Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm
Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm
复制标题
基于求和的多目标进化算法的随机风电经济排放调度问题
DOI：
10.1016/j.ins.2016.01.081
复制
发表时间：
2016
影响因子：
8.1
通讯作者：
P. N. Suganthan
中科院分区：
文献类型：
--
作者：
B. Y. Qu;J. J. Liang;Y. S. Zhu;Z. W. Wang;P. N. Suganthan
近年来，风能等可再生能源已被用作减少污染排放的最有效途径之一。将基于求和的多目标差分进化算法应用于随机风电环境下的经济排污调度问题。利用威布尔概率分布函数对风电的随机性进行建模，将不确定性作为具有随机变量的系统约束来处理。该算法结合了可行解约束处理技术的优越性。为了验证所提方法的有效性，以IEEE30节点6发电机标准风电试验系统(考虑和不考虑损耗)为例，将燃料成本和排放作为两个相互冲突的目标同时进行优化。此外，本文还对一个较大的含风电场的40发机组系统进行了求解。将SMODE产生的结果与使用NSGAII以及文献报道的一些技术获得的结果进行了比较。结果表明，SMODE算法能产生更好且一致的解。
In recent years, renewable energy sources such as wind energy have been used as one of the most effective ways to reduce pollution emissions. In this paper, a summation based multi-objective differential evolution (SMODE) algorithm is used to optimize the economic emission dispatch problem with stochastic wind power. The Weibull probability distribution function is used to model the stochastic nature of the wind power and the uncertainty is treated as the system constraints with stochastic variables. The algorithm is integrated with the superiority of feasible solution constraint handling technique. To validate the effectiveness of the proposed method, the standard IEEE 30-bus 6-generator test system with wind power (with/without considering losses) is studied with fuel cost and emission as two conflicting objectives to be optimized at the same time. Besides, a larger 40-generator system with wind farms is also solved by the proposed method. The results generated by SMODE are compared with those obtained using NSGAII as well as a number of techniques reported in literature. The results reveal that SMODE generates superior and consistent solutions.