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Estimating the benefits of electric vehicle smart charging at non-residential locations: A data-driven approach

机译:估算非住宅位置的电动汽车智能充电的好处:一种数据驱动的方法

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摘要

In this paper, we use data collected from over 2000 non-residential electric vehicle supply equipments (EVSEs) located in Northern California for the year of 2013 to estimate the potential benefits of smart electric vehicle (EV) charging. We develop a smart charging framework to identify the benefits of non-residential EV charging to the load aggregators and the distribution grid. Using this extensive dataset, we aim to improve upon past studies focusing on the benefits of smart EV charging by relaxing the assumptions made in these studies regarding: (i) driving patterns, driver behavior and driver types; (ii) the scalability of a limited number of simulated vehicles to represent different load aggregation points in the power system with different customer characteristics; and (iii) the charging profile of EVs. First, we study the benefits of EV aggregations behind-the-meter, where a time-of-use pricing schema is used to understand the benefits to the owner when EV aggregations shift load from high cost periods to lower cost periods. For the year of 2013, we show a reduction of up to 24.8% in the monthly bill is possible. Then, following a similar aggregation strategy, we show that EV aggregations decrease their contribution to the system peak load by approximately 37% (median) when charging is controlled within arrival and departure times. Our results also show that it could be expected to shift approximately 0.25 kW h (-2.8%) of energy per non-residential EV charging session from peak periods (12 PM-6 PM) to off-peak periods (after 6 PM) in Northern California for the year of 2013. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在本文中,我们使用2013年从位于加利福尼亚北部的2000多种非住宅电动汽车供应设备(EVSE)收集的数据来估算智能电动汽车(EV)充电的潜在收益。我们开发了一种智能充电框架,以识别非住宅EV充电对负载聚合器和配电网的好处。通过使用这些广泛的数据集,我们旨在通过放宽这些研究中关于以下方面的假设,来改善专注于智能电动汽车充电益处的以往研究:(i)驾驶模式,驾驶员行为和驾驶员类型; (ii)有限数量的模拟车辆的可扩展性,以代表电力系统中具有不同客户特征的不同负载集合点; (iii)电动汽车的充电模式。首先,我们在表外研究EV聚合的好处,其中,当EV聚合将负载从高成本期转移到较低成本期时,使用分时定价模式来了解所有者的收益。对于2013年,我们显示每月账单最多可减少24.8%。然后,遵循类似的聚合策略,我们显示,当将充电控制在到达和离开的时间范围内时,EV聚合会将其对系统峰值负载的贡献降低约37%(中值)。我们的结果还表明,可以预计,每笔非住宅EV充电时段的能量消耗大约0.25 kWh(-2.8%)从高峰时段(12 PM-6 PM)转移到非高峰时段(6 PM之后)。 2013年为北加利福尼亚州。(C)2015 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Applied Energy》 |2015年第1期|515-525|共11页
  • 作者单位

    Carnegie Mellon Univ, Civil & Environm Engn, Pittsburgh, PA 15213 USA.;

    Lawrence Berkeley Natl Lab, Energy Storage & Distributed Resources Div, Berkeley, CA USA.;

    Lawrence Berkeley Natl Lab, Energy Storage & Distributed Resources Div, Berkeley, CA USA.;

    Carnegie Mellon Univ, Civil & Environm Engn, Pittsburgh, PA 15213 USA.;

    Carnegie Mellon Univ, Elect & Comp Engn, Pittsburgh, PA 15213 USA.;

    Lawrence Berkeley Natl Lab, Energy Storage & Distributed Resources Div, Berkeley, CA USA.;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Electric vehicles; Demand response; Non-residential loads; Data analysis;

    机译:电动汽车;需求响应;非住宅负荷;数据分析;

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