• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2019, Vol. 40 ›› Issue (3): 34-41.doi: 10.3969/j.issn.1000-7229.2019.03.005

• 储能系统关键技术 ·栏目主持 李建林教授级高级工程师· • 上一篇    下一篇

基于电动汽车V2G响应能力的储能容量配置方法

陈嘉敏,徐永海,张雪垠   

  1. 新能源电力系统国家重点实验室(华北电力大学) 北京市102206
  • 出版日期:2019-03-01
  • 作者简介:陈嘉敏(1994),女,硕士研究生,主要研究方向为电能质量分析与控制技术、电动汽车等; 徐永海(1966),男,教授,博士生导师,主要研究方向为电能质量分析与控制技术、新能源电力系统等; 张雪垠(1992),男,博士研究生,主要研究方向为电能质量分析与控制技术、柔性配电技术等。
  • 基金资助:
    北京市自然科学基金项目(3172036)

Energy Storage Sizing Method Considering V2G Response Capability

CHEN Jiamin , XU Yonghai, ZHANG Xueyin   

  1. State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
  • Online:2019-03-01
  • Supported by:
    This work is supported by Beijing Natural Science Foundation(No.3172036).

摘要: 电动汽车通过V2G(vehicle to grid)技术可作为移动储能单元参与电网运行。基于电动汽车交通行为特性,构造了考虑时间、能量和电池约束的电动汽车V2G响应能力边界,并将接入电网的电动汽车分成可响应和不可响应2类;针对可响应电动汽车提出定时段V2G响应能力预测模型,并利用蒙特卡洛法实现电动汽车集群V2G响应能力的量化计算。为了解决多种不确定因素下的供电可靠性问题,将风险价值理论引入储能容量配置中,建立了考虑电动汽车V2G的可靠性风险备用模型,用于确定一定可靠性置信度下的储能容量配置。最后通过算例对一天不同时刻下电动汽车V2G响应能力进行预测,并对比和分析了不同置信度、期望支撑时长以及电动汽车渗透率对储能容量配置的影响。

关键词: 电动汽车, V2G响应能力预测, 蒙特卡洛, 储能, 风险价值, 可靠性

Abstract: Utilizing vehicle to grid (V2G) technology, electric vehicles (EVs) can behave as mobile energy storage units to participate in power grid operation. Due to the traffic behavior characteristics, the V2G response capability boundaries of EV considering time, energy and battery restraint are constructed. Then,the electric vehicles connected to the grid are divided into two types: responsive and non-responsive. Finally, time-limit V2G response capability forecast models for EV and EV groups are established. On the other side,the value-at-risk(VaR) method is introduced into the sizing of energy storage capacity, and a reliability-at-risk(RaR) model is established to calculate the storage capacity under a certain reliability confidence level. The methods proposed in this paper are applied and verified in the examples.

Key words: electric vehicles, V2G response capability, Monte Carlo, energy storage, value-at-risk (VaR), reliability

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