Genetic algorithm optimization of a modified peak shaving energy storage system with mixed refrigerant system
Seyyed Amirreza Abdollahi1, Arash Nourbakhsh Sadabad2, Seyed Amirreza Mousavi Alamdardehi3
1Faculty of Mechanical Engineering , University of Tabriz , Tabriz, Iran.
Scientific Reports
|November 20, 2025
Summary
This study optimizes liquefied natural gas (LNG) peak-shaving systems for power plants using a genetic algorithm. The optimized system significantly reduces energy consumption and offers strong economic returns, enhancing energy security.
Area of Science:
- Thermodynamics and Energy Systems Engineering
- Chemical Engineering and Process Design
- Computational Optimization and Simulation
Background:
- Natural gas shortages during peak demand periods, especially winter, challenge power generation reliability.
- Seasonal liquefaction and storage of natural gas (LNG) offers a solution for energy security and mitigating supply disruptions.
- Existing LNG peak-shaving systems require optimization for thermodynamic and economic efficiency across diverse power plant applications.
Purpose of the Study:
- To design and optimize a mixed refrigerant (MR) based liquefied natural gas (LNG) production cycle for peak-shaving applications.
- To develop a multi-domain optimization framework integrating thermodynamic and economic performance metrics.
- To adapt the optimized LNG cycle for gas-fired power plants facing seasonal fuel supply challenges.
Main Methods:
- Utilized a genetic algorithm (GA) coupled with Aspen HYSYS simulations for thermodynamic modeling and optimization.
- Minimized specific energy consumption (SEC) by optimizing refrigerant composition, pressure levels, and flow rates.
- Evaluated energy, exergy, and economic performance simultaneously within the optimization framework.
Main Results:
- Achieved a 12% reduction in specific energy consumption (SEC), lowering it from 0.37 to 0.31 kWh/kg.
- The optimized system demonstrated an exergy efficiency of 37% and a coefficient of performance (COP) of 2.4.
- Economic viability confirmed with a net present value (NPV) of $4.2 million, an internal rate of return (IRR) of 13.1%, and a 6.5-year payback period.
Conclusions:
- The proposed GA-based optimization framework is technically effective for LNG peak-shaving systems.
- The optimized system presents a economically viable solution for enhancing energy security in power generation.
- The methodology is adaptable for broader deployment in power plants experiencing seasonal natural gas supply challenges.
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