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

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.