4.7 Article

Micro-scale trigenerative compressed air energy storage system: Modeling and parametric optimization study

Journal

JOURNAL OF ENERGY STORAGE
Volume 26, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.est.2019.100944

Keywords

Compressed air energy storage; Trigeneration; Thermodynamic modeling; Parametric study; Optimization

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Funding

  1. Natural Sciences and Engineering Research Council of Canada (NSERC)
  2. company Sigma Energy Storage Inc. [386141936]

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In this paper, a trigenerative compressed air energy storage system is considered giving priority to the electric energy production with the objective to apply it at a micro-scale, typically a few kW. A whole detailed thermodynamic model of the system is developed including the existing technological aspects and the relations between components. The study then focuses on investigating the mutual effects of the design parameters and their influences on the system performances, energy density and heat exchanger footprints via a parametric study. From this analysis, it is found that the temperature of the thermal energy storage, the number of compression stages and the effectiveness of heat exchangers should be selected as a trade-off between the system efficiencies, heat exchangers footprints and the required number of expansion stages. Meanwhile, the selection of the maximum storage pressure is a choice whether to increase the energy density or the system efficiencies. An optimal design guideline of the above key parameters is then provided. This guideline, the method and the procedure presented in this paper can be applied to the optimization of the trigenerative compressed air energy storage and could be extended for the adiabatic one with minor changes. Based on existing technologies and using an optimal set of parameters, the round trip electrical efficiency of our system remains low at 17%, while the comprehensive efficiency reaches 27.2%. The poor performances are mainly linked to the exergy losses in the throttling valve and the low values of the component efficiencies at a micro-scale. The most optimization potentials are also addressed.

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