We study the interdependence between transportation and power systems considering decentralized renewable generators and electric vehicles (EVs). We formulate the problem in a stochastic multi-agent optimization framework considering the complex interactions between EV/conventional vehicle drivers, \revi{renewable}/conventional generators, and independent system operators, with locational electricity and charging prices endogenously determined by markets. We show that the multi-agent optimization problems can be reformulated as a single convex optimization problem and prove the existence and uniqueness of the equilibrium. To cope with the curse of dimensionality, we propose ADMM-based decomposition algorithm to facilitate parallel computing. Numerical insights are generated using standard test systems in transportation and power system literature.
@article{arxiv.2101.00908,
title = {A Stochastic Multi-Agent Optimization Framework for Interdependent Transportation and Power System Analyses},
author = {Zhaomiao Guo and Fatima Afifah and Junjian Qi and Sina Baghali},
journal= {arXiv preprint arXiv:2101.00908},
year = {2021}
}