Optimal Planning of Electric Vehicle Battery Centralized Charging Station Based on EV Load Forecasting

被引:35
作者
He, Chenke [1 ]
Zhu, Jizhong [1 ]
Lan, Jing [1 ]
Li, Shenglin [1 ]
Wu, Wanli [1 ]
Zhu, Haohao [1 ]
机构
[1] South China Univ Technol, Sch Elect Power Engn, Guangzhou 510641, Peoples R China
基金
中国国家自然科学基金;
关键词
Planning; Batteries; Load modeling; Roads; Costs; Load forecasting; Forecasting; Battery centralized charging station (BCCS); distribution system; electric vehicle (EV) spatial-temporal load forecasting; photovoltaic (PV); swapping electric vehicle (SEV); DISTRIBUTION NETWORK; POWER DISTRIBUTION; ENERGY-STORAGE; TRANSPORTATION; FACILITIES; STRATEGY; DEMAND; SYSTEM; MODEL; PV;
D O I
10.1109/TIA.2022.3186870
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This article studies the planning of swapping electric vehicle (SEV) battery centralized charging station (BCCS) based on EV spatial-temporal load forecasting. First, according to the road topology of the planning area, the road topology model is built. The driving energy consumption process and endurance mileage model of EVs are developed. Then, we analyze the travel demand of people, the driving segments of SEVs (e.g., swapping private EVs and swapping taxis) are analyzed. Next, the spatial distribution model of SEVs is built. Thus, the spatial-temporal load model of SEVs is established. The sizing and locating planning model based on operation analysis is established for BCCSs. In addition, the photovoltaic (PV) configuration model in the urban grid is also established. Finally, considering the coupling relationship between urban distribution system and power line pipes, a grid planning method considering power line pipes is proposed. The planning model of BCCS aimed at minimizing annual comprehensive cost considering distribution system with PV is constructed. The MATLAB/YALMIP toolbox is applied to optimize the planning model in this article, and the feasibility and effectiveness of the proposed method are verified.
引用
收藏
页码:6557 / 6575
页数:19
相关论文
共 42 条
[1]   Stochastic Planning for Optimal Allocation of Fast Charging Stations and Wind-Based DGs [J].
Amer, Abdelrahman ;
Azzouz, Maher A. ;
Azab, Ahmed ;
Awad, Ahmed S. A. .
IEEE SYSTEMS JOURNAL, 2021, 15 (03) :4589-4599
[2]  
[Anonymous], 2012, PROC INT C SUSTAIN P, DOI DOI 10.1049/CP.2012.1846
[3]  
[Anonymous], 2021, NATL HOUSEHOLD TRAVE
[4]  
[Anonymous], 2014, 2014 IEEE PES ASIA P
[5]   Optimal sizing of PV and battery-based energy storage in an off-grid nanogrid supplying batteries to a battery swapping station [J].
Ban, Mingfei ;
Yu, Jilai ;
Shahidehpour, Mohammad ;
Guo, Danyang .
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2019, 7 (02) :309-320
[6]   Agent-Based Aggregated Behavior Modeling for Electric Vehicle Charging Load [J].
Chaudhari, Kalpesh ;
Kandasamy, Nandha Kumar ;
Krishnan, Ashok ;
Ukil, Abhisek ;
Gooi, H. B. .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (02) :856-868
[7]   Design and Planning of a Multiple-Charger Multiple-Port Charging System for PEV Charging Station [J].
Chen, Huimiao ;
Hu, Zechun ;
Luo, Haocheng ;
Qin, Junjie ;
Rajagopal, Rain ;
Zhang, Hongcai .
IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (01) :173-183
[8]   Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles' Spatial-Temporal Transfer Randomness [J].
Cheng, Shan ;
Wei, Zhaobin ;
Shang, Dongdong ;
Zhao, Zikai ;
Chen, Huiming .
IEEE ACCESS, 2020, 8 :124084-124096
[9]  
Chenke He, 2021, 2021 6th International Conference on Power and Renewable Energy (ICPRE), P1192, DOI 10.1109/ICPRE52634.2021.9635414
[10]   Reinforcement Learning-Based Load Forecasting of Electric Vehicle Charging Station Using Q-Learning Technique [J].
Dabbaghjamanesh, Morteza ;
Moeini, Amirhossein ;
Kavousi-Fard, Abdollah .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (06) :4229-4237