Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
IET Smart Grid Pub Date : 2025-02-17 DOI:10.1049/stg2.12210
Manuel Treutlein, Marc Schmidt, Roman Hahn, Matthias Hertel, Benedikt Heidrich, Ralf Mikut, Veit Hagenmeyer
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引用次数: 0

Abstract

Distribution system operators (DSOs) face challenges such as restructuring distribution grids for climate neutrality and managing grid consumption and generation. Measurements within the grid are crucial for DSOs, yet many low-voltage (LV) grids lack measurement devices. To address this, an approach is proposed to estimate pseudo-measurements for non-measured LV feeders using regression models. The models are based on feeder metadata, which includes the number of grid connection points, installed power of equipment, and billing data in the downstream LV grid. The authors also incorporate weather, calendar, and timestamp data as model features and use the existing measurements as the model target. For evaluation, a dataset of 2323 LV feeders is used and peak metrics for magnitude, timing, and shape of consumption and feed-in are introduced, inspired by the BigDEAL challenge. The authors employ XGBoost, a multilayer perceptron (MLP), and a linear regression (LR) model, finding that XGBoost and MLP outperform LR. The results demonstrate that this approach effectively adapts to varying conditions and generates realistic load curves from feeder metadata. Additionally, the authors elaborate on feeders where pseudo-measurements exhibit deficiencies. This method could be extended to other grid levels such as substations and contribute to research in load modelling, state estimation, and LV load forecasting.

Abstract Image

利用配电系统运营商的元数据为低压馈线生成峰值感知伪测量
配电系统运营商(dso)面临着诸如调整配电网以实现气候中和和管理电网消费和发电等挑战。电网内的测量对dso至关重要,但许多低压(LV)电网缺乏测量设备。为了解决这个问题,提出了一种使用回归模型估计非测量低压馈线的伪测量的方法。该模型基于馈线元数据,其中包括电网连接点的数量、设备的安装功率和下游低压电网的计费数据。作者还将天气、日历和时间戳数据作为模型特征,并使用现有的测量数据作为模型目标。为了进行评估,使用了2323个低压馈线的数据集,并在BigDEAL挑战的启发下,引入了消耗和馈电的幅度、时间和形状的峰值指标。作者使用XGBoost,多层感知器(MLP)和线性回归(LR)模型,发现XGBoost和MLP优于LR。结果表明,该方法能有效地适应各种工况,并能根据馈线元数据生成真实的负荷曲线。此外,作者详细说明了伪测量显示缺陷的馈线。该方法可以推广到变电站等其他电网层面,并有助于负荷建模、状态估计和低压负荷预测的研究。
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
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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