Scientists in China have created an optimization technique for an electric vehicle charging station that uses PV, battery storage and a vehicle-to-grid effect. Given the uncertainty of PV generation and the arbitrariness of EV taxes, the system uses optimization on two levels, day doing and intra-day.
A research group of Chinese Shanghai Jiao Tong University has developed a new optimization strategy for a cargo station for electric vehicle (EV) that depends on a PV system and storage solutions.
“This study introduces a vehicle-to-schedule (V2G)-defender Operation optimization strategy for EV charging stations with PV and Energy Storage (ES) integration,” the team said. “There is a day-Ahead Power Purchet planning model based on two-stage Distributional Robust Optimization (TDRO), which demonstrates benefits in balancing economic efficiency with uncertainty risks. To tackle intra-day stochastity control (MPC) based on the MPC) based on the MPC) based on the MPC-based) based EV attack station is presented. “
The strategy was tested on a case study in Shanghai. It regards the uncertainty of PV generation and the arbitrariness of the EV tax, while both day-light and intra-day optimization are applied.
The TDRO is used as a daily Power Procurement planning model that absorbs two decision-making phases in the context of the electricity market.
In the first phase, the model determines the amount of electricity it must buy. It uses PV predictions and estimated electricity prices for use time, while it also estimates the condition of cargo (SOC) for the batteries. In the second phase, the model regards the prediction errors of the PV system to complete the day-Ahead Power Purchasing Plan. The results are then refined on Intrad-Day.
Image: Shanghai Jiao Tong University, International Journal of Electrical Power & Energy Systems, CC by 4.0
“The MPC-rolling optimization interval is subdivided into two parts: One is the control interval based on short-term prediction information, and the other is the interval based on information about the day light,” the group explained. “Considering The Balance Between Computational Efficiency and Real-Time Performance, and Combining the Practical Scheduling Operations of Charging Stations, The Time Scale for Intra-Day Rolling Optimization is Set to 15 Min. The Rolling Optimization Interval Length Time of the Rollining Time of the Rollining Time of the Rollininging Time of the Rollininging Time of the Rollininging Time of the Rollininging Time of the Rollininging Time of the Rollininging Time of the Rollining Time of the Rollining Time of the Rollining Time of the Rollining Time of the Rollining. Decisions are made based on a Combination of short-term and day-ahead forecasting information. ”
A simulation of the optimization strategy was then carried out on the basis of data from an EV charging station in Shanghai, China. The station is equipped with 80 DC fast charging, each with a loading force of approximately 100 kW. The parking lots have PV panels with a total capacity of 500 kW, while two battery containers with six locks with a total capacity of 1,080 kWh are also connected on location. The EV -charging data were collected from July to August 2024, with a sample size of 19,570 vehicle trips.
The analysis showed that compared to the original “unordered” charge, the operational costs of two typical days were analyzed by 17.80% and 13.51% respectively.
“Joint optimization by V2G and ES can better lower the peak loads compared to the use of ES alone,” the scientists concluded. “With a peak load of 2,608.96 kW during the evening peak on weekdays, PV-es optimization can reduce 11.57% of the peak load, while PV-EVS optimization can achieve a 23.81% reduction.”
Their findings were presented in “V2G improved operation optimization strategy for EV charging station with photovoltaic and energy storage integrationPublished in the International Journal of Electrical Power & Energy Systems. Academics of the Chinese state grid Shanghai Municipal Electric Power Company and Nari technology have contributed to the study.
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