A distribution system state estimation for an efficient integration of electric vehicle charging infrastructure into low-voltage grids

Publikationstyp
Conference Paper
Publikationsdatum
2019
Sprache
English
Author
Ritt, Bendic 
Scheffer, Volker 
Ipach, Hanko orcid-logo
Becker, Christian orcid-logo
Institut
Elektrische Energietechnik E-6 
TORE-URI
http://hdl.handle.net/11420/5043
Citation
ETG-Fachbericht 160 "Netzregelung und Systemführung" (2019)
Contribution to Conference
ETG-Kongress 2019 
Publisher Link
https://ieeexplore.ieee.org/document/8835980
Scopus ID
2-s2.0-85101720259
Due to political reasons, a growth in the usage of electric vehicles (EV) is expected for the coming years. The consequenceis an increasing amount of EV charging operations, where the charging devices are mainly connected to the low-voltagelevel of the distribution grid. A high simultaneity factor of the charging processes together with inherent high and constantload demands can lead to a higher stress on grid assets and in extreme situations to violations of grid operation constraints.For a resolution of this problem, direct load management represents a favorable instrument in the view of distributionsystem operators. An efficient load management scheme, which acts on the loads only in critical grid situations, demandsa grid state estimation of high accuracy. A deteriorating factor for the estimation quality in low-voltage grids is the verylow-availability of real-time measurements.This work presents a methodology for implementing an accurate state estimation scheme for low-voltage grids, withoutthe need for real-time measurements and solely based on historical smart meter data. For the realization of the objective,the methodology consists of the synthesis of two concepts. The first concept implements a state estimation algorithmfor low-voltage grids using three-phase line models. In order to handle the low-availability of real-time measurementdata, a concept for the generation of pseudo-measurements flanks the first concept. Hereby, neural networks trained withhistorical smart meter data are used to estimate the real-time load behavior of household loads.A series of exemplary simulations in a typical low-voltage grid from the city of Hamburg proves the applicability of thedeveloped concept.