this study but analyzed separately in Paper IV. The selected modelling period was from 2013 to 2040, which allows observation of the effects of new investments after the retirement of existing infrastructure. Existing generation capacity was incorporated as residual capacity into the model baseline. Additionally, medium-term commissioned projects were added, while long-term planned projects were added only if these were part of the cost-optimal solution. Planned medium-term investments in transmission infrastructure to connect the four main nodes were included as described by Comité Nacional de Despacho de Carga (2012). Further long-term connections were optimized. Because domestic gas production costs have a direct effect on the electricity generated by gas-fired technologies, the natural gas production chain was modelled in detail. Certified proven, probable, and possible reserves were introduced into the model along with capital and operational costs for developing wells based on Chávez-Rodríguez et al. (2016). Investments in exploration and well-developing are higher for possible reserves (mostly unconventional shale gas) than probable reserves, and also higher than proven reserves. Therefore, production costs for natural gas varied depending on how much natural gas could be extracted and the need for developing additional wells. Gas export agreements with Brazil and Argentina were introduced in the model until the fulfilment of the current agreements in 2026 and 2019 respectively. Future exports were subject to cost optimization with an upper capacity limit equal to the existing pipeline capacity. If the production cost (together with transportation and taxes) of natural gas was higher than the gas export price, and if such natural exports contributed to minimize the NPV of the system as whole, then gas exports occurred. Gas export prices were projected using a simple econometric relation with international oil prices (current contractual export prices are indexed to international spot prices) for which official projections from the West Texas International oil price were taken from (EIA, 2016). WACC and carbon tax scenarios

In the model developed for Paper II, the discount rate was based on the “Guidelines on the Assessment of Investment Analysis” by the CDM Executive board. These suggest the discount rate to be used for the analysis of CDM projects should be equal to the WACC representative of the country (CDM, 2011). The WACC has an effect on system development, and it may be influenced for strategic reasons like moving an economy to

Chapter 4. Methodological approach and modelling tools

low carbon development. Therefore, Paper II, explored hypothetical WACC scenarios different from the mandated values (12% set by Ministerial Resolution 01/2000). This study contributes to the debate about the impact of lowering financing cost and “de-risking” renewable energy investments to emissions mitigation.

Because appropriate adjustments for opportunity costs and risk cannot be precisely estimated (Steckel and Jakob, 2018), several scenarios were developed for comparison. The base scenario considered the current WACC of 12% and other scenarios ranging from 5 to 15%. In total, five WACC (discount rate) scenarios of 5%, 8%, 10%, 12%, and 15% were studied. The importance of financing costs for power systems decarbonization have often been overlooked in global assessments as previously pointed (Schmidt, 2014; Ondraczek, Komendantova and Patt, 2015), therefore this aspect is one of the main contributions of this study.

Carbon taxation was selected as the instrument of emissions mitigation, and it was implemented as a surcharge on fossil fuel consumption proportional to the quantity of carbon dioxide emitted when burned. A set of scenarios were explored to assess the effect of different carbon tax values on the long-term power generation decarbonization. In total, twelve carbon tax scenarios were modelled with values of 0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 US$/tCO2 and combined with the four WACC scenarios, for a total of 60 combined scenarios. To alleviate the uncertainty of demand projections, fuel costs, and the steady reductions of renewable capital costs, sensitivity scenarios were performed to quantify the effects on the model results. Two scenarios for demand were carried with +10% and -10% of the reference electricity demand projections; similarly, variations of +10% and -10% were introduced for natural gas and diesel market prices. Lastly, projections of renewable learning curves (also known as experience curves) were taken from the World Energy Outlook to define two additional scenarios (optimistic and pessimistic) for renewable technology costs (IEA, 2019).

4.2.3 Exploring Bolivia’s potential cross-border electricity trade Historically, hydropower has been the foundation of the development of the power sector in South America (SA), currently accounting for as much as the 65% of its total electricity generation (IRENA, 2016; IEA, 2019).

Chapter 4. Methodological approach and modelling tools

Hydropower potential in SA has large geographic variability, with large runoff potential in the Amazon region and large elevation potential in the tropical high plateau regions (Zhou et al., 2015). Despite this large hydropower potential, not all countries can make use of this resource because of technical and economic constraints along with other environmental, ecological, socio-economic and geopolitical restrictions (Zhou et al., 2015; Sovacool and Walter, 2019). When comparing the exploitable hydropower potential with the electricity demand in countries in SA, Chile, for example, has nearly enough exploitable potential to meet its domestic demand completely (Zhou et al., 2015). Other countries such as Bolivia have an exploitable hydropower potential larger than their domestic demand. Cross-border electricity trade can allow countries to gain access to a more diversified portfolio of power generation plants. It can also facilitate the incorporation of high shares of intermittent renewable energy and alleviate the effects of climate change (Bahar and Sauvage, 2013). In SA, international trade could facilitate a cost-optimal use of hydropower resources; this, in addition to existing dam-reservoir storage capacities, could ease the incorporation of other intermittent renewable energy. Despite this potential, limited trade occurs in SA because there is little long-distance transmission infrastructure and no regulatory cross-border electricity trading system. Only 5% of the electricity produced in SA countries is traded, with Brazil being the largest importer (CIER, 2018). To meet growing demands in countries with future energy deficits, several bi-national hydropower generation projects and interconnection lines began to be included in national development plans (IHA, 2019). The Bolivian government identified three strategic large-scale hydropower dams in its National Electricity Plan 2025 along with their electricity export potential. The three are Cachuela Esperanza, with 990 MW installed capacity; El Bala, with 1680 MW; and the Río Grande hydropower complex, with 2882 MW. Investments for these projects are estimated to reach up to US$ 8.8 billion (Ministry of Hydrocarbons and Energy, 2014). The National Power Plan highlights the need for additional studies to estimate investments in long-distance transmission lines (between 1500 and 2500 km, approximately) to connect these strategic projects to their main load destinations abroad, namely the Brazilian southeast subsystem and Argentina (Ministry of Hydrocarbons and Energy, 2014). Since Bolivia has planned for other generation projects to serve its domestic demand, the energy generated by the dams could be used almost exclusively for export. Despite Bolivia’s ambitious plans for hydropower

Chapter 4. Methodological approach and modelling tools

exports, hydrological complementarities with its main export markets have still not been studied. Moreover, other countries in SA (notably Peru and Paraguay) are also considering using large hydropower plants for their own power capacity expansion and trade (IADB, 2016), becoming potential competitors with Bolivia in the long term. In Paper IV, the OSeMOSYS South America Model Base (SAMBA) was used to examine the trade potential of Bolivia and its role in facilitating the deployment of renewable energy in SA. The SAMBA model includes ten countries in SA: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay and Venezuela. Each power system is modelled separately with existing and potential trade links between each other (Pinto de Moura, Legey and Howells, 2018). Modifications to that model were introduced to increase the detail of the power system of Bolivia. In addition, an updated database with its current power capacity and commissioned projects in the medium term were included. Note that the model does not take decentralized generation into account. For every country, 15 electricity generation technologies were considered in the analysis with aggregated data on installed capacity. Domestic transmission and distribution capacity and cross-border transmission capacities were also included in the model. Currently, Bolivia does not trade (import or export) electricity with its neighbors. For the modelling period, transmission capacity to export hydroelectricity was added with an upper limit equivalent to the capacity of the new hydropower projects (thus limiting to a 100% surplus export capacity).

Key model assumptions, limitations and delimitations

As mentioned in Section 4.1.2, the optimization model assumes perfect market competition, meaning the electricity is traded at marginal production cost and distorting effects such as market power do not occur. The model also assumes perfect foresight, meaning that decisions in the market are based on perfect information on future parameters influencing the optimization. Therefore, the effects of delays in the deployment of committed power plants (which are common for large hydropower plants) and changes in any of the cost parameters (such as fossil-fuel prices) were not investigated. The intermittency of renewable technologies was assumed to be compensated with the dispatch of more flexible technologies. Further grid stability analysis due to increased trade is necessary, but beyond the scope of this study. Although important, the effects of climate variability on hydropower generation were not

Chapter 4. Methodological approach and modelling tools

investigated. Other important factors beyond the scope of the analysis include environmental and ecological effects, economic feasibility, and macro-economic effects.

Hydropower trade Scenarios

Four export scenarios were considered in Paper IV, with each scenario modelling a combination of the three identified large hydropower projects (see Table 3 and Figure 18). These scenarios were previously assessed in the Power Expansion Plan of Bolivia (Plan Eléctrico, PE), for which the electricity export potential was estimated assuming that the hydropower energy would primarily serve domestic demand and surplus electricity could be exported (Ministry of Hydrocarbons and Energy, 2014). In the PE analysis, however, compatibilities or interactions with importing countries were not studied. Therefore, Paper IV expands the analysis using the OSeMOSYS SAMBA model in which the trade balance between countries was cost-optimized. Additionally, the paper assesses to what extent hydropower trade affects investments on power generation in neighboring countries.

Table 3. Strategic large hy dropower export scenarios planned by the gov ernment of Boliv ia.

Scenario Strategic large hydropower plants Total Average Capacity, MW capacity factor 1 Cachuela Esperanza (990 MW) and Río Grande 3872 0.49 Hy dro Power Complex (2882 MW)

2 Cachuela Esperanza (990 MW) and El Bala (1680 2670 0.79 MW)

3 Cachuela Esperanza (990 MW), El Bala (1680 MW) 3220 0.74 and part of the Río Grande Hy dro Power Complex (550 MW)

4 Río Grande Hy dro Power Complex (2882 MW) 2882 0.43

4 3.272 3.22 2.882 2.67 3 1 - El Bala 2

GW 2 - Cachuela Esperanza

1 3- Rio Grande

0 Sc1 Sc2 Sc3 Sc4 Figure 18. Hydropower export scenarios

Chapter 4. Methodological approach and modelling tools

Bargaining power To better understand the electricity trade relationships of Bolivia and its neighboring countries, its bargaining power was calculated following a cooperative games approach (as previously implemented in electricity trade scenarios in South America by Pinto de Moura et al. (2018). A bargaining situation is defined as any exchange situation in which a set of players (countries in this case) can engage in a mutually beneficial trade, but have conflicting interests over exactly how to cooperate (Muthoo, 2000). Because a voluntary exchange situation generates a gain or surplus value (or at least it is expected to do so due to different valuations between buyers and suppliers), the player with the higher bargaining power is expected to take the greater share of this surplus value (Harwick, 2013). The bargaining power of suppliers refers to the pressure that suppliers can put on buyers by raising their prices, reducing the availability or lowering the quality of their products (Porter, 2008). Determining bargaining power is a standard part of a business strategy. In cooperative games, the Shapley value is one way to calculate a fair distribution of the total gains to the players, assuming that they all collaborate (Ichiishi, 1983). This value can be obtained by calculating the expected contribution of each player to the total surplus value of the grand coalition (which is formed by all players participating in the game). The calculated value represents a player's bargaining power and represents the fair amount the player should expect from the shared surplus value (Naveiro, Mendes and Neves Medina, 2008). The Shapley value can be calculated with different methods. In this research, the method proposed by Straffin (1993) was used, in which the calculation was made for a particular player i and compute information on how often and how much each player contributes to the grand coalition Following this methodology, the bargaining power was calculated when a player i over a n number of players was added to a coalition S of size s to form a grand coalition. The value of the coalition v(S) is the sum of all the expected gains that the members of S can obtain by cooperation. The value that player i contributes to the coalition is represented by the difference of the value of the coalition minus the value of the coalition, excluding the value contributed by the player i. Mathematically, this is equivalent to v(S)- v(S-i). Since the contribution to the value of the coalition depends on the order in which the players join the coalition, the contribution of player i occurs for those orderings in which i is preceded by the (s-1) other players in S and followed by the other (n-s) players not joining coalition S. The

Chapter 4. Methodological approach and modelling tools

number of combinations for this ordering can be mathematically represented by (s-1)!(n-s)!. Hence, the Shapley value for the player i is represented by Equation 5: 2푛−1 1 휑 = ∙ ∑ (푠 − 1)! ∙ (푛 − 푠)! ∙ [푣(푆) − 푣(푆 − 𝑖)] Equation 5 푖 푛! 푖 ∈ 푆 The calculation of the Shapley value is made for all scenarios in which 10 countries (players) participate. To determine the bargaining power of a given country, its contribution is calculated over a total number of possible coalitions equal to 512 or 29. The Shapley values were calculated using results of the optimization of the accumulated trade (imports and exports in TWh) over the modelling period 2013–2058, where the grand coalition gain is the sum of the electricity traded across all international transmission lines in the continent (Pinto de Moura et al., 2017). Therefore, the more a country contributes to the cross-border trade, the higher its bargaining power. Countries that do not add value to any coalition (non-trading countries) have a Shapley value of zero. Note that these players do not change the Shapley value of other players (Straffin, 1993).

