基于自适应遗传-粒子群优化算法的风电场微观选址优化

为了减小尾流效应对风电场发电量的影响，提高风能利用率，提出了一种自适应权重的遗传-粒子群优化算法(genetic-particle swarm optimization algorithm, GA-PSO)。首先，以风电场单位发电成本为目标函数，风机坐标为优化变量，通过在优化变量的速度更新中加入惯性权重，以改变算法的寻优速度；其次，在WASP软件选址的基础上，对风电机组进行布局优化；进而，将计算结果与遗传算法(genetic algorithm, GA)、萤火虫算法(firefly algorithm, FA)和粒子群(particle swarm algorithm, PSO)优化算法进行对比。结果表明：运用PGOA算法优化后的风电场单位发电成本为2 016元/GWh,减少了232元/GWh,年发电量为82.633 GWh,比优化前提高了8.538 GWh,同时尾流损失减小了1.12%。可见研究结论对未来的风电场微观选址具有一定指导意义。

Micro-location and Optimization of Wind Farm Based on the Adaptive GA-PSO Algorithm

In order to reduce the impact of the wake effect on the wind farm power generation, to improve the utilization rate of wind energy, an optimization algorithm-adaptive weight Genetic-Particle Swarm Optimization Algorithm (GA-PSO) is proposed. Firstly, the cost per unit of produced energy and coordinates of wind turbines were regarded as the target function and variable respectively, inertial weight was added to the speed update of optimization variable to change the search speed. Secondly, the layout of wind turbines was optimized based on micro-site selection of WASP. Eventually, the calculation results were also compared with the genetic algorithm(GA), firefly algorithm(FA) and particle swarm algorithm(PSO) optimization algorithms. The results show that the cost per unit of produced energy in the wind farm optimized by PGOA algorithm is 0.2016 ten thousand yuan/GWh , reducing 0.0232 ten thousand yuan/GWh and the annual power generation is 82.633 GWh, increased by 8.538 GWh compared with before optimizing, meanwhile the wake loss is decreased by 1.12%. The research results are guiding significance to the future micro-site selection of wind farms.