Despite electric vehicles (EVs) gaining traction in many industrialized nations, their presence in Africa remains nascent. African cities are some of the fastest motorizing cities in the world, with major implications for the vehicle stock being adopted. While EVs are emerging as a critical solution for countries to achieve their Nationally Determined Contributions as part of the Paris Agreement, while also tackling urban air pollution, and affordability, we acknowledge the many unique challenges that African cities face in growing EV adoption, especially in the capacity of their electricity grids to sustain the EV transition – in fact, grids in many African cities often have generation and distribution constraints that may potentially be exacerbated by a transition to EVs.
In partnership with colleagues at University of Amherst, Massachusetts, I recently collaborated in a study that is the first comprehensive analysis of the impact of electric vehicle fleet expansion on electricity grids in African cities, leveraging data on local urban mobility patterns. Our study was just recently published in Nature’s Scientific Reports.

We focus on Nairobi, Kenya, a dynamic and fast-growing metropolitan area that is anticipated to have 10 million residents by 2050, and a leader in the EV transition. Our study explores the influence of different fleet conversion rates across key vehicle classes – including public transport buses, commercial fleet vehicles, and private domestic vehicles – on bulk power supply and the electricity distribution grid in Nairobi. Our work offers unique insights into the potential for electric mobility in Nairobi’s evolving urban landscape, serving as a blueprint for understanding the challenges faced by growing African metros aiming to incorporate EVs.
Our methodology involves building granular models that simulate traffic patterns, electric vehicle charging, and electricity grid transformer utilization. Inputs to these models include hourly traffic data from Uber’s Movement platform, trip surveys from transportation modelers, asset ownership data from the USAID Demographic and Health Surveys, and electricity consumption and transformer data made available by the national electricity utility of Kenya. We combine these models to investigate the effects of progressively larger rates of electric vehicle adoption on bulk electricity supply, and also estimate the overloading of transformers, an important indicator of grid stability.

We find that adoption of electric vehicles across the public transportation and commercial fleet sectors generally improves grid conditions in the city. This is due to such adoption largely adding consumption during periods when the grid is otherwise significantly below peak capacity. However, widespread conversion of private vehicles – the largest vehicle class in Nairobi – to electric vehicles, can substantially exacerbate peak electric load by up to 30%, leading to the accelerated overload of transformers, forcing both an increase in electricity outages as well as early equipment replacement costs of up to USD$15 million within five years of fleet conversion. These outcomes would have major implications for energy services in cities like Nairobi, and could be detrimental to the viability of EVs as a solution for air quality and for achieving climate goals.

Our study explores important actions to mitigate these outcomes – crucial insights for policy makers and stakeholders in the electric mobility sector. We quantify the impact of incrementally introducing managed electric charging by introducing coordination logic into the charging model. Our findings show that coordinated charging would not only shave the peak electricity demand relative to baseline, but could actually reverse the situation, improving grid efficiency – for instance, coordinated charging of electric vehicles at a 30% adoption rate could be the equivalent of adding a $10 million Battery Energy Storage System to the Nairobi grid, and could represent a 20% cost savings in early equipment replacements. This finding demonstrates the critical nature of a managed fleet conversion and coordinated charging.

Finally, our study also considers the potential effects of local driving and charging behavior on system planning. For instance, in cities like Nairobi, many drivers maintain only slightly above empty gas tank levels during their daily trips, fueling at stations as needed. If this behavior translates to the equivalent of low range anxiety for electric vehicles, such drivers may have less frequent charging events than high range anxiety consumers who may recharge their battery more often. Such behavior differences in charging could result in a halving of peak demand. Again, these results point to the importance of a managed fleet conversion, and the importance of collecting data on behavior patterns for system design.

This study is the first of its kind for African cities, exploring urban electric mobility transitions through high resolution modeling. The implications of our findings are crucial for today’s electric utility, municipal transport planner, regulators and national policy makers. for delivering successful electric mobility transitions across African cities. Practically, stakeholders such as Kenya Power and the Energy Regulatory Commission of Kenya, will immediately be able to leverage the results of our research. Our methods are replicable for other primary and secondary cities across the continent and can help improve the planning of efficient and effective African energy and mobility transitions.
Look out for more research in this topic coming soon.