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.

中文翻译：

---

用于相互依赖的交通和电力系统分析的随机多智能体优化框架

考虑到分散的可再生发电机和电动汽车 (EV)，我们研究了交通和电力系统之间的相互依赖关系。考虑到电动汽车/传统汽车驾驶员、可再生能源/传统发电机和独立系统运营商之间的复杂相互作用，以及由市场内生决定的位置电力和充电价格，我们在随机多智能体优化框架中制定了该问题。我们表明多智能体优化问题可以重新表述为单凸优化问题，并证明了均衡的存在性和唯一性。为了应对维数灾难，我们提出了基于乘法器（ADMM）的分解算法的交替方向方法来促进并行计算。