基于静态评价粒子群的风电场微观选址方法

WIND FARM MICRO-SITING BASED ON PARTICLE SWARM OPTIMIZATION WITH STATIC EVALUATION

提出静态评价粒子群算法实现连续空间微观选址。根据风的时间和空间连续变化特性评价风电场出力,以风机台数固定为前提条件,以风电场发电量最大为优化目标,以风机之间的最小允许距离为约束条件,建立连续空间微观选址的优化数学模型。采用静态惩罚函数法评价不同微观选址方案的优化性能,采用粒子群算法实现微观选址问题的优化求解。仿真试验分析了惩罚参数和粒子群参数对优化结果的影响,比较了本文方法与经验方法、离散网格优化方法的区别。仿真结果表明,本文方法可大幅度提高微观选址方案的风电场发电量和风能利用效率。

The particle swarm optimization algorithm （PSO） with static evaluation was proposed to optimize positions of turbines in the continuous space. The electricity generation by the wind farm was evaluated according to the continuous characteristics of wind in time and space. The number of turbines was assumed to be fixed, and the op- timization objective was to extract more energy from the wind farm, the optimization model of micro-siting was for- mulated in the continuous space based on the minimumdistance between turbines. The static penalty method was employed to evaluate the performances of distinct micro-siting schemes. PSO was employed to solve the formulated micro-siting problem. Simulation tests analyzed the effect of penalty parameter and PSO parameters on optimization results, and compared the distinction of the proposed method from empirical method and discrete optimization method. Simulation results demonstrate that the electricity generation by the wind farm and the wind exploitable efficiency can be greatly improved by the proposed method.