Spatial Modelling of Electrical Loads in Distribution Grids Based on Socioeconomic Data

Trageser, M.; Vertgewall, C.; Kurth, Marcel

doi:10.1049/icp.2021.2107

Cited by 2 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, the method is well suited to determine residential supply tasks. However, it should always be seen as a supplement to existing methods such as [4]. In regions with high quality geospatial data, the results of these methods are usually better.…”

Section: Discussionmentioning

confidence: 99%

“…In future research, we will combine our method with the method presented in [4]. With the combined data sources, a higher quality for supply tasks can be achieved.…”

Section: Discussionmentioning

confidence: 99%

“…While the modelling of synthetic time series (load and generation) and charging profiles is continuously discussed in research, the geo-referencing of electricity demands is still a challenge due to limited data availability. Previous studies such as [2][3][4][5][6] which tackle this topic use socioeconomic data, geospatial data, digital orthophotographs (DOP), or measurement data. Socioeconomic data contains information on the population and building structure of a region.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the data set, the data includes house perimeters up to information about building height and roof shape. Methodologies that use geospatial data as data basis make use of available building information [3,4]. However, data in OSM is uploaded by users noncommercially so that completeness and accuracy cannot be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modelling residential supply tasks based on digital orthophotography using machine learning

Schumann

Böttcher

Hälsig

et al. 2022

CIRED Porto Workshop 2022: E-Mobility and Power Distribution Systems

View full text Add to dashboard Cite

In order to achieve the climate targets, electrification of individual mobility is essential. However, grid integration of electrical vehicles poses challenges for the electrical distribution network due to high charging power and simultaneity. To investigate these challenges in research studies, the network-referenced supply task needs to be modeled. Previous research work utilizes data that is not always complete or sufficiently granular in space. This is why this paper presents a methodology which allows a holistic determination of residential supply tasks based on orthophotos. To do this, buildings are first identified from orthophotos, then residential building types are classified, and finally the electricity demand of each building is determined. In an exemplary case study, we validate the presented methodology and compare the results with another supply task methodology. The results show that the electricity demand deviates from the results of a reference method by an average 9%. Deviations result mainly from the parameterization of the selected residential building types. Thus, the presented methodology is able to model supply tasks similarly as other methods but more granular.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In future research, we will combine our method with the method presented in [4]. With the combined data sources, a higher quality for supply tasks can be achieved.…”

Section: Discussionmentioning

confidence: 99%