基于改进鲸鱼算法的微网复合储能系统容量优化配置

李玲玲; 王鑫; 郎永波; 贾立凯; 王昕

李玲玲,王鑫,郎永波,贾立凯,王昕.基于改进鲸鱼算法的微网复合储能系统容量优化配置[J].电测与仪表,2019,56(16):104-110.
LI Lingling,WANG Xin,LANG Yongbo,JIA Likai,WANG Xin.Capacity Configuration of Microgrid Composite Energy Storage System Based on Improved Whale Optimization Algorithm[J].Electrical Measurement & Instrumentation,2019,56(16):104-110.

基于改进鲸鱼算法的微网复合储能系统容量优化配置

Capacity Configuration of Microgrid Composite Energy Storage System Based on Improved Whale Optimization Algorithm

DOI：10.19753/j.issn1001-1390.2019.016.017

中文关键词: 复合储能容量配置多目标优化差分进化鲸鱼优化算法

英文关键词:composite energy storage, capacity allocation, multi-objective optimization, the differential evolution whale optimization algorithm

基金项目:国家自然科学基金(61673268)

作者中文名	作者英文名	单位
李玲玲	LI Lingling	上海电力学院电气工程学院
王鑫	WANG Xin	国网吉林省电力有限公司延边供电公司
郎永波	LANG Yongbo	国网吉林省电力有限公司延边供电公司
贾立凯	JIA Likai	国网吉林省电力有限公司延边供电公司
王昕	WANG Xin	上海交通大学电工与电子技术中心

摘要点击次数: 1747

中文摘要:

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,采用本文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,通过算例将本文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。

英文摘要:

Aiming at the random and intermittent characteristics of the distributed power supply in the microgrid, a method to optimize the configuration of the composite energy storage capacity is proposed to ensure its economical and reliable operation. This paper proposes a method for optimizing the configuration of composite energy storage capacity. Firstly, an optimization configuration model of composite energy storage capacity is established to establish 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. The constraints of the composite energy storage system, the tie-line transmission power constraints, the micro-turbine output power constraints, and the renewable energy power constraints are constraints. Secondly, 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. At the same time, the utilization ratio of the micro-network connection line has also been improved, and the rational use of resources has been realized.

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