This project developed a high-fidelity, scalable modeling platform to forecast medium- and heavy-duty (MHD) electric truck charging demand and evaluate its operational, capital, and environmental impacts on the Texas grid. Leveraging a modular transportation-grid co-simulation architecture, 96 charging scenarios were analyzed across variations in charging logic, rates, depot locations, seasons, and market adoption. Results indicate that charging behavior—specifically the timing and rate of charging—plays a more significant role in total costs than physical siting of infrastructure along the I-45 corridor. Midnight charging consistently lowers grid operating costs (which account for ~88% of total annualized costs) compared to charging during daytime load peaks (8 AM–5 PM), and higher charging rates reduce operating costs by minimizing congestion and better utilizing off-peak generation. Capital costs, primarily attributed to distribution system upgrades, comprise ~12% of total yearly costs.

Environmental outcomes are influenced by the emissions intensity of marginal generation, with off-peak charging yielding stronger emissions-reduction potential provided cleaner nighttime generation is available. Rate design strategies that shift load away from high locational marginal price (LMP) periods further reduce costs by 0.1–9% and help alleviate transmission congestion. Using these insights, ElectroTempo has commercialized depot identification and energy impact modeling software now deployed by members of the Texas Public Power Corridor—including Austin Energy, New Braunfels Utilities, Lower Colorado River Authority, CPS Energy, and Metropolitan Edison—to support real-world electrification planning.

This dataset is the result of the charging demand simulation that underpinned the subsequent grid and environmental impact analyses.