Photovoltaic Substation Project Location and Line Selection Planning Method Based on Intelligent Algorithm

This study proposes a photovoltaic substation engineering site selection and line planning method based on intelligent algorithms to improve complex site selection areas' planning efficiency and scientificity. This method comprehensively utilizes unmanned aerial vehicle oblique photography, multi-objective genetic algorithm, differential evolution algorithm, and adaptive algorithm. Integrating advanced technology and optimization algorithms achieves efficient and accurate substation site selection and line planning. A multi-objective optimization model is proposed by collecting geographic information through drone technology and establishing a three-dimensional model, considering environmental impact, power supply reliability, and construction costs. This method was comprehensively compared with traditional genetic algorithms and other methods in the experimental stage. The experimental results show that the method proposed in this paper performs well in multiple indicators. Compared to traditional methods, this approach effectively reduces the total cost and significantly improves power supply reliability, achieving a 9% improvement. At the same time, the convergence speed has also been accelerated by 600 generations, reflecting the efficiency and superiority of the algorithm in solving complex problems. The method proposed in this paper improves the efficiency of substation site selection and line planning. It provides new ideas and technical means for research in related fields, verifying intelligent algorithms' practical application value and potential in power engineering.