4.2.4 Increasing sugarcane agricultural yield and use of its biomass residues for electricity and 2G biofuel production

In order to produce the biofuel needed to meet mandated production and blending targets in an environmentally benign way, several aspects of the ethanol production chains must be assessed. There is a large potential to enhance the environmental benefits of bioenergy production by optimizing the agricultural production chain (by increasing yields, restricting biomass residues burning and using abandoned residues/wastes to produce bioenergy, among other strategies) (Bordonal et al. 2018; Seabra et al. 2011; Macedo et al. 2008). In addition to higher energy yields, emissions reductions can be achieved when biomass residues (i.e. surplus bagasse and sugarcane residues tops and trash) are used for electricity generation and/or second-generation (2G) ethanol production (Dias, Junqueira, Jesus, et al., 2012; Dias et al., 2013; Khatiwada et al., 2016; Birru, Erlich and Martin, 2019).

Chapter 4. Methodological approach and modelling tools

Increasing sugarcane yields can be achieved by reducing growth limiting factors (water and nutrients) and growth reducing factors (pests, diseases, weeds, insects and pollutants) (van Dijk, et al. 2017; van Ittersum, et al. 2013; van Ittersum et al. 1997). In the industrial production of ethanol, higher energy yields can be achieved by using biomass residues in efficient/advanced cogeneration to generate bioelectricity and/or by the biochemical conversion of biomass into 2G ethanol (Dias, Cunha, et al., 2011; Dias, Da Cunha, et al., 2011; Seabra et al., 2011; Dias, Junqueira, Cavalett, et al., 2012; World Energy Council – Bioenergy, 2017).

In Paper V, an innovative framework was designed to examine 1) potential yield improvements in sugarcane production systems using a tiered approach of agricultural management levels/practices, and 2) alternative uses of sugarcane biomass residues for bioelectricity and 2G ethanol production in biorefineries. The study performed techno-economic optimization to evaluate cost-optimal upgrades of agricultural production systems and to evaluate the location and size of different biorefinery configurations. A lifecycle approach was used to quantify emissions from feedstock production to bioenergy generation and to estimate emission savings from the least-cost solution. In addition, a multisource nexus approach was used to model interactions with the water, energy and land- use systems. Key model components

Model time-frame and spatial zonation The selected modelling timeframe is from 2013 to 2030 with a temporal resolution of one year. The base year (2013) was selected as the year of the latest National Agricultural Census. The end year is set in 2030, five years after the mandated production targets. Two zonation schemes were combined to divide the territory of Bolivia into a manageable number of regions aiming to capture similar hydrological, climatic, physiographic and agricultural activity characteristics (Figure 19). Regions not suitable for sugarcane production were excluded from this analysis.

Chapter 4. Methodological approach and modelling tools

Figure 19. Harmonized zonation schemes. a. Ov erlay of the 96 watersheds of the Water Balance of Boliv ia (Ministerio de Medio Ambiente y Agua, 2017) with the 19 agro-productiv e zones f rom the National Irrigation Plan (Ministerio del Agua, 2007). b. Zonation scheme with 27 harmonized regions. Figure reprinted f rom Paper V.

Energy demand projections for the Transport sector

In the model, demands were exogenously defined and price-inelastic. Demand projections were generated using correlations of historical data of end-products with macro-level growth drivers such as gross domestic product, GDP, and population. Projections of fuel demands for the transport sector were based on an accounting model developed in Paper III9. Using a bottom-up approach, energy demands were allocated to a set of transport modalities (e.g. private and public transport of passengers, freight transport) for a given base year (2013). Projections to 2030 were generated using top-down drivers (population and GDP projections). For a more detailed description, the reader is referred to Paper III and Paper V.

Two demand scenarios were modelled (Figure 20). The baseline scenario used GDP projections deriving from historical trends, and the alternative scenario used projections of higher GDP growth. For both scenarios, government targets for ethanol production in 2025 were introduced. Note that volumes of the blend increase steadily while volumes of pure gasoline decline in the period 2018–2025. For the period 2025–2030, ethanol demands are projected in both scenarios assuming that pure gasoline is no longer commercialized by 2030 and is replaced by E25.

9 Residential, commercial, industrial, agriculture and mining sectors were modelled separately generating projections per fuel type.

Chapter 4. Methodological approach and modelling tools

Energy demand of gasoline-engine cars

80 30% Gasoline- Alternative

60 Gasoline - Baseline 20% 40 Avoided gasoline PJ imports-alternative 10% 20 Avoided gasoline

imports-baseline Ethanol v/v blend % Ethanol 0 0% % Ethanol in blend 2013 2018 2023 2028

Figure 20. Comparisson of gasoline and ethanol demand projections for baseline and alternative scenarios. Figure reprinted f rom Paper V.

Modelling sugarcane agriculture production The four main processes of the sugarcane agricultural production chain were modelled (Figure 21). Land-preparation and planting of sugarcane commonly occur every 5 or 6 years, and after this period (called the ratoon period), sugarcane is replanted. Agricultural operations of fertilization, irrigation, and application of agrochemicals occur every year and these are accounted for in the model per hectare of cultivated land. Harvesting operations and transportation of sugarcane to the mill occur every year and these are accounted for in the model per ton of sugarcane produced.

Figure 21. Scheme of the processes modelled in the agricultural production unit. Figure reprinted f rom Paper V.

In developing nations such as Bolivia, a variety of agricultural production and management systems typically co-exist in large geographical areas (Bernués and Herrero, 2008; de Oliveira Bordonal et al., 2018). Therefore, a characterization of agriculture production systems is necessary to evaluate opportunities for agriculture intensification. Typification of three agriculture management and input levels (high, intermediate and low) was adopted from the Global Agro-Ecological Zones (GAEZ) project (Fischer et al., 2012). In the GAEZ approach, soil (and terrain) characteristics and agro-climatic yield-reducing and yield-limiting factors are associated with crop-productivity at three generic levels of agricultural inputs and management assumptions. Table 4 summarizes the characteristics of each agriculture production system according to Fischer et al. (2012).

Chapter 4. Methodological approach and modelling tools

Table 4. Agriculture management input lev els.

Features High Intermediate Low Management assumption Adv anced, f ully Improv ed, semi- Traditional, no mechanized mechanized mechanization Production assumption Commercial Partially market- Subsistence production. oriented. oriented. Cultiv ars High-y ielding Improv ed v arieties Traditional v arieties Labour intensity Low Medium Intensiv e Fertilizers, pesticides, Ideally implemented Used to some extent Minimal herbicides and weed control Water supply Rainf ed or irrigated Rainf ed or irrigated Rainf ed or irrigated

A classification methodology was used to differentiate three levels of mechanization in sugarcane crops in Bolivia using micro-data from the Agricultural Census. For a detailed description of the methodology and results of the classifications, the reader is referred to the Supplementary Materials in Paper V. The outputs of the classification were used to calculate the areas of each agriculture production system in each region of the model for the base year. The water requirements for sugarcane irrigation were estimated following the guidelines for computing crop water requirements from FAO (Paper 56–Irrigation and drainage) (Allen, Pereira, Raes, & Smith, 1998). The crop water needs are estimated on a monthly basis using the crop- coefficient approach (Equation 6) in which the effect of weather conditions are incorporated into the reference crop evapotranspiration (ETo) and crop characteristics into the k c coefficient. Equation 6 퐸푇푐 = 퐸푇표 ∙ 푘푐

Estimates of reference crop evapotranspiration were carried out in previous work (Ministerio de Medio Ambiente y Agua (MMAyA), 2017). Results for the 퐸푇표 from the WBB at basin-level and specific to each land-cover type were aggregated into the 27 regions of our model (and for each land- cover type modelled). Indicative nominal values for the single crop coefficient k c for sugarcane in ratoon were adopted from (Allen et al., 1998). Irrigation from superficial and underground sources were considered. Average electricity demand for irrigation for pumping water (superficial or underground possibilities), water efficiency and irrigation technology costs were adopted from Kahil et al. (2018). Micro-data from the census was used to classify current agriculture production systems into three agriculture management and input levels (high, intermediate and low). In addition to input levels, agriculture

Chapter 4. Methodological approach and modelling tools

production was classified into rain-fed and irrigated production. The input- level categories were adopted from the GAEZ project (Fischer et al., 2012). Each input level had its characteristics in crop production associated with a theoretical yield and production costs. Production with high inputs will require higher costs than production with intermediate and low inputs, but will provide the highest yield.

Gridded data (5x5 degree resolution) of attainable agro-climatic yields of sugarcane production under each of the six management input levels was weighted-averaged in each of the 27 regions modelled. Figure 22 illustrates the differences in yields for each agriculture management level. The highest agro-climatically attainable yield under irrigated and rainfed conditions are found in Regions 12 and 24 with 132 tc/ha and 103 tc/ha respectively. The lowest agro-climatically attainable yield under irrigated agriculture is found in Region 23 with 36 tc/ha, and the lowest yield under rainfed conditions is found in Region 21 with 11 tc/ha. Note that the regions at the highest elevations with colder climates (Region 18, 19, 21 and 23) have the lowest soil suitability for sugarcane production and therefore the lowest agro-climatically attainable yields, even when cultivated with high-input agriculture.

IRRIGATED

RAINFED

Figure 22. Agroclimatic yields for rainfed and irrigated conditions Weighted av eraged v alues f or high, intermediate and low inputs in tonne/ha. Figure reprinted f rom Paper V.

Chapter 4. Methodological approach and modelling tools

Industrial production modelling Before the promulgation of the Law for Vegetable Derived Additives, sugarcane was used exclusively for sugar production. Today, four of the seven sugar factories (which also own most of the distillation units) have expanded their milling and distillation capacities and implemented molecular sieves to dehydrate ethanol to meet the production of contracted ethanol volumes. Other recently implemented biorefinery plants in Aguai and Guabirá produce anhydrous ethanol as the main product from sugarcane juice. For simplicity, existing sugar mills were modelled as the configuration illustrated in Figure 23. Capacity upgrades and insertion of molecular sieves in the period 2013–2020 were inserted in the model. For the modelled sugarcane mills, refined sugar was the primary product, and hydrated ethanol was a secondary product deriving from molasses fermentation and distillation. The sugarcane mill produced heat and electricity to meet its internal energy requirements using a conventional bagasse-based combined heat and power (CHP) plant. Excess electricity was exported to the grid, but not all the bagasse was used efficiently. Although upgrades in CHP could be evaluated (to increase the efficiency of electricity production or to generate surplus bagasse for 2G ethanol), these possibilities were not assessed in the study. The conventional sugar refinery model does not include sugarcane stalk trash in the process. Ethanol dehydration is a process inserted to produce anhydrous ethanol. Data on the existing capacity of the different sugar factories and distilleries were collected per facility and accounted at the regional level. The capacities of existing sugar factories and ethanol refineries in each region were aggregated. For simplicity, existing and new investments in sugar factories produced hydrated ethanol as a sub-product from molasses. New investments in sugar factories were set to cost-optimization (if current installed capacity was insufficient to supply future sugar demands). New investments in alternative biorefinery configurations were required to supply the increasing ethanol demands in 2030. Figure 23 shows the evaluated biorefinery configurations. Each configuration used cane juice to produce ethanol as the primary product. Operational data and costs from a specialized optimization model by Dias, Cunha, et al., (2011) and Dias et al. (2013) were adopted in this model. The first configuration (Biorefinery EtOH 1G) produced first-generation ethanol only, and biomass was used for heat and electricity generation through conventional CHP. The second configuration (Biorefinery EtOH1G+electricity)

Chapter 4. Methodological approach and modelling tools

produced 1G ethanol and used efficient CHP, generating electricity surplus from bagasse and trash combustion to export to the grid. In the third configuration (Biorefinery EtOH 1G2G), efficient CHP was also employed, and bagasse and trash residues were further converted into second-generation ethanol (2G). Conversion efficiencies and techno- economic information for the three technologies are presented in Paper V.

