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Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging

Huber, Julian; Dann, David; Weinhardt, Christof

Abstract (englisch):
Users charging the batteries of their electric vehicles in an uncoordinated manner can present energy systems with a challenge. One possible solution, smart charging, relies on the flexibility within each charging process and controls the charging process to optimize different objectives. Effective smart charging requires forecasts of energy requirements and parking duration at the charging station for each individual charging process. We use data from travel logs to create quantile forecasts of parking duration and energy requirements, approximated by upcoming trip distance. For this task, we apply quantile regression, multi-layer perceptrons with tilted loss function, and multivariate conditional kernel density estimators. The out-of-sample evaluation shows that the use of local information from the vehicle's travel data improves the forecasting accuracy by 13.7% for parking duration and 0.56% for trip distance compared to the data generated at the charging stations. In addition, the analysis of a case study shows that using probabilistic forecasts can control the interruption of charging processes more efficiently compared to point forecasts. ... mehr



Originalveröffentlichung
DOI: 10.1016/j.apenergy.2020.114525
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2020
Sprache Englisch
Identifikator ISSN: 0306-2619
KITopen-ID: 1000105492
Erschienen in Applied energy
Band 262
Seiten Article: 114525
Schlagworte Charging coordination; Demand-side flexibility; Electric vehicles; Probabilistic forecasts; Smart charging
Nachgewiesen in Scopus
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