# A Stochastic Multi-Agent Optimization Framework for Interdependent Transportation and Power System Analyses

arXiv: https://arxiv.org/abs/2101.00908 · HTML: https://mlsanity.com/paper/2101.00908/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2021.

## Built on by (most cited first, 1 of 1)

- [Analyzing the Travel and Charging Behavior of Electric Vehicles -- A Data-driven Approach](https://mlsanity.com/paper/2106.06475/) (2021)

## Abstract (arXiv)

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, revirenewable/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.