Dynamic Smart Charging Through Integrated Agent-based Simulation and Optimization Modeling
Abstract
Coordinated electric vehicle (EV) charging can improve alignment between transportation operations and power-system conditions as electrification expands. This study develops an integrated simulation-optimization framework that embeds demand-aware charging (DAC) and grid-responsive charging, including unidirectional smart charging (V1G) and vehicle-to-grid (V2G), within an agent-based model, enabling dynamic feedback between optimized vehicle (dis)charging decisions and evolving transportation-system states. Using the Austin 6-county region, the framework evaluates 15,000 shared autonomous electric vehicles (SAEVs) under DAC, V1G, fleet-only V2G, and combined fleet-plus-household V2G. Results show coordinated strategies concentrate charging toward midday and provide selective evening discharge while maintaining service performance. Under favorable pricing, V2G reduces net CO2 emissions from 45 kg/SAEV/day under DAC to below 40 kg and lowers emission intensity by up to 14% to 0.14 kg per vehicle-mile traveled. DAC and V1G offer near-term options, whereas high V2G costs may hinder adoption. Realized benefits depend on tariffs, vehicle availability, and V2G maturity.
Related
- Mathematical modeling, simulation and optimization via (port-Hamiltonian) differential-algebraic equations
- Topology-Aware Integrated Sensing, Communication, Charging in Massive Low-Altitude Wireless Network
- Nonlinear Trajectory Optimization Models for Uncrewed Aerial Vehicles with Mobile Charging Support
- Dynamic Structural Equation Modeling in Blimp
- On The Relevance Of Using Temperature For Mechanical Fault Detection In Industrial Robots Based On Dynamic Modeling
BibTeX
@misc{su2026dynamic,
title = {{Dynamic Smart Charging Through Integrated Agent-based Simulation and Optimization Modeling}},
author = {Lin Su and Kara M. Kockelman},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.08748},
eprint = {2611.08748},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.08748},
}
RIS
TY - UNPB
TI - Dynamic Smart Charging Through Integrated Agent-based Simulation and Optimization Modeling
AU - Lin Su
AU - Kara M. Kockelman
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.08748
UR - https://arxiv.org/abs/2611.08748
AB - Coordinated electric vehicle (EV) charging can improve alignment between transportation operations and power-system conditions as electrification expands. This study develops an integrated simulation-optimization framework that embeds demand-aware charging (DAC) and grid-responsive charging, including unidirectional smart charging (V1G) and vehicle-to-grid (V2G), within an agent-based model, enabling dynamic feedback between optimized vehicle (dis)charging decisions and evolving transportation-system states. Using the Austin 6-county region, the framework evaluates 15,000 shared autonomous electric vehicles (SAEVs) under DAC, V1G, fleet-only V2G, and combined fleet-plus-household V2G. Results show coordinated strategies concentrate charging toward midday and provide selective evening discharge while maintaining service performance. Under favorable pricing, V2G reduces net CO2 emissions from 45 kg/SAEV/day under DAC to below 40 kg and lowers emission intensity by up to 14% to 0.14 kg per vehicle-mile traveled. DAC and V1G offer near-term options, whereas high V2G costs may hinder adoption. Realized benefits depend on tariffs, vehicle availability, and V2G maturity.
ER -