Suitability of Photovoltaic Power Station Sites Based on Particle Swarm Optimization Model of Fuzzy Hierarchical Analysis—Taking Qujing City of Yunnan Province as Example

With the global shift in energy systems and the growing adoption of renewable energy, photovoltaic power generation has become widely implemented worldwide as a clean and efficient energy source. Therefore, scientific and rational locating methods for photovoltaic power stations has become essential. This study employs a particle swarm optimization (PSO) model based on the fuzzy analytic hierarchy process (FAHP) for evaluating the site-suitability of photovoltaic power stations. Taking Qujing City in Yunnan Province as an example, this study comprehensively considered 13 factors in three categories: meteorology, physical geography, and location. In addition, such topographic factors as planar curvature, profile curvature, and LSW were added to the physical geography factors for consideration. In previous studies, less attention has been paid to these factors. Spatial analysis and data integration of influencer factors in the study area were carried out in geographic information system (GIS) technology, with weights assigned for each factor in combination with a fuzzy analytic hierarchy process, but a low consistency effect was delivered. To optimize the consistency effect, the particle swarm optimization algorithm was introduced, and weights with good consistency effects were obtained. Based on such weights, the suitability evaluation was carried out to select photovoltaic power stations. The evaluation results were compared with those obtained from the fuzzy analytic hierarchy process and the genetic algorithm (GA) optimization model based on FAHP and were further verified in the area of the photovoltaic power stations built. It is demonstrated that the accuracy rate of the suitability of Qujing photovoltaic power station site selection based on the particle swarm optimization model of the fuzzy analytic hierarchy process was the highest at 99.3%.