Developing countries confront the challenge of generating more electricity to meet demands in a sustainable manner. According to the World Bank’s 2015 Global Tracking Framework, roughly 15% of world population (or 1.1 billion people) lack access to electricity, and many more rely on poor quality electricity supplies. In September 2015, the United Nations General Assembly adopted Agenda 2030 comprised of a set of 17 Sustainable Development Goals (SDGs) and defined by 169 targets. “Ensuring access to affordable, reliable, sustainable and modern energy for all by 2030” is the seventh goal (SDG7). While energy access refers to more than electricity, it is the central focus of this work.

Models addressing electrification and access typically need large volumes of reliable energy-related data and information, which in most developing countries have been limited or not available. This paucity of information has decelerated energy planning in the developing World. That situation has fundamentally changed with increasing availability and application of Geographic Information Systems (GIS). GIS layers can provide location specific energy-related information that has not been previously accessible. The focus of this thesis lies on integrating a simple electricity supply model into GIS. In so doing a novel open source spatial electrification tool is developed. It estimates power capacity needs and associated investment (and other) costs for achieving universal access to electricity in developing countries.

The dissertation includes a cover essay and six appended papers presenting quantitative methods on coupling selected aspects of GIS and energy systems. It strives to answer three key research questions. 

The first research question is: What is the spatially explicit renewable energy potential that can be technically and economically exploited? This information is currently either missing or scattered in developing countries. The provision of low cost, locally available energy can provide a significant opportunity to empower a better standard of living. The first paper presents a GIS based approach to assess the onshore technical wind energy potential on the African continent by applying socioeconomic and geographic restrictions regarding the localization of wind farms and state of the art wind data analysis. The second paper builds on this knowledge and moves one step further by assessing the economic potential and providing cost indicators to assess the viability of wind power (this time in India). The third paper maps the economic wind power potential in Africa based on the methodologies developed in the two preceding papers. Not only wind power but most energy resources have a spatial nature and their availability is linked to geography. Evaluating these other energy sources (solar, hydro etc.) are included and analysed in Papers IV-VI.

The second research question is: what is the least-cost set of technologies needed to meet different levels of electricity use accounting for different geographies? Increasing access to electricity effectively requires, inter alia, strategies and programmes that address and account for the geographical, infrastructural and socioeconomic characteristics of a country or region. Paper IV introduces a GIS based methodology to inform electrification planning. It builds on the previous work by taking into account the techno-economic wind, and other resource mapping. This methodology is applied in Nigeria in order to determine the least cost technology mix considering the country’s infrastructure and resource availability on a spatial basis. Paper V utilizes this method and in so doing demonstrates the importance of geospatial calculations in energy access planning. It highlights differences in investment estimates between alternate scenarios with regards to energy demand and technology deployment. Paper VI enhances this methodology and applies it to every square kilometre of Sub-Saharan Africa. The method is subsequently implemented in an Open Source Spatial Electrification Tool (OnSSET) to facilitate education, repeatability and further research.

Finally, the third question is: Are there gains to be had by linking geographically explicit analysis with typical (non-spatially explicit) long term energy systems models? The work shows that not only do long-term systems models influence geospatially optimal technology deployment. But vice versa, their output influences long term systems models’ investment profile.  That is because the geospatial disaggregation allows for a better determination of grid versus off-grid connections, and in turn power demand on the national grid. This thesis demonstrates that energy system models should take into consideration the geographic dimension of energy-related parameters, as these play a fundamental role in determining the optimal energy system of a region.