Data-Driven Evaluation for Demand Flexibility of Segmented Electric Vehicle Chargers in the Korean Residential Sector

• Main Authors: Keon Baek, Sehyun Kim, Eunjung Lee, Yongjun Cho, Jinho Kim
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Energies
• Subjects: demand response; demand flexibility; electric vehicle; data analysis
• Online Access: https://www.mdpi.com/1996-1073/14/4/866

The rapid spread of renewable energy resources has increased need for demand flexibility as one of the solutions to power system imbalance. However, to properly estimate the demand flexibility, demand characteristics must be analyzed first and the corresponding flexibility measures must be validated. Thus, in this study, a novel approach is proposed to evaluate the demand flexibility provided by Electric Vehicle Chargers (EVC) in the residential sector based upon a new process of electric charging demand characteristic data analysis. The proposed model estimates the frequency, consistency, and operation scores of the flexible demand resource (FDR) during identified ramp-up/down intervals presented in our previous work. The scores are included in the components that calculate the flexibility score referring that the closer it is to 1, the higher utilization as an FDR. A case study was conducted by considering EV user segmentation based on their demand characteristic analysis. The results confirm that flexibility scores of segmented EVC groups are about 0.0273 in ramp-up and ramp-down intervals. Based on the experimental results, the flexibility score can be utilized for multi-dimensional analysis and verification in perspectives of seasonality, participation time interval, customer group classification, and evaluation. Thus, the proposed method can be used as an indicator to determine how a segmented EVC group is adequate to participate as an FDR while suggesting meaningful implications through EVC demand data analysis.