# An EV Charging Scheduling Mechanism Based on Price Negotiation
## 1 Introduction  
Electric vehicles (EVs) are an important branch of renewable energy vehicles, and will undoubtedly play an important role in reducing greenhouse gas emissions and maintaining sustainable development. However, the large-scale charging of EVs which have access to the power grid will bring great challenges, such as increased load pressure of the power grid, an increasing difference between the peak and valley load, and so on [1; 2; 3; 4]. To deal with these challenges, reasonable scheduling of EV users' charging behavior and the effective utilization of power generation using renewable energy resources are promising solutions [5; 6; 7; 8; 9]. The EV charging price is an important and effective factor in realizing the scheduling of EV users' charging behavior. Moreover, the reasonable utilization of EV charging price will not only promote the operators to invest in the charging station, but can also improve the enthusiasm of people to use EVs [10; 11]. Currently, research on EV charging prices keeps growing with the increasing development of EVs. Most research mainly focuses on the reasonable charging price model and strategy by considering the profits of charging station operators and the EV user, the cost and travelling requirements of EV users, and the utilization of renewable energy resources [11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. Another paper [11], presents a competitive charging station pricing strategy where each charging station optimizes its charging price based on the prediction of the EVs' charging station selection decisions, and the other station's pricing decision, in order to maximize its profit. The actual electricity prices and predicted prices are both utilized to reduce the cost of each single EV with a presented batch reinforcement-learning (RL) algorithm [12]. Another paper [13], analyzes the charging pricing space in centralized charging and discharging mode, and analyzes the benefit levels of charging station operators at different charging price levels. The authors of [14] proposed a decentralized optimizationalgorithm for plug-in vehicles (PEVs) during grid-vehicle (G2V) and vehicle-grid (V2G) operations by using the economic model predictive control technique. It considers the state-of-charge (SoC) at a given time as a discrete state space to investigate PEV performance and the schedule of V2G/G2V cycles for the maximum economic benefits of EV users. The authors of [15] presented an optimal scheduling of V2G using the genetic algorithm to minimize the power grid load variance. It allows G2V whenever the actual power grid loading is lower than the target loading, while conducting V2G whenever the actual power grid loading is higher than the target loading. V2G scheduling is used to implement the appropriate peak loading shaving and load leveling services for the grid load variance minimization. The authors of [16] proposed an energy management scheme (EMS) for a charging station that combines the photovoltaic (PV) and energy storage units (ESU) with the grid. The EMS has the capability to charge EV with a constant per unit price at, and below, the level of solar grid parity, and can reduce the economic loss of the charging station due to cheaper and constant price charging with the involvement of a valley-filling operation by both ESU and EV, along with selling the surplus PV energy to the grid. The authors of [17] studied the utilization of EVs and their used-batteries in supporting small-scale energy management systems. The developed system performs a peak-load shift by utilizing EVs, used EV batteries, and PV panels. The authors of [18] proposed an optimal charging strategy of the EV rational user with different time and mileages based on the time-of-use power price to reduce the cost of EV users and the power network loss rate and achieve the win-win goal of EV users and the electric power company.