Research on optimization methods for multi-energy expansion supply plans in industrial parks based on genetic...

Area of Science:

• Energy Systems Engineering
• Operations Research
• Sustainable Manufacturing

Background:

• Global warming necessitates industrial energy structure adjustments, including integrating zero-carbon sources like wind and solar.
• Current optimization software for integrated energy systems often lacks generalizability and fails to account for facility capacity expansion.
• Designing cohesive multi-energy systems presents challenges in complexity and cost.

Purpose of the Study:

• To develop a generalized optimization model for multi-energy expansion supply systems in industrial factories.
• To minimize the cost of integrated energy supply systems while facilitating capacity expansion.
• To promote the adoption of green energy structures in traditional and new factories.

Main Methods:

• Development of a Multi-energy Expansion Supply system optimization model.
• Application of the Genetic Algorithm (GA) for cost optimization.
• Verification against commercial software and analysis of dynamic vs. fixed installation scenarios.

Main Results:

• The GA model achieved 23.19% higher total cost savings and a 4-year faster payback period compared to commercial software.
• Dynamic installation demonstrated an 8.4% cost saving and a 2-year faster payback compared to fixed installation.
• Optimizing Combined Heat and Power (CHP) units resulted in 40% lower initial investment and 36% higher utilization.

Conclusions:

• The developed GA-based model offers a superior and more adaptable approach to optimizing industrial multi-energy systems.
• The model effectively reduces costs and accelerates payback, supporting the green transformation of factory energy structures.
• This optimization strategy enhances the efficiency and economic viability of integrating diverse energy sources.