Dispatchable Capability of Eclectic Vehicle Clusters Considering Temporal-Spatial Characteristics

doi:10.12204/j.issn.1000-7229.2021.12.010

Abstract

Abstract:

Electric vehicles (EVs) can balance grid voltage through V2G (vehicle-to-grid) technology as mobile storage, but their temporal-spatial dual uncertainty will affect the safe operation of the grid. To solve this problem, considering the uncertainty of EVs, this paper proposes an evaluation method for V2G response-ability and energy storage capacity of EVs. The temporal-spatial behavior of the EVs is simulated by Monte Carlo method using the random travel chain. On the basis of the Gaussian mixture model, the probability model of the charging station load and the SOC (state of charge) of the EVs in the station are established. The voltage regulation ability index and the V2G response capacity index of EVs are then proposed to assess the response-ability of EVs in different periods. The simulation compares the dispatchable capacity of EVs and the recovery effect on grid voltage in different periods. Finally, the results verify the effectiveness of the proposed evaluation method.

Key words: electric vehicle, trip-chain, Gaussian mixture model, temporal-spatial characteristics, dispatchable capacity

CLC Number:

TM73

LIU Junqi, JIANG Shujun, WANG Zhaoqi, ZHANG Bo, WANG Chen. Dispatchable Capability of Eclectic Vehicle Clusters Considering Temporal-Spatial Characteristics[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(12): 83-92.

