【摘要】 为提升荒漠地区光伏电站发电量，以新疆克拉玛依地区2 640 MW大型集中式并网光伏电站为例，提出一种基于粒子群优化（PSO）算法的光伏电站智能设计方法。该方法将光伏组件型号选择、安装倾角、阵列间距等设计参数作为优化变量，以年发电量最大化为目标函数，同时考虑投资成本约束，构建多目标优化模型。通过PVsyst软件进行发电量仿真，结合MATLAB编程软件实现PSO算法优化求解。仿真结果表明，与传统设计方法相比，提出的智能优化方法可使新建2 640 MW光伏电站年发电量提升3.2%，达到41.26×10⁸ kW·h，投资回收期缩短0.5年。项目建成后每年可减少CO₂排放量约7.176×10⁷t，具有显著的经济效益和社会效益，为大型光伏电站智能化设计提供了新思路。更多还原

【Abstract】 The 2 640 MW large scale centralized grid-connected photovoltaic power station in Karamay,Xinjiang,is taken as an example in this paper,and an intelligent design method for photovoltaic power stations is proposed,based on the Particle Swarm Optimization（PSO） algorithm. This method uses photovoltaic module type selection,installation angle,array spacing and other design parameters as optimization variables,with annual power generation maximization as the objective function and investment costs as constraints,to construct a multi-objective optimization model. Power generation simulation is performed using the PVsyst software combined with MATLAB programming to implement PSO algorithm optimization. Simulation results show that compared with traditional design methods,the intelligent optimization method proposed in this paper can increase the annual power generation of the 2 640 MWphotovoltaic power stations by 3.2% to 41.26×10⁸ kW·h,and shorten the investment payback period by 0.5 years. After completion,the project can reduce CO₂ emissions by approximately 7.176×10⁷ t per year,with significant economic and social benefits. The proposed method provides new ideas for intelligent design of large scale photovoltaic power stations.更多还原