Identification of Optimal Parameters for a Small-Scale Compressed-Air Energy Storage System Using Real Coded Genetic Algorithm

Thomas Guewouo, Lingai Luo, Dominique Tarlet and Mohand Tazerout
Additional contact information
Thomas Guewouo: Laboratoire de Modélisation et de Simulation Multi Echelle (MSME), CNRS UMR 8208, Université Paris-Est, 5 Bd Descates, F-77454 Marne-la-Vallée CEDEX 2, France
Lingai Luo: Laboratoire de Thermique et Energie de Nantes (LTEN), CNRS UMR 6607, Université de Nantes, La Chantrerie, Rue Christian Pauc, B.P. 50609, F-44306 Nantes CEDEX 3, France
Dominique Tarlet: Laboratoire de Thermique et Energie de Nantes (LTEN), CNRS UMR 6607, Université de Nantes, La Chantrerie, Rue Christian Pauc, B.P. 50609, F-44306 Nantes CEDEX 3, France
Mohand Tazerout: GEPEA UMR CNRS 6144, BP 406, 37 boulevard de l’Université, 44602 Saint Nazaire, France

Energies, 2019, vol. 12, issue 3, 1-32

Abstract: Compressed-Air energy storage (CAES) is a well-established technology for storing the excess of electricity produced by and available on the power grid during off-peak hours. A drawback of the existing technique relates to the need to burn some fuel in the discharge phase. Sometimes, the design parameters used for the simulation of the new technique are randomly chosen, making their actual construction difficult or impossible. That is why, in this paper, a small-scale CAES without fossil fuel is proposed, analyzed, and optimized to identify the set of its optimal design parameters maximizing its performances. The performance of the system is investigated by global exergy efficiency obtained from energy and exergy analyses methods and used as an objective function for the optimization process. A modified Real Coded Genetic Algorithm (RCGA) is used to maximize the global exergy efficiency depending on thirteen design parameters. The results of the optimization indicate that corresponding to the optimum operating point, the consumed compressor electric energy is 103.83 kWh and the electric energy output is 25.82 kWh for the system charging and discharging times of about 8.7 and 2 h, respectively. To this same optimum operating point, a global exergy efficiency of 24.87% is achieved. Moreover, if the heat removed during the compression phase is accounted for in system efficiency evaluation based on the First Law of Thermodynamics, an optimal round-trip efficiency of 79.07% can be achieved. By systematically analyzing the variation of all design parameters during evolution in the optimization process, we conclude that the pneumatic motor mass flow rate can be set as constant and equal to its smallest possible value. Finally, a sensitivity analysis performed with the remaining parameters for the change in the global exergy efficiency shows the impact of each of these parameters.

Keywords: small-scale compressed-air energy storage (SS-CAES); energy storage; exergy analysis; optimization; Real Coded Genetic Algorithm (RCGA); Violation Constraint-Handling (VCH) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/3/377/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/3/377/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:3:p:377-:d:200698

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().