Dynamic EV Charging Pricing Methodology for Facilitating Renewable Energy With Consideration of Highway Traffic Flow

Abstract

Electric vehicles (EVs) are expected to play a critical role in future transportation systems. A number of countries have published roadmaps aiming to facilitate the adoption of EVs on the road. It is estimated that existing charging facilities will not be able to satisfy the tremendous charging demands of a dramatically increasing number of EVs. Following the rapid development of artificial intelligence and mobile communication technology, certain charging pricing mechanism is expected to influence the charging behavior of EV drivers. In order to maximize the working efficiency of highway charging facilities and the consumption of the renewable energy near charging facilities, this paper proposes a pricing methodology taking into account the charging facility service ratio, traffic flow and renewable energy generation. To support the adoption of the proposed pricing methodology, forecasts of hour-by-hour traffic flow and renewable generation as well as calculation of the shortest paths to different charging stations (CSs) are investigated. A road network testbed based on the Dublin traffic network is established to evaluate the proposed pricing methodology. It is discovered that for certain wind-rich CSs, the proposed pricing methodology can increase the consumption rate of wind energy by up to 82.97%, with an average improvement of 30.73%; for certain solar-rich CSs, it can improve the level of solar energy consumption by up to 59.50% and an average increase of 29.28% is achieved. The proposed pricing methodology can also reduce traffic jams to some extent at both peak and off-peak times. INDEX TERMS Electric vehicle, charging stations, charging pricing methodology, renewable energy consumption. NOMENCLATURE

A Customer-Centered Smart Charging Strategy Considering Virtual Charging System

“…The first vital parameter that has a great impact on the EV's decision is the charging price offered by each charging station participating in VCS. The following nonlinear model is considered to determine the price score for each charging station [4]. This nonlinear model is selected to represent the EVs drivers' behavior in the real world where the EV does not select another charging station to charge at it due to a small difference in the charging price.…”

“…In addition, EVs have become costcompetitive with conventional vehicles due to the decline in the price of batteries in the last few years [3]. Despite the explosive growth of EVs, they suffer from long charging periods and limited infrastructure, where existing charging facilities can't satisfy the enormous demands of a significantly growing number of EVs [4]. Moreover, they have many negative consequences on the distribution network like increasing peak demand, thermal overloading, voltage deviations, and power imbalance [5]- [9].…”

“…Most of the existing research focuses on reducing the charging time and offering different charging options for EVs' drivers to facilitate their charging process. The authors in [4] presented a price strategy for charging EVs to utilize renewable energy generation while considering the traffic flow. The authors in [7] proposed a routing strategy for EVs' drivers to the most suitable charging station that satisfies the minimum charging time, traveling distance, and charging cost.…”

Dynamic Model for the EV’s Charging Infrastructure Planning Through Finite Element Method

“…The charging spots located in areas periodically visited by small number of EV owners were associated with a lower popularity. A pricing methodology considering the charging facility service ratio, traffic flow and renewable energy generation, was proposed and tested in [8]. This method maximizes the use of charging stations and renewable energy sources applied to the Dublin (Ireland) traffic.…”

Energy price forecasting for optimal managing of electric vehicle fleet

“…It, therefore, becomes necessary to organise and plan the recharge in order to overcome these problems [5–8]. Both from a fleet and a network point of view, it would be useful to have a tool able to have an economic advantage to recharge when the network has more availability and to be discouraged when the network has to supply other more critical loads [9–13]. Then, it becomes necessary to implement a new market agent known as demand aggregator or EV fleet operator.…”