Energy management scheduling of a smart factory with carbon capture and storage, carbon emission quota cap-and-trade, and green energy trading

Many governments have capped carbon emission quotas (CQs) on factories to reach net-zero emissions. Despite efforts through improvements in manufacturing processes and materials or certified carbon offsetting, factories often find it challenging to meet these CQ caps. Participation in government-led cap-and-trade (CAT) systems allows factories to trade surplus CQs to meet their respective caps. Previous studies on energy management scheduling has largely ignored the potential for optimizing CQ trading within CAT systems, especially when integrated with carbon capture and storage (CCS) systems. This study proposes an energy management scheduling system for factories that integrates battery energy storage systems, CCS, CQ trading, and green energy trading. We develop a mixed-integer programming model for cost-efficient energy management scheduling, encompassing decisions on electricity usage, energy storage, carbon capture and storage, green energy trading, and CQ trading. Additionally, this study incorporates a ladder-type carbon trading pricing (LTCTP) mechanism, in which carbon prices increase progressively with higher emission levels, thereby influencing the factory's operational decisions. Simulation results demonstrate that the LTCTP mechanism effectively incentivizes lower emissions and enables profitable carbon trading, generating surplus revenue and improving carbon trading performance by 31.18 % compared to fixed pricing. This study also features a new algorithm combining simplified harmony search (SHS) with self-adaptive variable neighborhood search (SAVNS) to enhance both global and local search efficiencies. Experimental results demonstrate high solution quality and stability of the algorithm, significantly reducing total costs by integrating CCS and the LTCTP model.