Fog-Computing-Based Joint Flow Calculation Method of Coupled Power and Transportation Network

Abstract

In recent years, the rapid growth of electric vehicles (EVs) has led to frequent interactions and tight coupling of the power distribution network (PDN) and transportation network (TN). To effectively analyze the dynamic interactions between PDN and TN, this paper proposes a joint traffic-power flow calculation method using a fog computing architecture, which combines macroscopic traffic flows with microscopic individual vehicle characteristics, improving the link transmission model to describe the dynamic traffic transmission among links in TN and between charging stations (CSs) and TN, as well as the dynamic charging and queuing process of EVs at CSs. Moreover, this paper develops a travel time estimation model and an energy consumption estimation model for EVs, effectively describing the dynamic power transmission among EVs, CSs, and PDN. Numerical results show that the method can effectively capture the spatio-temporal interactions among PDN, TN, CSs, and EVs, even under system incidents. The scalability and computational efficiency of this method are demonstrated through testing on a large-scale coupled network.