摘要
为了使风光水联合发电系统达到经济效益最大化优化调度的目的,针对粒子群算法在进化过程中易早熟、后期收敛速度慢并且精度较低的特点,提出一种动态调整学习因子的免疫粒子群算法。该算法对学习因子进行非对称线性动态调整,增强前期的全局搜索能力,以及后期的局部搜索能力,快速得到全局最优解。该算法在文中联合系统的求解中得到很好的应用,显著提高了搜索精度,表明了模型和算法的有效性。
To realize the optimized dispatching with maxi- mum economic benefits of the hybrid photovohaie and wind power generation system, an immune particle swarm optimization algorithm based on dynamically changing learning factors is proposed to address the premature, low precision and slow convergence in the evolutionary process of the PSO algorithm. The algorithm can do dissymmetric linear dynamic adjustment to the learning factors, and strengthen the early stage global search ability and late stages local search ability and quickly obtain the global optimal solution. The algorithm has good application in solving the combined system described in the paper, and significantly improves the search precision, demonstrating the effectiveness of the model and algorithm.
出处
《电网与清洁能源》
2014年第2期76-80,87,共6页
Power System and Clean Energy
基金
国家自然科学基金资助项目(61273144)
北京市自然科学基金资助项目(4122071)~~
关键词
学习因子
免疫粒子群算法
风光水联合系统
经济性调度
learning factors
immune particle swarm opti-mization algorithm
hybrid photovohaic and wind power gene-ration system
economical dispatch
