Probabilistische Methode zur Modellierung des Ladeverhaltens von Elektroautos anhand gemessener Daten elektrischer Ladestationen – Auslastungsanalysen von Ladestationen unter Berücksichtigung des Standorts zur Planung von elektrischen Stromnetzen

Abstract

In diesem Beitrag wird eine neue probabilistische Methode aufgezeigt, um das Ladeverhalten von Elektrofahrzeugen an Ladestationen durch eine unterschiedliche Anzahl an Elektroautos mit verschiedenen Anschlussleistungen (3,7/11/22/50 kW) zu analysieren. Die Eingangsdaten für diesen Bottom-up Ansatz basieren auf den gemessenen Daten unterschiedlicher Ladestationen mit standortspezifischem Verhalten (öffentliche, betriebliche, Einkaufzentrum Ladestationen). Die durchgeführten Simulationen beantworten aktuelle Fragestellungen im Rahmen der Elektromobilität. Ebenfalls werden zu erwartende energetische Auslastungen innerhalb eines Tages sowie sinnvolle Aufstellungsorte unter Berücksichtigung der Anzahl von elektrischen Ladestationen betrachtet.Der vorgestellte standortspezifische gesteuerte Gleichzeitigkeitsfaktor sowie ungesteuerte Gleichzeitigkeitsfaktor ist für die zukünf-tige Planung der Anzahl und Aufstellungsorte sowie der Integration von Schnell-bzw. Langsam-Ladestationen in das Niederspannungsnetz maßgebend. Schlüsselwörter: probabilistische Modellierung; Elektromobilität; elektrische Ladestationen; Gleichzeitigkeitsfaktor; Niederspannungsnetz; Planung elektrischer Netze Probabilistic method of modelling the charging behaviour of electric vehicles based on measured data from electric charging stations-utilization analysis of charging stations for electrical grid planning under consideration of the location. In this contribution a novel probabilistic approach is shown to analyse and model the charging behaviour of a various number of electric vehicles on different locations for different installed power of the electric charging stations (3,7/11/22/50 kW). The input data for this bottom-up approach are based on measured data for different locations (public, company and shopping centre charging stations). The performed simulations answer actual questions in the context of electric mobility with respect to the expected energy usage within a day, as well as the relevant locations and number of electric charging stations. The presented location-specific controlled coincidence factors and uncontrolled coincidence factors are decisive for future grid planning. These factors, depending on the number of charging stations and their specific locations are important to integrate the fast and slow charging stations in low voltage networks.

Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach

“…Furthermore, the arrival and departure time, the distance travelled, the EV type, and the charging strategy are determined for each charging process within a cell and user group. Due to the stochastic nature of the mobility behaviour, the determination of the distance travelled, arrival time (= start charging process), departure time, and EV type is performed via a probabilistic approach using a random number generator, similar to references [31,50,51]. Another important parameter is the charging strategy.…”

Stochastic Modeling of the Charging Behavior of Electromobility

“…Moreover, around 210 thousand electric vehicles are expected to occupy the roads in Austria by 2020. These goals assume public acceptance on a broader range along with a broad charging infrastructure [5]. A recent study [6] shows that around 3700 charging stations, of which 528 have fast charging capabilities, were publicly available in Austria at the end of 2017.…”

“…In previous works [2,5,[8][9][10], it is assumed that PEV usage does not differ from conventional ICE vehicle usage. In [5], real data from the city Graz have been used to generate random variables from probability distribution functions for extensive bottom-up calculations for three different charging power levels and different levels of PEV penetration. Yunus et al utilized grid data, and Matlab generated load profiles to simulate impacts on distribution transformer loading and system bus voltage profiles.…”