风电并网的电力负荷优化分配算法  

Optimized Load Distribution of Electricity for Grid-connected Wind Power

作　　者：鲁宇明[1] 李登科[2] 

机构地区：[1]南昌航空大学航空制造工程学院,江西南昌330063 [2]南昌航空大学信息工程学院,江西南昌330063

出　　处：《计算机仿真》2016年第11期101-104,130,共5页Computer Simulation

基　　金：国家自然科学基金(61262019);江西省自然科学基金(2015BAB207065);江西省图像处理与模式识别重点实验室开放基金(第三批)

摘　　要：风能是一种无污染的可再生绿色能源,但同时具有很强的随机性,不可调度性和波动性,与发电机组并网的的经济负荷分配问题很难用经典的数学方法来求解。针对上述问题,在已有的粒子群算法,文化算法和文化粒子群算法等等研究成果的基础上,进一步探讨信念空间进化机制以及知识结构的更新规则并提出一种改进的动态改变惯性权重双重更新的并行文化粒子群算法。最后在含风电并网的动态经济负荷分配问题进行优化求解并与其它几种算法对比来验证了改进算法在寻优能力的可行性以及优越性。Wind power is a kind of renewable green energy resource which is clean and inexhaustible, but also random, non-scheduling and volatile. It is difficult to allocate the economic load with integration of wind power generators by classical mathematical methods. To overcome this problem, the mechanism of evolution and the update rules of the knowledge structure in belief space are explored, and an improved parallel cultural swarm particle optimization allocation algorithm, which is double updated based on dynamic inertia weight, is proposed on the basis of research on particle swarm optimization, the cultural algorithm and the cultural particle swarm optimization. Experiment results show that this algorithm has feasibility on solving allocation of the economic load with integration of wind power generators, and it is better than other existing algorithms.

关 键 词：风电 经济负荷分配 惯性权重 文化粒子群算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]