Figure 23. Biorefinery technological options. a. Bioref inery f or 1G ethanol production using sugarcane molasses, conv entional CHP plant (no sugarcane trash recov ery ). b. Biorefinery for 1G ethanol production using sugarcane juice, conv entional CHP plant (no sugarcane trash recov ery ). c. Bioref inery f or 1G ethanol production with ef f icient CHP and export of surplus electricity generation (50% of sugarcane trash recov ery ). d. Bioref inery for 1G and 2G ethanol production with ef f icient CHP (50% of sugarcane trash recov ery ). Figure reprinted from Paper V.

Net energy analysis of ethanol production For each biorefinery configuration, the lifecycle cost, emissions and energy use were compared. The feedstock supply chain (agricultural production) was incorporated in the assessment for high, intermediate and low input agricultural management levels. The timeframe for the LCA was 15 years, which accounts for the average lifetime of the machinery and accounts for three ratoon periods of five years each (Figure 24).

Chapter 4. Methodological approach and modelling tools

Three indicators were used to compare the energy balance of ethanol production for the four technology options, namely the net energy value (NEV), net renewable energy value (NREV), energy ratio (ER). The NEV of bioethanol is the difference between the energy content of the bioethanol produced and the total primary energy inputs (fossil plus renewable) in the entire biofuel production chain. A positive NEV implies that more energy is extracted from the fuel than is consumed during the production of the fuel. NREV is similar to NEV, the major difference being that only fossil fuel consumption is considered as input. A positive NREV implies that more energy is extracted from the fuel than the amount of fossil energy consumed. ER is the ratio of the low heating value (LHV) of ethanol to the fossil energy required to produce it (Khatiwada and Silveira, 2017). Table 5 describes the formulas used to calculate each indicator.

Figure 24. Schematic of the system boundaries for the lifecycle assesment. Figure reprinted f rom Paper V.

Table 5. Formulas f or each energy balance indicator,

Indicator Units Formula

NEV (MJ/l) NEV = LHVEtOH – (ERenewable + Efossil )

NREV (MJ/l) NREV = LHVEtOH – Efossil

ER (MJtot /MJfossil fuel ) ER = (EEtOH + Eelectricity surplus) / Efossil

Interactions with other systems Figure 25 illustrates the interconnections of the systems modelled and the flows of energy/water/land resources to secondary, tertiary, and final demands of commodity products. For illustration purposes, the energy

Chapter 4. Methodological approach and modelling tools

system was simplified to represent the extraction and transformation of fossil and renewable resources to generate electricity, diesel and gasoline. The complete system representation is presented in Paper II. If domestic resources are not sufficient to meet the domestic demands, the model can import these fuels at market prices. The energy model from Paper II and the demand projections from Paper III were integrated into a single model. From the energy system, electricity is supplied to the water system for water pumping for the domestic sector. Electricity is also supplied to the land-use system for pumping water for irrigation. Electricity surplus generated by the biorefinery is connected to the national grid. Diesel is supplied to the land-use system to supply machinery use, such as tractors, harvesters, and mechanical seeders.

The representation of the water system uses results from the recently published Water Balance of Bolivia (WBB) (Ministerio de Hidrocarburos, 2019). WBB is a hydrogeological and water systems model developed using the Water Evaluation and Planning System software (WEAP). The hydrological model was developed by the Stockholm Environment Institute in collaboration with the Ministry of Environment and Water of Bolivia. Released in 2017, the WBB is the first national effort that unifies diverse information about water supply in Bolivia. The model uses data from 1980 to 2016 to generate water balances. The water balance for each region was calculated by overlaying the water inflows and outflows of the basins contained. The water balance is specific for each land cover type. The components represented are precipitation, evapotranspiration, groundwater recharge and run-off water, and these are introduced as water to area ratios. Additional components from the water balance were aggregated into run-off water flow, namely, surface runoff, interflow, base flow and river flood inflow. Results of the hydrological model for the years 2013, 2014, 2015 and 2016 were introduced in the integrated CLEWs model.

The effects of climate change will affect the water ratios in future years, however, these were beyond the scope of this project. Surface and groundwater are available in each region and are used to supply domestic water use, crops irrigation and water for livestock. Although variations in precipitation influence changes in rainfed yields, these were not studied. (See Sridharan et al. (2019) for further exploration of these connections together with climate scenarios.)

Chapter 4. Methodological approach and modelling tools

Figure 25. Simplified representation of the systems modelled and the nexus interactions. Figure reprinted f rom Paper V.

4.2.5 Energy demand projections In Paper III, an accounting energy demand model consisting of six sectoral sub-models was developed to generate energy demand projections for 2035. The objective was to provide insights about the actual use of energy and the effect of targets on fuel switching and energy-saving measures on future energy demand. Results of demand projections were introduced in Paper II and Paper V. A mix of bottom-up and top-down approaches were used to model energy demand for the residential,

Chapter 4. Methodological approach and modelling tools

transport, commercial, industrial, agriculture and mining sectors. The LEAP modelling tool was used to develop the energy demand model and scenarios. Historical data from the National Energy Balance of Bolivia 2000–2014 was used (at the time of this writing, these were the latest available for Bolivia). Each sector was subdivided into a set of sub-sectors for which further data was available. Energy demand in every sector or subsector was linked to key growth assumptions about different “activity units”. For example, the unit of activity governing residential energy demand was the population in each year. Official projections of key activity units were used to generate the projection. Every unit of activity had a set of technologies (with respective energy intensities and efficiencies) producing a unit of service. For example, to produce a unit of passenger-km per year (units of transport service per year), different modalities of public transport are available, from which a large bus with a full capacity of 50 passengers would be more efficient than a minibus with a full capacity of 10 passengers. At the same time, every technology is disaggregated into fuel-use possibilities. For example, cookstoves using LNG, charcoal, and electricity are available to produce energy to cook a certain number of meals per year. Each technology can be used for cooking, but electric cookstoves are more energy efficient than charcoal cookstoves. For sectors for which little information was available, such as the commercial sector, historical data on annual growth on energy demand was correlated with annual growth of the gross value-added (GVA) of the commercial sector. Energy requirements of planned energy-intensive projects were accounted for in the model and introduced separately. Additionally, medium-term government fuel-switch targets (such as the number of gas-pipeline residential connections per year or number of car engine conversions from gasoline to compressed gas) were introduced in a fuel-switch scenario. Lastly, potential measures for energy savings deriving from data from a report on energy audits were used for a scenario about energy savings (Ministerio de Hidrocarburos y Energía, 2011).

Chapter 4. Methodological approach and modelling tools

Figure 26. Radial representation of the energy demand model. BM: Biomass; CNG: Compressed Natural Gas; DO: Diesel Oil; EE: Electricity ; GL: Gasoline; KE: Kerosene; LPG: Liquif ied Petroleum NG: Natural Gas; RW: Solar. Reprinted f rom Paper III.

Chapter 5. Results and discussion

5 Results and discussion

This chapter is divided in four sub-sections, each of which compiles the modelling results and insights gained from every analysis individually. This chapter compiles results presented and discussed in detail the appended papers with the aim to address the thesis research questions posed in Section 1.1.2.

5.1 The role of decentralized systems and renewable energy for achieving universal access to electricity The economic competitiveness of solar, diesel or hybrid micro-grids depends largely on location-specific data, such as electricity demand, solar resource, diesel cost at the site, among other considerations. In all instances modelled, solar micro-grids have a higher levelized cost of electricity (LCOE) than hybrid and diesel micro-grids because these require a large battery to achieve the same reliability, and therefore require higher investment costs. When international diesel prices are applied, hybrid micro-grids are more cost-competitive than diesel micro-grids. However, when diesel subsidies are applied (such as the current subsidized price of 0.53 US$/liter), diesel micro-grids are more competitive than hybrid micro-grids. This is shown in Figure 27 with boxplots of the LCOE results obtained for all instances modelled when international diesel prices (no subsidy) and subsidized diesel prices are applied. To achieve the same reliability, hybrid micro-grids require larger capital investments than diesel micro-grids, while diesel micro-grids require higher variable costs than hybrid micro-grids. Taken together, these results show that current diesel subsidies applied to the power sector in Bolivia are an effective economic disincentives to technological alternatives using renewable energy, such as hybrid micro-grids.

Chapter 5. Results and discussion

Figure 27. LCOE results for the scenarios with/without diesel subsidy Boxplots of the LCOE of solar-only , hy brid and diesel-only micro-grids f or all instances modelled with international diesel price and subsidized diesel costs. Note that results for solar-battery microgrids are not inf luenced by diesel prices.

To meet Bolivia’s universal access to electricity in 2025, results at the national level show that hybrid micro-grids could supply electricity to nearly 2% of the newly electrified population. Of this population, about 6% live within 10 km of high-voltage grid transmission lines, and 36% live within 10 km of off-grid medium voltage lines. The remainder of the

Chapter 5. Results and discussion

population lives at father away from the existing transmission infrastructure. Other decentralized alternatives such as micro-grid hydropower, diesel stand-alone and PV stand-alone could electrify 0.2%, 3.9% and 16.3% of the newly electrified population by 2025, respectively. Given the relatively extensive area covered by the grid, a large number of new connections derive from grid extension (76% of the newly electrified population). 99.4% of the settlements electrified by grid extension are located within 10 km from the current grid network. Figure 28 illustrates results from the geospatial electrification model for each of the 19,280 communities of Bolivia.

The investment cost per household varies largely depending on the electrification technology. For households electrified with grid-extension, the investment cost is proportional to the distance to the transmission lines, and decreases with increasing population density. The average investment costs for grid-extension, micro-grids hybrid, micro-grid hydropower, stand-alone diesel and stand-alone PV are US$ 1159, 1134, 1054, 241, and 1380 per household. Total investments in new grid connections in Bolivia will require a sum of US$973 million. The total investments for mini-grid systems are calculated to be US$10 million, for micro-grid systems, US$26 million, and for stand-alone systems, US$258 million.

The least-cost optimization results also show that 87.8% of the total electrified population could be electrified by grid-extension by 2025 (compared to 98.3% in 2012). This means that a larger percentage of the electrified population will gain access by decentralized generation (12.2% of the total population electrified by decentralized systems in 2025 compared to 1.7% in 2012). To meet its residential demand and reach its electrification targets by 2025, Bolivia will need to increase grid capacity by 251 MW and off-grid capacity by 59 MW. Assuming a future grid generation mix from Paper II, together with the results presented in this analysis, 62% of the additional generation capacity (grid and off-grid) would have to derive from renewable technologies by 2025.

Chapter 5. Results and discussion

Figure 28. Geographical representation of the least-cost electrification technology at the community level. Micro-grids are plotted with circle-shaped sy mbols f or diesel (red), hy dro (cyan), PV (orange) and hy brid (y ellow). Grid extension with a cross (light blue), stand-alone diesel with a cross (light red) and stand-

alone PV with cross (light green) sy mbols. Figure reprinted f rom Paper I.

5.1.1 Influence of diesel subsidies and capital costs of micro- grid components in the cost-competitiveness of micro-grids

To highlight the results of the cost scenarios (see scenario details in Section 4.2.1), three indicators are reported for the communities that belong to the population threshold considered for micro-grids (communities with 50 to 550 households with no connection to the grid in the base year). Number of new connections, new capacity added, and investment costs per type of electrification technology are evaluated separately for each scenario. Figure 29.a compares the results for new connections; Figure 30.b, new capacity, and Figure 29.c, investments per technology type.

For the scenarios with low and high capital cost (SC1 and SC2 respectively), the smallest settlements switch from hybrid micro-grids to

Chapter 5. Results and discussion

stand-alone when capital costs increase; the opposite occurs when there is a reduction in capital costs. This marginal difference, however, does not account for reliability differences between stand-alone and micro-grid systems. For the first diesel price subsidy scenario (SC3), there is a slight increase in the cost-competitiveness of hybrid micro-grids and a significant increase for diesel stand-alone compared to the reference scenario. For the highest diesel subsidy scenario (SC4), diesel-fueled technologies gain cost- competitiveness over other renewable technologies. This switch from micro-grid to stand-alone systems is explained by marginal differences in the LCOE and does not consider differences in reliability. Although stand- alone systems are more expensive per unit of power capacity, these do not require expensive distribution lines and connection costs compared to micro-grids. Further considerations about reliability should be introduced to stand-alone systems in the OnSSET algorithms for an effective cost- comparison with micro-grids, since these provide only limited hours of electricity.

