Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea

Moon-Jong Jang, Taehoon Kim and Eunsung Oh ()
Additional contact information
Moon-Jong Jang: Smart Power Distribution Laboratory of Korea Electric Power Research Institute, Korea Electric Power Corporation, Daejeon 34056, Republic of Korea
Taehoon Kim: Basic Research Center for Electric Power of Korea Electric Power Research Institute, Korea Electric Power Corporation, Seoul 08826, Republic of Korea
Eunsung Oh: Department of Electrical and Electronic Engineering, School of Aviation Multidisciplinary, Hanseo University, Seosan 31962, Republic of Korea

Sustainability, 2023, vol. 15, issue 10, 1-16

Abstract: With the widespread use of electric vehicles (EVs), the potential to utilize them as flexible resources has increased. However, the existing vehicle-to-grid (V2G) studies have focused on V2G operation methods. The operational performance is limited by the amount of availability resources, which represents the flexibility. This study proposes a data-driven modeling method to estimate the V2G flexibility. A charging station is a control point connected to a power grid for V2G operation. Therefore, the charging stations’ statuses were analyzed by applying the basic queuing model with a dataset of 1008 chargers (785 AC chargers and 223 DC chargers) from 500 charging stations recorded in Korea. The basic queuing model obtained the long-term average status values of the stations over the entire time period. To estimate the V2G flexibility over time, a charging station status modeling method was proposed within a time interval. In the proposed method, the arrival rate and service time were modified according to the time interval, and the station status was expressed in a propagated form that considered the current and previous time slots. The simulation results showed that the proposed method effectively estimated the actual value within a 10% mean absolute percentage error. Moreover, the determination of V2G flexibility based on the charging station status is discussed herein. According to the results, the charging station status in the next time slot, as well as that in the current time slot, is affected by the V2G. Therefore, to estimate the V2G flexibility, the propagation effect must be considered.

Keywords: data analysis; demand response; demand side management; electric vehicle; flexibility; modeling; vehicle to grid; V2G (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/10/7938/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/10/7938/ (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:15:y:2023:i:10:p:7938-:d:1145461

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 ().