Multi-time-scale carbon emission assessment and low-carbon scheduling optimization for energy-intensive enterprises

Energy-intensive enterprises are major contributors to global carbon emissions and face dual pressures from time-of-use electricity pricing and dynamic carbon factors. This study develops a multi-time-scale low-carbon scheduling framework that integrates daily and weekly horizons within an improved multi-objective particle swarm optimization algorithm. The method combines constraint repair, Pareto archiving, and a weight-guided decision mechanism to ensure feasibility and balanced trade-offs between cost and emissions. Daily optimization enhances short-term responsiveness to fluctuating price and emission signals, while weekly optimization captures representative operating patterns and improves long-term stability. Time-series aggregation and clustering are employed to reduce computational complexity without compromising fidelity. Case studies on representative energy-intensive enterprises verify that the proposed framework consistently achieves effective cost-emission trade-offs, with enterprise-specific differences in sensitivity to optimization weights. The results demonstrate that the framework provides a practical and scalable tool for guiding energy-intensive industries toward sustainable and low-carbon operations under the dual-carbon strategy.