Suzhou Electric Appliance Research Institute
期刊號(hào): CN32-1800/TM| ISSN1007-3175

Article retrieval

文章檢索

首頁(yè) >> 文章檢索 >> 最新索引

基于改進(jìn)蜻蜓算法的混合儲(chǔ)能容量?jī)?yōu)化配置

來源:電工電氣發(fā)布時(shí)間:2024-08-30 15:30 瀏覽次數(shù):32

基于改進(jìn)蜻蜓算法的混合儲(chǔ)能容量?jī)?yōu)化配置

黃禮燦,秦斌
(湖南工業(yè)大學(xué) 電氣與信息工程學(xué)院,湖南 株洲 412007)
 
    摘 要:針對(duì)傳統(tǒng)方法在風(fēng)光儲(chǔ)能系統(tǒng)容量?jī)?yōu)化配置過程中求解精度較低、效率較慢等問題,提出一種改進(jìn)蜻蜓算法(IDA)。通過采用 Logistic 混沌初始化和非線性慣性權(quán)重兩種策略對(duì)原始蜻蜓算法進(jìn)行改進(jìn),使算法能夠在初始化階段分布更均勻,全局搜索和局部開發(fā)更加協(xié)調(diào),同時(shí)更快鎖定最優(yōu)解區(qū)域;構(gòu)建混合儲(chǔ)能系統(tǒng)容量?jī)?yōu)化模型,以儲(chǔ)能裝置的生命周期費(fèi)用作為目標(biāo)函數(shù),并考慮負(fù)荷缺電率、儲(chǔ)能系統(tǒng)能量等約束條件。使用 MATLAB 軟件對(duì)算例進(jìn)行仿真分析,通過 3 種算法的仿真結(jié)果對(duì)比發(fā)現(xiàn),采用改進(jìn)蜻蜓算法蓄電池個(gè)數(shù)有所減少,全生命周期費(fèi)用也相對(duì)降低,有較好的經(jīng)濟(jì)適用性。
    關(guān)鍵詞: 混合儲(chǔ)能;容量配置;改進(jìn)蜻蜓算法;混沌初始化;蓄電池;全生命周期費(fèi)用
    中圖分類號(hào):TM734 ;TM912     文獻(xiàn)標(biāo)識(shí)碼:A     文章編號(hào):1007-3175(2024)08-0001-07
 
Optimal Configuration of Hybrid Energy Storage Capacity Based on
Improved Dragonfly Algorithm
 
HUANG Li-can, QIN Bin
(College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China)
 
    Abstract: Aiming at the problems of lower solution accuracy and slower efficiency of traditional methods in the process of capacity optimization and configuration of wind energy storage system, an improved dragonfly algorithm (IDA) is proposed. The original dragonfly algorithm is improved by adopting two strategies: Logistic chaotic initialization and nonlinear inertia weights, so that the algorithm can be more uniformly distributed in the initialization stage, the global search and local development can be more coordinated and lock the optimal solution region more quickly at the sametime, constructing the capacity optimization model of the hybrid energy storage system, taking the life-cycle cost of the storage device as the objective function and considering load power shortage rate, energy storage system and other constraints. Finally, this paper uses MATLAB software to simulate and analyze the algorithm, the simulation results of the three algorithms are compared and found, using the improved dragonfly algorithm, the number of storage batteries is reduced, the cost of full life-cycle is also relatively reduced and it has better economic applicability.
    Key words: hybrid energy storage; capacity configuration; improved dragonfly algorithm; chaotic initialization; storage battery; full life-cycle cost
 
參考文獻(xiàn)
[1] 荊濤,陳庚,王子豪,等. 風(fēng)光互補(bǔ)發(fā)電耦合氫儲(chǔ)能系統(tǒng)研究綜述[J]. 中國(guó)電力,2022,55(1) :75-83.
[2] 亞夏爾·吐爾洪, 王小云, 常清, 等. 基于改進(jìn) NSGA-Ⅲ 的微電網(wǎng)儲(chǔ)能多目標(biāo)優(yōu)化配置[J]. 電工電氣,2024(3) :21-28.
[3] 曾志輝,劉云鵬,韋延方,等. 基于改進(jìn)蝙蝠算法的混合儲(chǔ)能系統(tǒng)容量?jī)?yōu)化配置[J] . 河南理工大學(xué)學(xué)報(bào)(自然科學(xué)版),2023,42(5) :130-136.
[4] 王欣,譚永怡,秦斌. 改進(jìn) MOGOA 及其在風(fēng)儲(chǔ)容量優(yōu)化配置中的應(yīng)用[J] . 電力科學(xué)與技術(shù)學(xué)報(bào),2024,39(2) :159-169.
[5] 周天沛,孫偉. 風(fēng)光互補(bǔ)發(fā)電系統(tǒng)混合儲(chǔ)能單元的容量?jī)?yōu)化設(shè)計(jì)[J] . 太陽(yáng)能學(xué)報(bào),2015,36(3) :756-762.
[6] 陳天,蔡澤祥,謝鵬,等. 基于改進(jìn)微分進(jìn)化算法的風(fēng)光互補(bǔ)系統(tǒng)發(fā)電容量?jī)?yōu)化配置[J]. 電力科學(xué)與技術(shù)學(xué)報(bào),2017,32(3) :22-28.
[7] 張沖,榮娜. 基于改進(jìn)粒子群算法的新能源側(cè)儲(chǔ)能容量配置[J] . 電網(wǎng)與清潔能源,2022,38(10) :98-105.
[8] 陳明,張靠社. 基于改進(jìn)布谷鳥算法的風(fēng)光儲(chǔ)聯(lián)合供電系統(tǒng)儲(chǔ)能容量?jī)?yōu)化配置研究[J] . 電網(wǎng)與清潔能源,2016,32(8) :141-146.
[9] 張子恒,吳定會(huì),楊朝輝,等. 基于改進(jìn)差分進(jìn)化算法的微網(wǎng)容量?jī)?yōu)化配置[J] . 控制工程,2023,30(1) :90-97.
[10] MIRJALILI S.Dragonfly algorithm:A new metaheuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J].Neural Computing and Applications,2016,27(4) :1053-1073.
[11] 吳忠強(qiáng),趙德隆,王云青,等. 基于改進(jìn)蜻蜓算法的蓄電池參數(shù)辨識(shí)[J] . 電機(jī)與控制學(xué)報(bào),2020,24(12) :152-160.
[12] 李茂林,王勇杰,介丹. 基于多種群蜻蜓算法的四旋翼自抗擾姿態(tài)優(yōu)化控制[J] . 計(jì)算機(jī)應(yīng)用與軟件,2022,39(12) :328-334.
[13] 王波,王浩,杜曉昕,等. 基于亞群和差分進(jìn)化的混合蜻蜓算法[J] . 計(jì)算機(jī)應(yīng)用,2023,43(9) :2868-2876.
[14] 薛飛,馬鑫,田蓓,等. 基于改進(jìn)蜻蜓算法的光伏全局最大功率追蹤[J].中國(guó)電力,2022,55(2) :131-137.
[15] 馬丙泰,劉海濤,張匡翼,等. 基于學(xué)習(xí)因子異步變化 CPSO 混合儲(chǔ)能容量?jī)?yōu)化配置[J]. 自動(dòng)化與儀器儀表,2022(7) :125-130.
[16] 唐浩,楊國(guó)華,王鵬珍,等. 基于改進(jìn)粒子群算法的風(fēng)光蓄互補(bǔ)發(fā)電系統(tǒng)容量?jī)?yōu)化[J] . 電測(cè)與儀表,2017,54(16) :50-55.