Abstract
Strategies for meeting Sustainable Development Goal 7 of providing access to electricity for all recognize the important role that off-grid solutions will need to play. Mini-grids will from part of this response, yet little data exists on household demand from these customers. Predicting demand accurately is a crucial part of planning financially viable mini-grid systems, so it is important to understand demand as fully as possible. This paper draws on metered data from two solar PV diesel hybrid mini-grid sites in Tanzania. It presents an analysis of load profiles from the different sites and categorizes households by demand characteristics. The paper then combines load profile data with household demographic and electrical asset ownership data to explore factors behind distinct load profile patterns of use. It concludes that load profiles are determined by a complex mix of appliance ownership, occupancy, and socio-economic status.
Highlights
Understanding Load Profiles ofThe headline target for Sustainable Development Goal 7 is target 7.1: to “ensure universal access to affordable, reliable and modern energy services” by 2030
The same team has published further work demonstrating how mini-grid customers in Tanzania can be divided into five distinct load profile types [26]
In order to inform modelling approaches, the analysis provides some understanding of the factors that lie behind electrical load profiles exhibited by different groups of mini-grid customers
Summary
The headline target for Sustainable Development Goal 7 is target 7.1: to “ensure universal access to affordable, reliable and modern energy services” by 2030. Academics including Mandelli et al have developed software which attempt to generate these profiles using mathematical models [20,21,22] These approaches rely on load profiles from consumer groups in similar contexts to the mini-grid in question, but data from low income households in Africa are lacking. The same team has published further work demonstrating how mini-grid customers in Tanzania can be divided into five distinct load profile types [26] This kind of customer segmentation, based on clustering techniques, is important to energy utilities more widely and can be used, for example, for energy efficiency campaigns, pricing, energy forecasting, and distributed generation planning [24]. In order to inform modelling approaches, the analysis provides some understanding of the factors that lie behind electrical load profiles exhibited by different groups of mini-grid customers
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