C Mathematical and Quantitative Methods
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(2023)
PV hosting capacity provides utilities the knowledge of the maximum amount of solar installations possible to accommodate in low voltage grids such that no operational problems arise. As the quantification of the hosting capacity requires data collection, grid modelling, and often time-consuming simulations, simplified estimations for large-scale applications are of interest. In this paper, Bayesian statistical inference is applied to estimate the hosting capacities of more than 5000 real feeders in Austria. The results show that the hosting capacity of 95% of the total feeders can be estimated with a mean error below 20% by only having knowledge of a random sample of 5%. Moreover, the hosting capacity estimation at a regional level shows a maximum error below 9%, also relying on a random sample of 5% of the total feeders. Furthermore, the approach proposed provides a methodology to assess new parameters aiming to improve the accuracy of the hosting capacity estimation at a feeder level.
Die Digitalisierung verändert Geschäftsmodelle und betriebliche Prozesse. Zugleich beeinflussen auch eine verbesserte Datenverfügbarkeit und leistungsfähige analytische Methoden das Controlling und erfordern vermehrt das Einbringen statistischer und informationstechnologischer Fertigkeiten und Kenntnisse. An einem Fallbeispiel aus dem Marketing-Controlling zeigt der Beitrag den Einsatz von Methoden der Business Analytics und thematisiert die Aufgaben des Controllings im digitalen Zeitalter.