Multi-scenario modeling for spatiotemporal distribution of battery-swapping heavy-duty truck load considering multi-source information interaction

Research on the load characteristics of battery-swapping electric heavy-duty trucks (BS-EHDTs) is critical for system scheduling and participation in demand response. Existing research remains insufficient in addressing the highly intensive and stochastic travel characteristics of BS-EHDTs, and lacks feedback mechanisms that integrate multi-source information such as travel routes, pricing mechanisms, and grid conditions, thereby limiting its practical applicability under real-world conditions. To overcome these limitations, this paper establishes a multi-scenario spatiotemporal distribution model of BS-EHDT load using Monte Carlo simulation. The BS-EHDTs, battery swapping stations, road network, and electric distribution network are individually modeled, and systematically coupled via a cloud platform mechanism, which enables the interaction of multi-source information. The model incorporates both objective factors (e.g., traffic, temperature) and subjective factors (e.g., driver behavior and psychology). Based on the current and anticipated operating modes of BS-EHDTs, this paper designs two operating scenarios: the closed scenario and the open scenario. Simulation results respectively show that integrating BS-EHDT clusters into the grid increases regional peak loads by 4.01 % and 2.06 % under the closed and open scenarios. However, the open scenario achieves better load balancing, reducing average waiting time by 0.03 h, maximum swapping duration by 0.45 h, peak load by 4.08 MW, and the peak-valley ratio by 1.23 %. These results demonstrate the significant potential of vehicle-to-grid (V2G) coordination in improving grid efficiency and operational stability.