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Optimal Load Dispatch of Gas Turbine Power Generation Units based on Multiple Population Genetic Algorithm

Optimal Load Dispatch of Gas Turbine Power Generation Units based on Multiple Population Genetic Algorithm
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摘要 In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective. In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.
出处 《Engineering(科研)》 2013年第1期197-201,共5页 工程(英文)(1947-3931)
关键词 Gas TURBINE generation UNITS LOAD DISPATCH MPGA Optimization Gas turbine generation units Load dispatch MPGA Optimization
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  • 1万文军,周克毅,胥建群,徐啸虎.动态系统实现火电厂机组负荷优化分配[J].中国电机工程学报,2005,25(2):125-129. 被引量:48
  • 2马晓茜,王毅,廖艳芬.火电厂机组负荷优化调度软件系统的设计与实现[J].华南理工大学学报(自然科学版),2006,34(6):112-116. 被引量:12
  • 3张成文,苏森,陈俊亮.基于遗传算法的QoS感知的Web服务选择[J].计算机学报,2006,29(7):1029-1037. 被引量:103
  • 4王治国,刘吉臻,谭文,杨光军.基于快速性与经济性多目标优化的火电厂厂级负荷分配研究[J].中国电机工程学报,2006,26(19):86-92. 被引量:67
  • 5Guan X,Luh P B,Zhang L.Nonlinear approximation method in Lagrangian relaxation-based algorithms for hydrothermal scheduling[J].IEEE Trans on PWRS,1995,10(2):245-252.
  • 6曹文亮.热力发电厂机组负荷优化分配的研究(硕士学位论文)[D].保定:华北电力大学,2003.
  • 7张保衡.大容量火电机组调峰运行的寿命管理[M].北京:水利电力出版社,1998.
  • 8Canfora G, Dipenta M, Esposito R, Villani M L. A lightweight approach for QoS - aware service composition[ C]. In Proc. of the 2th International Conference on Service Oriented Computing. New York, 2004, pp. 232 -239.
  • 9Canfora G, Dipenta M, Esposito R. An approach for QoS -aware service compositionbased on genetic algorithms[ C]. In Proc. of. the 2005 Conference on Genetic and Evolutio - nary Computation. Washington, 2005 : 1069 - 1075.
  • 10Zhang Liang- Jie, Li Bing, Chao Tian, et al. On demand web services -based business process composition [ C ]. IEEE International Conference on System, Man, and Cybemetics( SMC 03) , Washington, USA, 2003, 4057 -4064.

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