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Charging scheduling of hybrid energy storage systems for EV charging stations

  • The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV powerThe growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.show moreshow less

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Metadaten
Author:Gülsah ErdoganORCiD, Wiem Fekih Hassen
URN:urn:nbn:de:bvb:739-opus4-14128
DOI:https://doi.org/10.3390/en16186656
Parent Title (English):Energies
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of first Publication:2023/09/16
Publishing Institution:Universität Passau
Release Date:2024/04/17
Tag:GRU; HESS; LSTM; PV power; RNN; cost reduction; load demand; scheduling optimization
Volume:2023
Issue:16
Article Number:6656
Page Number:29 Seiten
Institutes:Fakultät für Informatik und Mathematik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
open_access (DINI-Set):open_access
Funding Acknowledgement:Gefördert durch den Open-Access-Publikationsfonds der Universitätsbibliothek Passau.
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International