Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D*

doi:10.23919/CJEE.2022.000005

Chinese Journal of Electrical Engineering ›› 2022, Vol. 8 ›› Issue (1): 53-62.doi: 10.23919/CJEE.2022.000005

• Regular Papers • Previous Articles Next Articles

扫码分享

Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D^*

Shurong Peng¹, Heng Zhang^1,*, Yunhao Yang², Bin Li¹, Sheng Su¹, Shijun Huang¹, Guodong Zheng¹

1. School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China;
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China

Received:2021-06-10 Revised:2021-10-12 Accepted:2021-11-20 Online:2022-03-25 Published:2022-04-08
Contact: *E-mail: 1326360169@qq.com
Supported by:
*Research Foundation of Education Bureau of Hunan Province (20A021) and National Natural Science Foundation of China (51777015).

Abstract

Abstract: The charging load of electric vehicles (EVs) has a strong spatiotemporal randomness. Predicting the dynamic spatiotemporal distribution of the charging load of EVs is of great significance for the grid to cope with the access of large-scale EVs. Existing studies lack a prediction model that can accurately describe the dual dynamic changes of EVs charging the load time and space. Therefore, a spatial-temporal dynamic load forecasting model, dilated causal convolution-2D neural network (DCC-2D), is proposed. First, a hole factor is added to the time dimension of the three-dimensional convolutional convolution kernel to form a two-dimensional hole convolution layer so that the model can learn the spatial dimension information. The entire network is then formed by stacking the layers, ensuring that the network can accept long-term historical input, enabling the model to learn time dimension information. The model is simulated with the actual data of the charging pile load in a certain area and compared with the ConvLSTM model. The results prove the validity of the proposed prediction model.

Key words: Time and space dynamic prediction, dilated convolution, charging load, convolutional neural network

Shurong Peng, Heng Zhang, Yunhao Yang, Bin Li, Sheng Su, Shijun Huang, Guodong Zheng. Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D^*[J]. Chinese Journal of Electrical Engineering, 2022, 8(1): 53-62.

References

[1] Y Cao, Y Liu, L Que, et al.Multi-objective bi-level real-time charging/discharging dispatch with coordination of BSS and grid.Electric Power Automation Equipment, 2015, 35(4): 1-7.
[2] B Qian, D Shi, P Xie, et al.Simulation analysis on zigzag transformer-based AC-DC combined distribution network.Automation of Electric Power Systems, 2014, 38(2): 64-69, 84.
[3] P Chen, Q Meng, Y Zhao.The electric vehicle charging load calculation based on the Monte Carlo method.Journal of Electrical Engineering, 2016, 11(11): 40-46.
[4] R Chen, Y He, F Chen, et al.Long-term daily load forecast of electric vehicle based on system dynamics and Monte Carlo simulation.Electric Power, 2018, 51(9): 126-134.
[5] S Bae, A Kwasinski.Spatial and temporal model of electric vehicle charging demand.IEEE Trans on Smart Grid, 2012, 3(1): 394-403.
[6] H Liang, I Sharma, W Zhuang, et al.Plug-in electric vehicle charging demand estimation based on queueing network analysis.2014 IEEE PES General Meeting | Conference & Exposition, National Harbor, 2014: 1-5.
[7] Y Luo, Z Song, Z Hu, et al.Forecasting charging load of plug-in electric vehicles in China. 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA, 2011: 1-8.
[8] Y Shao, Y Mu, X Yu, et al.A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model.Proceedings of the CSEE, 2017, 37(18): 5207-5219, 5519.
[9] Q Zhu, J Chen, D Shi, et al.Learning temporal and spatial correlations jointly: A unified framework for wind speed prediction.IEEE Transactions on Sustainable Energy, 2020, 11(1): 509-523.
[10] L Liu, Y Li, Y Cao, et al.Transient rotor angle stability prediction method based on SVM and LSTM network. Electric Power Automation Equipment, 2020, 40(2): 129-139.
[11] M B Arias, S Bae.Electric vehicle charging demand forecasting model based on big data technologies.Applied Energy, 2016(183): 327-339.
[12] S Xin, Q Lei, T Li, et al.Short-term load forecasting for electric vehicle charging stations based on time series distance measuring. 2017 IEEE 13th International Conference on Electronic Measurement & Instruments, 2017: 417-423.
[13] E S Xydas, C E Marmaras, L M Cipcigan, et al.Forecasting electric vehicle charging demand using support vector machines. 2013 48th International Universities’ Power Engineering Conference (UPEC), Dublin, 2013: 1-6.
[14] X Dong, Y Mu, H Jia, et al.Planning of fast EV charging stations on a round freeway.IEEE Transactions on Sustainable Energy, 2016, 7(4): 1452-1461.
[15] M Majidpour, C Qiu, P Chu, et al.Modified pattern sequence-based forecasting for electric vehicle charging stations. 2014 IEEE International Conference on Smart Grid Communications (Smart Grid Comm), Venice, 2014: 710-715.
[16] M Mostafa.Time series prediction for electric vehicle charging load and solar power generation in the context of smart grid. Los Angeles: University of California, 2016.
[17] W Liu, R Long, X Xu, et al.Forecasting model of short-term EV charging load based on data freshness and cross entropy.Automation of Electric Power Systems, 2015, 32(10): 2951-2954.
[18] Z Wang.Optimization of charging infrastructure siting for electric taxis in Beijing. Beijing: Beijing Jiaotong University, 2017.
[19] J Zhang, Y Zheng, D Qi.Deep spatio-temporal residual networks for citywide crowd flows prediction. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17). AAAI Press, 2017: 1655-1661.
[20] X Shi, Z Chen, H Wang, et al.Convolutional LSTM network: A machine learning approach for precipitation nowcasting.Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015(1): 802-810.

