Integrated economic and environmental optimization for industrial consumers: A dual-objective approach with multi-carrier energy systems and fuzzy decision-making

Industrial energy systems require innovative optimization strategies to simultaneously minimize costs and reduce environmental impact. This study presents a dual-objective optimization model that integrates economic and environmental considerations within a multi-carrier energy hub framework. The proposed approach incorporates a peak load management strategy to optimize energy consumption patterns, a fuzzy decision-making method to handle operational uncertainties, and an enhanced non-dominated sorting genetic algorithm II to improve multi-objective optimization efficiency. The model employs Pareto-optimal solutions, offering decision-makers a structured method to balance cost reduction and emissions minimization. The effectiveness of the proposed framework is validated through case studies, demonstrating that peak load management significantly flattens load profiles, optimizes distributed energy resources, and reduces reliance on grid electricity during peak hours. The enhanced non-dominated sorting genetic algorithm II ensures better convergence toward optimal trade-offs, improving computational performance. Results indicate that the integration of peak load management leads to a 0.93 % reduction in operational costs and a 0.79 % decrease in carbon emissions compared to conventional energy management approaches. These findings underscore the potential of the developed model in enhancing industrial energy efficiency and sustainability, providing a robust and adaptable solution for modern industrial consumers.