Limited data on global power infrastructure makes it difficult to respond to challenges in electricity access and climate change. Although high-voltage data on transmission networks are often available, medium- and low-voltage data are often non-existent or unavailable. This presents a challenge for practitioners working on the electricity access agenda, power sector resilience or climate change adaptation. Using state-of-the-art algorithms in geospatial data analysis, we create a first composite map of the global power system with an open license. We find that 97% of the global population lives within 10 km of a MV line, but with large variations between regions and income levels. We show an accuracy of 75% across our validation set of 14 countries, and we demonstrate the value of these data at both a national and regional level. The results from this study pave the way for improved efforts in electricity modelling and planning and are an important step in tackling the Sustainable Development Goals.

Achieving universal access to electricity is a development challenge many countries are currently battling with. The advancement of information technology has, among others, vastly improved the availability of geographic data and information. That, in turn, has had a considerable impact on tracking progress as well as better informing decision making in the field of electrification. This paper provides an overview of open access geospatial data and GIS based electrification models aiming to support SDG7, while discussing their role in answering difficult policy questions. Upon those, an updated version of the Open Source Spatial Electrification Toolkit (OnSSET-2018) is introduced and tested against the case study of Malawi. At a cost of $1.83 billion the baseline scenario indicates that off-grid PV is the least cost electrification option for 67.4% Malawians, while grid extension can connect about 32.6% of population in 2030. Sensitivity analysis however, indicates that the electricity demand projection determines significantly both the least cost technology mix and the investment required, with the latter ranging between $1.65–7.78 billion.

Sub-Saharan Africa has been at the epicenter of an ongoing global dialogue around the issue of energy poverty. More than half of the world’s population without access to modern energy services lives there. It also happens to be a sub-continent with plentiful renewable energy resource potential. Hydropower is one of them, and to a large extent it remains untapped. This study focuses on the technical assessment of small-scale hydropower (0.01–10 MW) in Sub-Saharan Africa. The underlying methodology was based on open source geospatial datasets, whose combination allowed a consistent evaluation of 712,615 km of river network spanning over 44 countries. Environmental, topological, and social constraints were included in the form of constraints in the optimization algorithm. The results are presented on a country and power pool basis.

The authors wish to make a change in author names (adding new author—Dimitrios Mentis) to this paper

Linear and mixed-integer optimisation models are widely used across economics and engineering to study resource allocation, infrastructure planning, and energy-system transitions. Algebraic modelling languages such as GNU MathProg (GMPL), AMPL, and GAMS let researchers write these models close to their mathematical form, keeping them transparent and reviewable even without extensive coding experience. However, as models grow in scale, translating algebraic formulations into solver-ready sparse matrices becomes a major computational bottleneck. This paper introduces MOSOX, a Rust-based command-line tool and library that compiles a targeted subset of GMPL model and data files into sparse matrices, covering the constructs required by the OSeMOSYS energy-system model family. It expands sets, parameters, variables, objectives, and constraints into matrices, exports them in standard MPS format, and can solve models directly via the HiGHS solver. On OSeMOSYS benchmarks, MOSOX compiles matrices up to 6.5 times faster than GLPK's glpsol while also reducing peak memory use on the largest tested model. By combining fast, low-memory compilation with the readability of GMPL and solver-independent output, MOSOX - developed within the Climate Compatible Growth Program - supports reproducible, auditable, and automatable optimisation workflows for large-scale energy-system modelling.