Logo

Design and optimization of a compressed air energy storage (CAES) power plant by implementing genetic algorithm

Shamshirgaran, S. Reza and Ameri, M and Khalajiassadi, Morteza and Ahmadi, M. Hossein (2016) Design and optimization of a compressed air energy storage (CAES) power plant by implementing genetic algorithm. Mechanics & Industry, 17 (109). ISSN 2257-7777

[img] PDF
1241Kb

Abstract

Today all engineering efforts are focused on the optimum utilization of available energy sources. The energy price is a critical subject regarding the present global conditions over the world. The strong penalties of CO2 generation have forced the designers to develop systems having the least pollution. Almost two thirds of electrical output energy of a conventional gas turbine (GT) is consumed by its compressor section, which is the main motivation for the development of Compressed Air Energy Storage (CAES) power plants. The main objective of this paper is to obtain the optimum parameters through which the CAES GT cycle can be designed effectively. The cost-benefit function as a target function has been maximized using the Genetic Algorithm. The Thermoflex software has been used for the CAES cycle modeling and design calculation. Meanwhile the sensitivity analysis results have shown that the net annual benefit and the discharge time duration of CAES plant decrease by increasing the fuel price. In addition, the optimal recuperator effectiveness increases with increasing the fuel price until it reaches its maximum value. Therefore, one can conclude that the future design modifications of the system as well as the variation in operation strategy of the existing plant will be based on the varying fuel price.

Item Type:Article
Impact Factor:0.477
Uncontrolled Keywords:CAES / gas turbine / genetic algorithm / energy storage / optimization
Academic Subject One:Mechanical Engineering
Academic Subject Two:Academic Department - Mechanical Engineering - Energy - Sustainable energy - Solar power
Academic Subject Three:Renewable Energy
Departments / MOR / COE:Mission Oriented Research > Energy
ID Code:11887
Deposited By: Dr Morteza Khalaji Assadi
Deposited On:07 Oct 2016 01:42
Last Modified:07 Oct 2016 01:42

Repository Staff Only: item control page

Document Downloads

More statistics for this item...