Research on the EV charging load estimation and mode optimization methods

• Autorzy: Zhan Zhiyan , Dong Kailang , Pang Xiaochen , Zhao Hongfei , Wang Aifang

Identyfikatory

DOI

10.24425/aee.2019.130686

• Języki publikacji: EN

Abstrakty

EN

With the increasing number of electric vehicles (EVs), the disordered charging of a large number of EVs will have a large influence on the power grid. The problems of charging and discharging optimization management for EVs are studied in this paper. The distribution of characteristic quantities of charging behaviour such as the starting time and charging duration are analysed. The results show that charging distribution is in line with a logarithmic normal distribution. An EV charging behaviour model is established, and error calibration is carried out. The result shows that the error is within its permitted scope. The daily EV charge load is obtained by using the Latin hypercube Monte Carlo statistical method. Genetic particle swarm optimization (PSO) is proposed to optimize the proportion of AC 1, AC 2 and DC charging equipment, and the optimal solution can not only meet the needs of users but also reduce equipment investment and the EV peak valley difference, so the effectiveness of the method is verified.

Słowa kluczowe

EN

EVs; gap optimization; Latin hypercube sampling; Monte Carlo simulation

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2019
• Tom: Vol. 68, nr 4
• Strony: 831--842
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., wz.

Twórcy

autor

Zhan Zhiyan

• Department of Electrical and Information Engineering, Zhengzhou University of Light Industry China

autor

Dong Kailang

• Department of Electrical and Information Engineering, Zhengzhou University of Light Industry China

autor

Pang Xiaochen

• Department of Electrical and Information Engineering, Zhengzhou University of Light Industry China

autor

Zhao Hongfei

• Maintenance Department, Jiangsu Electric Power Company China

autor

Wang Aifang

• State Grid Henan Electric Power Company, Zhengzhou Electric Power Supply Company China

Bibliografia

• [1] Yang Bo, Chen Wei, Wen Minghao et al., Probabilistic Load Modeling of Electric Vehicle Charging Stations, Automation of Electric Power Systems, vol. 38, no. 16, pp. 67–73 (2014).
• [2] Gao Ciwei, Zhang Liang, A Survey of Influence of Electrics Vehicle Charging on Power Grid, Power System Technology, vol. 35, no. 2, pp. 127–131 (2011).
• [3] Ma Jinxiang, Fan Xinnan, Wu Zhixiang et al., Optimal scheduling of electric vehicle charging and discharging based on two dimensions of time and space, Electrical Application, vol. 34, no. 24, pp. 140−146 (2015).
• [4] Zhang Weige, Xie Feixiang, Hu Mei, Research on short-term load forecasting methods of electric buses charging station, Power System Protection and Control, vol. 41, no. 4, pp. 61–66 (2013).
• [5] Li Yafang, Huang Mei, Zhang Weige, An Estimation Method for Daily Charging Load of Electric Taxis, Automation of Electric Power Systems, vol. 38, no. 10, pp. 55–60 (2014).
• [6] Yuan Zhengping, Zhou Wei, Wang Wenbin, Charging Load Forecasting Method for Electric Vehicles, East China Electric Power, vol. 41, no. 12, pp. 2567–2572 (2013).
• [7] Wu Kuihua, Sun Wei, Zhang Xiaolei et al., Electric Vehicle Charging Load Modeling and Impact of Power Grid Load Characteristics, Shangdong Dianli Jishu, vol. 195, no. 5, pp. 5−21 (2013).
• [8] Zhang Di, Jiang Jiuchun, Zhang Weige et al., Economic Operation of Electric Vehicle Battery Swapping Station Based on Genetic Algorithms, Power System Technology, vol. 37, no. 8, pp. 2102–2107 (2013).
• [9] http://shumo.neepu.edu.cn/index.php/Home/zxdt/news/id/35.html, accessed May 2018.
• [10] Zhou Hongwei, Sevseral Commonly Methods on Inspection of Normal Distribution, Journal of Nanjing Xiaozhuang University, to be published.
• [11] Zhu Lulu, The Monte Carlo method and application, MFA Thesis, Faculty of Mathematics and Statistics, Central China Normal University, Wuhan (2014).
• [12] Chen Lulu, Qiu Jianlin, Chen Yanyun et al., Improved hybrid optimization algorithm and particle swarm optimization, Computer Engineering and Design, vol. 38, no. 2, pp. 396–399 (2017).
• [13] Jiang Xiaochao, Research on Energy Management for Local Distribution Network Considering Electric Vehicles and Renewable Energy, MFA Thesis, Faculty of Electrical engineering, Beijing Jiaotong University, Beijing (2015).

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).