Routing Optimization of Electric Vehicles for Charging With Event-Driven Pricing Strategy

2021 ◽  
pp. 1-14
Author(s):  
Yue Xiang ◽  
Jianping Yang ◽  
Xuecheng Li ◽  
Chenghong Gu ◽  
Shuai Zhang
Keyword(s):  
Electric Vehicles ◽  
Pricing Strategy ◽  
Routing Optimization ◽  
Event Driven

scholarly journals Optimal Pricing Strategy of Electric Vehicle Charging Station for Promoting Green Behavior Based on Time and Space Dimensions

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xiaomin Xu ◽  
Dongxiao Niu ◽  
Yan Li ◽  
Lijie Sun
Keyword(s):  
Electric Vehicles ◽  
Optimization Problems ◽  
Power Grid ◽  
Economic Benefits ◽  
Pricing Strategy ◽  
Optimal Pricing ◽  
Electricity Pricing ◽  
Optimal Pricing Strategy ◽  
Charging Stations ◽  
The Impact

Considering that the charging behaviors of users of electric vehicles (EVs) (including charging time and charging location) are random and uncertain and that the disorderly charging of EVs brings new challenges to the power grid, this paper proposes an optimal electricity pricing strategy for EVs based on region division and time division. Firstly, by comparing the number of EVs and charging stations in different districts of a city, the demand ratio of charging stations per unit is calculated. Secondly, according to the demand price function and the principle of profit maximization, the charging price between different districts of a city is optimized to guide users to charge in districts with more abundant charging stations. Then, based on the results of the zonal pricing strategy, the time-of-use (TOU) pricing strategy in different districts is discussed. In the TOU pricing model, consumer satisfaction, the profit of power grid enterprises, and the load variance of the power grid are considered comprehensively. Taking the optimization of the comprehensive index as the objective function, the TOU pricing optimization model of EVs is constructed. Finally, the nondominated sorting genetic algorithm (NSGA-II) is introduced to solve the above optimization problems. The specific data of EVs in a municipality directly under the Central Government are taken as examples for this analysis. The empirical results demonstrate that the peak-to-valley ratio of a certain day in the city is reduced from 56.8% to 43% by using the optimal pricing strategy, which further smooth the load curve and alleviates the impact of load fluctuation. To a certain extent, the problem caused by the uneven distribution of electric vehicles and charging stations has been optimized. An orderly and reasonable electricity pricing strategy can guide users to adjust charging habits, to ensure grid security, and to ensure the economic benefits of all parties.

scholarly journals Dynamic Time-Of-Use Pricing Strategy for Electric Vehicle Charging Considering User Satisfaction Degree

2020 ◽  
Vol 10 (9) ◽  
pp. 3247 ◽  
Author(s):  
Qian Zhang ◽  
Yue Hu ◽  
Weiyu Tan ◽  
Chunyan Li ◽  
Zhuwei Ding
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
User Satisfaction ◽  
Pricing Strategy ◽  
Satisfaction Degree ◽  
Electric Vehicle Charging ◽  
Micro Grid ◽  
Vehicle Charging ◽  
Time Of Use Pricing ◽  
Dynamic Time

In order to solve the problem that the static peak-valley price for electric vehicles cannot truly reflect the relationship between electricity supply and demand, as well as the fact that the low utilization rate of renewable energy in the micro-grid, a dynamic time-of-use pricing strategy for electric vehicle charging considering user satisfaction degree is proposed, to achieve the goal of friendly charging for the micro-grid. Firstly, this paper researches the travel patterns of electric vehicles to establish the grid connection scenes and predict the controllable capacity of electric vehicles. Secondly, the charging preferences of different types of users are studied, and a comprehensive satisfaction degree model is set up to obtain different users’ charging strategies. Furthermore, the paper raises a pricing strategy on account of the dispatching requirements of the micro-grid, and realizes the effective dispatch of electric vehicle charging load based on price signals. Finally, we gain the dynamic time-of-use charging price, using the strategy proposed above, and the economic benefits brought to the micro-grid and electric vehicle users are analyzed, which validates the rationality and effectiveness of the pricing strategy.

