基于粒子群算法的百千瓦级风热机组配置优化

Configuration Optimization of 100-kilowatt Wind Turbine Heating System Based on Particle Swarm Optimization

风热机组是一种利用风力机直接驱动热泵制热的清洁供暖系统,高效环保,但是初期投资大,回收周期长,亟待进一步成本优化。本文以百千瓦级风热机组为研究基础,在典型年冬季逐时风速工况条件下,对其进行成本优化。构建风热机组的仿真模型与经济性模型,以风力机叶片长度、塔架高度、齿轮箱增速比、蓄热装置容量等因素为变量,以一次能源利用率>120%为约束条件,利用粒子群优化算法,综合考虑投入资本的时间价值和环境成本,将风热机组与现有传统供热方式进行对比,获得风热机组各部件的匹配最优组合,以实现风热机组制热单价最低。

The wind turbine heating system(WTHS)is a kind of clean heating equipment that uses the wind turbine to directly drive the heat pump for heating.It has the characteristics of high efficiency and environmental protection,but the initial investment is large and the recovery period is long.Further cost optimization is urgently needed.In this paper,based on the research of a 100-kilowatt-level WTHS,under the conditions of hourly wind speed in typical winter,the cost is optimized.Construct the simulation model and economic model of WTHS,taking the wind turbine blade length,tower height,speed increase ratio of the speed change mechanism,heat storage device and evaporation and condensation equipment capacity as variables,and the primary energy utilization rate>120%as Constraint conditions,the use of particle swarm optimization(PSO)algorithm to obtain the matching optimal combination of the components of the WTHS,in order to achieve the lowest heating unit price of the WTHS.Comprehensively considering the time value and environmental cost of invested capital,the WTHS is compared with the existing traditional heating method to predict the future industrialization trend of the WTHS.