@techreport{BruecklEllerAdeltetal., author = {Br{\"u}ckl, Oliver and Eller, Johannes and Adelt, Christian and Gschossmann, David and Kraus, Hermann and Rauch, Johannes and Sippenauer, Thomas and Susewind, Andreas and Wildfeuer, Matthias}, title = {Zuk{\"u}nftige Beschaffung von Blindleistung II : Endbericht zum Dienstleistungsauftrag}, pages = {143}, abstract = {Die vorliegende Studie befasst sich mit der Ermittlung des Bedarfs an Blindleistung f{\"u}r das gesamte deutsche Stromnetz auf Grundlage des NEP 2030 (2019) bis zum Jahr 2035. Die Verteilungsnetze werden dabei in ihrem Blindleistungsverhalten m{\"o}glichst realit{\"a}tsnah nachgebildet. Neben Maßnahmen zur Senkung des Blindleistungsbedarfs werden M{\"o}glichkeiten zur effizienten Deckung aufgezeigt. Dar{\"u}ber hinaus werden die mit der Blindleistungsbereitstellung verbundenen Kosten sowie die Methodik der Be-darfsermittlung beleuchtet.}, language = {de} } @inproceedings{KlitsmanBruecklEller, author = {Klitsman, Maksym and Br{\"u}ckl, Oliver and Eller, Johannes}, title = {Analysis and simulation of the population driving behaviour and charging processes at different charging station types with electric vehicles}, series = {NEIS 2021, Conference on Sustainable Energy Supply and Energy Storage Systems: 13-14. September 2021, Hamburg}, booktitle = {NEIS 2021, Conference on Sustainable Energy Supply and Energy Storage Systems: 13-14. September 2021, Hamburg}, editor = {Schulz, Detlef}, publisher = {VDE-Verlag}, isbn = {978-3-8007-5651-3}, pages = {254 -- 260}, abstract = {Electric vehicles are one of the important components of the energy transition, which is necessary for the CO2 emission reduction. In order to estimate the influence of electromobility on electric grids at present day and in the future, the generation of realistic charge profiles is necessary. The main objective of this study is the development of an algorithm, which generates realistic charge time series of electric vehicles. The very huge and detailed input surveys enable to define different population groups, representing specific driving behaviour. These groups are then composed and scaled up, according to the statistical population structure of a chosen town or city in Germany, and show typical driving behaviour of all inhabitants. The charge profiles are calculated for several charge station types, based on daily electric vehicle user mobility, day type and household type. In addition, charge process occurrence is evaluated through a model, involving charge infrastructure presence, attractiveness of corresponding charge station type and charge process necessity.}, language = {en} }