Optimization of a cavern‐based compressed air energy storage facility with an efficient adaptive genetic algorithm

Abstract

Due to the dynamic interactions of the components of cavern-based compressed air energy storage plants, optimizing this system is challenging and a small change in the design parameters, such mass flow rate, compression ratio, expansion ratio can significantly alter the efficiency of the entire system. An adaptive genetic algorithm has been invoked to overcome this challenge, with system efficiency and exergy efficiency as the objective functions. The proposed method provides more flexibility to the optimization process; instead of using fixed rates for the mutation percentage, it is adjusted individually based on the feedback from both the intensity of a component's distributions in the design space and the relative objective function of that component in comparison to other components. The method is utilized for the efficiency optimization of an 80 MW plant with 9 of freedom. For performing the optimization process an automatic coupling between HYSYS and MATLAB was used. The proposed search algorithm discovered a significantly wider range of data by increasing the chance of design parameters relocation with efficient shifting of the searching domain. Outcomes indicated the merit of the algorithm by 6.4% increase in the efficiency of the plant as well as notably decreasing the number of objective function evaluations.

Appendix: Sample Design and Optimization Projects

“…With the appropriate engineering background after mastering Engineering Design and Optimization of Thermofluid Systems, you joined a frontier company to work on designing a cavern-based CAES system; see, for example (Ebrahimi et al, 2020). It is a 100 MW system for supplying electricity during a high-demand period of up to 8 hours at a time.…”

Chemically-informed data-driven optimization (ChIDDO): leveraging physical models and Bayesian learning to accelerate chemical research

“…10 In recent years, BO has been implemented for various applications in the chemical sciences including materials discovery and prediction of their properties, [11][12][13][14][15][16][17][18][19][20][21] design of reactors and chemical processes, [22][23][24][25][26][27][28][29][30][31][32][33] and the optimization of energy storage materials and devices. [34][35][36][37][38] Data-driven optimization methods such as BO learn and evolve with new experimental data, but they lack a priori knowledge of the physical laws that dictate the behavior of the chemical system under study. This can result in the need for large experimental campaigns to accurately model and find the optimal combination of parameters for a given objective function.…”

Accumulated and transient exergy analyses of pneumatic systems with isochoric and isobaric compressed air storage tanks

“…Pneumatic systems powered by compressed air are widely used in industrial and non‐manufacturing sectors 1 . Air can be compressed and regulated to the desired pressure for various applications, such as power transmission, control, conveying, spraying, vacuum generation, compressed air energy storage, hybrid vehicle, hypersonic wind tunnel, geological air gun, air explosion extraction, and so on 2‐7 . In many facilities, compressed air is usually regarded as the “fourth utility” after electricity, water, and natural gas.…”

“…They do not provide any detail or an accurate result. Exergy‐based method, on the other hand, is a promising method that can be used in all pneumatic systems, furnishing detailed and accurate results 2,4,6,7,12,16,17 …”