Electric Vehicles En-Route Charging Navigation Systems: Joint Charging and Routing Optimization

2019 ◽  
Vol 27 (2) ◽  
pp. 906-914 ◽  
Author(s):  
Chensheng Liu ◽  
Min Zhou ◽  
Jing Wu ◽  
Chengnian Long ◽  
Yebin Wang
Keyword(s):  
Electric Vehicles ◽  
Navigation Systems ◽  
Routing Optimization

A Discrete Event-Driven Model for Electric Vehicles Predictive Charging

2015 ◽  
Vol 6 (2) ◽  
pp. 23-46
Author(s):  
Azizbek Ruzmetov ◽  
Ahmed Nait-Sidi-Moh ◽  
Mohamed Bakhouya ◽  
Jaafar Gaber ◽  
Marie-Ange Manier
Keyword(s):  
Electric Vehicles ◽  
Road Traffic ◽  
Discrete Event ◽  
Behavior Modeling ◽  
Charging System ◽  
Event Driven ◽  
Operation Techniques ◽  
Max Algebra ◽  
Operating Constraints ◽  
Charging Stations

Very great research efforts have been made in the last decades to further develop and promote electric vehicles (EVs), their charging infrastructures, and operation techniques. However, little attention has been paid so far to the management of their charging planning, EVs assignment and mainly drivers' assistance to get into adequate charging stations (CSs). The charging planning and EVs assignment need to be predicted taking into consideration all operating constraints of charging systems including EV characteristics, status of CSs, road traffic, etc. This paper presents a discrete event driven model for EVs predictive charging. The authors mainly focus on behavior modeling of the charging system using (max, +) algebra and Petri nets. The model is then used to anticipate maximum charging times and charging rates of EVs while respecting their various constraints.

scholarly journals Goods Delivery with Electric Vehicles: Electric Vehicle Routing Optimization with Time Windows and Partial or Full Recharge

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 285
Author(s):  
Tomislav Erdelić ◽  
Tonči Carić
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Combustion Engine ◽  
Time Windows ◽  
Neighborhood Search ◽  
Routing Problem ◽  
Charging Station ◽  
Routing Optimization ◽  
Mode Of Transport

With the rise of the electric vehicle market share, many logistic companies have started to use electric vehicles for goods delivery. Compared to the vehicles with an internal combustion engine, electric vehicles are considered as a cleaner mode of transport that can reduce greenhouse gas emissions. As electric vehicles have a shorter driving range and have to visit charging stations to replenish their energy, the efficient routing plan is harder to achieve. In this paper, the Electric Vehicle Routing Problem with Time Windows (EVRPTW), which deals with the routing of electric vehicles for the purpose of goods delivery, is observed. Two recharge policies are considered: full recharge and partial recharge. To solve the problem, an Adaptive Large Neighborhood Search (ALNS) metaheuristic based on the ruin-recreate strategy is coupled with a new initial solution heuristic, local search, route removal, and exact procedure for optimal charging station placement. The procedure for the O(1) evaluation in EVRPTW with partial and full recharge strategies is presented. The ALNS was able to find 38 new best solutions on benchmark EVRPTW instances. The results also indicate the benefits and drawbacks of using a partial recharge strategy compared to the full recharge strategy.

scholarly journals Routing Optimization for Shared Electric Vehicles with Ride-Sharing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Chuanxiang Ren ◽  
Jinbo Wang ◽  
Yongquan You ◽  
Yu Zhang
Keyword(s):  
Electric Vehicles ◽  
Optimization Model ◽  
Operation Mode ◽  
Utilization Rate ◽  
Total Cost ◽  
Routing Optimization ◽  
Ride Sharing ◽  
Proposed Model ◽  
Demand Information ◽  
User Demand

Shared electric vehicles (SEVs) are becoming a new way for urban residents to travel because of their environmental protection, energy saving, and sustainable development. However, at present, the operation mode of shared electric vehicles has the problem that the vehicle cannot be utilized efficiently. For this reason, this paper studied the mode of SEVs with ride-sharing (MSEVRS) and SEVs routing optimization under this mode. Firstly, the operation principle of MSEVRS is presented, which includes the collection of user demand information and SEVs information and the routing optimization of SEVs, both of which are completed by the user and SEVs management center. Secondly, the routing optimization model of SEVs with ride-sharing is proposed, in which the SEVs operation cost, user time cost, user rental cost, and user ride-sharing bonus are taken into account. And the genetic algorithm is designed to solve the model. Finally, a case study is carried out to illustrate the effectiveness of the proposed model. The results show that the proposed routing optimization model achieves the optimal SEVs route, realizes the MSEVRS, and improves the utilization rate of SEVs. Compared with the current SEVs mode (CSEVM), the MSEVRS reduces the number of vehicles, user rental cost, the total cost of users, and the total cost of user and company of SEVs. And the total distance is reduced, which means saving energy. Moreover, it shows that MSEVRS obtains a better cost performance and service for users and has a better application prospect.

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