Effect of electric vehicles, heat pumps, and solar panels on low-voltage feeders: Evidence from smart meter profiles

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS
Thijs Becker , Raf Smet , Bruno Macharis , Koen Vanthournout
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Abstract

Electric Vehicles (EVs), Heat Pumps (HPs) and solar panels are Low-Carbon Technologies (LCTs) that are being connected to the Low-Voltage Grid (LVG) at a rapid pace. One of the main hurdles to understand their impact on the LVG is the lack of recent, large electricity consumption datasets, measured in real-world conditions. We investigated the contribution of LCTs to the size and timing of peaks on LV feeders by using a large dataset of 42,089 smart meter profiles of residential LVG customers. These profiles were measured in 2022 by Fluvius, the Distribution System Operator (DSO) of Flanders, Belgium. The dataset contains customers that proactively requested higher-resolution smart metering data, and hence is biased towards energy-interested people. LV feeders of different sizes were statistically modeled with a profile sampling approach. For feeders with 40 connections, we found a contribution to the feeder peak of 1.2 kW for a HP, 1.4 kW for an EV and 2.0 kW for an EV charging faster than 6.5 kW. A visual analysis of the feeder-level loads shows that the classical duck curve is replaced by a night-camel curve for feeders with only HPs and a night-dromedary curve for feeders with only EVs charging faster than 6.5 kW. Consumption patterns will continue to change as the energy transition is carried out, because of e.g. dynamic electricity tariffs or increased battery capacities. Our introduced methods are simple to implement, making it a useful tool for DSOs that have access to smart meter data to monitor changing consumption patterns.

Abstract Image

电动汽车、热泵和太阳能电池板对低压馈线的影响:来自智能电表的证据
电动汽车(ev)、热泵(HPs)和太阳能电池板都是低碳技术(lct),正在快速连接到低压电网(LVG)。要了解它们对LVG的影响,主要障碍之一是缺乏最近在现实条件下测量的大型电力消耗数据集。通过使用42,089个住宅LVG用户的智能电表资料的大型数据集,我们研究了lct对低压馈线峰值大小和时间的贡献。这些剖面是由比利时法兰德斯的配电系统运营商(DSO) Fluvius于2022年测量的。该数据集包含主动请求更高分辨率智能计量数据的客户,因此偏向于对能源感兴趣的人。采用剖面抽样的方法对不同尺寸的低压馈线进行了统计建模。对于具有40个连接的馈线,我们发现对馈线峰值的贡献为马力1.2 kW,电动汽车1.4 kW,电动汽车2.0 kW,充电速度高于6.5 kW。对饲主负荷的可视化分析表明,仅配备hp的饲主的经典鸭子曲线被夜间骆驼曲线所取代,而仅配备充电速度大于6.5 kW的电动汽车的饲主的夜间单峰曲线所取代。随着能源转型的进行,由于动态电价或电池容量的增加,消费模式将继续发生变化。我们介绍的方法易于实现,使其成为可访问智能电表数据的dso的有用工具,以监测不断变化的消费模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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