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

• Published in: Mechanics & industry : an international journal on mechanical sciences and engineering applications Vol. 17; no. 1; p. 109
• Main Authors: Reza Shamshirgaran, S., Ameri, M., Khalaji, M., Hossein Ahmadi, M.
• Format: Journal Article
• Language: English
• Published: Villeurbanne EDP Sciences 2016
• Subjects: CAES; Compressed air; Design; Design modifications; Design optimization; Energy storage; Fuels; gas turbine; Gas turbines; genetic algorithm; Genetic algorithms; optimization; Power plants; Regenerators; Sensitivity analysis
• ISSN: 2257-7777; 2257-7750; 2257-7750
• DOI: 10.1051/meca/2015047

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.