基于改进型GAGA算法的智能电网有功优化调度研究  

Research on Intelligent Optimization of Smart Grid Based on Improved GAGA Algorithm

作　　者：李端超 黄少雄 汪伟 梁肖 王松 徐栋哲[2] LI Duanchao;HUANG Shaoxiong;WANG Wei;LIANG Xiao;WANG Song;XU Dongzhe(State Grid Anhui Electric Power Co.,Ltd.,Hefei 230022,Anhui,China;University of Science&Technology of China,Hefei 230051,Anhui,China)

机构地区：[1]国网安徽省电力有限公司,安徽合肥230022 [2]中国科学技术大学,安徽合肥230051

出　　处：《电网与清洁能源》2018年第10期54-59,共6页Power System and Clean Energy

基　　金：国家自然科学基金资助项目(10475079);国家电网公司科技项目(521200130M0R)~~

摘　　要：针对智能电网有功优化调度问题,在传统格雷码加速遗传(GAGA)算法的基础上引入渔夫捕鱼算法来实现迭代算法的收缩搜索;构建Hadoop云平台实现对智能电网有功优化调度的应用。实例验证表明:改进型GAGA算法通过引入农夫捕鱼算法提高了单次迭代时间成本,减少了总的计算迭代次数,降低了总计算时间,同时保证了算法不会陷入局部最优解;在高复杂度的智能电网有功优化调度方面降低了计算量,提高了算法的计算效率。For the active optimization scheduling in smart grids,this paper introduces a farmer fishing algorithm based on GAGA algorithm to realize the contractive search of iterative algorithm.The Hadoop cloud platform is constructed to realize the active optimization scheduling of smart grids.The experimental results show that the improved GAGA algorithm can increase the time cost of single iterations,but reduce the total number of iterations and the total computation time,and thus it can prevent the algorithm from falling into the local optimal solution.The improved GAGA algorithm improves the computational efficiency of the original algorithm and reduces the computational complexity in the highly complex intelligent grid active optimization scheduling.

关 键 词：智能电网 云计算 改进GAGA 有功优化 

分 类 号：TM74[电气工程—电力系统及自动化]