A Stochastic Multiagent Optimization Framework for Interdependent Transportation and Power System Analyses

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 multiagent optimization framework considering the complex interactions between EV/conventional vehicle drivers, renewable/conventional generators, and independent system operators, with locational electricity and charging prices endogenously determined by markets. We show that the multiagent 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 the alternating direction method of multipliers (ADMM)-based decomposition algorithm to facilitate parallel computing. Numerical insights are generated using standard test systems in the transportation and power system literature.