Geo-referenced synthetic low-voltage distribution networks: A data-driven approach

Abhilash, Bandam; Syranidou, Chloi; Linßen, Jochen; Stolten, Detlef

doi:10.1109/isgteurope52324.2021.9639916

Cited by 5 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we describe the study area and the evaluation criteria before presenting the results. A meaningful comparison between the proposed method and the methods found in literature is infeasible due to several factors, such as a focus on different voltage levels [13], [14], different grid styles [9], and missing implementation details [8], [12], [15]. Thus, we focus on the evaluation of the impact of various available data sources and the comparison with real grid topology data provided by DSO.…”

Section: Discussionmentioning

confidence: 99%

“…Other approaches, such as [12] focus on the German power grid. This approach utilizes OSM data and the known total number of LV networks in Germany to generate a total of 500 000 LV distribution grid topologies.…”

Section: Related Workmentioning

confidence: 99%

“…The decision variable x i,j equals 1 if the path between node i and node j is part of the solution, and 0 otherwise. (12) ensures symmetry while (13) makes sure that each station is connected to two other ones. The last constraint (14) ensures that the solution is indeed a single 20 kV ring and not just a union of other, smaller rings.…”

Section: B Stage 1: Automated Generation Of the 20kv Gridmentioning

confidence: 99%

See 2 more Smart Citations

Open Data-Driven Automation of Residential Distribution Grid Modeling With Minimal Data Requirements

Weber,

Janecke,

Çakmak

et al. 2024

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

In the present paper, we introduce a new method for the automated generation of residential distribution grid models based on novel building load estimation methods and a two-stage optimization for the generation of the 20 kV and 400 V grid topologies. Using the introduced load estimation methods, various open or proprietary data sources can be utilized to estimate the load of residential buildings. These data sources include available building footprints from OpenStreetMap, 3D building data from OSM Buildings, and the number of electricity meters per address provided by the respective distribution system operator (DSO).For the evaluation of the introduced methods, we compare the resulting grid models by utilizing different available data sources for a specific suburban residential area and the real grid topology provided by the DSO. This evaluation yields two key findings: First, the automated 20 kV network generation methodology works well when compared to the real network. Second, the utilization of public 3D building data for load estimation significantly increases the resulting model accuracy compared to 2D data and enables results similar to models based on DSO-supplied meter data. This substantially reduces the dependence on such normally proprietary data.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B Stage 1: Automated Generation Of the 20kv Gridmentioning

confidence: 99%

See 1 more Smart Citation

Open Data-Driven Automation of Residential Distribution Grid Modeling With Minimal Data Requirements

Weber,

Janecke,

Çakmak

et al. 2024

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…Geo-referenced synthetic electrical distribution networks for Germany are estimated using data corresponding to residential buildings. Before the tagged residential building data for Germany was included in this model, the geo-referenced synthetic electrical low-voltage distribution networks developed had a percentage error of 33% when validated against the overall low-voltage network length for Germany [53]. However, when classified residential buildings are included in the geo-referenced synthetic distribution network generator model, a percentage error of 0.89% is obtained.…”

Section: Discussionmentioning

confidence: 99%

Classification of Building Types in Germany: A Data-Driven Modeling Approach

Abhilash

Busari

Syranidou

et al. 2022

Data

View full text Add to dashboard Cite

Details on building levels play an essential part in a number of real-world application models. Energy systems, telecommunications, disaster management, the internet-of-things, health care, and marketing are a few of the many applications that require building information. The essential variables that most of these models require are building type, house type, area of living space, and number of residents. In order to acquire some of this information, this paper introduces a methodology and generates corresponding data. The study was conducted for specific applications in energy system modeling. Nonetheless, these data can also be used in other applications. Building locations and some of their details are openly available in the form of map data from OpenStreetMap (OSM). However, data regarding building types (i.e., residential, industrial, office, single-family house, multi-family house, etc.) are only partially available in the OSM dataset. Therefore, a machine learning classification algorithm for predicting the building types on the basis of the OSM buildings’ data was introduced. Although the OSM dataset is the fundamental and most crucial one used for modeling, the machine learning algorithm’s training was performed on a dataset that was prepared by combining several features from three other datasets. The generated dataset consists of approximately 29 million buildings, of which about 19 million are residential, with 72% being single-family houses and the rest multi-family ones that include two-family houses and apartment buildings. Furthermore, the results were validated through a comparison with publicly available statistical data. The comparison of the resulting data with official statistics reveals that there is a percentage error of 3.64% for residential buildings, 13.14% for single-family houses, and −15.38% for multi-family houses classification. Nevertheless, by incorporating the building types, this dataset is able to complement existing building information in studies in which building type information is crucial.

show abstract

“…T HE increasing utilization of distributed renewable generation, electric vehicles, and heat pumps poses substantial challenges to power distribution grids (PDGs) [1], and their adoption is expected to rise due to the climate strategies that aim to reduce carbon emissions worldwide. Therefore, concerns such as voltage stability, reverse power flows [2], and overloading of transformers [3] are becoming increasingly pressing.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Inference of Geo-Referenced Power Distribution Grids Using Open Data

Oneto,

Gjorgiev,

Tettamanti

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Power distribution grids host an increasing amount of distributed renewable generators, electric vehicles, and heat pumps worldwide. Distribution grids, however, were not designed with the goal of incorporating large shares of these technologies. These soaring challenges demand accurate and realistic grid models to assess the need for operation strategies and reinforcements that ensure reliable and economic management. Nevertheless, real models are often unavailable due to privacy and security concerns or a lack of digitized data from distribution system operators. To address this issue, we present a framework for large-scale inference of geo-referenced low- and medium-voltage grid models using publicly accessible information on power demand and transport infrastructure. First, we develop a clustering algorithm, which detects load areas served by distribution grids. Then, we obtain the graphical grid layout, i.e., a graph with the street and pathway geometries and the load point connections inside the load area. Next, we introduce a selection method for line types that assigns cost-effective conductors to grid lines while ensuring operational constraints. We demonstrate the effectiveness of our approach by inferring all the low- and medium-voltage infrastructure in Switzerland. Remarkably, the inferred grids present overall power requirements and line lengths statistically aligned with reference grids.</p>

show abstract

Geo-referenced synthetic low-voltage distribution networks: A data-driven approach

Cited by 5 publications

References 17 publications

Open Data-Driven Automation of Residential Distribution Grid Modeling With Minimal Data Requirements

Open Data-Driven Automation of Residential Distribution Grid Modeling With Minimal Data Requirements

Classification of Building Types in Germany: A Data-Driven Modeling Approach

Large-Scale Inference of Geo-Referenced Power Distribution Grids Using Open Data

Contact Info

Product

Resources

About