|Table of Contents|

Optimization of Generator Unit Commitment Including AGC Based on Improved Genetic Algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2009年06期
Page:
801-805
Research Field:
Publishing date:

Info

Title:
Optimization of Generator Unit Commitment Including AGC Based on Improved Genetic Algorithm
Author(s):
ZHANG Jun-fang1QIN Hong-xia2JIA Jin3WU 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
Keywords:
genetic algorithm principle of equal incremental rate generator unit commitment automatic generation control
PACS:
TM73
DOI:
-
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.

References:

[ 1] 余廷芳, 林中达. 部分解约束算法在机组负荷优化组合中的应用[ J]. 中国电机工程学报, 2009, 29( 2): 107-112.
[ 2] Valenzuela J, SmithAE. A seededmemetic algorithm for large unit commitment problems[ J]. Journal of Heuristics, 2002, 8( 2): 173-195.
[ 3] 李铁苍, 周黎辉,张光炜, 等. 基于粒子群算法的火电厂机组负荷优化分配[ J]. 华北电力大学学报, 2008, 35( 1): 44-47.
[ 4] 贾德香, 程浩忠,熊虎岗, 等. 考虑控制性能标准的 AGC机组经济补偿研究[ J]. 中国电机工程学报, 2007, 27( 31): 52-56.
[ 5] 王民量, 张伯明, 夏清. 考虑多种约束条件的机组组合新算法[ J]. 电力系统自动化, 2000, 24( 12): 29- 34.
[ 6] WalshMP, O. MalleyMJ. Augmentedhopfieldne-t work for unit commitment and economic dispatch[ J]. IEEETransonPower Systems, 1997, 12( 4): 1765-1774.
[ 7] 吴金华, 吴耀武,熊信艮, 等. 机组优化组合问题的随机tabu搜索算法[ J]. 电网技术, 2003, 27( 10): 35- 38.
[ 8] 范宏, 韦化. 改进遗传算法及其在机组优化组合中的应用[ J]. 电力系统及其自动化学报, 2004, 16( 4): 46-50.
[ 9] 张振宇, 葛少云, 刘自发. 粒子群优化算法及其在机组优化组合中应用[ J]. 电力自动化设备, 2006, 26( 5): 28- 31.
[ 10] 吴金华, 吴耀武, 熊信艮. 基于退火演化算法和遗传算法的机组优化组合算法[ J]. 电网技术, 2003, 27( 1): 26- 29.

Memo

Memo:
-
Last Update: 2012-11-19