Multi-terminal grid system contains wind power, photovoltaic power generation, energy storage equipment and other distributed power sources, and its operating characteristics are complex and variable, so the traditional optimization method is difficult to effectively deal with the scheduling decision-making problem under multi-objective constraints. In this paper, an adaptive particle swarm optimization algorithm based on information interaction mechanism is proposed to address the problems of low information interaction efficiency and insufficient computational accuracy in multi-terminal grid scheduling optimization. The method constructs a multi-objective optimal scheduling model with the objectives of lowest operation cost, maximum environmental benefit and highest system security, adopts a chaotic initialization strategy to enhance the diversity of particle swarms, designs an information interaction mechanism to enhance the global search capability of the algorithm, and establishes an adaptive updating strategy to avoid local optimum. The performance of the algorithm is verified by the ZDT test function, and the results show that the SP value of the improved algorithm on the ZDT3 function is only 0.0096, which is 75% of that of the traditional PSO algorithm; the GD value is 1.9224e-04, which is better than that of the traditional algorithm, which is 2.0905e-04. In the actual multi-terminal grid scheduling experiments, the maximum load of the user is 2200kW, and the daily load rate is 44.5%. The total load fluctuation range of the grid is optimized from 400-900 MW to 560-760 MW after scheduling with the proposed method.This study provides effective technical support for the optimal scheduling of multi-terminal grids, and significantly improves the stability and economy of system operation.
