电力系统自动化Issue(5)：62-70,9.DOI:10.7500/AEPS20150409009
用于提高风电场运行效益的电池储能配置优化模型
An Optimization Model of Battery Energy Storage System Configuration to Improve Benefits of Wind Farms
摘要
Abstract
More benefits can be earned for wind farms integrated with a battery energy storage system(BESS)by improving the acceptance of wind power.Firstly,this paper proposes a double optimization model for battery energy storage system considering grid structure. The optimal configuration node, power, capacity of BESS are determined with the aim of incremental benefits maximization for wind and storage joint system compared with wind farms only considering system security constraints in the outer planning model.The benefits maximization of wind and storage joint system is chosen as the objective function,and the output of generating units,wind farm,BESS as decision variables.In the constraints,power balance,spinning reserve, power and capacity of BESS are considered in the inner optimization model. A numerical optimization algorithm,which is based on an improved empirical competition algorithm is proposed to calculate the model. Finally,the validity of this model is verified in an improved IEEE 1 18-node system.Case results suggest that the best configuration of BESS can increase wind farm benefits,meanwhile both benefits increments and the improvement of abandoned wind show an increasing trend with reduction in investments or the increase in grid price.Furthermore,an appropriate initial capacity of BESS is able to improve the benefits of composite BESS and wind generation system.And on the premise of guaranteed convergence,the improved empirical competition algorithm can effectively increase the computing speed compared with the traditional one.关键词
电池储能系统/网架结构/风电接纳能力/双层优化模型/改进帝国竞争算法Key words
battery energy storage system/grid structure/acceptance of wind power/double optimization model/improved empirical competition algorithm引用本文复制引用
徐国栋,程浩忠,方斯顿,马则良,张建平,朱忠烈..用于提高风电场运行效益的电池储能配置优化模型[J].电力系统自动化,2016,(5):62-70,9.基金项目
国家重点基础研究发展计划(973计划)资助项目(2014CB23903) （973计划）
国家自然科学基金资助项目(51337005)。This work is supported by National Basic Research Program of China (973 Program)(No.2014CB23903) and National Natural Science Foundation of China(No.51337005) （51337005）
