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Comparison of Small EV Charging Station's Load Forecasts and its Impact on the Operational Costs

Stein, Alexander ORCID iD icon 1; Starosta, Anna Sina ORCID iD icon 1; Schwarz, Bernhard ORCID iD icon 1; Munzke, Nina ORCID iD icon 1; Hiller, Marc 1
1 Elektrotechnisches Institut (ETI), Karlsruher Institut für Technologie (KIT)

Abstract:

For an energy management system (EMS) of a charging station (CS), information on future load is crucial. Existing models primarily focus on load forecasting for large charging stations. In this study, three different load forecasting models based on real data from a public CS with two charging points are developed. The models include two persistent models and one model that utilizes a machine learning algorithm.
To assess the impact of forecasting accuracy on operational costs, a case study with dynamic electricity prices and a stationary battery storage is conducted. Using the load predictions, a mixed-integer linear programming problem is formulated to optimize the scheduling of the stationary battery charging.


Postprint §
DOI: 10.5445/IR/1000162902
Veröffentlicht am 17.10.2023
Originalveröffentlichung
DOI: 10.1109/SEST57387.2023.10257538
Scopus
Zitationen: 3
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Elektrotechnisches Institut (ETI)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 27.09.2023
Sprache Englisch
Identifikator ISBN: 979-83-503-9790-1
KITopen-ID: 1000162902
HGF-Programm 37.12.02 (POF IV, LK 01) Design,Operation & Digitalization of the Future Energy Grids
Erschienen in International Conference on Smart Energy Systems and Technologies (SEST 2023)
Veranstaltung International Conference on Smart Energy Systems and Technologies (SEST 2023), Mugla, Türkei, 04.09.2023 – 06.09.2023
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 6 S.
Projektinformation SKALE (BMWK, 01MV19004D)
Schlagwörter charging infrastructure, load forecast, energy management system (EMS), stationary battery storage
Nachgewiesen in Dimensions
Scopus
Relationen in KITopen
Globale Ziele für nachhaltige Entwicklung Ziel 7 – Bezahlbare und saubere Energie
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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