Modeling and Forecasting Self-Similar Power Load Due to EV Fast Chargers

In this paper, we consider modeling and prediction of power loads due to fast charging stations for plug-in electric vehicles (EV). The first part of this paper is to simulate work of a fast charger activity by exploiting empirical data that characterize EV user behavior. The second part describes t...

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Veröffentlicht in:	IEEE transactions on smart grid 2016-05, Vol.7 (3), p.1620-1629, Article 1620
Hauptverfasser:	Korolko, Nikita, Sahinoglu, Zafer, Nikovski, Daniel
Format:	Artikel
Sprache:	eng
Schlagworte:	Aggregates Automotive components Computer simulation Demand forecasting Electric power generation Electric vehicles Forecasting Fractals Load modeling Mathematical models Modelling Predictive models Regression analysis Self-similarity System-on-chip Time series Time series analysis
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Beschreibung
Zusammenfassung:	In this paper, we consider modeling and prediction of power loads due to fast charging stations for plug-in electric vehicles (EV). The first part of this paper is to simulate work of a fast charger activity by exploiting empirical data that characterize EV user behavior. The second part describes the time series obtained by this simulator and its properties. We show that the power load aggregated over a number of fast chargers (after deseasonalizing and elimination of the linear trend) is a self-similar process with the Hurst parameter 0.57
ISSN:	1949-3053 1949-3061
DOI:	10.1109/TSG.2015.2458852