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

TL;DR

This work presents an approach to estimate pseudo-measurements for non-measured LV feeders based on the metadata of the respective feeder using regression models, and introduces peak metrics inspired by the BigDEAL challenge for the peak magnitude, timing and shape for both consumption and feed-in.

Abstract

Distribution system operators (DSOs) must cope with new challenges such as the reconstruction of distribution grids along climate neutrality pathways or the ability to manage and control consumption and generation in the grid. In order to meet the challenges, measurements within the distribution grid often form the basis for DSOs. Hence, it is an urgent problem that measurement devices are not installed in many low-voltage (LV) grids. In order to overcome this problem, we present an approach to estimate pseudo-measurements for non-measured LV feeders based on the metadata of the respective feeder using regression models. The feeder metadata comprise information about the number of grid connection points, the installed power of consumers and producers, and billing data in the downstream LV grid. Additionally, we use weather data, calendar data and timestamp information as model features. The existing measurements are used as model target. We extensively evaluate the estimated pseudo-measurements on a large real-world dataset with 2,323 LV feeders characterized by both consumption and feed-in. For this purpose, we introduce peak metrics inspired by the BigDEAL challenge for the peak magnitude, timing and shape for both consumption and feed-in. As regression models, we use XGBoost, a multilayer perceptron (MLP) and a linear regression (LR). We observe that XGBoost and MLP outperform the LR. Furthermore, the results show that the approach adapts to different weather, calendar and timestamp conditions and produces realistic load curves based on the feeder metadata. In the future, the approach can be adapted to other grid levels like substation transformers and can supplement research fields like load modeling, state estimation and LV load forecasting.

Figures (9)

• Figure 1: Classification of LV feeders into measured ($A$) and non-measured feeders ($B$). The symbols represent substations with three LV feeders. The present paper aims to estimate pseudo-measurements for non-measured feeders $B$ by learning the relation between measurements of feeders in $A$ and the feeder metadata like the number of houses or the installed power of heat pumps.
• Figure 2: Model pipeline with the pipeline steps, input data and output data. The data flows are differentiated with respect to measured feeders $A$ and non-measured feeders $B$ as described in \ref{['sec:Problem statement']}.
• Figure 3: Daily profile of the measurements from all feeders for different quantiles in $15$ minute resolution after applying the data cleaning and feature engineering specified in \ref{['subsec:experimental_setup-data_set']}.
• Figure 4: Distribution of metadata at LV feeders divided into consumers, batteries and producers (left) as well as the average consumption data of commerce and industry (right). The x-axis is scaled based on the categories solar and g1 respectively. The y-axis describes the fraction of feeders where the category is greater than zero.
• Figure 5: Visualization of the used cross-validation.