OPTIMIZATION OF THE DESIGN AND OPERATION OF A HYBRID OFF-GRID SYSTEM WITH THE OPEN ENERGY MODELLING FRAMEWORK

Electricity is a very precious commodity and its access on large scale has promoted significant improvements in all the society sectors. Nevertheless, nowadays around 770 million people lack access to affordable, reliable, and sustainable forms of energy, which are essential factors for improving living conditions. Most of them live in rural areas of developing countries, which are often isolated, low-densely populated and characterized by poor infrastructures and services. The global push to reach the Sustainable Development Goals (SDGs) by 2030 has emphasized the role of rural electrification, and off-grid small-scale generation represents one of the most interesting options to pursue. In this regard, hybrid renewable energy systems (HRESs) are expected to play a key role, because they are designed to overcome the fluctuating nature of renewable energy sources (RESs) ensuring the availability of power when one of the generation units experiences intermittencies. However, determining the best local supply solution represents a very complicated task requiring different generation technologies and configuration systems. For this purpose, sophisticated, flexible and powerful modelling tools are essential to build up strong models that depict in the best possible way the behaviour of real systems, and are able to find the optimal solution to the optimization problem. Furthermore, in order to properly cope with the increasing share of intermittent RESs and storage technologies, it is necessary to perform analyses that cover high temporal and spatial resolutions. The large number of time-varying input data, such as solar irradiance and electrical demand profiles, in addition to the complexity of energy system configurations, often make the optimization problems computationally intractable. This issue can be addressed using clustering analysis in order to aggregate time series of input data in periods representative of the original time series. The aim of this thesis work is threefold. The first one is to introduce the energy poverty issue worldwide, and to analyze possible approaches to overcome it. The second aim it to present the open energy modelling framework, oemof, modelling approach, to optimize the design and operation of a hybrid off-grid energy system. Finally, the third aim is to investigate the effects on the optimization results of using a reduced data set obtained with clustering techniques. One expected result of using time-series clustering approaches compared to the entire dataset is the reduction of computational time of the optimization procedure with no significant impact on the accuracy of the results.