Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network

Munseok Chang, Sungwoo Bae, Gilhwan Cha and Jaehyun Yoo
Additional contact information
Munseok Chang: Department of Electrical Engineering, Hanyang University, Seoul 04763, Korea
Sungwoo Bae: Department of Electrical Engineering, Hanyang University, Seoul 04763, Korea
Gilhwan Cha: Algorigo Software Development Inc., Seoul 06103, Korea
Jaehyun Yoo: Algorigo Software Development Inc., Seoul 06103, Korea

Sustainability, 2021, vol. 13, issue 24, 1-17

Abstract: With the widespread use of electric vehicles, their charging power demand has increased and become a significant burden on power grids. The uncoordinated deployment of electric vehicle charging stations and the uncertainty surrounding charging behaviors can cause harmful impacts on power grids. The charging power demand during the fast charging process especially is severely fluctuating, because its charging duration is short and the rated power of the fast chargers is high. This paper presents a methodology to analyze and forecast the aggregated charging power demand from multiple fast-charging stations. Then, pattern of fast-charging power demand is analyzed to identify its irregular trend with the distribution of peak time and values. The forecasting model, based on long short-term memory neural network, is proposed in this paper to address the fluctuating of fast-charging power demand. The forecasting performance of the proposed model is validated in comparison with other deep learning approaches, using real-world datasets measured from fast-charging stations in Jeju Island, South Korea. The results show that the proposed model outperforms forecasting fast-charging power demand aggregated by multiple charging stations.

Keywords: deep learning; electric vehicle; fast-charging power demand; forecasting model; power system; road transport; sustainable transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/24/13783/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/24/13783/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:24:p:13783-:d:701865

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().