The framework developed in Paper I raises the possibility of identifying, with more technical accuracy, the geographical location in which micro- grids are cost-effective. The results applied to Bolivia (Figure 28) demonstrate that in the studied conditions, hybrid micro-grids are cost- effective in numerous communities. Note that these results are valid only if a diesel subsidy reform occurs. Fossil fuel subsidies are remarkably widespread in developing countries for several socioeconomic reasons (Szabó et al., 2011). For a small economy, a meaningful change in subsidy schemes would consequently produce large macroeconomic effects through economy-wide changes in sectoral relative prices and demands (Coxhead and Grainger, 2018). It may seem counterintuitive to remove fossil fuels in order to foster energy access, but, because fossil fuel subsidies are used to promote affordable energy, capital subsidies for renewable energy could be used instead to compensate for this market distortion (Szabó et al., 2013). Because electrification planning diversifies with the inclusion of decentralized alternatives, different affordability and financing concerns emerge. Further actors should be considered by electricity planners and policymakers to address the entire range of affordability concerns for both grid and off-grid rural consumers. More importantly, better coordination among national stakeholders is needed to develop a local renewable energy industry able to mobilize public finances for rural electrification projects.

Chapter 5. Results and discussion

a) New connections b) New capacity c) Capital Investments

30 150 120 100 100 80 20

60 MW 50 40 10 $million

Thousandhh 20

0 0 0

SC2 SC3 SC1 SC4

REF

SC4 SC1 SC2 SC3

REF

SC2 SC1 SC3 SC4 REF

Grid extension Standalone PV Standalone Diesel Micro-grid Hydro Micro-grid Diesel Micro-grid Hybrid

Figure 29: Summary of the sensitivity analysis results for selected communities. a. New connections per electrif ication technology . b. New capacity added per electrif ication technology. c. Inv estment costs per electrif ication technology in present v alue. Figure reprinted f rom Paper I.

5.1.2 Surrogate models validation Figure 30 shows the plots comparing the predicted and computed values of the LCOE for each surrogate model and the respective statistics (see structure in Section 4.2.1). When looking at the regression diagnosis- testing results, we can conclude that the accuracy of the regression fit is sufficient, with low scores reported for the Mean Absolute Error (MAE) and low scores for the Root Mean Squared Error (RMSE). Specifically for solar-only micro-grids, the models were not able to predict the LCOE as accurately as for the other two models, due to the non-linear characteristics of battery sizing. Looking at the R- squared scores, we find a regression correlation higher than 95% for all models, with the only exemption of solar-only micro-grids with a correlation of 92%. Looking at the Durbin Watson (DW) residual autocorrelation test score, we found no autocorrelation in any of the models (DW = 2 means no autocorrelation). Each coefficient of the regression was found statistically significant, with non-significant standard errors and non-multi-collinearity found with the Variance Inflation Factor (VIF) test (see the complete regression statistics in Paper I Supplementary Materials). These results indicate the surrogate models are able to predict sufficiently the LCOE for rural microgrids for high-level electrification applications without the need for a computationally intensive optimization for each specific case. A detailed comparison of the electrification results between the original OnSSET algorithms and the new proposed methodology is presented in Paper I.

Chapter 5. Results and discussion

Solar+battery microgrids Hybrid microgrids 1 0.5 R2=98.372 0.4 0.8 MAE=1.444 0.4 RMSE=0.047 0.6 0.3 DW=2.054 0.4 R2=92.072 0.3 MAE=11.068 0.2 0.2 RMSE=1.346 DW=1.865 0.2

0 0.1 LCOE US$/kWh predicted, LCOE 0.3 0.5 0.7 0.9 1.1 US$/kWh predicted, LCOE 0.1 0.3 0.5 LCOE computed, US$/kWh LCOE computed, US$/kWh Diesel microgrids

0.5 R2=99.973 MAE=0.637 0.4 RMSE=0.005 DW=1.686 0.3

0.2

0.1

0.1 0.3 0.5 LCOE US$/kWh predicted, LCOE LCOE computed, US$/kWh Figure 30: Comparisson prodicted and computed values for each microgrid configuration

5.2 The role of financing costs and carbon pricing in power systems decarbonization

In Paper II, the future development of the Bolivian power system was evaluated under a variety of carbon tax scenarios with the goal of illustrating an economic phenomenon that has been recognized in similar studies (Schmidt, 2014; Ondraczek, Komendantova and Patt, 2015; Hirth and Steckel, 2016; Creutzig et al., 2017). Results from this study demonstrate that high capital costs, expressed as high WACC, encourage the use of fossil fuels and undermine effective carbon pricing. In other words, when future costs are more highly discounted, the influence of carbon taxes, which are also future costs, decreases. Adding to previous work, Paper II used marginal abatement cost curves to compare the effect of a number of carbon prices over their abatement potential for a number of WACC scenarios. The result of this exercise shows that in order to

Chapter 5. Results and discussion

achieve a deep decarbonization of the power sector of developing and emerging economies, policy instruments to reduce the cost of capital are required to make carbon pricing effective. Long-term power capacity expansion models compare different electricity generation alternatives based on data on available energy resources and future trends in technology and cost. Despite the significant reductions in capital cost of renewable energy, fossil-fueled generation can be significantly cheaper over its lifetime than low-carbon alternatives if access to capital is expensive (Schmidt, 2014). Renewable energy technologies (RET) are characterized by their high upfront capital costs and their low (practically zero) variable cost, as opposed to fossil-fueled power plants, which usually have lower capital costs but often higher variable cost (fuel costs). This difference in cost structure matters, particularly for power systems planning. The levelized cost of electricity (LCOE) is the metric commonly used to compare the lifetime net present costs of different power generation alternatives. If the objective is to minimize costs, the technology with the least LCOE is recommended for investment. To illustrate the effect of WACC on the LCOE, Figure 31 compares the LCOE for combined cycle gas turbine (CCGT), wind power (capacity factor 0.35) and medium-size hydropower (capacity factor 0.5) for WACC scenarios of 5%, 12% and 20%. As WACC values increase, the generation cost increases for all technologies, but at different rates for each cost component. When WACC values are reduced from 20% to 5%, technologies with a high proportion of the upfront capital costs achieve larger reductions of their LCOE than technologies with low upfront costs. This trend can be easily observed for wind and hydropower in this example. For CCGT, capital investment costs represent from 30% to 60% of the total cost depending on the WACC. In contrast, for wind power and medium hydropower, the capital investment cost represents from 80% to 95% of the total costs. When comparing the LCOE at different WACC, wind and hydropower options become 48% and 56% more expensive at 12% WACC compared to 5% WACC, which is different from gas-fired generation costs that become only 19% more expensive. These differences in costs become more pronounced for wind power and hydropower with a WACC of 20%. In Paper II, the long-term cost-optimal power system configuration was obtained for each of the 60 scenarios combining WACC values and carbon taxes (see Section 4.2.2). First, the least-cost power generation mix and

Chapter 5. Results and discussion

transmission infrastructure was compared between scenarios. Next, the carbon intensity of each power system configuration was compared and discussed. Effects on natural gas reserves were also discussed. Lastly, results of the average abated costs and total abated emissions were illustrated in the light of average multiyear marginal abatement cost curves.

CCGT Wind Power Hydropower 30 30 30

20 20 20 Fuel Variable 10 10 10

Fixed

LCOE (c$/kWh) LCOE LCOE (c$/kWh) LCOE

0 0 0 Capital LCOE (c$/kWh) LCOE 5% 12% 20% 5% 12% 20% 5% 12% 20% Figure 31. Numerical representation. LCOE breakdown per cost component f or CCGT, Wind power and Hy dropower f or WACC of 5%, 12% and 20% f or y ear 2013, 2020 and 2030. Cost assumptions taken f rom(IEA, 2019).

5.2.1 Effects on the optimal generation mix

Medium-term projects (committed up to 2025) were ‘forced’ into the solution and these were found sufficient to meet the projected demand. New investments in additional capacity are needed from 2026 onwards to supply the increasing demand. As described in the previous section, we expect new uncommitted investment choices to vary with the WACC. Figure 32 shows the accumulated new investments over the period 2013– 2040 for the five WACC scenarios where no carbon tax is included. At lower values (5%, 8% and 10%), large hydro (with no fuel costs, but high up-front capital) is invested in. However, at higher values (12 and 15%) and combined with low gas costs, it becomes economical to increase the load factor of old gas power plants and invest in new gas power plants. The solid red line in the figure represents the total accumulated new capacity, which decreases with increasing WACC, because thermal power plants have a higher load factor.

Chapter 5. Results and discussion

100% 6.8

L-Hydro 75% 6.6 OCGT CCGT 50% M_Hydro 6.4 S_Hydro 25% Wind

New New Capacity,GW Solar Technology share,% 0% 6.2 Other renewable

Accumulated New Capacity

DR=5% DR=8%

DR=12% DR=15% DR=10%

Figure 32. Accumulated new power generation capacity in the period 2013-2040 for all WACC scenarios when no carbon tax is included. Figure reprinted f rom Paper II.

Figure 33 compares the accumulated new investments in high and extra- high voltage transmission capacity. The blue-scaled colors represent the transmission lines in each node, the red-scaled colors the transmission lines between nodes, the yellow-scaled colors the transmission lines to connect each node with the near decentralized mini-grids, and the grey-scaled colors the extra-high voltage transmission connecting strategic large- hydropower projects to the nearest node. The scenarios that insert large- scale hydropower plants (5%, 8% and 10% WACC) have a higher share of new transmission lines connecting between-nodes than the scenarios that only expand their capacity with gas-fueled technologies (12%, 15% WACC). The solid red line in Figure 33 shows the differences in new transmission capacity. These results reveal that WACC values less than 8% incentivize the deployment of renewable energy projects by encouraging interconnections between nodes. In Bolivia, the large hydropower potential in the north of the country can be used to supply energy to other nodes with less hydropower capacity.

Chapter 5. Results and discussion

100% 8.0 T500_LHBL~NO 75% 6.0 T230_SU~OG T230_WE~OG T230_NO~OG 50% 4.0 T230_WE~SU T230_CE~SU T230_CE~WE 25% 2.0 T230_CE~NO T230_SU~SU T230_WE~WE

Technology share,% 0% 0.0 T230_NO~NO New New Capacity,GW T230_CE~CE

Accumulated New Capacity

DR=5% DR=8%

DR=12% DR=15% DR=10% Figure 33. Accumulated new transmission capacity 2013–2040 for all WACC scenarios. Figure reprinted f rom Paper II.

5.2.2 Effects on the power system carbon intensity Figure 34 shows the variation of the average carbon intensity (Figure 33.a) and the accumulated emissions avoided (Figure 33.b) for each combination of carbon tax and WACC scenarios. Each point in the diagrams represents a single simulation with averaged values for 2013- 2040. The results numerically show how carbon prices and the WACC interact to lead long-term emission reductions in the power system. When no carbon tax is levied, the average carbon intensity of the 5% WACC scenario is 148 kgCO2/MWh. To reach the same average carbon intensity in a scenario with 8% WACC, a carbon tax of 30 US$/tCO2 is needed. For higher WACC values of 10%, 12% and 15%, carbon taxes of 50, 70 and more than 100 US$/tCO2 are necessary to reach the same decarbonization effect of the scenario with 5% WACC and no carbon tax, respectively (see black dashed line in the left chart in Figure 33.a). The findings of this numerical exercise show quantitatively that a high cost of capital (WACC) reduces the decarbonization effectiveness of carbon taxes. These results are particularly relevant for low and middle-income countries (LMIC) where the costs of capital are predominantly high and which are currently investing in new fossil-fueled power generation capacity. As presented in Figure 33.b, the results also show that carbon emissions need to be priced higher than 5, 10 and 30 US$/tCO2 to abate any emissions for the WACC scenarios of 10%, 12% and 15%, respectively, which implies that carbon taxation itself cannot lead to significant emission reductions when WACC is high.

Chapter 5. Results and discussion

300 80

225 60 WACC=5% 150 40 WACC=8%

WACC=10%

gCO2/kWh MMtCO2 75 20 WACC=12% WACC=15%

0 0 0 25 50 75 100 0 25 50 75 100 Carbon Tax, US$/tCO2 Carbon tax, US$/tonneCO2 Figure 34. Average emission intensity and total accumulated emissions a. Av erage multiy ear (2013–2040) carbon intensity v s. carbon tax f or f ive WACC scenarios. b. Total av oided emissions v s carbon tax f or f ive WACC scenarios. Figure reprinted f rom Paper II.

