基于小世界邻域PSO的含风场电力系统经济调度

ECONOMIC DISPATCH OF WIND FARM INTEGRATED POWER SYSTEM BASED ON SMALL WORLD NEIGHBORHOOD PSO

以聚类系数大且平均路径短的NW(Newman-Watts)型小世界网络作为粒子群的拓扑结构,提出NW型小世界邻域粒子群优化算法(SW-PSO)。典型高维基准测试函数表明,该算法能有效避免基本PSO算法因群体间信息交互过快而陷入局部最优解的问题。将新算法应用于含风电场电力系统经济调度问题的优化求解,并根据优化调度的实际需求采用相应的调整策略以修正粒子,保证粒子在可行域中飞行寻优,使新算法的寻优精度显著提高。仿真实例表明,SW-PSO算法比传统PSO算法寻优效果好,是一种求解复杂大规模非线性规划问题的新方法。

Studies demonstrated that the topological structure of PSO has a great influence on its optimization ability. Thus, the NW small world neighborhood PSO （SW-PSO） algorithm was proposed which adopted the NW small world network with high clustering coefficient and short average path length as the topological structure of PSO. Typical test functions with high scales were employed as test examples, and the result showed that this new algorithm can avoid premature convergence that the basic PSO had due to rapid information exchange between populations. Then SW-PSO algorithm was applied to solve the economic dispatch of wind farm integrated power system. Moreover, the corresponding adjustment strategies to practical demand were proposed to correct the particle optimizing in the feasible region so that the optimization accuracy of SW-PSO was significantly improved. The result indicated that SW-PSO is more effective than traditional PSO and is a new solution for the complex large-scale non-linear programming problem.