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    RESEARCH  |  IMPACT SERIES  |  SOLVING SUSTAINABLE

    Comparing EV scenarios

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    Contributor: Ryan Preclaw
    12 Jul 2023

    Modelling the potential impact of 100% electric vehicle (EV) adoption is a complex challenge. By combining a group of experts, sizable data sets and AI-powered analytical tools, the Barclays Investment Sciences team considered three possible scenarios for how full EV adoption could play out: cheap, easy, and green.

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    Cost comparison for the scenarios

    They considered both household costs and system costs associated with the three scenarios: easy, cheap and green.

    The model suggests that California could leverage EVs to go full green at a direct cost slightly higher than current combined gasoline and electricity bills. When the estimated social costs of carbon emissions are added, it would become cheaper to be green than to maintain the status quo.

    Looking at system costs, the model indicates a potential maximum spend of about US$7bn in new energy generating capacity to service peak demand for personal vehicles. This is less than 2.5% of the amount Californians would comparably spend on gasoline. The model estimates that a fully green transition to EV could cost up to US$300bn, but that would lead to gasoline cost savings in excess of US$350bn over 20 years.

    Carbon emissions across the scenarios

    The easy and cheap scenarios would both result in a significant drop in carbon emissions caused by transport. However, the reduction created by going full green would be almost double. In this scenario, the reduction could potentially be over 100% because returning power to the grid would also reduce the carbon produced by other household activities. This translates to a near halving of household carbon production in the easy and cheap scenarios, and the potential for houses to become net-zero in the green scenario.

    Comparing power demand and sources

    Within each scenario, power demand undulates through different peaks and troughs as demand rises and falls and is supplied by differing levels of charging infrastructure. With the easy scenario, demand peaks later in the day, with the cheap scenario this is mostly shifted to the early morning hours as charging is constrained, and the green scenario sees the peak spread out through the day.

    The maximum green scenario implies a large increase in power coming from renewables such as solar and wind, and a considerable drop in natural gas. Notably, the easy and cheap scenarios would both require an increase in natural gas supply.

    Shifts in consumer behaviour

    Over 90% of EV drivers prefer charging at home, with almost half considering low-cost at-home charging to be the most important economic factor in EV adoption.

    In the easy scenario, consumer behaviour would lead to significant demand peaks as people come home from work and plug their car into the grid at the same time. In the cheap scenario, dynamic price shifting could reposition these peaks and reduce strain on infrastructure. However, with a wider penetration of public charging points and the introduction of bi-directional charging that allows EVs to return energy to the grid, a max green scenario would see both reduced strain and fewer restrictions on access to charging points.

    EV infrastructure requirements

    Greater use of EVs would of course necessitate increased infrastructure to support them. These include at-home chargers, and new plants to handle peak demand in the easy scenario. The green scenario would also require more distributed charging points and a considerable increase in renewable energy infrastructure.

    Importantly, to fully leverage a 100% EV shift in a maximum green scenario, policymakers may need to ask manufacturers to make two-way charging standard equipment on EVs, to enable power to be transferred back to the grid.

    Source: Barclays Research

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    Science behind the modelling

    To explore the model and methodology behind the analysis, hear from Ryan Preclaw around how he and the team harnessed AI to analyse millions of data points.

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    About the expert

    Ryan Preclaw

    Global Head of Investment Sciences

    Ryan Preclaw is the head of Investment Sciences, a group that creates investment insights by combining alternative data, data science, and traditional research. Previously, he was a Director in Credit Strategy, where he focused on special situations, event-driven strategies, and industries facing fundamental transitions. Ryan has also worked as a coverage banker in Barclays' Communications and Media group. Prior to joining Barclays, Ryan worked as an economist at NERA Economic Consulting and London Economics International. Ryan received his M.B.A. from the University of Chicago in 2008, his M.A. from Western University in 2001, and his B.A. from the University of Alberta in 2000.

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