Genetic algorithm optimization of a modified peak shaving energy storage system with mixed refrigerant system

Natural gas peak-shaving through seasonal liquefaction and storage provides an effective solution to mitigate fuel supply disruptions and improve energy security in power generation. This study presents the design and multi-domain optimization of a liquefied natural gas (LNG) production cycle using a mixed refrigerant (MR) system. The proposed cycle is optimized thermodynamically and economically, making it adaptable to a wide range of gas-fired power plants. A genetic algorithm (GA) is applied to minimize specific energy consumption (SEC), with the optimization process directly coupled to Aspen HYSYS^® (Aspen Technology, Inc., Version 11, https://www.aspentech.com/en/products/engineering/aspen-hysys ) simulations for accurate thermodynamic modeling. The framework simultaneously evaluates energy, exergy, and economic performance by optimizing refrigerant composition, pressure levels, and flow rates. The methodology is demonstrated through a case study of the Shahid Mofateh power plant in Iran, which experiences recurring natural gas shortages during winter. The GA-optimized configuration achieved a 12% reduction in SEC (from 0.37 to 0.31 kWh/kg), an exergy efficiency of 37%, and a coefficient of performance (COP) of 2.4. Economic analysis shows a net present value (NPV) of 4.2 million USD, an internal rate of return (IRR) of 13.1%, and a payback period of approximately 6.5 years. These results demonstrate that the proposed GA-based optimization framework is both technically effective and economically viable, and can be adapted for broader deployment in LNG-based peak-shaving systems across power plants with similar seasonal energy challenges.