Exergy analysis and exergoeconomic optimization of a constant-pressure adiabatic compressed air energy storage system. (December 2017) - Mazloum, Youssef; Sayah, Haytham; Nemer, Maroun
Highlights: A thermodynamic model of a constant-pressure adiabatic compressed air energy storage system is developed. An exergoeconomic optimization is performed to optimize the storage system in a cost–effective way. The highest and the most costly exergy destruction occurs in the gas turbines. Reducing the compressors efficiency and improving the heat exchangers pinch are advantageous. The efficiency increases in a logarithmic shape with the objective function. Abstract: The renewable energy penetration into the electrical grid is considerably increasing. However, the intermittence of these sources breaks the balance between supply and demand for electricity. Hence the importance of the energy storage technologies, they allows restoring the balance and improving the penetration of the renewable energy in the power generation mix. Thus, this paper discusses the thermodynamic modeling and the exergoeconomic analysis of an isobaric adiabatic compressed air energy storage (IA-CAES) system performed by a computer simulation using" Dymola" . The constant-pressure air storage and the recovery of the compression heat lead to overcome the drawbacks of the conventional CAES system such as the losses due to the storage pressure variation, the wastage of the compression heat and the use of fossil fuel sources. A steady state model is then developed to perform an energy and exergy analyses of the IA-CAES system and evaluate the exergy losses distribution in this system. The efficiency of the storage system is 55.1% and the energy density is 11.9 kWh/m 3 . An optimization is also carried out by using exergoeconomic principles in order to minimize an objective function including investment cost of equipments and operating cost. The exergoeconomic analysis is performed by the specific exergy costing (SPECO) method and the optimizer used is OmOptim which is a genetic algorithms based optimizer. As results, the efficiency is improved by 2.7% and the consumed electricity cost is reduced by 2.8% whereas the capital investment is reduced by 5.6%. A sensitivity analysis is finally carried out to estimate the effects of some key parameters on the objective function and the system's efficiency, such as the storage system capacity, the ambient temperature and the fuel cost.