水利水电技术2018，Vol.49Issue(3)：176-185,10.DOI:10.13928/j.cnki.wrahe.2018.03.026
计及风电不确定性的区域互联动态经济优化调度方法
Uncertainty of wind electric power-considered dynamic-economic optimal dispatching method for regional interconnected power system
摘要
Abstract
Combined with the operation characteristics of the regional interconnected power system that includes wind farms,a dynamic-economic optimal dispatching model for the regional interconnection with the consideration of the fuel cost,power-generating unit operation and maintenance costs and risk cost of the system is established by taking the factors of the uncertainties of the output and load of wind farm as well as the restriction on the safety operation of power system into account.Moreover,the model proposed herein is solved with an optimized method that integrates the Latin hypercube sampling,scenario-reduction method and self-learning differential algorithm.By this method,a large amount of samples are created with the Latin hypercube sampling technique at first in accordance with the wind electric power and load prediction errors,and then the created samples are reduced with the scenario-reduction method.Secondarily,global optimization is carried out with the self-learning differential algorithm,and then the optimal dispatching schemes corresponding to all the scenarios concerned are obtained.The result shows that the adoption of this method can not only simulate the characteristics of the uncertainties of wind electric power and load,but can also avoid establishing those more complicated random models,thus the modelling and solving difficulties are to be lowered as well.Meanwhile,the self-learning differential algorithm proposed herein has better convergence characteristics and robustness.Therefore,the optimal method proposed herein has a referential value for the optimal dispatching of the regional interconnected power system.关键词
区域互联电力系统/不确定性/拉丁超立方采样/场景缩减法/自学习差分算法Key words
regional interconnected power system/uncertainty/Latin hypercube sampling/scenario reduction method/self-learning differential algorithm分类
动力与电气工程引用本文复制引用
林艺城,孟安波,殷豪,陈云龙..计及风电不确定性的区域互联动态经济优化调度方法[J].水利水电技术,2018,49(3):176-185,10.基金项目
广东省科技计划项目(2016A010104016) （2016A010104016）
广东电网公司科技项目(GDKJQQ20152066) （GDKJQQ20152066）
