Paper Infomation
Study on the Impact Evaluation of Different Charging and Discharging Strategies for Electric Vehicles
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Author: Yinbo Zhang
Abstract: Because electric vehicles have significant energy saving and emission reduction potential, it has become an important transformation direction in the future transportation field. However, the charging and discharging behavior of electric vehicles is mainly determined by the user, and every car connected to the grid will cause random power load fluctuations. In the case that coal is the main power supply structure in China, the random charging behavior without regulation will lead to the emission reduction benefit in the transportation field to the power generation end, making the energy-saving emission reduction goal of the development of electric vehicles lose practical significance. Therefore, the development of electric vehicles needs to focus on charge-discharge strategies. There are three kinds of charging and discharging strategies: disordered charging strategy, ordered charging strategy and V2G strategy. This paper summarizes the factors affecting the charging and discharging load of electric vehicles, including the type of electric vehicles, battery capacity, daily mileage, charging start time, charging and discharging power, electricity price, etc. Based on statistical probability, a probability statistical model is established for these major influencing factors, and Monte Carlo simulation method is used to predict the load of electric vehicles. The daily load curves to meet the electric demand of electric vehicles under different charging and discharging strategies are obtained. Then, the actual data of Chongqing Municipality was used for simulation to explore the impact of disordered charging, orderly charging and V2G on the power grid, environment and users under actual conditions for large-scale EV users, so as to provide decision-making basis for stakeholders and put forward scientific, reasonable and forward-looking management suggestions based on the conclusions.
Keywords: Electric Vehicles, Charging and Discharging Strategies, Monte Carlo Simulations
References:
[1] 李晓辉,李磊,刘伟东等.基于动态交通信息的电动汽车充电负荷时空分布预测[J].电力系统保护与控制,2020,48(01):117-125.
[2] 万井培.电动汽车直流充电桩接入对电网谐波的影响分析[J].电工技术,2022, 575(17):9-11.
[3] 张美霞,吴子敬,杨秀.基于动态能耗模型与用户心理的电动汽车充电负荷预测[J].现代电力,2022,39(06):710-719.
[4] Sovacool B K,Kester J, Noel L,et al.Actors, business models, and innovation activity systems for vehicle-to-grid (V2G) technology: A comprehensive review[J]. Renewable and Sustainable Energy Reviews,2020,131:109963.
[5] 陶莉,高岩,朱红波.以极小化峰谷差为目标的智能电网实时定价[J].系统工程学报,2020,35(03):315-324.
[6] 罗维祥,常喜强,伏睿,等考虑供需需求的电动汽车充放电调度策略[J].电力系统及其自动化学报,2022,34(07):106-112.
[7] 李咸善,鄂璇.分档电价和碳配额激励的含电动汽车微电网优化调度策略[J]科学技术与工程,2022,22(16):6537-6546.
[8] 胡思洋,廖凯,杨健维,等基于V2G技术的城市电网供电恢复策略[J/OL].电力自动化设备:1-17[2023-02-09].
[9] Ma S C,Yi B W,Fan Y.Research on the valley-filling pricing for EV charging considering renewable power generation[J].Energy Economics,2022,106:105781.
[10] Sovacool B K,Kester J, Noel L,et al.Actors, business models, and innovation activity systems for vehicle-to-grid (V2G) technology: A comprehensive review[J]. Renewable and Sustainable Energy Reviews,2020,131:109963.
[11] Bauer C,Hofer J,Althaus H J,et al.The environmental performance of current and future passenger vehicles: Life cycle assessment based on a novel scenario analysis framework[J].Applied Energy,2015,157:871-883.
[12] 王振报.电动汽车策略对碳排放的影响评估——以美国马里兰州一体化模型应用为例[J].城市交通,2021,19(05):66-72＋16.
[13] 李利军,孙凯扬,李艳丽.碳达峰背景下雄安电动货车减碳量测评研究[J].交通节能与环保,2021,17(02):5−12.
[14] 邵丹,李涵.城市客运交通电动化碳减排效益和碳达峰目标——以上海市为例[J].城市交通,2021,19(95):53−58.
[15] 张钰,张玥,韩新阳,等碳排放最小化条件下电动汽车有序充电策略研究[J].中国电力,2020,53(04):147-154.
[16] 项,宋永华,胡泽春,等电动汽车参与V2G的最优峰谷电价研究[J].中国电机工程学报,2013,33(31):11.
[17] 黄贵鸿,雷霞,芦杨,等考虑用户满意度的电动汽车用户侧最优智能充放电策略[J].电力系统保护与控制.2015.43(24):40-47.
[18] 谭维玉.计及用户需求侧响应的电动汽车充放电电价制定策略研究[D]：[硕士学位论文]．重庆：重庆大学，2019．
[19] Goh H H,Zong L,Zhang D,et al.Orderly charging strategy based on optimal time of use price demand response of electric vehicles in distribution network[J].Energies,2022,15(5):1869.
[20] Kolawole O,Al-Anbagi I.Optimizing electric vehicles charging cost for frequency regulation support in a smart grid[C].2017 International Conference on Open Source Systems & Technologies (ICOSST).(Lahore, Pakistan):IEEE,2017:1-6.
[21] 杨镜司,秦文萍,史文龙,等基于电动汽车参与调峰定价策略的区域电网两阶段优化调度[J].电工技术学报,2022,37(01):58-71.
[22] Amamra S A，Marco J.Vehicle-to-grid aggregator to support power grid and reduce electric vehicle charging cost[J].IEEE Access,2019,7:178528-178538.
[23] 周晨瑞,盛光宗,李升.考虑电动汽车接入的微电网多目标优化调度[J/OL].电气工程学报:1-8[2023-02-09].
[24] 郭继孚.推动城市交通碳达峰､碳中和的对策与建议[J].可持续发展经济导刊,2021,23(03):22−23.