Figure 35 shows how high costs of capital (that is, higher WACC scenarios) affect the transformation of the power system under carbon taxes of 50 and 100 US$/tCO2. For a 5% WACC and 50 US$/tCO2, gas- fired generation is rapidly replaced by medium and large-scale hydropower (and solar power in a lesser extend), reaching a cost-optimal electricity mix comprising 94% renewable energy by 2040. For a carbon tax of 100 US$/tCO2, wind and solar power capacity are increased to achieve almost full decarbonization (97% renewable energy) by 2040. A similar pattern was observed for higher WACC scenarios, however, the effectiveness of carbon taxation is limited with increasing WACC. By 2040, a carbon tax of 100 US$/tCO2 can lead to an electricity mix comprising 94%, 82%, 81% and 68% for WACC of 8%, 10%, 12% and 15% respectively.

For all scenarios with a carbon tax, a large share of decarbonization is led by investments in large-scale hydropower. Medium-scale hydropower capacity is introduced to the power system in the form of committed projects (currently in construction or planned for deployment) and not by cost-optimization. Potential large-scale cascading dams on the El Bala river (in the north node) have the highest capacity factor in the country (avg. 0.87) compared to other identified hydropower projects and compared to other renewable energy sources. Therefore, when carbon taxes are applied, the large-scale dams from El Bala are introduced as the least-cost technology in part or full-capacity.

Chapter 5. Results and discussion

5% WACC 8% WACC 10% WACC 50 50 50

40 40 40

30 30 30

20 20 20 TWh 10 10 10

0 0 0

2040 2040 2030 2030 2030 2040

2030 2030 2030 2040 2040 2040

2040 2030 2040 2030 2040 2030 0 50 100 0 50 100 0 50 100 Carbon price US$/tCO2 Carbon price, US$/tCO2 Carbon price, US$/tCO2 12% WACC 15% WACC 50 50 Other renewable 40 40 Wind 30 30 Solar TWh 20 20 2 Large hydro 10 10 0 Medium hydro 0 0 3

0 Thermal

2030 2040 2030 2040 2030 2040

2030 2030 2040 2040 2040 2030 0 50 100 0 50 100 Carbon price, US$/tCO2 Carbon price, US$/tCO2 Figure 35. Generation mix for selected carbon taxes for 2030 and 2040.

5.2.3 Carbon abatement cost curves For all scenarios, the overall costs and technology-specific abatement can vary significantly depending on the timing of investments. At high CO2 emissions costs (>50 US$/tCO2), committed projects in CCGT installed over 2013–2025 are partially phased out or completely stranded before the completion of their economic lifetime. Figure 36 compares the average abatement costs (y-axis) and the accumulated abated emissions (x-axis), commonly referred in the literature as Marginal Abatement Cost Curves, (MACC) for each scenario. For every carbon tax scenario, the abated emissions are calculated by subtracting the accumulated emission for that scenario from the accumulated emissions of the base scenario with no carbon tax.

Chapter 5. Results and discussion

4 Abatement Abatement Cost, US$/tCO2

0 0 20 40 60 80 100 120 Abated Emissions, tCO2

WACC=5% WACC=8% WACC=10% WACC=12% WACC=15% Figure 36. Abatement costs and abated emissions for each scenario of carbon tax and WACC. The abatement costs are calculated as total discounted sy stem abatement costs (excluding carbon tax rev enue) in US$/av oided tCO2 f or 2013-2040. Figure reprinted f rom Paper II.

Since the present values are discounted with different rates, the MACC do not aim to provide numerical values of abatement costs. Still, the illustration provides a comparative visualization of the abatement costs and abated emissions for all carbon tax and WACC scenarios. In this plot, it is possible to identify carbon tax ranges for which significant emissions reductions are obtained with a comparably smaller increase in carbon abatement costs. The larger abated emissions per unit of abatement cost occur at carbon tax of 50 US$/tCO2 for the 5% WACC scenario, 40 US$/tCO2 for the 8% WACC scenario, 20 US$/tCO2 for the 10% WACC scenario, 30 US$/tCO2 for the 12% WACC scenario and 80 US$/tCO2 for the 15% WACC scenario.

Chapter 5. Results and discussion

5.2.4 Effects on natural gas reserves Due to a gradual increase in the domestic demand for natural gas (its continued use for power generation and natural gas exports), proven (1P discovered and yet to be discovered), probable (2P) and possible (3P and shale) certified gas reserves decline fast (see Figure 37). Starting in 2021, Bolivia will have to invest in shale gas reserves, relying completely on these from 2038 onwards. The 5% WACC scenario consumes 1.1 TCF (trillion of cubic feet) of natural gas less than the 12% and 15% WACC scenarios. One of the findings from the demand analysis in Paper III shows that the Bolivian domestic demand for natural gas will increase on average at 6.6% per year between 2012 and 2035, mostly driven by fuel-switch policies in the transport and industrial sectors and natural gas industrialization projects. However, domestic demand will not surpass exports, which will still account for 74% of Bolivia’s supply in 2030. Bolivia’s conventional natural gas production is low-cost, estimated in 0.28 US$/MMBTU for developed wells and 2.17 US$/MMBTU for undeveloped proven wells (tax-excluded and transport costs) by Chávez-Rodríguez et al. (2016). Continually increasing demands in the domestic and international markets (exports) will require large investments in exploration, development and operation for technically recoverable resources in the long-term. Chávez- Rodriguez et al. estimate these cumulative investments to be US$0.5 billion for exploration (CAPEX), US$13.3 billion for well development (CAPEX), and US$5.8 billion in operation (OPEX) leading production costs (tax-excluded and transport costs) to rise from 0.28 US$/MMBTU in 2013 to 4.64 US$/MMBTU in 2040. Contractual natural gas export prices are indexed to international reference oil prices and are higher than international prices found in North America (Henry Hub prices). At the time of this writing, projections of international oil prices were expected to rise from 7.48 US$/MMBtu in 2016 to 18 US$/MMBtu in 2040, leading to gas export prices to increase from 4.43 US$/MMBtu in 2016 to 9.23 in 2040. Under these assumptions, exports will continue to be beneficial. Although oil prices have declined in recent years (which is different from projected trends at the time of this research), in 2019, export prices to Argentina were found to be, on average, 6.25 US$/MMBtu and 5.07 US$/MMBtu to Brazil, still higher than the Henry Hub spot price of 2.56 US$/MMBtu (CEIC, 2020; EIA, 2020).

Chapter 5. Results and discussion

P1-dev P1-und P2 P3 Shale Average price 1500 6

1000 4

PJ 500 2

0 0 USD/MMBTU

2017 2023 2029 2035 2039 2015 2019 2021 2025 2027 2031 2033 2037 2013 Figure 37. Natural gas reserves required to supply domestic demand and exports and average natural gas production cost. Results shown f or the base scenario (12% WACC and no carbon tax). Prov en dev eloped (P1-dev ), prov ed undev eloped (P1-und), Probable (P2), Possible (P3) and Shale reserv es.

5.3 The role of the cross-border electricity trade in South America

5.3.1 Bolivia’s hydropower export potential The Ministry of Hydrocarbons and Energy assessed four hydropower export scenarios in the Electric Plan 2025 (EP) using the computational tool OptGen (Ministry of Hydrocarbons and Energy, 2014). Electricity surplus with export potential was calculated as follows. Electricity generated by the export-oriented plants was assumed to be absorbed by the national grid within inter-nodal restrictions10 on transmission capacity, displacing generation from hydropower, renewable and thermoelectric power plants. Electricity from export-oriented hydropower plants that cannot be taken by the national grid was accounted for as surplus electricity as was the displaced electricity from power plants in the grid. Results from this study are reported on a monthly basis as the total hydropower and thermoelectric surplus for one single year, 2025, without further estimations of the surplus electricity available in the following years. In this thesis, the electricity surplus was estimated using a different approach. The large-scale hydropower projects of each scenario were connected (with technologies representing the transmission lines) to the national grid and to demand sites in neighboring countries. For each

10 Restrictions on transmission capacity and congestion were evaluated to balance the demand and supply between nodes

Chapter 5. Results and discussion

scenario, a capacity bound was inserted in the transmission lines for exports equal to the total generation capacity (in GW) of the export- oriented hydropower projects from 2025 to 2030. This constraint allows the model to optimize the investments in transmission lines to export surplus electricity without exceeding the capacity of the export-oriented projects. From 2030 onwards, the export capacity constraint was removed, allowing to each scenario to expand its export capacity. The dispatch of the strategic hydropower projects was cost-optimized in every time-slice to supply the demand and/or to export. It is expected that electricity would be exported if the domestic demand was already met by the power plants connected to the grid. However, the model will not export if the importing country has met its demand using its local energy, which can happen for example during the rainy season when countries sharing the same basin can have hydropower surpluses at the same time. Results for 2025 Since the OptGen model is a single-year model, we can only compare results for the year 2025 with the OSeMOSYS SAMBA model. By 2025, all hydropower projects will reach their total installed capacity (see Figure 38). Results of the cross-border trade potential found by these analyses are smaller when compared to the results from the Electric Plan for the four scenarios (see Table 6). The differences in the results are attributed in part because of similarities in seasonal hydropower production patterns among Bolivia, Brazil and Argentina11 and in part because of the more detailed description of the generating capacity expansion alternatives in all countries in the OSeMOSYS SAMBA model. Of the four export scenarios, Scenario II has the largest trade potential for Bolivia in both models. It is interesting to note that despite the smaller installed capacity of hydropower projects in Scenario II (2,670 MW) as compared to Scenario I (3,872 MW), Scenario II has a higher cross-border trade potential than Scenario I. The reason is that in Scenario II, the average capacity factor of the hydro projects considered is higher (0.79) than in Scenario I (0.49). Scenario IV has the lowest export potential and it has the lowest complementarity of hydro patterns with Brazil compared to the other scenarios.

11 Hydropower production is modelled as average monthly patterns (capacity factors) for every trading country. Hydrological complementarities in the overall rainy and dry seasons increase trade while similarities in hydropower production reduce opportunities to export hydropower that cannot be stored.

Chapter 5. Results and discussion

Results for the period 2025–2058 Table 6 summarizes the accumulated electricity potential exports over the period 2025–2058. Comparing the export capacities in this time-frame, Scenario II has the higher accumulated trade potential for Bolivia, with exports to Brazil up to 679 TWh. Scenarios I, III and IV export 678 TWh, 635 TWh and 471 TWh, respectively. Scenario IV also shows a potential trade up to 9 TWh from Bolivia to Argentina. Although the investments in the strategic hydropower projects (and transmission lines connecting Bolivia to demand points) could bring potential benefits in terms of export revenues, these could not be sufficient to cover the investment costs if the capacity is underutilized. This is shown in Figure 38.a, where differences in future exports (2025–2058) are found among all scenarios. Such differences in exports are explained by the characteristics of each hydropower complex (mostly related to capacity factors), hydrological compatibilities with importing countries and the investments in additional transmission lines needed to export electricity (which vary in length). From 2030 onwards, there is a steady increase of electricity exports from Bolivia to Brazil in all scenarios. This increased export capacity is achieved by new hydropower projects. By 2059, near 40 TWh of hydropower are exported in all scenarios, which represents the 23% of Bolivia’s technically exploitable capacity (173 TWh). Error! Reference source not found. shows t he annual imports and exports in Scenario II, it can be observed that Bolivia and Peru become the largest exporters (of hydroelectricity) in the long- term while Paraguay export volumes reduce in 2059 to one-fifth of its exports in 2014. As a change in the network topology (interconnections between countries) and new generation capacity influence the bargaining power and the distribution of welfare of all countries, the next section discusses implications on surplus allocation using results from a cooperative games theory approach (using the Shapley value).

Chapter 5. Results and discussion

Table 6. Boliv ia’s potential electricity surplus trade in 2025. Scena- Strategic hydropower Potential trade in 2025, TWh Accumulated rio projects trade 2025-2058 EP- OSeMOSYS- Diferen in SAMBA OptGen SAMBA ce, % model, TWh

I Cachuela Esperanza (990 23.1 13.8 -40% 678 MW) and Río Grande Hy dro Power Complex (2,882 MW). Total capacity of 3,872 MW

II Cachuela Esperanza (990 22.0 14.4 -35% 679 MW) and El Bala (1,680 MW). Total capacity of 2.670 MW

III Cachuela Esperanza (990 24.6 16.0 -35% 635 MW), El Bala (1,680 MW) and part of the Río Grande Hy dro Power Complex (550 MW). Total Capacity of 3,220

IV Río Grande Hy dro Power 17.4 9.2 -47% 480 Complex. Total capaity of 2,882 MW.

