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

TL;DR

This paper presents a stochastic multi-agent optimization framework that models the interdependence of transportation and power systems, incorporating renewable energy, electric vehicles, and market dynamics, with a focus on computational efficiency.

Contribution

It introduces a novel multi-agent optimization model for interdependent systems and proposes an ADMM-based algorithm for scalable computation.

Findings

The multi-agent problem can be reformulated as a convex optimization.

Existence and uniqueness of the equilibrium are proven.

The proposed algorithm enables efficient parallel computation.

Abstract

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.