[1]张俊芳,秦红霞,贾 晋,等.基于改进遗传算法的AGC机组优化组合研究[J].南京理工大学学报(自然科学版),2009,(06):801-805.
　ZHANG Jun-fang,QIN Hong-xia,JIA Jin,et al.Optimization of Generator Unit Commitment Including AGC Based on Improved Genetic Algorithm[J].Journal of Nanjing University of Science and Technology,2009,(06):801-805.
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基于改进遗传算法的AGC机组优化组合研究
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《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2009年06期
页码:
801-805
栏目:
出版日期:
2009-12-30

文章信息/Info

Title:
Optimization of Generator Unit Commitment Including AGC Based on Improved Genetic Algorithm
作者:
张俊芳 1 ;  秦红霞 2 ;  贾 晋 3 ;  吴军基 1
1.南京理工大学动力工程学院,江苏南京210094; 2. 北京四方继保自动化股份有限公司, 北京100085; 3. 安徽省巢湖供电公司调度所,安徽巢湖238000
Author(s):
ZHANG Jun-fang1; QIN Hong-xia2; JIA Jin3; WU Jun-ji1
1.School of Power Engineering,NUST,Nanjing 210094,China;2.Beijing Sifang Automation Co.,Ltd.,Beijing 100085,China;3.Dispatching Station,Anhui Provincial Chaohu Electric Power Company,Chaohu 238000,China
关键词:
遗传算法;  等微增法;  机组优化组合;  自动发电控制
Keywords:
genetic algorithm;  principle of equal incremental rate;  generator unit commitment;  automatic generation control
分类号:
TM73
摘要:
为降低发电成本,该文对自动发电控制(AGC)机组优化组合问题进行了研究。基于改进遗传算法,建立了包含AGC的机组优化组合模型;针对遗传算法存在的不足,结合包含AGC机组优化组合模型的特殊性,提出了可变长二进制编码;设计了专门的遗传操作,并采用等微增法对其中的连续变量进行了处理。将所研究的算法和模型应用于包含16台机组24时段的机组优化系统中,仿真结果表明该改进遗传算法的计算结果优于实数编码方法结果11.33%,并在搜索区间及收敛速度等方面都具有较好的性能,适用于大、中型发电系统。
Abstract:
To reduce the generating cost,a method for generator unit commitment including automatic generation control(AGC) is studied here.Based on the improved genetic algorithm,a new model of generator unit commitment including AGC is established.For the existing deficiencies of the standard genetic algorithm and particularity of the model on generator unit commitment including AGC,a variable-length binary encoding is proposed and a special genetic operation is designed,in which the principle of equal incremental rate is used for the continuous variables.The simulations of the 16-machine and 24-hour system show that the results from the improved genetic algorithms and mode optimize 11.33% compared with the results from real encoding.A preferable performance is achieved in search range and convergence speed.The method is suitable for large and medium generating systems.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 张俊芳( 1965- ), 女,副教授, 主要研究方向:电力系统优化、运行与控制, E-mail:zjf807@163. com。
更新日期/Last Update: 2012-11-19