Load adjustment planning method for distribution network transformer areas based on Simulated Annealing-Quantum Ant Colony algorithm

Aiming at the problems of numerous complex constraints and the need to take into account multi-objective optimization in the generation of transformer area load adjustment schemes, this paper proposes a distribution network transformer area load adjustment planning method based on simulated annealing - quantum ant colony algorithm. Firstly, an optimization model was constructed with the goals of balancing the load rate of the transformer area, reducing the load adjustment cost, minimizing the variation of power supply distance and stabilizing the voltage. Secondly, a collaborative solution algorithm combining simulated annealing and quantum ant colony is proposed. The random disturbance of the simulated annealing algorithm is utilized to avoid the quantum ant colony algorithm falling into local optimum. Finally, the simulation verification through the actual transformer area data shows that the proposed method can effectively improve the quality and efficiency of the load adjustment scheme and provide strong technical support for the operation and maintenance of the power system.