Electric Vehicle Integration using Large-Scale Combined Transmission and Distribution Grid Models
Diana Wallison, Lyric Haylow, Jessica Wert, Jonathan M. Snodgrass, Thomas J. Overbye, Yanzhi, Xu
TL;DR
This paper introduces a unified co-simulation framework that integrates transportation demand, land use, demographics, and emissions with large-scale transmission and distribution grids to optimize EV deployment, particularly for truck charging. By coupling a regional Travel Demand Model with a large-scale AC-OPF-based co-simulation (via HELICS) for a synthetic Texas grid, the study analyzes 2,304 EV charging snapshots across 96 scenarios, mapping loads to distribution networks with Voronoi-based service areas and enabling end-to-end optimization down to distribution nodes. Key findings show that load-shifting strategies driven by marginal-cost thresholds can significantly reduce transmission capital and operating costs, while midnight charging offers substantial cost savings though with complex emissions trade-offs as generation mixes shift. The framework demonstrates scalability to millions of distribution nodes and provides actionable insights for optimal truck-depot placement and charging-infrastructure sizing, underscoring the potential for coordinated T&D optimization in EV integration planning.
Abstract
In this paper, we propose a unifying co-simulation framework integrating transportation demand, grid assets, land use, demographics, and emissions to optimally accelerate electric vehicle (EV) development as well as measure the impact of EV integration. 96 urban and long-haul truck charging demand simulations were developed and integrated into a combined transmission and distribution (T&D) simulation, encompassing the Houston/Dallas/Fort Worth area. The T&D scenarios are then used to develop cost optimization strategies to determine optimal placement and sizing of truck charging infrastructure that minimize infrastructure costs.