Electric Vehicle Dispatching Strategy Considering the Participation of Load Aggregators in the Group Optimization Mode

• Published in: 2023 5th International Conference on Power and Energy Technology (ICPET) pp. 1111 - 1116
• Main Authors: Zu, Wenjing, Ma, Xing, Yi, Tegele, Li, Peng, Yang, Qinchen, Zhang, Yihan, Zhang, Hongkai, Li, Huixuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.07.2023
• Subjects: Carbon dioxide; Costs; Dispatching; dispatching strategy; electric vehicle; Electric vehicle charging; Fluctuations; group optimization; load aggregator; Power grids; Uncertainty
• DOI: 10.1109/ICPET59380.2023.10367654

Under the background of the "Double Carbon Targets as an important element of carbon emission reduction in transportation industry, the development scale of electric vehicles (EVs) is increasing, and the management difficulty is also increasing. At the same time, the increasing penetration rate of EVs on the grid side brings uncertainty to the grid and makes the scheduling control of EVs increasingly difficult. Accordingly, this paper proposes an EV dispatching strategy considering the participation of load aggregators in the group optimization mode and builds a relevant model for simulation verification. First, the locations of EV charging stations are simulated according to the local population distribution, and a scatter diagram of charging station distribution is drawn. On this basis, the EVs and EV charging stations are bound by the characteristic of more stable EV charging and discharging behaviors in a certain area, and the group management of scattered EVs is transformed into the group management of EV charging stations with fixed locations. The k-means method is used to cluster the EVs and assign them to each aggregator for management, and each aggregator provides the predicted charging and discharging power of EVs in its jurisdiction. Finally, each aggregator is assigned a dispatching plan according to the proportion of the predicted power of each aggregator in the total predicted power. This paper takes the data for a typical day of a certain area as an example to simulate the optimal dispatching of EVs. The results show that the EV dispatching strategy proposed in this paper, which considers the participation of load aggregators in the group optimization mode, can easily and quickly cluster EVs, thus effectively reduce user costs, smooth out net load fluctuations, develop individualized dispatching plans for different aggregators, and provide reference for the dispatching center to allocate dispatching plans.