Spatial-temporal Dynamic Forecasting of EVs Charging Load Based on DCC-2D^*

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 9

Recommended Articles

Metrics

Comments

[1]	CHEN Zhuyun, ZHONG Qi, HUANG Ruyi, LIAO Yixiao, LI Jipu, LI Weihua. Intelligent Fault Diagnosis for Machinery Based on Enhanced Transfer Convolutional Neural Network [J]. Journal of Mechanical Engineering, 2021, 57(21): 96-105.
[2]	CHE Changchang, WANG Huawei, NI Xiaomei, LIN Ruiguan, XIONG Minglan. Residual Life Prediction of Aeroengine Based on 1D-CNN and Bi-LSTM [J]. Journal of Mechanical Engineering, 2021, 57(14): 304-312.
[3]	YE Zhuang, YU Jianbo. Feature Extraction of Gearbox Vibration Signals Based on Multi-Channels Weighted Convolutional Neural Network [J]. Journal of Mechanical Engineering, 2021, 57(1): 110-120.
[4]	DONG Shaojiang, PEI Xuewu, WU Wenliang, TANG Baoping, ZHAO Xingxin. Rolling Bearing Fault Diagnosis Method Based on Multilayer Noise Reduction Technology and Improved Convolutional Neural Network [J]. Journal of Mechanical Engineering, 2021, 57(1): 148-156.
[5]	JIANG Hongquan, HE Shuai, GAO Jianmin, WANG Rongxi, GAO Zhiyong, WANG Xiaoqiao, XIA Fengshe, CHENG Lei. An Improved Convolutional Neural Network for Weld Defect Recognition [J]. Journal of Mechanical Engineering, 2020, 56(8): 235-242.
[6]	LI Yu,YANG Liulin. Identification of Single-phase-to-earth Fault in Distribution Network Based on Convolutional Neural Network [J]. Journal of Electrical Engineering, 2020, 15(3): 22-30.
[7]	HU Niaoqing, CHEN Huipeng, CHENG Zhe, ZHANG Lun, ZHANG Yu. Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks [J]. Journal of Mechanical Engineering, 2019, 55(7): 9-18.
[8]	LI Haichao, LIU Jingfeng, XIE Jibing, WANG Xin. GTAW Penetration Prediction Model Based on Convolution Neural Network Algorithm [J]. Journal of Mechanical Engineering, 2019, 55(17): 22-28.
[9]	Chen Peng,Meng Qinghai,Zhao Yanjin. The Electric Vehicle Charging Load Calculation Based on the Monte Carlo Method [J]. Journal of Electrical Engineering, 2016, 11(11): 40-46.