Mechanics & Industry
Volume 17, Number 1, 2016
|Number of page(s)||8|
|Published online||13 November 2015|
Design and optimization of a compressed air energy storage (CAES) power plant by implementing genetic algorithm
1 Mechanical and Energy Engineering Dept., ACE, Shahid Beheshti University, Tehran, Iran
2 Universiti Teknologi PETRONAS, Perak, 31750 Tronoh, Malaysia
3 Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis New City, Iran
a Corresponding author: firstname.lastname@example.org
Received: 13 January 2015
Accepted: 19 June 2015
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.
Key words: CAES / gas turbine / genetic algorithm / energy storage / optimization
© AFM, EDP Sciences 2015
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.