Multi-scenario Demand Response Strategy Based on DEMATEL-AISM for Electric Vehicles

doi:10.12204/j.issn.1000-7229.2023.03.010

Abstract

Abstract:

With the continuous increase of renewable energy penetration, the volatility and randomness of the power grid are increasing, and demand-side resource supply will become more and more important. Electric vehicles (EVs) account for a relatively large share of demand-side resources, but existing studies have rarely considered the multi-factor interaction of individual and social factors in the process of EV participating in demand response. Therefore, an EV multi-scenario demand-response charging scheduling strategy based on the decision-making trial and evaluation laboratory-adversarial interpretive structure modeling method (DEMATEL-AISM) is constructed in this paper. Firstly, a data mining method is used to analyze the operating characteristics of charging stations and EV charging characteristics in multiple scenarios, and an EV charging load characteristic model is constructed. Secondly, the DEMATEL-AISM algorithm is used to analyze the multi-factor coupling relationship affecting EV charging behavior in multiple scenarios, and the dominant factors are explored. Finally, according to the analysis of the dominant factors in multiple scenarios, a user regulation strategy is formulated under the influence of multiple factors. Through simulation, it is verified that the method proposed in this paper can effectively smooth out the peak and valley levels of load, node voltage fluctuations is reduced, the stability and economy of the demand side of the power system is improved.

Key words: electric vehicles, demand response, decision-making trial and evaluation laboratory-adversarial interpretive structure modeling method (DEMATEL-AISM), multi-factor impact degree assessment

CLC Number:

TM73

YAO Yin, ZHU Yedong, LI Dongdong, ZHOU Bo, LIN Shunfu. Multi-scenario Demand Response Strategy Based on DEMATEL-AISM for Electric Vehicles[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(3): 93-104.

Figures/Tables 17

Fig.1 Calculation flow chart of DEMATEL-AISM

Fig.2 Load curves of EV charging station for different functional areas and dates

Fig.3 Hierarchy diagram of UP and DOWN topology

Fig.4 The weights of factors in the integrated system affecting users' charging behavior

Table 1 Distribution of coupling weights among important factors

Fig.5 Grid load curve under different DR strategies

Fig.6 Levels of load peak-to-valley differences and load fluctuation under different DR strategies

Table 2 System voltage stability indicators under different DR strategies

Table 3 Voltage stability indicators for each functional area under different DR strategies

