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

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.

Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors

“…The optimal state-action function in Equation (20) shows that the optimal policy is to determine the local optimal action at each decision time t because the expected reward from the onward decision time is the optimal value. Therefore, the optimal action is determined as follows:…”

“…For our V2G system analysis, we utilized CS information sourced from the Korea Environment Corporation [20], which offers critical insights into the operational dynamics of the CS infrastructure. The selected CS was equipped with three DC chargers, each boasting a charging power of 50 kWh, which served as a practical foundation for evaluating the proposed V2G operation strategy.…”

“…This metric serves as a critical benchmark against which the performance improvements introduced by the proposed DRL-based V2G strategy are measured, underscoring the potential of our approach in enhancing the accuracy and reliability of SPG forecasting. For our V2G system analysis, we utilized CS information sourced from the Korea Environment Corporation [20], which offers critical insights into the operational dynamics of the CS infrastructure. The selected CS was equipped with three DC chargers, each boasting a charging power of 50 kWh, which served as a practical foundation for evaluating the proposed V2G operation strategy.…”