An Unmanned Electric Vehicle Cluster Scheduling Model for Overload Management of Multi-Voltage-Level Feeders

As the new type of power system evolves, the magnitude of user load on the distribution network side continues to expand, resulting in increasingly pronounced overload issues across feeders of various voltage levels. Achieving asynchronous interconnection among feeders at various voltage levels poses significant challenges, thereby constraining the flexibility of load transfers within the distribution network. The rise of electric vehicles has introduced a novel pathway for electric energy transfer, extending it from the power grid to the road network. Moreover, the advent of unmanned electric vehicle clusters has enhanced the precision and controllability of electric vehicle scheduling. Against the backdrop described above, this paper develops a scheduling framework for unmanned electric vehicle clusters for overload management of multi - voltage - level feeders. During the peak load period, a mixed integer optimization model is constructed by combining the distribution of unmanned electric vehicle clusters on the road network level and the load distribution of multi voltage level feeders. To achieve an efficient resolution of the scheduling strategy, the whale optimization algorithm is employed to address the aforementioned mixed-integer programming model. Finally, the algorithm effect is tested on a multi - voltage - level distribution network composed of a 33 - node feeder at 12.66 kV and 0.8 kV and a 37 - node feeder.