Fig.7 Cost and profits under each strategy

Table A1 Possibility value of consumer participating in demand response

Table A2 Peak and valley prices

Table A3 Fuzzy influence matrix among factors

Table A4 Coupling weight values among factors

Fig.B1 Charging start time of EVs in different functional areas

Fig.B2 Single-time charge capacity of EVs in different functional areas

Fig.B3 IEEE 33-bus system

References 26

[1]	王萍萍, 许建中, 闫庆友, 等. 计及灵活性负荷资源需求响应和不确定性的楼宇微网调度双层优化模型[J]. 电力建设, 2022, 43(6): 128-140. doi: 10.12204/j.issn.1000-7229.2022.06.014
	WANG Pingping, XU Jianzhong, YAN Qingyou, et al. A two-level scheduling optimization model for building microgrids considering demand response and uncertainties of flexible load resources[J]. Electric Power Construction, 2022, 43(6): 128-140. doi: 10.12204/j.issn.1000-7229.2022.06.014
[2]	WANG J Y, BHARATI G R, PAUDYAL S, et al. Coordinated electric vehicle charging with reactive power support to distribution grids[J]. IEEE Transactions on Industrial Informatics, 2019, 15(1): 54-63. doi: 10.1109/TII.9424 URL
[3]	王瑞东, 吴杰康, 蔡志宏, 等. 含广义储能虚拟电厂电-气-热三阶段协同优化调度[J]. 电网技术, 2022, 46(5): 1857-1868.
	WANG Ruidong, WU Jiekang, CAI Zhihong, et al. Three-stage collaborative optimal scheduling of electricity-gas-heat in virtual power plant with generalized energy storage[J]. Power System Technology, 2022, 46(5): 1857-1868.
[4]	蔡黎, 张权文, 代妮娜, 等. 规模化电动汽车接入主动配电网研究进展综述[J]. 智慧电力, 2021, 49(6): 75-82.
	CAI Li, ZHANG Quanwen, DAI Nina, et al. Review on research progress of large-scale electric vehicle access to active distribution network[J]. Smart Power, 2021, 49(6): 75-82.
[5]	公安部交通管理局. 全国汽车达3.15亿辆第三季度汽车月均新增量明显高于上半年,连续3个月保持较快增长[EB/OL]. (2022-10-08)[2022-11-15]. https://www.mps.gov.cn/n2254314/n6409334/c8719751/content.html.
[6]	MEJIA-RUIZ G E, CÁRDENAS-JAVIER R, ARRIETA PATERNINA M R, et al. Coordinated optimal volt/var control for distribution networks via D-PMUs and EV chargers by exploiting the eigensystem realization[J]. IEEE Transactions on Smart Grid, 2021, 12(3): 2425-2438. doi: 10.1109/TSG.2021.3050443 URL
[7]	史亮, 葛晓琳, 顾闻, 等. 考虑需求响应的电动汽车充电负荷研究[J]. 电测与仪表, 2022, 59(7): 42-47.
	SHI Liang, GE Xiaolin, GU Wen, et al. Research on charging loads of electric vehicles considering demand response[J]. Electrical Measurement & Instrumentation, 2022, 59(7): 42-47.
[8]	侯慧, 王逸凡, 赵波, 等. 价格与激励需求响应下电动汽车负荷聚集商调度策略[J]. 电网技术, 2022, 46(4): 1259-1269.
	HOU Hui, WANG Yifan, ZHAO Bo, et al. Electric vehicle aggregator dispatching strategy under price and incentive demand response[J]. Power System Technology, 2022, 46(4): 1259-1269.
[9]	阎怀东, 马汝祥, 柳志航, 等. 计及需求响应的电动汽车充电站多时间尺度随机优化调度[J]. 电力系统保护与控制, 2020, 48(10): 71-80.
	YAN Huaidong, MA Ruxiang, LIU Zhihang, et al. Multi-time scale stochastic optimal dispatch of electric vehicle charging station considering demand response[J]. Power System Protection and Control, 2020, 48(10): 71-80.
[10]	史文龙, 秦文萍, 王丽彬, 等. 计及电动汽车需求和分时电价差异的区域电网LSTM调度策略[J]. 中国电机工程学报, 2022, 42(10): 3573-3587.
	SHI Wenlong, QIN Wenping, WANG Libin, et al. Regional power grid LSTM dispatch strategy considering the difference between electric vehicle demand and time-of-use electricity price[J]. Proceedings of the CSEE, 2022, 42(10): 3573-3587.
[11]	王丹, 孙冬冬, 胡庆娥, 等. 基于最优能量状态调节的电动汽车需求响应及其对配电网的影响[J]. 电力建设, 2018, 39(5): 86-94. doi: 10.3969/j.issn.1000-7229.2018.05.011
	WANG Dan, SUN Dongdong, HU Qinge, et al. Demand response of electric vehicles based on optimal energy status regulation and its impact on distribution network[J]. Electric Power Construction, 2018, 39(5): 86-94. doi: 10.3969/j.issn.1000-7229.2018.05.011
[12]	蔡黎, 葛棚丹, 代妮娜, 等. 电动汽车入网负荷预测及其与电网互动研究进展综述[J]. 智慧电力, 2022, 50(7): 96-103.
	CAI Li, GE Pengdan, DAI Nina, et al. Review of research progress on load prediction and grid interaction of electric vehicles[J]. Smart Power, 2022, 50(7): 96-103.
[13]	姚蓝霓, 李钦豪, 杨景旭, 等. 考虑电动汽车充放电支撑的配用电系统综合无功优化[J]. 电力系统自动化, 2022, 46(6): 39-47.
