太阳能道路集成：多重不确定性下耦合电-交通系统的两阶段鲁棒调度

Abstract Solar roads (SRs) offer potential for urban energy transition in transportation sector and addressing the scarcity of urban photovoltaic land, yet integrating them into power systems tightly couples transportation and power grids, posing safety challenges amidst SR uncertainties. This paper presents a robust scheduling method for the coupled power-transportation systems (CPTS) with SRs, addressing multiple uncertainties. It proposes an enhanced traffic assignment model (ETAM) to optimize both generation benefits of SRs and user travel costs, forming a joint optimal flow model for the CPTS. A two-stage robust scheduling model considers uncertainties in solar radiation, traffic demand, and power load. Phase one determines energy storage (ES) unit operations and electricity purchases, while phase two identifies worst-case scenarios, such as SRs operation, based on phase one decisions. The model, transformed into Mixed Integer Linear Programming (MILP) through convex relaxation, is solved using the Column and Constraint Generation (C&CG) algorithm, demonstrating efficacy through case studies .