With the growing push toward decarbonizing the transportation sector, fleet electrification is gaining momentum. Fleet vehicles, particularly buses and heavy-duty trucks, contribute significantly to total emissions. Compared to individually owned EVs, fleet EVs are likely to employ fast chargers that can introduce substantial, concentrated power demands, leading to more pronounced localized impacts on the grid. Moreover, the adoption dynamics of fleet EVs differ markedly from those of private EVs in terms of adoption rate, deployment scale, and geographic distribution. Fleet electrification presents significant challenges. Power grids are highly sensitive to increased loads, and infrastructure upgrades involve substantial costs. Commercial EV fleets, on the other hand, typically operate on fixed or predictable schedules, making them well-suited for load management and the employment of site-wide smart charging technologies.

Previous studies analyzing the impact of fleet electrification on the power grid often rely on fleet load curves generated through simulations using stochastic models. These models typically incorporate publicly available data on driving patterns, charging behavior, EV charging characteristics, and assumptions about charging infrastructure. While this is a widely accepted and effective approach, it tends to fall short in capturing localized driving and charging behavior, daily variations, seasonal trends, and behavioral shifts.

This study utilizes real-world data to reduce model abstraction and improve the accuracy of load estimates of EV Fleets. We aim to address whether local grid infrastructure can support the electrification of existing fleets, particularly when enhanced by grid-aware control systems. The analysis draws on anonymized load curve data from diverse EV fleets, spanning multiple sites per fleet type, including taxis, buses, delivery vehicles, and trucks. The comparative study is conducted on a range of urban and semi-urban settings, each characterized by varying levels of EV adoption and grid demand patterns. The analysis is performed for distinct locations chosen based on the mix of different types of charging hubs. To estimate the total charging demand for a location, the demand curve representing different fleet types is scaled based on the number of charging hubs per fleet type and is aggregated.

Preliminary calculations suggest a reduction in peak load of 32% for bus fleets, 36% for truck fleets, and 53% for fleet taxis can be achieved while satisfying the energy demand with smart charging solutions at fleet depots. These estimates are based on real-world data from single representative sites for each fleet type analyzed over a week, where demand was observed to be high. In the final study, we aim to explore the potential of load shaping using grid-level load estimates, cost savings, and greenhouse gas emission reduction for the locations under consideration.