TY  - EJOU
AU  - Luo, Yuming 
AU  - Zhang, Wei 
AU  - Zhao, Anqi 
AU  - Gou, Ling 
AU  - Chen, Li 
AU  - Yang, Yaling 
AU  - Wang, Xiaoping 
AU  - Liu, Shichang 
AU  - Qi, Huiqing 
AU  - Hu, Shilai 

TI  - Optimization of Operating Parameters for Underground Gas Storage Based on Genetic Algorithm
T2  - Energy Engineering

PY  - 2025
VL  - 122
IS  - 8
SN  - 1546-0118

AB  - This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety. Previous research primarily focused on integrating reservoir, wellbore, and surface facility constraints, often resulting in broad constraint ranges and slow model convergence. To solve this problem, the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs, while considering extreme peak-shaving demands. This approach effectively narrows the constraint range. Subsequently, a collaborative optimization model with maximum gas production as the objective function is established, and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques. Finally, this methodology was applied to optimize operational parameters for Gas Storage T. The results demonstrate: (1) The convergence of the model was achieved after 6 iterations, which significantly improved the convergence speed of the model; (2) The maximum working gas volume reached 11.605 × 10<sup>8</sup> m<sup>3</sup>, which increased by 13.78% compared with the traditional optimization method; (3) This method greatly improves the operation safety and the ultimate peak load balancing capability. The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.
KW  - Underground gas storage; operational parameter optimization; extreme peak-shaving constraints; genetic algorithm; model

DO  - 10.32604/ee.2025.066507