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 >> 往年索引

基于改進(jìn)MNPSO算法的微電網(wǎng)經(jīng)濟(jì)運(yùn)行優(yōu)化研究

來(lái)源:電工電氣發(fā)布時(shí)間:2022-07-18 15:18 瀏覽次數(shù):380

基于改進(jìn)MNPSO算法的微電網(wǎng)經(jīng)濟(jì)運(yùn)行優(yōu)化研究

柳勇,楊國(guó)華,吳宣儒,劉煜,李思維
(寧夏大學(xué) 物理與電子電氣工程學(xué)院,寧夏 銀川 750021)
 
    摘 要:為研究各種改進(jìn)的粒子群優(yōu)化算法對(duì)微電網(wǎng)的經(jīng)濟(jì)運(yùn)行優(yōu)化,通過(guò)構(gòu)建微電網(wǎng)經(jīng)濟(jì)運(yùn)行優(yōu)化模型,用多個(gè)正態(tài)隨機(jī)數(shù)擾動(dòng)粒子群算法速度和位置的演進(jìn)方向,對(duì)比了改進(jìn)粒子群算法的收斂性和不同應(yīng)用環(huán)境下的優(yōu)化性能,采用實(shí)際簡(jiǎn)單協(xié)調(diào)風(fēng)光儲(chǔ)的微電網(wǎng)算例進(jìn)行驗(yàn)證分析,證明了改進(jìn)算法的優(yōu)化效果并驗(yàn)證了優(yōu)化微電網(wǎng)經(jīng)濟(jì)運(yùn)行的科學(xué)性。
    關(guān)鍵詞: 微電網(wǎng);改進(jìn)粒子群優(yōu)化算法;正態(tài)隨機(jī)數(shù);優(yōu)化性能
    中圖分類(lèi)號(hào):TM734     文獻(xiàn)標(biāo)識(shí)碼:A     文章編號(hào):1007-3175(2022)07-0014-08
 
Research on an Improved Particle Swarm Algorithm with Many
Normal Random Number Disturbances
 
LIU Yong, YANG Guo-hua, WU Xuan-ru, LIU Yu, LI Si-wei
(School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China)
 
    Abstract: This paper constructed an optimized model of the economic operation of the microgrid to optimize the microgrid for studying different improved particle swarm optimization.It employed many normal random numbers to disturb the speed and evaluation direction of the particle swarm optimization. In addition, it compared the astringency of the evolutional particle swarm optimization and the optimal performance under diverse application environments.This paper takes the example of the solar energy storage microgrid to do the analysis. It verifies the improved effect of the evolutional algorithm. Moreover, it validates the scientificity of optimizing the economic operation of the microgrid.
    Key words: microgrid; improved particle swarm optimization algorithm; normal random number; optimized performance
 
參考文獻(xiàn)
[1] 戴旭凡,陸奎,宋丹. 基于混沌映射的自適應(yīng)退火型粒子群算法的微電網(wǎng)優(yōu)化經(jīng)濟(jì)調(diào)度[J] . 蘭州文理學(xué)院學(xué)報(bào)(自然科學(xué)版),2021,35(4) :70-74.
[2] 王強(qiáng)杰,沈達(dá),鄔晶,等. 基于天牛須-粒子群算法的微電網(wǎng)日經(jīng)濟(jì)調(diào)度優(yōu)化[J] . 上海電機(jī)學(xué)院學(xué)報(bào),2021,24(1) :39-46.
[3] 李星辰,袁旭峰,李沛然,等. 基于改進(jìn) QPSO 算法的微電網(wǎng)多目標(biāo)優(yōu)化運(yùn)行策略[J] . 電力科學(xué)與工程,2020,36(12) :22-29.
[4] 李海濤,崔樹(shù)春,聞楓. 基于 r-BBMOPSO 算法的微電網(wǎng)優(yōu)化運(yùn)行方法[J] . 廣東電力,2020,33(8) :78-85.
[5] 陳深,肖俊陽(yáng),黃玉程,等. 基于改進(jìn)量子粒子群算法的微網(wǎng)多目標(biāo)優(yōu)化調(diào)度[J] . 電力科學(xué)與技術(shù)學(xué)報(bào),2015,30(2) :41-47.
[6] 劉燕華,張楠,張旭. 考慮儲(chǔ)能運(yùn)行成本的風(fēng)光儲(chǔ)微網(wǎng)的經(jīng)濟(jì)運(yùn)行[J] . 現(xiàn)代電力,2013,30(5) :13-18.
[7] 柳勇,王萱政,李思維,等. 面向泛在電力物聯(lián)網(wǎng)的毫秒級(jí)分層分區(qū)精準(zhǔn)切負(fù)荷系統(tǒng)研究[J] . 電工技術(shù),2021(5) :145-148.
[8] 柳勇. 一種實(shí)時(shí)綜合賦權(quán)評(píng)判決策的切負(fù)荷策略研究[D]. 銀川:寧夏大學(xué),2021.
[9] 劉振,張梅. 常見(jiàn)幾種分布隨機(jī)數(shù)產(chǎn)生原理及實(shí)現(xiàn)途徑[J] . 中阿科技論壇(中英文),2020(11) :95-97.
[10] 劉剛,耿健,楊冬梅,等. 基于高斯擾動(dòng)的改進(jìn)混合粒子群算法研究[J] . 工業(yè)控制計(jì)算機(jī),2021,34(3) :12-14.
[11] KROHLING R A.Gaussian swarm:a novel particle swarm optimization algorithm[C]//IEEE Conference on Cybernetics and Intelligent Systems,2004.
[12] 岳小雪,鄭云水,林俊亭. 自適應(yīng)變異的蝙蝠算法[J].計(jì)算機(jī)測(cè)量與控制,2015,23(2):516-519.
[13] 鄭云水,岳小雪,林俊亭. 帶有高斯變異的混合蛙跳蝙蝠算法[J]. 計(jì)算機(jī)應(yīng)用研究,2015,32(12):3629-3633.
[14] 周璨, 董偉廣, 鐘建偉, 等. 基于改進(jìn)粒子群算法的配電網(wǎng)無(wú)功優(yōu)化[J] . 物聯(lián)網(wǎng)技術(shù),2020,10(1):33-35.