Research on Low-Carbon Scheduling of Energy-Intensive Projects Considering Multi-Mode Resource Constraints and Dynamic Electricity Prices

Abstract

Energy-intensive industries constitute the primary source of energy consumption and carbon emissions. To achieve the "Dual Carbon" goals, advancing the low-carbon transformation of such industries is of vital significance. Nevertheless, current production scheduling for energy-intensive projects rarely coordinates time-of-use electricity prices, multi-shift production and low-carbo requirements in an integrated optimization framework. To address this gap, this study develops a multi-objective scheduling framework incorporating multiple resource scheduling rules and dynamic fluctuations in energy prices. The framework pursues three optimization objectives: minimizing carbon emissions, cutting energy costs, and maintaining reasonable project durations. Interms of numerical solution, this paper improves the Nondominated Sorting Genetic Algorithm III (NSGA-III) and adopts the Analytic Hierarchy Process (AHP) to determine the weight of each objective, thereby realizing dynamic equilibrium of scheduling schemes under multiple constraints. Case verificat on demonstrates that the proposed model can effectively reduce carbon emissions and energy expenditures of energy-intensive projects while improvin resource utilization efficiency. The research outcomes can provide practical guidance and references for low-carbon production scheduling in energy-intensive industries.