摘要
针对传统方法在风储容量优化配置过程中求解精度低、效率低等问题,提出一种改进多目标蝗虫优化算法(improved multi-objective grasshopper optimization algorithm,IMOGOA),采用Fuch混沌映射、余弦自适应参数和莱维飞行三种策略进行改进,使算法的初始解分布更均匀、全局探索和局部开发更协调,同时增强了算法跳出局部最优的能力。对改进算法和多目标粒子群等多个算法进行性能测试对比,实验结果表明改进算法具有更好的寻优精度和稳定性。将该算法应用于风电场混合储能系统容量优化配置,对比其他算法,改进算法能够快速找出Pareto最优解集,在满足系统要求的同时,最大限度降低混合储能系统成本,可以验证算法改进策略的有效性和应用于实际优化问题的适用性。
Aiming at the problems of low solution accuracy and efficiency in the optimal capacity allocation of energy storage system in wind farms with traditional methods,an improved multi-objective grasshopper optimization algorithm(IMOGOA)is proposed.Three strategies including Fuch chaos mapping,cosine adaptive parameters,and Levy flight are adopted for improvement,which makes the initial solution distribution of the algorithm more uniform,global exploration and local development more coordinated,and enhances the ability for algorithm to jump out of the local optimum.Performance tests are conducted to compare the improved algorithm with multiple algorithms such as multi-objective particle swarm optimization and et al.Experimental results show that the improved algorithm has better optimization accuracy and stability.When applied to the optimal capacity allocation for hybrid energy storage system in wind farms,compared with other algorithms,the improved algorithm can quickly find the Pareto optimal solution set.While meeting the system requirements,it minimizes the cost of the hybrid energy storage system,which verifies the effectiveness of the algorithm on improving strategy and its applicability to practical optimization problems.
作者
王欣
谭永怡
秦斌
WANG Xin;TAN Yongyi;QIN Bin(College of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou 412007,China)
出处
《电力科学与技术学报》
CAS
CSCD
北大核心
2024年第2期159-169,共11页
Journal of Electric Power Science And Technology
基金
国家自然科学基金(62033014)
湖南省自然科学基金(2021JJ50006
2022JJ50074)。
关键词
风电场
储能容量优化配置
蝗虫优化算法
Fuch混沌映射
莱维飞行
wind farms
optimal capacity allocation of energy storage
grasshopper optimization algorithm
Fuch chaos mapping
Levy flight
