改進(jìn)黑洞粒子群算法在電力系統(tǒng)環(huán)保經(jīng)濟(jì)調(diào)度中的應(yīng)用
宗超凡，代奇跡，趙海麗
(貴州大學(xué) 電氣工程學(xué)院，貴州 貴陽 550000)
摘 要： 針對(duì)電力系統(tǒng)中的環(huán)保經(jīng)濟(jì)調(diào)度問題，采用了一種改進(jìn)隨機(jī)黑洞粒子群算法進(jìn)行優(yōu)化計(jì)算。該算法通過引入隨機(jī)黑洞策略、慣性權(quán)重和學(xué)習(xí)因子動(dòng)態(tài)更新及小概率隨機(jī)變異改進(jìn)了粒子群算法，提高了全局搜索能力，穩(wěn)定了計(jì)算結(jié)果，加快了收斂速度。通過對(duì)IEEE30 系統(tǒng)仿真計(jì)算，結(jié)果驗(yàn)證了該算法的有效性和優(yōu)越性，系統(tǒng)的經(jīng)濟(jì)性和電能質(zhì)量都得到了提升。
關(guān)鍵詞： 隨機(jī)黑洞策略；動(dòng)態(tài)更新技術(shù)；小概率隨機(jī)變異；環(huán)保經(jīng)濟(jì)調(diào)度
中圖分類號(hào)：TM744 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：1007-3175(2016)01-0033-04
Application of Improved Black Hole Particle Swarm Optimization Algorithm in Environmental Economic Dispatch of Power System
ZONG Chao-fan, DAI Qi-ji, ZHAO Hai-li
（The Electrical Engineering College, Guizhou University, Guiyang 550000, China）
Abstract: In allusion to the problem of environmental economic dispatch in power system, this paper proposed a kind of improved random black particle swarm optimization algorithm to carry out optimal computation. This paper introduced random black hole strategy,dynamic updates of inertia weight and learning factor, small probability random mutation to improve the particle swarm optimization algorithm and raised the global searching ability, stability of the results and acceleration of the convergence rate. The proposed algorithm is used to calculate the environmental economic dispatch in IEEE30 system. The simulation results show the effectiveness and superiority of this algorithm, and the power system has more economical efficiency and better power quality.
Key words: random black hole strategy; dynamic updating technology; small probability random mutation; environmental economic dispatch
參考文獻(xiàn)
[1] 劉自發(fā)，張建華. 一種求解電力系統(tǒng)經(jīng)濟(jì)負(fù)荷問題的改進(jìn)微分進(jìn)化算法[J]. 中國電機(jī)工程學(xué)報(bào)，2008，28(10)：100-105.
[2] 周任軍，李紹金，李紅英，等. 空間粒子群優(yōu)化算法及其在電力系統(tǒng)環(huán)保經(jīng)濟(jì)負(fù)荷分配中的應(yīng)用[J].電力自動(dòng)化設(shè)備，2014，34(9)：7-12.
[3] 魯忠燕，鄧集祥，汪永紅. 基于免疫粒子群算法的電力系統(tǒng)無功優(yōu)化[J]. 電網(wǎng)技術(shù)，2008，32(24)：55-59.
[4] 李如琦，李芝榮，王維志，等. 基于差分策略的多目標(biāo)電力系統(tǒng)無功優(yōu)化[J]. 電網(wǎng)技術(shù)，2012，36(12)：170-175.
[5] 冉曉洪，周任軍，李湘華，等. 計(jì)及等排性能系數(shù)的冷熱電多聯(lián)供環(huán)境經(jīng)濟(jì)調(diào)度[J]. 電力系統(tǒng)自動(dòng)化設(shè)備，2013，33(9)：94-99.
[6] 何啟明. 基于改進(jìn)粒子群算法的多目標(biāo)無功優(yōu)化[D]. 成都：西南交通大學(xué)，2009.
[7] 程杉. 含分布式電源的配電網(wǎng)多目標(biāo)優(yōu)化問題研究[D]. 重慶：重慶大學(xué)，2013.
[8] 亢國棟，孫偉，楊海群，等. 基于改進(jìn)粒子群優(yōu)化算法的火電廠機(jī)組負(fù)荷分配[J]. 計(jì)算機(jī)測(cè)量與控制，2015，23(2)：593-596.
[9] Zhang Junqi, Liu Kun, Tan Ying, et al.Random Black Hole Particle Swarm Optimization and Its Application[C]//Proceedings of IEEE Conference on Neural & Signal Processing,2008：359-365.
[10] Kalyanmoy Deb, Sachin Jain.Multi-Speed G e a r b o x D e s i g n U s i n g M u l t i - O b j e c t i v e
E v o l u t i o n a r y A l g o r i t h m s [ J ] . J o u r n a l o f Mechanical Design, 2003，125(3)：609-619.
[11] Abido M A.Multi-objective particle swarm optimization for environmental economic dispatch problem[J].Electric Power Systems Research, 2003，79(7)：1105-1113.
