Intelligent Algorithms for Voltage Level Collaborative Planning and Power Supply Capacity Optimization

Abstract

In this paper, an optimization strategy for voltage level planning using intelligent algorithms is constructed to improve the power supply capacity (PSC) and overall performance of the power grid. The Laida criterion is used to clear abnormal data, and the weighted average method is used to supplement the missing data;  the adaptive Particle Swarm optimization algorithm (APSO) is used to construct a voltage-level collaborative planning model, through flexible adjustment of inertial weights and acceleration factors, to achieve a balance between global search and local fine-tuning; by combining particle swarm optimization (PSO) and Genetic algorithm (Genetic Algorithm, GA), a hybrid algorithm (PSO-GA) is formed, effectively avoiding the dilemma of local optimization by introducing random self-feedback variation and high-frequency cross-operation. The results show that the energy utilization rate and transmission efficiency of the APSO algorithm have increased to 94.3% and 92.8%, respectively; the PSC of the PSO-GA algorithm has increased by 20.61% and 22.44% under low-load and medium-load conditions, respectively. Both algorithms effectively solve the voltage planning challenges, significantly reduce energy consumption, enhance the synergy of the voltage level, and improve the maximum PSC.