	YAO Lanni, LI Qinhao, YANG Jingxu, et al. Comprehensive reactive power optimization of power distribution and consumption system with support of electric vehicle charging and discharging[J]. Automation of Electric Power Systems, 2022, 46(6): 39-47.
[14]	张良, 孙成龙, 蔡国伟, 等. 基于PSO算法的电动汽车有序充放电两阶段优化策略[J]. 中国电机工程学报, 2022, 42(5): 1837-1852.
	ZHANG Liang, SUN Chenglong, CAI Guowei, et al. Two-stage optimization strategy for coordinated charging and discharging of EVs based on PSO algorithm[J]. Proceedings of the CSEE, 2022, 42(5): 1837-1852.
[15]	张美霞, 蔡雅慧, 杨秀, 等. 考虑用户充电差异性的家用电动汽车充电需求分布分析方法[J]. 电力自动化设备, 2020, 40(2): 154-163.
	ZHANG Meixia, CAI Yahui, YANG Xiu, et al. Charging demand distribution analysis method of household electric vehicles considering users’ charging difference[J]. Electric Power Automation Equipment, 2020, 40(2): 154-163.
[16]	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]. 电网技术, 2022, 46(2): 632-645.
	ZHANG Meixia, SUN Quanjie, YANG Xiu. Electric vehicle charging load prediction considering multi-source information real-time interaction and user regret psychology[J]. Power System Technology, 2022, 46(2): 632-645.
[17]	LU Y Q, GU J, XIE D, et al. Integrated route planning algorithm based on spot price and classified travel objectives for EV users[J]. IEEE Access, 2019, 7: 122238-122250. doi: 10.1109/Access.6287639 URL
[18]	GUPTA V, KUMAR R, PANIGRAHI B K. User-willingness-based decentralized EV charging management in multi-aggregator scheduling[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5704-5715. doi: 10.1109/TIA.28 URL
[19]	盛裕杰, 郭庆来, 刘梦洁, 等. 多源数据融合的用户充电行为分析与充电设施规划实践[J]. 电力系统自动化, 2022, 46(12): 151-162.
	SHENG Yujie, GUO Qinglai, LIU Mengjie, et al. User charging behavior analysis and charging facility planning practice based on multi-source data fusion[J]. Automation of Electric Power Systems, 2022, 46(12): 151-162.
[20]	邵尹池, 穆云飞, 余晓丹, 等. “车-路-网”模式下电动汽车充电负荷时空预测及其对配电网潮流的影响[J]. 中国电机工程学报, 2017, 37(18): 5207-5219.
	SHAO Yinchi, MU Yunfei, YU Xiaodan, et al. A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model[J]. Proceedings of the CSEE, 2017, 37(18): 5207-5219.
[21]	方晓涛, 严正, 王晗, 等. 考虑“路-车-源-荷”多重不确定性的交通网与配电网概率联合流分析[J]. 电力系统自动化, 2022, 46(12): 76-87.
	FANG Xiaotao, YAN Zheng, WANG Han, et al. Analysis on probabilistic joint flow for transportation network and distribution network considering multiple uncertainties of road-vehicle-source-load[J]. Automation of Electric Power Systems, 2022, 46(12): 76-87.
[22]	刘志强, 张谦, 朱熠, 等. 计及车-路-站-网融合的电动汽车充电负荷时空分布预测[J]. 电力系统自动化, 2022, 46(12): 36-45.
	LIU Zhiqiang, ZHANG Qian, ZHU Yi, et al. Spatial-temporal distribution prediction of charging loads for electric vehicles considering vehicle-road-station-grid integration[J]. Automation of Electric Power Systems, 2022, 46(12): 36-45.
[23]	张巍, 王丹. 基于云边协同的电动汽车实时需求响应调度策略[J]. 电网技术, 2022, 46(4): 1447-1458.
	ZHANG Wei, WANG Dan. Real-time demand response scheduling strategy for electric vehicles based on cloud edge collaboration[J]. Power System Technology, 2022, 46(4): 1447-1458.
[24]	AMIRGHODSI S, NAEINI A B, MAKUI A. An integrated Delphi-DEMATEL-ELECTRE method on gray numbers to rank technology providers[J]. IEEE Transactions on Engineering Management, 2022, 69(4): 1348-1364. doi: 10.1109/TEM.2020.2980127 URL
[25]	石文超, 吕林, 高红均, 等. 考虑需求响应和电动汽车参与的主动配电网经济调度[J]. 电力系统自动化, 2020, 44(11): 41-51.
	SHI Wenchao, LÜ Lin, GAO Hongjun, et al. Economic dispatch of active distribution network with participation of demand response and electric vehicle[J]. Automation of Electric Power Systems, 2020, 44(11): 41-51.
[26]	王强钢, 田雨禾, 王健, 等. 计及充电站无功补偿的配电网日前-实时协调优化模型[J]. 电力系统自动化, 2021, 45(17): 27-34.
	WANG Qianggang, TIAN Yuhe, WANG Jian, et al. Coordinated day-ahead and real-time optimization model for distribution network considering reactive power compensation of charging station[J]. Automation of Electric Power Systems, 2021, 45(17): 27-34.