摘要
针对微电网中分布式电源存在的随机性与间歇性等特征,提出了一种微网复合储能容量优化配置的方法,以保证微电网经济可靠运行。该方法以复合储能系统全寿命周期成本最低、平滑可再生能源功率波动效果最好以及微网联络线利用率最高为目标,建立复合储能容量优化配置模型。在此模型的基础上,采用文章改进的差分进化鲸鱼算法求解得到复合储能系统容量最优配置。最后,通过算例将改进的算法结果与基本的鲸鱼算法、粒子群算法进行对比,验证了差分进化鲸鱼算法可以更合理地配置复合储能容量,使风光功率波动得到更有效的平抑,同时微网联络线利用率也得到了提高,实现了资源的合理利用。
Aiming at the random and intermittent characteristics of the distributed power supply in the micro-grid,a method to optimize the configuration of the composite energy storage capacity is proposed to ensure its economical and reliable operation.Firstly,an optimization configuration model of composite energy storage capacity is established to take a multi-objective function with the lowest life cycle cost of the composite energy storage system,the best smooth power of renewable energy,the highest utilization of the micro-net connection line,and the highest reliability of power supply.Secondly,on the basis of this model,a modified whale optimization algorithm was used to obtain the optimal configuration of the composite energy storage,and compared with the basic whale algorithm and the particle swarm algorithm.Finally,the results of this paper are compared with the basic whale algorithm and particle swarm algorithm by an example.Verification of differential evolution whale algorithm can more rationally configure composite energy storage capacity.It allows the wind power fluctuations to be more effectively suppressed.Meanwhile,the utilization ratio of the micro-network connection line has also been improved,and the rational use of resources has been realized.
作者
李玲玲
王鑫
郎永波
贾立凯
王昕
Li Lingling;Wang Xin;Lang Yongbo;Jia Likai;Wang Xin(School of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Yanbian Power Supply Company,State Grid Jilin Electric Power Co.,Ltd.,Yanbian 133001,Jilin,China;Center of Electrical & Electronic Technology,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《电测与仪表》
北大核心
2019年第16期104-110,共7页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(61673268)
关键词
复合储能
容量配置
多目标优化
差分进化鲸鱼优化算法
composite energy storage
capacity allocation
multi-objective optimization
the differential evolution whale optimization algorithm
