Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants

Energy ◽  
2011 ◽  
Vol 36 (10) ◽  
pp. 5886-5898 ◽  
Author(s):  
Pouria Ahmadi ◽  
Ibrahim Dincer ◽  
Marc A. Rosen
Keyword(s):  
Evolutionary Algorithm ◽  
Power Plants ◽  
Combined Cycle ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Optimal start-up control of combined cycle power plants using the multi-objective evolutionary algorithm

2017 ◽  
Vol 83 (847) ◽  
pp. 16-00433-16-00433 ◽  
Author(s):  
Yasuhiro YOSHIDA ◽  
Kazunori YAMANAKA ◽  
Atushi YAMASHITA ◽  
Norihiro IYANAGA ◽  
Takuya YOSHIDA
Keyword(s):  
Evolutionary Algorithm ◽  
Power Plants ◽  
Combined Cycle ◽  
Multi Objective ◽  
Start Up

Optimal start-up control of combined cycle power plants using the multi-objective evolutionary algorithm

2016 ◽  
Vol 2016.21 (0) ◽  
pp. D224
Author(s):  
Yasuhiro Yoshida ◽  
Kazunori Yamanaka ◽  
Atsushi Yamashita ◽  
Norihiro Iyanaga ◽  
Takuya Yoshida
Keyword(s):  
Evolutionary Algorithm ◽  
Power Plants ◽  
Combined Cycle ◽  
Multi Objective ◽  
Start Up

Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

2014 ◽  
Vol 494-495 ◽  
pp. 1715-1718
Author(s):  
Gui Li Yuan ◽  
Tong Yu ◽  
Juan Du
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Power Plants ◽  
Optimization Problem ◽  
Thermal Power ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Load ◽  
Optimal Load Distribution

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

Sign in / Sign up

Export Citation Format

Share Document