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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (6): 101-111.doi: 10.6040/j.issn.1672-3961.0.2020.128

• 电气工程 • 上一篇    下一篇

电动汽车虚拟储能可用容量建模

李蓓1(),赵松1,2,谢志佳1,牛萌1   

  1. 1. 中国电力科学研究院有限公司，北京 100192
    2. 华北电力大学控制科学与工程学院，北京 102206
  • 收稿日期:2020-04-20 出版日期:2020-12-20 发布日期:2020-12-15
  • 作者简介:李蓓(1980—)，女，山东威海人，高级工程师，博士，主要研究方向为规模化储能及应用. E-mail：libei@epri.sgcc.com.cn
  • 基金资助:
    中国电力科学研究院有限公司创新基金资助项目(5242001700H7)

Electric vehicle virtual energy storage available capacity modeling

Bei LI1(),Song ZHAO1,2,Zhijia XIE1,Meng NIU1   

  1. 1. China Electric Power Research Institute, Beijing 100192, China
    2. North China Electric Power University College of Control Science and Engineering, Beijing 102206, China
  • Received:2020-04-20 Online:2020-12-20 Published:2020-12-15

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摘要：

为实现对广域布点的电动汽车虚拟储能(electric vehicle virtual energy storage，EVVES)的合理调用，通过聚类划分参与EVVES服务的电动汽车类型，结合各类电动汽车车主的用车习惯(如日行驶里程、充放电规律、期望备用出行电量等)、市场对EVVES响应度的影响、可用储能容量冗余设计等多方因素，综合建立可供调度的虚拟储能容量估算模型；结合电力系统对储能参与系统紧急功率支撑、峰谷差调节以及平滑可再生能源输出等有偿辅助服务的技术需求，提出EVVES供需匹配依据。基于蒙特卡洛模拟方法，验证模型实时估算不同时段响应虚拟储能(virtual energy storage，VES)服务的可用容量的实用性，对后续开展EVVES的运营实践奠定了基础。

关键词: 电动汽车, 虚拟储能, 可用容量建模, 蒙特卡洛模拟, 聚类分析

Abstract:

In order to efficiently implement the virtual energy storage dispatch of electric vehicles in a wide area, the article focused on the types of electric vehicles that respond to electric vehicle virtual energy storage (EVVES) services by clustering, combined with the use habits of all kinds of electric vehicle owners (such as daily driving mileage, charging and discharging rules, expected standby travel electricity, etc.), the impact of market on EVVES responsiveness, redundant design of available energy storage capacity and other factors, a virtual energy storage (VES) capacity estimation model was established. Combined with the technical requirements of power system for energy storage to participate in system services, such as emergency power support, smooth renewable energy output, UPS/EPS, etc., the matching basis of supply and demand was proposed. Based on Monte Carlo simulation method, the practicability of the model to estimate the available capacity of response VES service in different time periods was verified, which layed the foundation for the operation practice of EVVES in future.

Key words: electric vehicle, virtual energy storage, available capacity modeling, Monte Carlo simulation method, clustering analysis

中图分类号: 

  • TM85

图1

电动汽车的状态分布"

表1

不同时段内电动汽车行驶状态"

时段 电动汽车状态
A类 B类
t1 M1 M21
t2 M21 M1
t3 M1 M21
t4 M21 M1
t5 M1 M21
t6 M21 M21
t0 M23 M23

图2

EVVES响应特性"

图3

EV日行驶里程概率密度曲线"

图4

停车时间概率密度拟合曲线"

图5

EVVES可用容量统计流程"

表2

电力系统对VES服务的不同技术要求"

需求分类 应用场景 功率规模/万kW 持续时间 响应时间
小时级 提供输变电设备容量扩充 1~30 1~6 h < 15 min
实现峰谷价差套利 ＜0.1 1~4 h < 15 min
提供负荷侧需求侧响应管理 ＜5 2~6 h < 15 min
提供分布式电源出力波动平滑服务 0.1~5 1~4 h ＜15 s
分钟级 提供短时备用容量支援服务 5~100 15 min~1 h < 1 min
提供二次调频服务 1~50 1~15 min 2.5~15 s
秒级 提供紧急功率支撑服务 10~100 1~30 s 1 ms~1 s

表3

电动汽车电池相关数据"

车辆类型 额定容量/(kWh) 电动汽车效率/% 百公里能耗/(kWh) 最大放电功率/kW
A类 A1 82 95 20.5 20
A2 95 92 17.5 23
A3 79 89 23.5 17
B类 B1 84 95 33.6 23.5
B2 88 90 36 16.5
B3 80 88 32.2 20

表4

响应VES服务调度的EV实体数量"

时间 NAves NBves
t0 26 0
t1 0 1
t2 25 1
t3 2 10
t4 324 5
t5 22 49
t6 239 50

表5

EVVES在不同时段的可用容量和"

时间 A类 B类 总和
t0 1 513 0 1 513
t1 0 52 52
t2 1 489 49 1 538
t3 126 457 583
t4 19 549 254 19 803
t5 1 252 2 580 3 832
t6 14 024 2 562 16 586

表6

EVVES在不同时段的可用功率和"

时间 A类 B类 总和
t0 520 0 520
t1 0 20 20
t2 500 20 520
t3 40 200 240
t4 6 480 100 6580
t5 440 980 1 420
t6 4 780 1 000 5 780

表7

不同持续时间集合中的EV数量分布"

时间 TL1 TL2 TL3
t0 26 26 25
t1 1 1 1
t2 26 26 26
t3 12 12 11
t4 327 327 323
t5 70 70 68
t6 286 286 279

表8

不同持续时间集合中EVVES的容量和"

时间 TL1 TL2 TL3
t0 374 374 369
t1 16 16 16
t2 360 360 360
t3 185 185 174
t4 4 692 4 692 4 642
t5 1 050 1 050 1 029
t6 4 138 4 138 4 120

表9

不同持续时间集合中EVVES的功率和"

时间 TL1 TL2 TL3
t0 520 520 500
t1 20 20 20
t2 520 520 520
t3 240 240 220
t4 6 540 6 540 6 460
t5 1 400 1 400 1 360
t6 5 720 5 720 5 580

图6

不同持续时间集合中EVVES的容量"

图7

不同持续时间集合中EVVES的功率"

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