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

In response to the problem of global warming, the factories are actively adjusting their energy use structure and significantly introducing zero-carbon energy sources such as wind and solar energy to reduce carbon dioxide emissions. The integration of diverse energy sources into a cohesive system presents significant challenges in terms of design complexity and cost. Currently, many researchers have designed some simulation software for optimization of integrated energy systems in industrial factories. However, these approaches are specific to single sites (i.e., not generalizable) and are typically not designed to anticipate capacity expansion of facilities. Herein, an optimization modeling of Multi-energy Expansion Supply system has been developed based on the Genetic Algorithm (GA) to optimize the cost of energy supply systems. This model has been used for optimization of multi-energy system in the new energy supply systems. The proposed method was verified against commercial software results, showing a higher total cost saving (23.19%) and faster payback time (5 years comparing to 9 years). Additional case was studied by comparing the dynamic installation and fixed installation, demonstrating 8.4% more total cost saving and faster payback time (2 years and 4 years). Furthermore, the same demand was fulfilled by different amount of CHP units, achieving 40% initial investment and 36% higher utilization rate. This model will promote the green transformation of the energy structure of traditional industrial factories and the optimization of multi-energy supply systems in new factories.