GreenEVT: Greensboro Electric Vehicle Testbed

TL;DR

This work tackles the need for coupled, high-fidelity simulations of electric power distribution and urban transportation to assess EV impacts on grids and evacuation performance. It introduces GreenEVT, an open-source co-simulation testbed that links OpenDSS and SUMO with high-fidelity SMART-DS Greensboro data and OpenStreetMap inputs, enabling simulation down to parcels and households. Demonstrations with small and large evacuation scenarios reveal how EV penetration and charging schedules influence grid overloads and evacuation effectiveness, highlighting benefits of coordinated charging and departure planning. The authors provide a data processing pipeline, workflow, and adaptable scripts to extend the platform to other regions, offering a practical validation environment for planning, control, and optimization strategies in coupled power-transport systems.

Abstract

The ongoing electrification of the transportation fleet will increase the load on the electric power grid. Since both the transportation network and the power grid already experience periods of significant stress, joint analyses of both infrastructures will most likely be necessary to ensure acceptable operation in the future. To enable such analyses, this paper presents an open-source testbed that jointly simulates high-fidelity models of both the electric distribution system and the transportation network. The testbed utilizes two open-source simulators, OpenDSS to simulate the electric distribution system and the microscopic traffic simulator SUMO to simulate the traffic dynamics. Electric vehicle charging links the electric distribution system and the transportation network models at vehicle locations determined using publicly available parcel data. Leveraging high-fidelity synthetic electric distribution system data from the SMART-DS project and transportation system data from OpenStreetMap, this testbed models the city of Greensboro, NC down to the household level. Moreover, the methodology and the supporting scripts released with the testbed allow adaption to other areas where high-fidelity geolocated OpenDSS datasets are available. After describing the components and usage of the testbed, we exemplify applications enabled by the testbed via two scenarios modeling the extreme stresses encountered during evacuations.

Figures (19)

• Figure 1: Conceptual figure of how the different data sources in the testbed are connected. Dashed links represent connections that are already in the respective source data, while the solid links were created through data processing scripts that come with the testbed.
• Figure 2: The urban-suburban distribution network of the SMART-DS Greensboro dataset plotted using OpenDSS.
• Figure 3: The area of the transportation network considered. Picture from OpenStreetMap.org.
• Figure 4: The road network imported in SUMO.
• Figure 5: The TAZs in the area of interest. In total, we have selected 106 TAZs, covering the city center's central area and its surroundings. The colored region contains the buses in the power grid network. The green outlined area denotes the six TAZs we are using in the small evacuation scenario described in Section \ref{['sec:scanarios']}.