Abstract
Globally about 800 million people live without electricity at home, over two thirds of which are in sub-Saharan Africa. Planning electricity access infrastructure and allocating resources efficiently requires a careful assessment of the diverse energy needs across space, time, and sectors. Because of data scarcity, most country or regional-scale electrification planning studies have however assumed a spatio-temporally homogeneous (top-down) potential electricity demand. Poorly representing the heterogeneity in the potential electricity demand across space, time, and energy sectors can lead to inappropriate energy planning, inaccurate energy system sizing, and misleading cost assessments. Here we introduce M-LED, a Multi-sectoral Latent Electricity Demand geospatial data processing platform to estimate electricity demand in communities that live in energy poverty. The platform shows how big data and bottom-up energy modelling can be leveraged together to represent the potential electricity demand with high spatio-temporal and sectoral granularity. We apply the methodology to Kenya as a country-study and devote specific attention to the implications for water-energy-agriculture-development interlinkages. A more detailed representation of the demand-side in large-scale electrification planning tools bears a potential for improving energy planning and policy.
Highlights
Electricity is a direct input to virtually every economic sector
About 800 million people live without electricity at home, over two thirds of which are in sub-Saharan Africa
To match the simulated electricity demand profiles with each population cluster detailed we evaluate the statistical association between the distribution of the population with electricity access across
Summary
Affordable, and reliable provision of power is a necessary condition for human livelihoods to prosper. This involves the achievement of most the United Nations’ Sustainable Development Goals (SDGs) [1, 2]. The choice of the most efficient electricity supply option and the size of the local generation capacity and storage system strongly depend on the assumed local demand. This demand is defined both by the hourly load curve and its peaks, and by the total energy consumption. In turn—provided a set of conditions is satisfied—the electricity input might improve the income of the whole community [15]
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