Figures/Tables 11

Fig.1 Flowchart of the EV traveling by Monte Carlo method

Fig.2 Response boundary of V2G using the charging and discharging strategy

Fig.3 Flowchart of discharging capacity assessment of EV clusters

Fig.4 Traffic-power grid

Fig.5 Charging station distribution in IEEE 33-node case

Table 1 Travel chain patterns

Fig.6 Charing power of EVs during 24 hours

Fig.7 Amount of EVs during 24 hours

Fig.8 Cumulative probability of EV’s dispatchable capability

Table 2 Energy storage capacity of charging stationskW·h

Table 3 Voltage recovery ability of V2G response

References 28

[1]	苏小林, 张艳娟, 武中, 等. 规模化电动汽车充电负荷的预测及其对电网的影响[J]. 现代电力, 2018, 35(1): 45-54.
	SU Xiaolin, ZHANG Yanjuan, WU Zhong, et al. Forecasting the charging load of large-scale electric vehicle and its impact on the power grid[J]. Modern Electric Power, 2018, 35(1): 45-54.
[2]	麻秀范, 李颖, 王皓, 等. 基于电动汽车出行随机模拟的充电桩需求研究[J]. 电工技术学报, 2017, 32(S2): 190-202.
	MA Xiufan, LI Ying, WANG Hao, et al. Research on demand of charging piles based on stochastic simulation of EV trip chain[J]. Transactions of China Electrotechnical Society, 2017, 32(S2): 190-202.
[3]	陈丽丹, 聂涌泉, 钟庆. 基于出行链的电动汽车充电负荷预测模型[J]. 电工技术学报, 2015, 30(4): 216-225.
	CHEN Lidan, NIE Yongquan, ZHONG Qing. A model for electric vehicle charging load forecasting based on trip chains[J]. Transactions of China Electrotechnical Society, 2015, 30(4): 216-225.
[4]	程杉, 魏昭彬, 赵子凯, 等. 考虑电动汽车时空接入随机性的充储电站有序充放电分散式优化[J]. 电力自动化设备, 2021, 41(6): 28-35.
	CHENG Shan, WEI Zhaobin, ZHAO Zikai, et al. Decentralized optimization of ordered charging and discharging for charging-storage station considering spatial-temporal access randomness of electric vehicles[J]. Electric Power Automation Equipment, 2021, 41(6): 28-35.
[5]	朱亚迪. 大数据驱动的城市轨道交通需求时空分布分析及预测方法研究[D]. 北京: 北京交通大学, 2019.
	ZHU Yadi. Big data-driven method of urban rail transit demand spatiotemporal distribution analysis and forecasting[D]. Beijing: Beijing Jiaotong University, 2019.
[6]	张洪财, 胡泽春, 宋永华, 等. 考虑时空分布的电动汽车充电负荷预测方法[J]. 电力系统自动化, 2014, 38(1): 13-20.
	ZHANG Hongcai, HU Zechun, SONG Yonghua, et al. A prediction method for electric vehicle charging load considering spatial and temporal distribution[J]. Automation of Electric Power Systems, 2014, 38(1): 13-20.
[7]	CHUKWU U C, MAHAJAN S M. V2G electric power capacity estimation and ancillary service market evaluation[C]// 2011 IEEE Power and Energy Society General Meeting. IEEE, 2011: 1-8.
[8]	LAM A Y S, LEUNG K C, LI V O K. Capacity management of vehicle-to-grid system for power regulation services[C]// 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2012: 442-447.
[9]	LAM A Y S, LEUNG K C, LI V O K. Capacity estimation for vehicle-to-grid frequency regulation services with smart charging mechanism[J]. IEEE Transactions on Smart Grid, 2016, 7(1): 156-166. doi: 10.1109/TSG.2015.2436901 URL
[10]	DONG X H, MU Y F, JIA H J, et al. Planning of fast EV charging stations on a round freeway[J]. IEEE Transactions on Sustainable Energy, 2016, 7(4): 1452-1461. doi: 10.1109/TSTE.2016.2547891 URL
[11]	VALIZADEH HAGHI H, QU Z H. A kernel-based predictive model of EV capacity for distributed voltage control and demand response[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 3180-3190. doi: 10.1109/TSG.2016.2628367 URL
[12]	张美霞, 孙铨杰, 杨秀. 考虑多源信息实时交互和用户后悔心理的电动汽车充电负荷预测[J/OL]. 电网技术:1-15[2021-07-23]. https://doi-org.manchester.idm.oclc.org/10.13335/j.1000-3673.pst.2021.0273.
	ZHANG Meixia, SUN Quanjie, YANG Xiu. Electric vehicle charging load prediction considering real-time multi-source information interaction and user regret[J/OL]. Power System Technology: 1-15[2021-07-23]. https://doi-org.manchester.idm.oclc.org/10.13335/j.1000-3673.pst.2021.0273.
[13]	HAN S, HAN S, SEZAKI K. Estimation of achievable power capacity from plug-in electric vehicles for V2G frequency regulation: Case studies for market participation[J]. IEEE Transactions on Smart Grid, 2011, 2(4): 632-641. doi: 10.1109/TSG.2011.2160299 URL
[14]	AGARWAL L, PENG W, GOEL L. Probabilistic estimation of aggregated power capacity of EVs for vehicle-to-grid application[C]// 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2014: 1-6.
[15]	翁国庆, 张有兵, 戚军, 等. 多类型电动汽车电池集群参与微网储能的V2G可用容量评估[J]. 电工技术学报, 2014, 29(8): 36-45.
	WENG Guoqing, ZHANG Youbing, QI Jun, et al. Evaluation for V2G available capacity of battery groups of electric vehicles as energy storage elements in microgrid[J]. Transactions of China Electrotechnical Society, 2014, 29(8): 36-45.
[16]	王浩林. 电动汽车时空多维度负荷预测及其可调度潜力容量评估方法[D]. 广州: 华南理工大学, 2020.
	WANG Haolin. Spatio-temporal multi-dimensional load forecasting of electric vehicles and its schedulable potential capacity assessment method[D]. Guangzhou: South China University of Technology, 2020.
[17]	吴巨爱, 薛禹胜, 谢东亮, 等. 电动汽车参与运行备用的能力评估及其仿真分析[J]. 电力系统自动化, 2018, 42(13): 101-107.
	WU Juai, XUE Yusheng, XIE Dongliang, et al. Evaluation and simulation analysis of reserve capability for electric vehicles[J]. Automation of Electric Power Systems, 2018, 42(13): 101-107.
[18]	CHUKWU U C, MAHAJAN S M. Modeling of V2G net energy injection into the grid[C]// 2017 6th International Conference on Clean Electrical Power (ICCEP). IEEE, 2017: 437-440.
[19]	YU T, YAO X P, WANG M S, et al. A reactive power evaluation model for EV chargers considering travelling behaviors[C]// 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT). IEEE, 2015: 2646-2651.
[20]	陈嘉敏, 徐永海, 张雪垠. 基于电动汽车V2G响应能力的储能容量配置方法[J]. 电力建设, 2019, 40(3): 34-41.
	CHEN Jiamin, XU Yonghai, ZHANG Xueyin. Energy storage sizing method considering V2G response capability[J]. Electric Power Construction, 2019, 40(3): 34-41.
[21]	TANG D F, WANG P. Probabilistic modeling of nodal charging demand based on spatial-temporal dynamics of moving electric vehicles[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 627-636.
[22]	LOJOWSKA A, KUROWICKA D, PAPAEFTHYMIOU G, et al. Stochastic modeling of power demand due to EVs using copula[J]. IEEE Transactions on Power Systems, 2012, 27(4): 1960-1968. doi: 10.1109/TPWRS.2012.2192139 URL
[23]	张曦予, 李秋硕, 陶顺, 等. 电动公交车充电站功率需求影响因素分析及建模[J]. 现代电力, 2014, 31(1): 28-33.
	ZHANG Xiyu, LI Qiushuo, TAO Shun, et al. Influence factors analysis and modeling of power demand for electric bus charging station[J]. Modern Electric Power, 2014, 31(1): 28-33.
[24]	于树良. 一种优化D算法最短路径实现方法的研究[J]. 科技信息, 2012(36): 640.
	YU Shuliang. Research on the shortest path realization method of advanced Dijkstra algorithm[J]. Science & Technology Information, 2012(36): 640.
[25]	FENG Y, LIU Y X, ZENG X M, et al. Power demand side response potential and operating model based on EV mobile energy storage[C]// 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2017: 1-6.
[26]	段雪, 张昌华, 张坤, 等. 电动汽车换电需求时空分布的概率建模[J]. 电网技术, 2019, 43(12): 4541-4550.
	DUAN Xue, ZHANG Changhua, ZHANG Kun, et al. Probabilistic modeling of battery swapping demand of electric vehicles based on spatio-temporal distribution[J]. Power System Technology, 2019, 43(12): 4541-4550.
[27]	BONDY J A, MURTY U S R. Graph theory[M]. London: Springer London, 2008.
[28]	李聪聪, 王彤, 相禹维, 等. 基于改进高斯混合模型的概率潮流解析方法[J]. 电力系统保护与控制, 2020, 48(10): 146-155.
	LI Congcong, WANG Tong, XIANG Yuwei, et al. Analytical method based on improved Gaussian mixture model for probabilistic load flow[J]. Power System Protection and Control, 2020, 48(10): 146-155.