A Distributed and Game-Theoretic-Based EV Charging Pricing Model Under Coupled Energy-Transportation-Information Networks
Xinxin Ge, Ge Wang, Rong-fu Sun, Fei Wang
Abstract
Optimized charging prices for the electric vehicles (EVs) under the coupled energy, transportation and information networks architecture is helpful to dig out the spatiotemporal flexibility potential of EVs and benefit the coupled networks in return. However, most existing studies scarcely investigated the whole process of optimized EV charging pricing issues under the coupled networks architecture. To this end, our main purpose in this study is to carry out an applicable architecture, design a sustainable business model for the EV charging related entities and optimize the EV charging prices. Firstly, an architecture and business model of the EV charging related entities is established, which reflects the energy, information and money flow among these entities. Secondly, a two-stage and distributed optimal charging pricing model is proposed for EV charging station (EVCS) based on game theory, taking the TAP-UE model into consideration. Lastly, the formulated pricing model is solved by the classical Gauss-Seidel algorithm. The proposed pricing model has been verified by the IEEE 33-node distribution system and a 12-node transportation network using the real-world data, the results showing that the utilization rate of distribution network assets is balanced, and the traffic congestion issue mitigated while the utility of EVs satisfied.