Trade Scenario II 120 Venezuela 80 Uruguay Paraguay 40 Peru 0 Ecuador TWh -40 Colombia Chile -80 Brazil

-120 Bolivia

2017 2023 2029 2035 2041 2047 2053 2020 2026 2032 2038 2044 2050 2056 2014 Figure 38. Trade model results 2014-2058 a) Annual export capacity by scenario, b) Imports and exports of electricity in Scenario II. Figure a) reprinted f rom Paper IV.

Chapter 5. Results and discussion

5.3.2 Bolivia’s bargaining power The theoretical bargaining power of Bolivia is calculated for all scenarios and presented in Table 7. Scenario II has the largest accumulated export potential. In Scenario II (shown in Table 8), Bolivia has a bargaining power equivalent to 10% of the total cross-border electricity trade (Shapley value), in spite of accounting for 21% of the exports (679 TWh, which makes it the third major exporter after Paraguay and Peru). Brazil has the largest bargaining power on the continent (46%) as it is the most important electricity importer (2917 TWh, mostly from Paraguay, Peru and Bolivia). Therefore, Paraguay and Peru also have significant bargaining powers, of 20% and 16%, respectively. The results on the surplus-allocation assessment highlight the competition in the long-term among Paraguay, Peru and Bolivia to export their electricity to Brazil in a future power market integration. The results also show that Bolivia has less bargaining power than its competitors in the long-term, as these countries obtain a higher share of the total surplus due to their larger export contributions in the medium-term.

Table 7. Accumulated exports and bargaining power f or all scenarios

Scenario I Scenario II Scenario III Scenario IV Country Exports Shapley Exports Shapley Exports Shapley Exports Shapley Value Value Value Value Argentina 54 128 53 127 53 128 56 132 Boliv ia 658 329 679 339 635 318 480 240 Brazil 131 1522 134 1525 133 1504 129 1434 Chile 5 5 4 5 4 5 4 5 Colombia 9 9 9 9 9 9 9 9 Ecuador 5 8 5 8 5 8 5 8 Paraguay 1304 652 1082 654 1309 654 1325 663 Peru 1082 541 1308 541 1084 542 1092 546 Uruguay 6 22 6 22 6 22 6 22 Venezuela 12 61 12 62 12 62 12 60 Total 3286 3286 3292 3292 3251 3251 3118 3118 Exports are in TWh and Shapley v alue in TWh equiv alent

Table 8. Accumulated exports and theoretical bargaining power in Scenario II. Country Exports Imports % of Cross- Shapley Theoretical (TWh) (TWh) border Trade Value, TWh Bargaining profit equivalent Power, % Argentina 53 201 2% 127 4% Boliv ia 679 0 21% 339 10% Brazil 134 2917 4% 1525 46% Chile 4 5 0% 5 0% Colombia 9 9 0% 9 0% Ecuador 5 11 0% 8 0% Paraguay 1082 0 40% 654 20% Peru 1308 0 33% 541 16% Uruguay 6 38 0% 22 1% Venezuela 12 112 0% 62 2% Total 3292 3292 100% 3292 100%

Chapter 5. Results and discussion

5.3.3 Impacts of trade on emissions from the power sector

In this section, differences in generation capacity are shown for Scenario II (the largest export potential) compared to a scenario in which Bolivia does not export electricity. Figure 39 shows the differences in generation capacity for Bolivia and Brazil (the only importer). It can be observed in Bolivia that although thermoelectric capacity is displaced by large-scale hydropower, also wind, solar, biomass and geothermal power are displaced. The hydroelectricity imported from Bolivia displaces also fossil-fueled generation (coal and natural gas), hydropower and renewable energy in Brazil. The total avoided emissions in Bolivia account for 66 million tCO2 and 275 million tCO2 in Brazil, showing that Brazil benefits more from the electricity trade in terms of emissions reductions than Bolivia. a) Bolivia 120 Large hydropower Cachuela Esperanza 80 El Bala 40 Wind Solar TWh 0 Biomass -40 Geothermal

-80 Gas

2013 2016 2022 2025 2031 2034 2043 2052 2055 2028 2037 2040 2046 2049 2058 2019 b) Brasil Imports from Bolivia 120 Imports from Paraguay 80 Imports from Perú 40 Imports from Argentina Nuclear TWh 0 Large hydro -40 Renewable -80 Hydropower

Fossil

2013 2016 2019 2022 2025 2028 2031 2037 2040 2043 2046 2049 2052 2055 2058 2034 Figure 39. Differences of the total installed capacity in Scenario 2 compared to the base scenario in which Bolivia does not export hydroelectricity

Chapter 5. Results and discussion

5.4 The role of increasing agricultural productivity and use of biomass residues for sustainable biofuel production

5.4.1 Agricultural production A lifecycle accounting of production costs, emissions and energy consumption was performed for three input management classes of sugarcane agriculture. Figure 40 compares the results of the net values calculated for average yields of 100, 70 and 30 tons of sugarcane per hectare (tc/ha) for irrigated high-inputs (Hi), intermediate (Ii) and low (Li) inputs production. Note that the values presented in this section are only an example and the modeled values vary for each of the 27 regions when applying region-specific potential yields. For this illustrative example, trash recovery was not included in the production process. The timeframe for the analysis was 15 years, which accounts for the average lifetime of the machinery, and accounts for three ratoon periods of five years each. The sugarcane production costs, energy use and emissions presented in Figure 40 are well within ranges of values found in previous work. For example, sugarcane production costs in Brazil have been reported as 18–32 US$/tc for yields in the range of 75–100 tc/ha (Dias, Cunha, et al., 2011; Cardoso et al., 2018). Emissions for high-input sugarcane production have been found to be 38–43 kgCO2,eq/tc for an average yield of 80 tc/ha (Seabra et al., 2011; Cardoso et al., 2018). Energy use for high-input sugarcane production was found with an average of 154 MJ/tc for a yield of 86.4 tc/ha (Seabra et al., 2011). For the average yields considered in this example, the lifecycle costs of one ton of sugarcane produced with Hi is 27% less costly than for Ii production and 61% less costly than Li production. The differences are attributed to the manual operations (labor), which have a larger share of Ii and Li. Although the production costs per unit of harvested area are higher for Hi (21% and 29% higher compared to Ii and Li), the costs per ton of sugarcane turn out to be lower for Hi because of higher yields achieved. The lifecycle emissions of one ton of sugarcane produced with Hi is 4% and 13% lower than Ii and Li production. Agriculture inputs, trash burning and decomposition and transportation are the more significant contributors to emissions in Hi and Ii production, accounting for 69% and 66% of total

100

Chapter 5. Results and discussion

emissions, respectively. For Li production, emissions are associated with manual harvesting, trash burning and decomposition, contribute to 62% of the total emissions. Lastly, the lifecycle energy use to produce a ton of sugarcane with Hi is 18% and 42% lower than Ii and Li, respectively. The largest contributor to Li and Ii is the energy use associated with human labor, while the energy consumed in Hi production is by the mechanized processes.

Production costs per tonne Production costs per ha

60 56 2 500 2 163 Irrigation 45 2 000 1 783 1 668 30 1 500 Transportation 30 22

1 000 Harvesting USD/tc USD/ha Agriculture inputs 15 500 Sugarcane farming

- -

Low

High

ediate

Interm Interm Energy Use Emissions Irrigation 369 400 60 53 48 Field emissions 260 46 300 45 214 Trash burning and decomposition

200 30 Transportation

MJ/tc kgCo2,eq/tc 100 15 Harvesting

- - Agriculture inputs

Low

High Low

High Sugarcane farming

diate

diate Interme Interme Figure 40. Comparison of the lifecycle production costs, emissions and energy use of sugarcane cultivated under high, intermediate and low input levels. Figure reprinted f rom Paper V.

To reach the targeted volumes of ethanol production and ethanol blending, the government estimates an annual increase of 180 km2 hectares of cultivated sugarcane area, reaching 2800 km2 by 2025 (from the current 1730 km2 in 2019) (YPFB 2018; Nolte 2018). In contrast, results of the optimization show that it is cost-effective to increase feedstock production with less cropland (1,600 km2 in 2025 and 1,839 km2 in 2030) and increased yields from the current 55.34 tc/ha to 76.8 tc/ha in 2025 and to 85.7 tc/ha in 2030 (as shown in Figure 41).

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Area Yield

3 Optimized 100 Optimized Historical 80 Historical 2 Gov. projections 60

1 tc/ha 40

20 thousand km2

0 0

1990 1995 2010 2015 2020 2000 2005 2025 2030

1995 2005 2010 2020 2030 2000 2015 2025 1990 Figure 41. Comparisson of agriculture area and national averaged yield between government projections and the optimization results Figure reprinted f rom Paper V.

As shown in Figure 42, the optimization results show that Region 12, located in the western lowlands of Santa Cruz, can maintain increasing production of sugarcane and reduce the cultivated area by 29% by increasing the share of high-input irrigated production. By 2030, Region 12 can produce 53% of total sugarcane demand in the country. The largest expansion occurs in Region 24, located in the northern rainforest between La Paz and Pando, where the cultivated area increases with high-input rainfed production. By 2030, Region 24 can supply 25% of the total sugarcane demand. Region 11, located in the western lowlands of Santa Cruz, can increase its cultivated area with irrigated intermediate-inputs production, supplying 9% of the total production by 2030. Region 8 located in the northern-east valleys of Cochabamba can supply 13% of the total production in 2030 with irrigated high-input production. Lastly, the cultivated area and production in Region 7, located in the southern valleys of Tarija, will reduce by 83% and 73% respectively due to less competitive yields compared to other regions.

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2 Area 1.5 1 0.5

thousand thousand km2

2015 2017 2021 2023 2029 2019 2025 2027 2013 Production 20 Region 24 15 Region 12 10 Region 11 5 Region 8 0

million tonne million Region 7

2013 2019 2023 2029 2017 2021 2025 2027 2015 Figure 42. Optimization results for the agriculture production model. Cultiv ated area and annual production in main producing regions. Figure reprinted f rom Paper V.

Figure 43 shows modelling results at the national level, aggregating the results by input level and by water supply modes (rainfed and irrigated). From 2020, the cultivated sugarcane area with high-inputs and irrigation (HI) increases continuously nearly reaching the upper limit (700 km2 from the upper limit of 730 km2 of sugarcane cropland with access to irrigation). The cultivated area with intermediate inputs and rainfed (IR) reduces in 38% in 2030 compared to its area in 2019. The land cultivated with high- input rainfed (HR) increases 25% of its area in 2030 compared to 2019. By 2030, the area under irrigation will be 38% of the total cultivated area and produce 53% of the total production. Because investments in high-inputs and mechanization allow increased yield, the average sugarcane production costs decrease from 26.7 US$/ton in 2013 to 23.6 US$/ton in 2030 for sugarcane production with no straw recovery and from 35.2 US$/ton in 2013 to 28.6 US$/ton in 2030 for production with straw recovery.

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a) Cropland area b) New cropland area 2 0.25 LI 1.5 0.2 II 0.15 1 HI 0.1 LR 0.5 0.05 IR

Thousandkm2 HR

0 0

2013 2019 2028 2016 2022 2025

2025 2028 2016 2019 2022 2013 c) Area d) Production

Rainfed 20 Rainfed 2 Irrigated Irrigated

1 10

milliontonne Thousandkm2

0 0

2013 2019 2021 2027 2029 2015 2017 2023 2025

2015 2019 2025 2029 2013 2017 2021 2023 2027

Figure 43. Optimization results for the agriculture production model by input level. a. Results of the sugarcane agricultural land by input lev el. b. Agricultural land expansion by input lev el. c. d. Accumulated area and production f or water use per category . Management input lev els acrony ms: LI (low-inputs with irrigation), II (intermediate-inputs with irrigation), HI (high-inputs with irrigation), LR (low-inputs and rainf ed), IR (intermediate inputs and rainf ed), HR (high-inputs and rainf ed). Figure reprinted f rom Paper V.

