Analysis of the Impacts of Distribution Connected PV Using High-Speed Datasets

J. Bank, B. Mather
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引用次数: 8

Abstract

High penetrations of distribution connected photovoltaic (PV) systems are becoming more common. However, the impact of these variable generators on system voltage and automatic voltage regulation equipment is not well quantified. In contrast to load which generally has some diversity, PV systems are often non-diverse over small geographic areas. Variability caused by PV can range from relatively slow changes in system voltage to high frequency impacts on real and reactive power. These changes have the ability to impact the operation of a distribution circuit from a protection, voltage control and load prediction/modeling point of view. This paper utilizes information from high resolution data acquisition systems developed at the National Renewable Energy Laboratory and deployed on a high-penetration PV distribution system to analyze the variability of different electrical parameters. High resolution solar irradiance data is also available in the same area which is used to characterize the available resource and how it affects the electrical characteristics of the study circuit. This paper takes a data-driven look at the variability caused by load and compares those results against times when significant PV production is present. Comparisons between the variability in system load and the variability of distributed PV generation are made. Additionally, the potential impacts of high-penetration PV on voltage regulation equipment such as capacitor banks and load tap changing transformers are quantified.
基于高速数据集的配网光伏影响分析
分布式光伏(PV)系统的高渗透率正变得越来越普遍。然而,这些变量发电机对系统电压和自动调压设备的影响并没有很好地量化。与负载通常具有一定的多样性相比,光伏系统在小的地理区域内通常是无多样性的。由PV引起的可变性可以从系统电压的相对缓慢变化到对实际功率和无功功率的高频影响。从保护、电压控制和负载预测/建模的角度来看,这些变化有能力影响配电电路的运行。本文利用国家可再生能源实验室开发的高分辨率数据采集系统的信息,并部署在高渗透光伏配电系统上,以分析不同电气参数的可变性。在同一地区也可获得高分辨率太阳辐照度数据,用于描述可用资源及其如何影响研究电路的电特性。本文采用数据驱动的方法研究由负载引起的可变性,并将这些结果与存在大量光伏生产的时间进行比较。对系统负荷的可变性与分布式光伏发电的可变性进行了比较。此外,还量化了高渗透光伏对电容器组和负载分接变换变压器等电压调节设备的潜在影响。
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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