Two-stage response model of bus station considering response degree feedback and day-night charging distribution

被引：0
|
作者：
Yao L. [1 ]
Zhang Y. [1 ]
Tang Y. [1 ]
Yang J. [1 ]
Zhou X. [1 ]
机构：
[1] School of Electric Power Engineering, South China University of Technology, Guangzhou
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2022年 / 42卷 / 10期
基金：
中国国家自然科学基金;
关键词：
demand response; electric bus; incentive settlement; intermittent power supply; two-stage charging;
D O I：
10.16081/j.epae.202204063
中图分类号：
学科分类号：
摘要：
The connection of large-scale electric bus charging load to the power grid leads to the increase of peak load and the decrease of power grid’s safety and economy. Therefore，a two-stage charging response model considering the response degree feedback and day-night charging distribution is proposed. A two-stage charging load model of electric buses is established，the control periods are determined based on the scheduling model of electric buses，and the charging state of each control period is taken as the control variable. The concept of travel margin coefficient is proposed，and the charging demand of electric buses is allocated between night and day to realize two-stage charging. The segmental incentive settlement mechanism of peak load shifting response based on the response degree is formulated. According to the response coefficient，the response is divided into under-response，effective response and over-response，and the bus station is guided to realize effective response through penalty coefficient and saturation coefficient. On this basis，the night-day two-stage charging response model is established，and the net expenditure per unit power is used as an index to maximize the bus station’s benefit. Based on particle swarm optimization algorithm，the optimization strategy with constraints is given by defining the constraint deviation degree function. Simulative results show that the proposed strategy can reliably complete the response task，the net expenditure per unit power of electric bus decreases significantly，and the response economic burden of the power grid is lighter than that of the fixed incentive method. © 2022 Electric Power Automation Equipment Press. All rights reserved.
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页码：62 / 69
页数：7
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共 19 条
  • [11] YANG Jingxu, ZHANG Yongjun, Optimization of charging load acceptance capacity based on ordered charging start-up mechanism and subsidy mechanism［J］, Electric Power Automation Equipment, 40, 11, pp. 122-130, (2020)
  • [12] CAO Wei, ZENG Xianwen, GAO Guige, Research on orderly charging and discharging of electric bus based on travel law ［J］, Journal of Shanghai Dianji University, 22, 6, pp. 345-351, (2019)
  • [13] DU J Y, LI F Q, LI J Q, Et al., Evaluating the technological evolution of battery electric buses：China as a case［J］, Energy, 176, pp. 309-319, (2019)
  • [14] LI Bin, Chang LIU, CHEN Huimiao, Et al., Coordinated charging of plug-in electric buses in fast charging stations based on mixed-integer programming［J］, Power System Technology, 40, 9, pp. 2623-2630, (2016)
  • [15] CHEN Lijuan, QIN Meng, GU Shaoping, Et al., Optimal dispatching strategy of electric bus participating in vehicle-to-grid considering battery loss［J］, Automation of Electric Power Systems, 44, 11, pp. 52-60, (2020)
  • [16] XIAO Bai, ZHU Jiaxun, JIANG Zhuo, Et al., Planning of electric bus charging station considering vehicle charging scheduling mechanism［J］, Electric Power Automation Equipment, 42, 1, pp. 148-155, (2022)
  • [17] YANG Jianwei, YANG He, ZHANG Xialin, Et al., A charging optimization strategy on charging and swapping station for electric buses based on optimization of switching rules and matching of buses and batteries［J］, Proceedings of the CSEE, 39, 8, pp. 2337-2347, (2019)
  • [18] WANG Haiwei, XU Jianzhong, CHEN Chen, A refined charging strategy for electric buses based on data-driven［J］, Advances of Power System & Hydroelectric Engineering, 37, 5, pp. 83-95, (2021)
  • [19] YANG Jingxu, LI Qinhao, ZHANG Yongjun, Et al., Peak shaving optimization modeling for demand response of multiple EV aggregators considering matching degree of power grid demand［J］, Electric Power Automation Equipment, 41, 8, pp. 125-134, (2021)
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