5.4.2 Industrial production

Figure 44 shows the optimal production capacity (existing and new) and operation of biorefineries at country-scale. The expansion of biorefinery production capacity is led by biorefineries co-producing electricity (EtOH+ELC). The model indicates the capacity of existing sugar refineries are sufficient to supply the domestic demand of sugar while reducing the exports volumes. Figure 45 shows the locations where investments on new biorefineries are best placed. Most of the new investments in biorefineries co-producing electricity occur on Region 24, located in the northern side of La Paz, where most of the high-input rainfed agricultural expansion occurs. From 2028, investments in EtOH+ELC occur in Region 8, located in the northern-east valleys of Cochabamba, where new agricultural production of sugarcane with irrigated high-input production occurs.

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Production capacity Production activity

800 800 600 600 400 400 200 200

0 0

millionEtOHlitres

2018 2020 2022 2024 2022 2024 2026 2028 2030 2028 2030 2018 2020 2026 EtOH 1G+2G EtOH+ELC EtOH 1G EtOH from molasses Total Capacity Figure 44. Total biorefinery capacity and activity in million liters of ethanol (EtOH) Figure reprinted f rom Paper V.

EtOH from molasses EtOH 1G 150 150

100 100

50 50

0 0

millionEtOH liter

millionEtOH liters

2013 2017 2019 2023 2025 2015 2021 2027 2029

2013 2015 2027 2029 2019 2021 2023 2025 2017 EtOH + ELC 500 Region 24 400 Region 12 300 200 Region 11 100 Region 8 0

Region 7

millionEtOHliters

2016 2019 2022 2025 2028 2013

Figure 45. Biorefinery capacity in million liters of ethanol (EtOH) per year in each region Figure reprinted f rom Paper V.

A lifecycle inventory for energy consumption, production costs, and emissions of the production of one liter of anhydrous ethanol was performed to compare the ethanol produced in each feedstock production type (input level). Figure 46 compares the results of the lifecycle inventories for each of the four biorefinery configurations. For an

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illustrative comparison, average yields of 100, 70 and 30 ton of sugarcane per hectare (tc/ha) are used for Hi, Ii and Li. For all biorefinery configurations, the fossil energy used in the production of one liter of anhydrous ethanol is nearly six times lower than the lower heating value of anhydrous ethanol (21.2 MJ/L). Because bagasse and trash (in the advanced biorefinery configurations) is used for energy, there is no demand for fossil fuels in the industrial phase. As a result, there is a strong benefit not only to the energy balance, but also to the GHG emissions associated with ethanol production. For all technologies, fossil energy use is estimated at an average of 3.5 MJ/L excluding the credits related to electricity surplus. GHG emissions are estimated at an average of 28 gCO2,eq/MJ, which is almost a third of the emissions of gasoline (81.77 gCO2,eq/MJ) (Macedo, Seabra and Silva, 2008). Regarding costs, feedstock production with low inputs increase the ethanol production costs more than the international price of anhydrous ethanol in 2020. When feedstock is produced with high and intermediate inputs, total production costs are lower than international prices. A breakdown by production process is presented in the appended Paper V with estimations of the avoided emissions of displaced gasoline and displaced gas-fired thermoelectricity generation.

Figure 46. Comparison of the lifecycle energy, production costs and emissions. – For ethanol produced f rom molasses, emissions, energy and costs f rom the upstream operations (cultiv ation, harv esting and transport) are partitioned as per economic allocation f ollowing a methodology illustrated in Khatiwada and Silv eira (2017). – Note that the v alues presented in this example include y ields of 100 tc/ha, 70 tc/ha and 30tc/ha f or high, intermediate and low-input production. The lif ecy cle costs, energy and emissions presented in this f igure will v ary f or ev ery region due to y ield dif f erences f or each agricultural production ty pe. Figure reprinted f rom Paper V.

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EMISSIONS 800 Ethanol transport and distribution 300 Processing to Ethanol

-200 Trash recovery and transport -700 Sugarcane production Avoided electricity

thousandtCO2,eq -1200 emissions

Avoided gasoline emissions

2014 2017 2019 2021 2023 2025 2026 2028 2030 2015 2016 2018 2020 2022 2024 2027 2029 2013

Figure 47. Emissions from ethanol production and avoided emissions from gasoline and electricity consumption Figure reprinted f rom Paper V.

Table 9 shows the energy balance indicators for the production of anhydrous ethanol in each biorefinery configuration. A negative value obtained in the net energy value (NEV) indicator means that the energy content of the ethanol produced is lower than the total primary energy inputs (fossil plus renewable) in the entire biofuel production chain. Higher negative values of the NEV show the increased use of biomass residues (sugarcane trash). Positive values of the NREV imply that more energy is extracted from ethanol than is consumed from fossil fuels during the production of one liter of ethanol. Higher values of the energy ratio (ER) imply that more energy is utilized from sugarcane (to generate energy products, 1G ethanol, 2G ethanol and electricity) than is consumed from fossil fuels used during the production of ethanol. The advanced biorefinery for 1G ethanol + electricity is the technology with the higher ER scores.

Table 9. Energy balance indicators f or ethanol production.

Units High Intermediate Low

Ethanol from Molasses NEV (MJ/l) -8.7 -9.2 -10.4 NREV (MJ/l) 18.6 18.1 17.0

ER (MJ tot /MJfossil fuel ) 10.4 9.7 7.0

Ethanol 1G from sugarcane juice NEV (MJ/l) -8.9 -9.4 -10.6 NREV (MJ/l) 18.6 18.1 17.0 ER (MJtot /MJfossil fuel ) 8.1 6.8 5.0

Ethanol 1G + electricity from sugarcane juice NEV (MJ/l) -13.9 -14.5 -16.1 NREV (MJ/l) 11.7 11.1 9.7 ER (MJtot /MJfossil fuel ) 11.1 9.5 7.1

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Ethanol 1G +2G from sugarcane juice NEV (MJ/l) -18.8 -19.3 -20.3 NREV (MJ/l) 18.7 18.3 17.3 ER (MJtot /MJfossil fuel ) 8.6 7.4 5.5

5.4.3 Biofuel support In this section, the effect of 2G ethanol support in the form of green certificates (CER) or incentives/subsidies is evaluated in terms of investments in new biorefineries. A biofuel incentive smaller than 7 US$/GJ does not affect investments in new biorefineries. Biofuel support in the range of 8–10 US$/GJ allows half of the new investments to be on the advanced 2G biorefinery by 2030. Without the biofuel support, production of 2G ethanol is not cost-competitive under the model assumption. Figure 48 shows the differences in the accumulated new investments in the period 2013–2030 for the six biofuel support prices.

ACCUMULATED NEW INVESTMENTS

12 EtOH-1G+2G

EtOH-1G+ELC PJ 6 EtOH-1G Molecular sieves 0 0 3 5 7 8 9 10 Biofuel support, USD/GJ

Figure 48. Total investments in biorefineries for each biofuel support scenario Accumulated new capacity of bioref ineries in units of PJ of ethanol produced. Note: Molecular sieves are installed on existing sugarmills producing hy drous ethanol. Figure reprinted f rom Paper V.

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6 Conclusions and scope for future research

This final chapter presents the overall conclusions by bringing together the knowledge compiled in this thesis. The thesis’ contributions are presented together with the limitations and scope for future work.

This thesis has brought together applied analytical advances to some of the most crucial areas for planning and investments in Bolivia’s energy sector. The main conclusions are presented in relation to the research questions introduced in Chapter 1. Contributions to the new body of academic knowledge are summarized in Table 10.

6.1 Research questions and insights

RQ1. From the perspective of least-cost electrification, to what extent can decentralized generation technologies (including renewable and non- renewable energy) help Bolivia achieve low-carbon universal access to electricity by 2025? Achieving universal access to electricity requires expanding and strengthening the supply and distribution of electricity with a variety of electrification solutions. In Bolivia, as well as in other countries facing the challenge of achieving universal electricity access, technical and economic constraints limit the provision of electricity via grid extension. Results from this thesis indicate that to reach electrification targets in Bolivia (in 2025), about 76% of the newly electrified population could be served by grid-extension and the remaining 24% served by decentralized systems. Therefore, the process to identify the best decentralized technology solution is not trivial.

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Because of their flexibility, fast deployment, capacity to harness renewable energy, and the ability to generate electricity at the consumption site, micro-grids will be a technology of importance for meeting electrification and decarbonization objectives simultaneously. Therefore, there is an increasing need for sound and efficient modelling techniques to assess their cost-competitiveness in high-level geographical settings. In this thesis, a methodology is proposed for generating surrogate models to estimate the LCOE of micro-grids based on geospatial data, interview- based information, techno-economic parameters and three specialized open-access modelling tools. The methodology proposed is demonstrated to be an advantageous alternative to one-by-one micro-grid systems optimization, especially at national-level settings where thousands of individual cases add to the computational efforts required to find the least- cost design. The added temporal granularity in load demand representation and the optimization methods for estimating the microgrid component size are important for obtaining accurate results. The satisfactory prediction capacity of the surrogate models allows for an increased level of detail in microgrids design and representation, making the proposed methodology a valuable addition to existing simplified methods commonly used in high- level geospatial electrification models. This innovative methodology has been applied to assess universal electricity access targets in Bolivia and attention is devoted to the analysis of enabling factors to increase the cost-competitiveness of hybrid micro- grids. To meet electrification targets with renewable energy, supportive policy and enabling regulatory frameworks are required to address the economic and financial barriers for investments in renewable energy technologies (RET). This thesis addresses two enablers that lead to increased competitiveness of RET: first, the reform of current fossil subsidy schemes, and second, the introduction of support schemes for reducing the capital investment of RET. Both increase the cost- competitiveness of hybrid (solar/battery/diesel) micro-grid systems over diesel-microgrids, and – to some extent – over stand-alone generation and grid electricity. The analyses show that removing existing diesel subsidies are a more effective way of increasing the cost-competitiveness of hybrid micro-grids over diesel microgrids than subsidizing half of the capital cost for micro-grid components (battery, PV panel and diesel generator). Without a fossil-fuel subsidy reform, the least-cost approach indicates that fossil-fueled technologies will dominate decentralized electrification (standalone and micro-grid technology options) in Bolivia. However, when

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a diesel subsidy reform is enforced, hybrid micro-grid systems could provide electricity to up to 2% of the total newly electrified population with an average capital investment cost of US$1,134 per household; solar home systems could provide electricity to 16% of the total newly electrified population with an average capital investment cost of US$1,380 per household, and the remaining 6% could be electrified with fossil-fueled decentralized technologies.

RQ 2. What is the potential impact of inserting carbon taxes and lowering financing costs on the transition to a low-carbon electricity generation system in Bolivia? This thesis evaluates conceptually and quantitatively how the combined effects of inserting carbon taxes and modifying the WACC affect the transformation of energy systems. To this aim, a techno-economic model representing the power sector in Bolivia was developed, introducing its particularities in demand, resources availability and power generation technologies. The combined decarbonization effects of lower financing costs and carbon taxes are quantified to provide insights on their importance and recommendations for climate policy. A significant finding of this thesis is that high cost of capital (WACC) reduces the decarbonization effectiveness of carbon taxes. At the current WACC of 12%, Bolivia will need to impose higher prices on carbon emissions to achieve the same levels of decarbonization of countries with lower costs of capital. Results from this thesis show that Bolivia (with current WACC of 12%) will require a carbon tax of 70 US$/tCO2 to achieve the same decarbonization effect in the long-term of planning with 5% WACC (commonly found in high-income countries) and zero-carbon tax. Moreover, to abate any emissions with carbon pricing in the long-term, carbon emissions need to be priced higher than 10 US$/tCO2 at current WACC of 12%. These results are particularly relevant for other developing and emerging economies where the cost of capital are predominantly high and which are currently investing in new fossil-fueled power generation capacity. As international investments and development assistance are moving away from fossil fuels, developing and emerging economies such as Bolivia will benefit from timely information to align policy-making with long-term green-growth goals. To this aim, carbon taxation is unquestionably important, however, this thesis demonstrates that a deep decarbonization of the power generation sector, in such contexts, requires very high carbon

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prices. Therefore, instruments to reduce the cost of capital in developing and emerging economies need to be considered as complementary policy to achieve power systems decarbonization. Such policy instruments could focus on supporting schemes for investments in RET, for example in the form of technology-specific feed-in tariffs. These conclusions are closely related to the debate on ‘de-risking’ renewable energy investments in developing and emerging economies and emphasize its importance in the context of green growth and for achieving global decarbonization goals.

