Multi-Objective Optimization Based on Life Cycle Assessment for Hybrid Solar and Biomass Combined Cooling, Heating and Power System

• Published in: Journal of thermal science 2024-05, Vol.33 (3), p.931-950
• Main Authors: Liu, Jiejie, Li, Yao, Meng, Xianyang, Wu, Jiangtao
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Biomass; Biomass energy production; Classical and Continuum Physics; Cooling; Design optimization; Emissions; Energy consumption; Engineering Fluid Dynamics; Engineering Thermodynamics; Environmental impact; Genetic algorithms; Greenhouse gases; Heat and Mass Transfer; Heating; Hybrid systems; Impact loads; Industrial parks; Life cycle assessment; Multiple objective analysis; Optimization; Optimization models; Pareto optimization; Physics; Physics and Astronomy; Pollutants; Raw materials; Renewable energy; Renewable resources; Solar energy; Sorting algorithms
• ISSN: 1003-2169; 1993-033X
• DOI: 10.1007/s11630-024-1953-9

The complementary of biomass and solar energy in combined cooling, heating and power (CCHP) system provides an efficient solution to address the energy crisis and environmental pollutants. This work aims to propose a multi-objective optimization model based on the life cycle assessment (LCA) method for the optimal design of hybrid solar and biomass system. The life-cycle process of the poly-generation system is divided into six phases to analyze energy consumption and greenhouse gas emissions. The comprehensive performances of the hybrid system are optimized by incorporating the evaluation criteria, including environmental impact in the whole life cycle, renewable energy contribution and economic benefit. The nondominated sorting genetic algorithm II (NSGA-II) with the technique for order preference by similarity to ideal solution (TOPSIS) method is employed to search the Pareto frontier result and thereby achieve optimal performance. The developed optimization methodology is used for a case study in an industrial park. The results indicate that the best performance from the optimized hybrid system is reached with the environmental impact load reduction rate (EILRR) of 46.03%, renewable energy contribution proportion (RECP) of 92.73% and annual total cost saving rate (ATCSR) of 35.75%, respectively. By comparing pollutant-eq emissions of different stages, the operation phase emits the largest pollutant followed by the phase of raw material acquisition. Overall, this study reveals that the proposed multi-objective optimization model integrated with LCA method delivers an alternative path for the design and optimization of more sustainable CCHP system.