Optimal energy trading for plug-in hybrid electric vehicles based on fog computing

Abstract

A large number of plug-in hybrid electric vehicles (PHEVs) have high mobility but a small battery capacity; thus, these vehicles urgently need to make charging and discharging decisions in real time. This paper proposes a new architecture based on fog computing for an Internet of Vehicles energy trading system, which we call a vehicle-mounted energy fog. This architecture includes a fog computing energy center (FCEC), which manages local energy trading and reduces the peak load energy trading for an external public energy company. We model the optimization problems for energy trading under two different types of FCECs: 1) a nonprofit-driven FCEC whose goal is solely to benefit the PHEV charging and discharging operations and 2) a profit-driven FCEC whose goal is to maximize its own profits while still guaranteeing that each PHEV achieves a non-negative utility. We also propose efficient algorithms for these two types of FCECs to seek optimal pricing and make supply demand decisions. Simulation results show that our proposed algorithms are superior to existing algorithms in terms of the convergence rate, the final objective value and the evenness of the Pareto solution set. Specifically, the evenness of the Pareto solution set is improved by 23% compared to the results of the existing algorithm.