A modeling framework to support the electrification of private transport in African cities: a case study of Addis Ababa

TL;DR

This study tackles the lack of mobility data for electric mobility planning in Africa by proposing a transferable modeling framework that uses open-source geospatial data to simulate intra-urban mobility, spatio-temporal charging demand, and EV-PV complementarity in data-scarce settings. It applies the framework to Addis Ababa with a hypothetical 100,000-vehicle private EV fleet, analyzing three charging archetypes and evaluating PV integration with PVlib-based production estimates. Key contributions include a self-calibrating gravity-based trip-distribution approach, a tractable two-step charging-demand model, and quantification of charging infrastructure needs and PV self-sufficiency across scenarios. The work demonstrates that large-scale private EV adoption could be accommodated with existing or modestly expanded infrastructure, and that PV can substantially offset charging loads, offering a practical, transferable tool for policymakers and planners in similar cities.

Abstract

The electrification of road transport, as the predominant mode of transportation in Africa, represents a great opportunity to reduce greenhouse gas emissions and dependence on costly fuel imports. However, it introduces major challenges for local energy infrastructures, including the deployment of charging stations and the impact on often fragile electricity grids. Despite its importance, research on electric mobility planning in Africa remains limited, while existing planning tools rely on detailed local mobility data that is often unavailable, especially for privately owned passenger vehicles. In this study, we introduce a novel framework designed to support private vehicle electrification in data-scarce regions and apply it to Addis Ababa, simulating the mobility patterns and charging needs of 100,000 electric vehicles. Our analysis indicate that these vehicles generate a daily charging demand of approximately 350 MWh and emphasize the significant influence of the charging location on the spatial and temporal distribution of this demand. Notably, charging at public places can help smooth the charging demand throughout the day, mitigating peak charging loads on the electricity grid. We also estimate charging station requirements, finding that workplace charging requires approximately one charging point per three electric vehicles, while public charging requires only one per thirty. Finally, we demonstrate that photovoltaic energy can cover a substantial share of the charging needs, emphasizing the potential for renewable energy integration. This study lays the groundwork for electric mobility planning in Addis Ababa while offering a transferable framework for other African cities.

Figures (10)

• Figure 1: Hourly electricity demand profile for Addis Ababa on a typical weekday, derived from national demand data provided by the National Grid Control Center and scaled to the city level. The scaling methodology is detailed in \ref{['app:demand_profile']}.
• Figure 1: Charging probability function as a function of daily EV energy use, for two battery capacities. The plot displays the average charging probability (solid line) along with the standard deviation. Results are derived from random sampling of 50,000 values.
• Figure 2: Overview of the three main steps of the modeling framework
• Figure 2: Histogram of the daily commuting distance (two-way) for the 100,000 simulated EVs.
• Figure 3: Map of the study area (Addis Ababa) showing administrative boundaries and traffic zones.