RQ3. What is the theoretical bargaining power of Bolivia and the potential decarbonization effect that can be achieved in a scenario of hydroelectricity trade with neighboring countries? This thesis evaluated Bolivia’s plans to export hydroelectricity using a continental-scale power systems model. Three different expansion alternatives (with initial operations in 2025) were evaluated in a scenario of power systems integration in South America in the long-term (period 2030-2058). To this end, a novel methodology of two-stages was applied. First, a power systems optimization model was used to obtain the least-cost expansion and operation of the power systems in South America; and then a second model was subsequently employed to identify cooperative game solutions to determine the bargaining power of each country. A valuable finding of this thesis is that investments in generation capacity the medium-term can influence negatively the long-term bargaining power of a country if the new capacity is underutilized due to hydrological incompatibilities with importing countries. Therefore, the impacts of hydrological complementarities (i.e. capacity factors and storage capacity) should be fully quantified over the economic and technical performance when evaluating different expansion alternatives. Furthermore, results from this assessment highlight the competition in the long-term among Paraguay, Peru and Bolivia to export their electricity to Brazil in a future of power systems integration. The results show that Bolivia has less bargaining power than its competitors in the long-term, as these countries obtain a higher share of the total surplus due to their larger export contributions in the medium-term. Moreover, Bolivia’s potential investments on hydroelectricity for exports can displace fossil-fueled generation domestically and in Brazil (the only importer). However, Bolivia would benefit less in terms of avoided emissions than Brazil (66 million tCO2 avoided in Bolivia and 275 million tCO2 avoided in Brazil). These results are particularly relevant to target power system integration

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plans in South America and to shed light on potential political economy issues.

RQ4. How can improvements in agricultural yield and advanced/efficient conversion technologies supply increasing biofuel production targets, thereby mitigating GHG emissions? The transition towards more sustainable production of sugarcane-based ethanol requires changes along the whole production chain; from the agricultural production of sugarcane to the industrial production of ethanol. The former requires moving from agricultural extensive practices to improved agricultural management practices to increase agricultural production per unit of land. The latter includes the use of agricultural residues for the generation of bioelectricity and the production of second- generation ethanol in a modern biorefinery setup. This thesis shows that Bolivia can cost-effectively reach its medium-term targeted volumes of ethanol production and ethanol blending with a moderate expansion of sugarcane cropland. Increased sugarcane production can be achieved on existing agricultural land by implementing modern farming systems and more effectively using agricultural inputs. Understanding the cost implications and benefits of sustainable agricultural intensification is paramount for designing policies to increase the environmental sustainability of the biofuel industry. The methodological approach of this thesis provides guidance on where financial investments for irrigation and increased input levels are best placed. This thesis demonstrates that demands for ethanol and sugar production can be met by increasing yields from the current country-wide average of 55.34 tc/ha to 85.7 tc/ha in 2030 and a cropland expansion from its current 1570 km2 to 1840 km2 in 2030. Advanced agricultural practices minimizing yield-limiting factors (irrigation and nutrient deficiencies) and yield-reducing factors (weds, pests and diseases) are necessary to narrow yield gaps. National efforts to increase the sustainability of biofuel production should focus on developing a system for innovation and knowledge transfer of advanced technologies and best practices to farmers (van Dijk et al., 2017). Also, policies facilitating access to affordable credit and insurance are required to allow capital-intensive investments in high-input and mechanized agriculture. To harness the full potential of sugarcane biomass, the efficient co- production of bioenergy can enable Bolivia to reach its targets of ethanol

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production while at the same time increasing its renewable energy output. The fact that most of the biorefinery infrastructure has yet to be built means that opportunities exist for investments in greater efficiency. Therefore, this thesis has also explored opportunities for increasing emission savings and adding economic and energy value to sugarcane biomass residues (bagasse and sugarcane trash) through efficient processes to generate bioelectricity and second-generation biofuels. The size and locations of biorefineries are identified for the minimization of the total system cost. This thesis demonstrates that with current technological advances in efficient co-generation, it is cost-optimal to invest in modern facilities to provide biofuel and export surplus electricity to the national grid. Biofuel support in the range of 8–10 US$/GJ is required for investments in second- generation biofuel to be cost-competitive.

6.2 Thesis contributions

Table 10 provides a summary of the thesis contributions to the academic knowledge. Although the list is not exhaustive, it brings an overview of the advancements to the broader body of knowledge in addition to the contributions to the country studied. Table 10. Thesis contributions. Methodological advances

– A new methodology was developed to expand the functionality of an existing open access modelling tool (OnSSET) by introducing new methods to capture time-dependent components for the optimal system design of micro-grids. – New methods were proposed to assess agricultural resources and agronomic potential in a least-cost optimization model (OSeMOSYS) using open-access data from an agro-ecological zones model (GAEZ). – Energy models were developed using open-access modelling tools and published in open access scientific journals, providing public access to the approach and input data, which creates opportunities for replicating the results, modifying the assumptions, and further improving the research work.

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New insights – … on the combined abatement potential of carbon taxes and lower financing costs on power systems decarbonization. – … on the effect of diesel subsidies and variations on the capital investment costs of batteries, PV modules and diesel generators on the cost-competitiveness of decentralized generation technologies (hybrid, diesel and solar-battery micro-grids, stand-alone PV and stand-alone diesel). – … on how the availability of relevant geospatial data has the potential to reinforce electrification modelling and planning. – … how linking a capacity expansion model with a cooperative games-theory model can help to estimate the bargaining power of alternative export scenarios and identify possible issues on the political economy of bilateral agreements. – … on how least-cost energy systems models can provide guidance on where financial investments on sustainable agriculture intensification and investments on advanced biorefineries are best placed. Applied analytical advances

– This is the first high-level geo-spatial electrification study applied to Bolivia using the open-source OnSSET modelling tool in which increased technical accuracy of the modelling of micro-grids was inserted by a new framework. Data from the most recent national census on population and households was used to delineate the current electrification status, and data from two survey campaigns were used to characterize the load demand profile of rural populations in two different climatic contexts. Cost-assumptions were evaluated in different scenarios to evaluate the impact of the least-cost electrification solution. – This is the first published bottom-up model of Bolivia’s power sector developed in the OSeMOSYS open-source modelling tool, in which four nodes were modelled with their respective energy resource potential, current installed power generation capacity and electricity demands, and allowing to electricity exchange between nodes. In addition, increased production costs of natural gas were introduced by inserting opportunity

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costs of natural gas exports, which incentivizes the development of currently untapped reserves. – This is the first energy demand accounting model (and projections) developed in close collaboration with the Ministry of Energy and Hydrocarbons in Bolivia in which energy demands were disaggregated by end-use. In this work, energy- intensive industrial projects slated for 2025 were added to the projections. In addition, government targets for fuel switch to natural gas and energy saving targets were introduced in a single model. – This is the first study assessing potential electricity exports of Bolivia using a continental electricity system model developed earlier by Pinto de Moura et al. (2018) in the OSeMOSYS open-source modelling tool. Each country was modelled with its respective energy resources potential, energy resources availability, installed capacity and committed investments for capacity expansion. – This is the first study assessing investments to improve the productivity of biofuels feedstock production and on improved biorefinery configurations on a least-cost optimization model.

6.3 Limitations and recommendations for future work

Regarding the electrification study, our results are necessary imprecise at the settlement-level due to required assumptions that do not capture dynamics and particularities at the micro-level. For example, migratory trends from isolated rural areas to urbanized centers were not considered in the population projections (which rely on simple country-level population growth rates). Additional data about consumer demand could allow for more detailed classifications of households in additional geographical, social and climatic contexts. In addition, a careful assessment of future demands of electricity for productive uses (such as electricity for manufacturing, agriculture and enterprises) would be beneficial for expanding the analysis which has been focused in this thesis on residential demand and community buildings (schools, health centers

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and public lighting). Also, further expansions of the developed methodology could include a broader portfolio of micro-grid technologies and fuels, including multi-source combinations of wind, solar, gas, biogas possibilities. Similarly, the methodology could be expanded to assess larger systems such as mini-grids, which require additional information about appliance ownership and usage habits, among other things, for better characterizing demand. The framework developed can easily be updated when additional data is available. Lastly, future work on this research should look at affordability and financing considerations for meeting desired electrification targets. Additional enabling forces should be studied to address the entire range of affordability and financing concerns for both grid and off-grid rural consumers. Regarding the power systems models, at the time of this writing, several projects originally envisioned for investment in the period 2015–2020 have not been deployed as planned. The construction of new units influences the electricity production of other units and the least-cost solution of additional capacity. These factors in turn influence the estimates on abatement costs and abatement potentials. The data included in these models will need to be updated in future modelling studies in order to meet current investments and account for other trends such as fuel prices, declining costs of renewable energy projects, and additional technologies that are not yet fully market-consolidated, such as fossil-fuel-based CCS and BioCCS. In this regard, conducting sensitivity analyses to cover some of these uncertainties, providing, for instance, an analysis of whether stringent mitigation targets could be achieved without making use of CCS based options, would be a valuable path to investigate. Regarding climate policy, future research on the effects that carbon taxes have on income distribution (heavier burden in the lower income groups) and competitiveness (production costs rise) will complement a broader understanding of the economy-wide effects of carbon taxes. In addition, further research about financial ‘de-risking’ instruments is required to add to the understanding of drivers that have greater potential for reducing the systemic market risk. Regarding the study on power systems integration in South America, the theoretical approach to identify the bargaining power of exporting and importing countries do not cover important aspects, such as political ones, and therefore cannot be qualified as definitive, but rather as part of an ongoing process. This research would benefit from a valuation of climate change vulnerability, which was not included in the long-term scenarios,

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to analyze effects on energy security, in the deployment of RET and on hydropower export potential. Furthermore, due to its limited temporal- resolution, the model developed cannot capture complementarities between large hydropower plants and other renewable energy technologies. Future studies assessing climatic complementarities (e.g. dry seasons having high wind speeds) and the potential to counteract short- term wind and solar variability with the flexible operation of hydropower would illustrate to policy makers on how to harness synergies between hydropower and RET. Finally, regarding the study on ethanol, several assumptions were made in order to model the processes and materials in the production chain. Future research could build upon this work using more accurate and up-to-date data for many of the parameters used in the model. Particularly, better quantitative microdata on use of agricultural inputs and mechanization could be combined with socio-economic data to investigate the factors influencing agriculture intensification and technology adoption. More importantly, further research is required in the field of sustainable agriculture intensification. Although this thesis approaches the cost of increasing productivity with the use of agricultural inputs and mechanization, it does not address the critical impact of agriculture intensification on environmental processes. Therefore, further CLEWs nexus interactions, effects of climate change and environmental effects of indirect land-use change (iLUC) emissions are directions for future work, adding important insights to the overall understanding on the sustainability of ethanol production. Lastly, climate change has effects on seasonal precipitation and therefore affects crop yields of rainfed agriculture. The study on biofuel feedstock production would benefit from this assessment and identify climate risk adaptation measures for improvements in agricultural production systems.

6.4 Thesis impact

This work would not have been possible without active collaboration with authorities and government officials from several ministries and planning institutions in Bolivia. In turn, the author was an energy modeler and trainer in capacity building workshops organized by the United Nations Division of Economic and Social Affairs (UNDESA) and the Unit for Analysis of Social and Economic Policies (UDAPE) in which government

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officials were trained to develop energy models using the Long-range Energy Alternatives Planning model (LEAP) and OSeMOSYS modelling tools. Datasets were made available during these activities and facilitated large parts of this thesis work. In addition, talking with officials from different ministries brought knowledge and expertise to model formulations. The models provided insights to the Ministry of Development Planning and UDAPE to write policy notes and submit the Intended Nationally Determined Contribution (INDC) to the Conference of Parties (COP21) in Paris in 2015. Building the country’s national energy system in OSeMOSYS was the first time such work has been conducted with an open-source modelling tool. This initiative was expanded later to building the first integrated CLEWs model for Bolivia. Much of the work carried out as consultant for this project led to the consolidation of Paper II, III and V. The methodology designed for Paper I was made in collaboration with two other research institutions (Politecnico di Milano and University of Liege), combining ultimately three open-source modelling tools into a single framework. This work was transferred to Universidad Mayor de San Simón in Cochabamba, Bolivia and successfully got research grant funding from the Sustainable Development Solutions Network to help deepen the research work.

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Chapter 7. Ref erences

7 References

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