Life cycle low-carbon capacity optimization planning of integrated energy systems in manufacturing enterprise parks

Life cycle low-carbon capacity optimization is widely acknowledged as an effective way to reduce carbon emissions and achieve sustainable development. Addressing the balance between carbon emission reduction and investment costs in integrated energy systems (IES) optimization of capacity configuration and incomplete life cycle boundaries issues, this paper establishes a multi-objective optimization model for IES in a whole life cycle boundary. The optimization is solved using Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with the entropy weight method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results reveal marginal effects between investment costs and carbon emissions. Compared to the original scenario, the life cycle low-carbon optimized plan reduces 56.1 % of carbon emissions and 18.8 % of costs, with 2 % of the carbon emissions and 7.3 % of the cost reduction coming from downstream. The relative proximity degree of the life cycle low-carbon optimization plan is twice that of purchasing green electricity, increasing 8.8 % of carbon emissions while reducing 24.9 % of costs. This indicates that green electricity purchases can only be used as a complementary strategy for sustainable development and should not be overly relied upon. This study focuses on balancing life cycle economics and carbon emissions by designing energy equipment capacity configurations of IES to achieve sustainable development in manufacturing enterprise parks.