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

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.

[1]  Sang-Bing Tsai,et al.  Models for forecasting growth trends in renewable energy , 2017 .

[2]  C. Fitzpatrick,et al.  Demand side management of electric car charging: Benefits for consumer and grid , 2012 .

[3]  Yifan Wu,et al.  Public recharging infrastructure location strategy for promoting electric vehicles: A bi-level programming approach , 2018 .

[4]  Y. Hao,et al.  Comprehensive policy evaluation of NEV development in China, Japan, the United States, and Germany based on the AHP-EW model , 2019, Journal of Cleaner Production.

[5]  Bangzhu Zhu,et al.  Exploring the effect of carbon trading mechanism on China's green development efficiency: A novel integrated approach , 2020 .

[6]  Willett Kempton,et al.  Electric-drive vehicles for peak power in Japan , 2000 .

[7]  Roberto Álvarez Fernández,et al.  A more realistic approach to electric vehicle contribution to greenhouse gas emissions in the city , 2018 .

[8]  Bowen Zhou,et al.  An electric vehicle dispatch module for demand-side energy participation , 2016 .

[9]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[10]  Chi-Hua Chen,et al.  An Arrival Time Prediction Method for Bus System , 2018, IEEE Internet of Things Journal.

[11]  Gang Liu,et al.  An ontology constructing technology oriented on massive social security policy documents , 2020, Cognitive Systems Research.

[12]  Hongcai Zhang,et al.  Pricing mechanisms design for guiding electric vehicle charging to fill load valley , 2016 .

[13]  Dongmin Yu,et al.  Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system , 2019, Energy.

[14]  Christian A. Klöckner,et al.  Positive and negative spillover effects from electric car purchase to car use , 2013 .

[15]  Jianhui Wang,et al.  Sustainability SI: Optimal Prices of Electricity at Public Charging Stations for Plug-in Electric Vehicles , 2016 .

[16]  Yixiong Feng,et al.  Driving preference analysis and electricity pricing strategy comparison for electric vehicles in smart city , 2019, Inf. Sci..

[17]  S. Baskar,et al.  NSGA-II algorithm for multi-objective generation expansion planning problem , 2009 .

[18]  D. Sarkar,et al.  Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm. , 2005 .

[19]  Qi He,et al.  Low-Cost and Confidentiality-Preserving Data Acquisition for Internet of Multimedia Things , 2018, IEEE Internet of Things Journal.

[20]  Fu Gu,et al.  An investigation of the current status of recycling spent lithium-ion batteries from consumer electronics in China , 2017 .

[21]  Chao Zhang,et al.  Research on the spatial-temporal distribution of electric vehicle charging load demand: A case study in China , 2020 .

[22]  Hitoshi Yano,et al.  Effects of Smart Charging of Multiple Electric Vehicles in Reducing Power Generation Fuel Cost , 2015 .

[23]  Vaneet Aggarwal,et al.  Control of Charging of Electric Vehicles Through Menu-Based Pricing , 2016, IEEE Transactions on Smart Grid.

[24]  Egoitz Martinez-Laserna,et al.  Sustainability analysis of the electric vehicle use in Europe for CO2 emissions reduction , 2016 .

[25]  Mohamed Elhoseny,et al.  Trust-based secure clustering in WSN-based intelligent transportation systems , 2018, Comput. Networks.

[26]  D. Morrey,et al.  Can electric vehicles significantly reduce our dependence on non-renewable energy? Scenarios of compact vehicles in the UK as a case in point , 2018, Journal of Cleaner Production.

[27]  Jian Ma,et al.  Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm , 2018, Journal of Advanced Transportation.

[28]  Kittisak Jermsittiparsert,et al.  Risk-constrained optimal operation of fuel cell/photovoltaic/battery/grid hybrid energy system using downside risk constraints method , 2020 .

[29]  Senthil Ragavan Valayapalayam Kittusamy,et al.  An enhanced whale optimization algorithm for vehicular communication networks , 2019, Int. J. Commun. Syst..

[30]  Christoph M. Flath,et al.  Deadline differentiated pricing in practice: marketing EV charging in car parks , 2014, Computer Science - Research and Development.

[31]  Kai Guo,et al.  Research on location selection model of distribution network with constrained line constraints based on genetic algorithm , 2019, Neural Computing and Applications.

[32]  Haibin Lv,et al.  Infrastructure Monitoring and Operation for Smart Cities Based on IoT System , 2020, IEEE Transactions on Industrial Informatics.

[33]  Kittisak Jermsittiparsert,et al.  An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm , 2020 .

[34]  Canbing Li,et al.  An Optimized EV Charging Model Considering TOU Price and SOC Curve , 2012, IEEE Transactions on Smart Grid.

[35]  Ying Fan,et al.  Does air pollution stimulate electric vehicle sales? Empirical evidence from twenty major cities in China , 2020 .

[36]  Jianzhong Wu,et al.  A charging pricing strategy of electric vehicle fast charging stations for the voltage control of electricity distribution networks , 2018, Applied Energy.

[37]  John W. Nicklow,et al.  Multi-objective automatic calibration of SWAT using NSGA-II , 2007 .