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Geospatial Open-Source Modelling for Integrated Energy Access Planning

New Tools and Methods to Bridge the Energy Access Gap

Time: Mon 2024-02-19 13.00

Location: F3 (Flodis), Lindstedtsvägen 26 & 28, Stockholm

Video link:

Language: English

Subject area: Energy Technology

Doctoral student: Babak Khavari , Energisystem

Opponent: Professor Erik Ahlgren,

Supervisor: Docent Francesco Fuso Nerini, Energisystem, KTH Climate Action Centre, CAC; Universitets lektor William Usher, Energisystem; Professor Mark Howells, Loughborough University, Imperial College London.

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In 2015 the United Nations (UN) General Assembly agreed on the Sustainable Development Goals (SDGs), a set of 17 goals defined by 169 targets to be reached by 2030. Amongst them is SDG 7. SDG 7 states “Ensure access to affordable, reliable, sustainable and modern energy for all”. The first target of SDG 7 mentions access to electricity and clean cooking specifically. Access to electricity brings with it myriad benefits across several sectors, including residential, health and education. Access to clean cooking can help reduce adverse health and environmental effects, as well as high opportunity costs related to current cooking practices, amongst other benefits. 

While SDG 7 has been recognized as a key pillar to achieve sustainable development, its achievement has remained elusive. As of 2021, 675 million people worldwide were estimated to lack access to electricity. The largest access gap is found in Sub-Saharan Africa (SSA) where only 50% of the population had electricity access. For clean cooking the situation is worse, with around 2.3 billion people globally lacking access. Again, the access gap is most pronounced in SSA with only 18% of the population in the region using clean cooking. The situation in SSA is further exacerbated by the fact that the population increases faster than the clean cooking access rate.

For electricity access modelling Geographic Information Systems (GIS) and the use of geospatial data is being increasingly leveraged. As every case in a study area is unique and requires context-specific information, the spatial dimension of GIS can help to more effectively model towards universal electricity access. Resource availability, fuel costs and access to infrastructure change spatially and a geospatial approach helps to capture this. Such reasoning can also be applied to clean cooking. Yet, at the time of writing this thesis, there was no geospatial tool comparing the relative costs and benefits of different cooking solutions. This work aims to advance the state-of-the-art in geospatial modelling approaches to support integrated energy planning towards universal electricity and clean cooking access.

Geospatial electrification modeling, while proven useful, is still a new field with many on-going developments. One such significant development was the move from raster population datasets to aggregated vector settlements. Raster datasets divide an area into uniform units with each unit including some piece of information about the area. It can be beneficial to have uniform units in modelling, but for this reason rasters fail to capture the size and shape that population settlements naturally have. On the other hand, aggregating raster datasets to vector settlements may impact modelling results. With this in mind, the first research question explores how the aggregation of data changes modelling results in geospatial electrification models. Paper I presents an open-source algorithm for the creation of aggregated vector settlements from raster data. In Paper I this algorithm is applied to 44 countries in SSA. As part of the algorithm, night-time lights are used to assess electrification rates within settlements and population density is used to assess the urban-rural divide of each country. The electrification rates and urban-rural divide is subsequently validated against survey data and compared to previous results. Following this, Paper II compares results produced by the Open Source Spatial Electrification Tool (OnSSET) as the level of population aggregation changes. This is done for three case studies (Benin, Malawi and Namibia), by producing 26 population bases for each country. Two of the population bases are rasters with different resolutions, three use the method developed in Paper I and 21 are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Paper II also presents the first Global Sensitivity Analysis (GSA) conducted for geospatial least-cost electrification models. The GSA enables comparisons between the importance of parameters that previous research has identified as important and the importance of population aggregation. 

The second research question explores if, and how, GIS can be used to develop clean cooking tools comparing different cooking solutions. To this end, Paper III presents OnStove. OnStove is the first geospatial tool comparing the relative costs and benefits of different cooking solutions. In Paper III the tool is described and applied for the first time to 44 countries in SSA. The tool is open-source and all data used to run the analysis (as well as the results) are published and made available for a broader public. Two main scenarios are developed for SSA assessing differences between current cooking practices and potential pathways for maximizing net-benefits (defined as total benefits minus total costs). In addition to the main scenarios, 680 additional scenarios are developed as part of a GSA to assess the impact of uncertainty of 33 parameters on key outputs.

Finally, the last research question assesses how integrated energy access planning impacts the results of existing geospatial electrification (OnSSET) and clean cooking (OnStove) tools. This is done by combining the two aforementioned tools in Paper IV in a case study of Kenya. The results describe how the least-cost technology mix and Levelized Cost of Electricity (LCoE) changes across Kenya as the increased electricity load following the inclusion of electric cooking is accounted for in OnSSET. On the cooking side the paper outlines how the competitiveness of electric stoves change as the electrification rate increase and the LCoE change. Paper IV also deepens the insights on research questions one and two as a new resolution is used to generate the population clusters using the algorithm developed in Paper I and new developments are done to OnStove.