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Data-Driven Assessment of Vehicle-to-Grid Capabilities in Supporting Grid During Emergencies: Case Study of Travis County, TX

Kelsey Nelson, Javad Mohammadi

TL;DR

The paper tackles grid resilience during extreme weather by analyzing vehicle-to-grid (V2G) capabilities in a data-driven framework for Travis County, TX. It fuses multi-modal data—EV registrations, demographics, outage histories, and willingness surveys—into a synthetic 173-bus transmission grid and uses ACOPF to evaluate emergency scenarios, projecting EV adoption to 2030–2040 via a PopGen 2.0-based synthetic population. A linear regression model links demographics to V2G participation, achieving $R^2 = 0.79$ on test data, and results show V2G can substantially reduce involuntary load shedding, with convergence by 2040; however, battery capacity imposes temporal limits on support. The findings support policy and infrastructure investment in bidirectional charging and V2G integration to enhance grid resilience during emergencies.

Abstract

As extreme weather events become more common and threaten power grids, the continuing adoption of electric vehicles (EVs) introduces a growing opportunity for their use as a distributed energy storage resource. This energy storage can be used as backup generation through the use of vehicle-to-grid (V2G) technology, where electricity is sent back from EV batteries to the grid. With enough participation from EV owners, V2G can mitigate outages during grid emergencies. In order to investigate a practical application of V2G, this study leverages a vast array of real-world data, such as survey results on V2G participation willingness, historical outage data within ERCOT, current EV registrations, and demographic data. This data informs realistic emergency grid scenarios with V2G support using a synthetic transmission grid for Travis County. The results find that as EV ownership rises in the coming years, the simultaneous facilitation of bidirectional charging availability would allow for V2G to play a substantial role in preventing involuntary load shed as a result of emergencies like winter storms.

Data-Driven Assessment of Vehicle-to-Grid Capabilities in Supporting Grid During Emergencies: Case Study of Travis County, TX

TL;DR

The paper tackles grid resilience during extreme weather by analyzing vehicle-to-grid (V2G) capabilities in a data-driven framework for Travis County, TX. It fuses multi-modal data—EV registrations, demographics, outage histories, and willingness surveys—into a synthetic 173-bus transmission grid and uses ACOPF to evaluate emergency scenarios, projecting EV adoption to 2030–2040 via a PopGen 2.0-based synthetic population. A linear regression model links demographics to V2G participation, achieving on test data, and results show V2G can substantially reduce involuntary load shedding, with convergence by 2040; however, battery capacity imposes temporal limits on support. The findings support policy and infrastructure investment in bidirectional charging and V2G integration to enhance grid resilience during emergencies.

Abstract

As extreme weather events become more common and threaten power grids, the continuing adoption of electric vehicles (EVs) introduces a growing opportunity for their use as a distributed energy storage resource. This energy storage can be used as backup generation through the use of vehicle-to-grid (V2G) technology, where electricity is sent back from EV batteries to the grid. With enough participation from EV owners, V2G can mitigate outages during grid emergencies. In order to investigate a practical application of V2G, this study leverages a vast array of real-world data, such as survey results on V2G participation willingness, historical outage data within ERCOT, current EV registrations, and demographic data. This data informs realistic emergency grid scenarios with V2G support using a synthetic transmission grid for Travis County. The results find that as EV ownership rises in the coming years, the simultaneous facilitation of bidirectional charging availability would allow for V2G to play a substantial role in preventing involuntary load shed as a result of emergencies like winter storms.

Paper Structure

This paper contains 8 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Literature Review
  4. Contributions and Paper Structure
  5. Multi Modal Data Sets
  6. Methodology and Key Assumptions
  7. Results and Discussion
  8. Conclusions

Figures (4)

  • Figure 1: An overview of how the proposed framework infuses geography and real-world data from Travis County (in Texas) to create a realistic assessment of how V2G could assist a grid under emergency conditions. The proposed framework consumes a wide range of multi-modal information from EV owners and grid conditions.
  • Figure 2: This map displays the actual location of buses where generators were taken offline during Winter Storm Urie (in black) and the synthetic grid's generators that were taken offline for this study (in red and blue).
  • Figure 3: Spatial distribution of V2G participants within Travis County test grid.
  • Figure 4: This figure shows how over time, V2G participants batteries will become depleted if all participating EVs dispatch power through a level 2 charger at time = 0.