Optimal design and deployment of isolated energy systems: The Bolivian pathway to 100 % rural electrification

During the past decades, the planet has undergone increased environmental pressure. This has led to a clear momentum towards the creation of a more sustainable world. In this context, the challenge to provide energy access for all in a fair and sustainable way is an enormous task. The international agency of energy has estimated that yearly investments of 55 billion USD are needed to reach set targets. To optimize the limited resources, researchers have focused on the use of geographical information systems (GIS) to better capture the spatial dimension and define least-cost pathways to universal energy access. The size of the deployment problem imposes to model dispersed energy demands and isolated energy systems in a simplified manner, which can lead to suboptimal solutions. In consequence, there is a need to capture the diversity of conditions in which these systems are deployed. The goal of this thesis is to contribute to the modeling of rural electrification processes through tailored models and methods. These tools are integrated into a coherent modeling framework, covering the whole value chain between accurate characterization of household demand to the macroscopic (national) planning of rural electrification. The models related to each relevant scale are soft-linked by defining common variables of interest. Then, methods to integrate the results of the more detailed models into the higher-level model are introduced. This approach provides additional technical insights and a better spatio-temporal optimum. The structure of this thesis reflects this bottom-up approach. It is organized in three parts. The first part deals with energy demand modeling in a rural context and presents the Bolivian case study. It introduces two methods to create stochastic load profiles depending on the available data (measurements or surveys). In addition, it explores the components of an ideal rural community and frames appliance ownership according to surveys in rural communities. Finally, demand curves at the household and community level are generated using an ad-hoc stochastic bottom-up profile generation model. The second part presents and applies an optimal sizing and operation framework for isolated energy systems in different contexts. The operational data from an existing microgrid in Bolivia is used as a benchmark and as a test case to test the model. Different sizing methods and formulations are compared, leading to the conclusion that a compromise must be found between system reliability and computational tractability. Finally, the trade-off between cost and lost load probability in single households equipped with a solar home system is analyzed. The third part deals with the creation of surrogate models for microgrid design and its use in GIS-based electrification models. The limitations of existing GIS tools are discussed and surrogate models are proposed as a solution to increase accuracy without compromising the solving time of the model. A methodology to create and validate surrogate models for rural electrification is introduced. Then, the OnSSET model is adapted and improved to integrate this new formulation. Finally, different electrification scenarios are computed for the case of Bolivia, where both hybrid microgrids and solar home systems proved to be essential technologies for the cost-optimal electrification of remote communities