A Multi-Time-Scale Low-Carbon Scheduling Method for Regional Integrated Energy Systems Under Chance-Constrained Programming

The uncertainty of wind and photovoltaic power generation results in carbon emissions of low-carbon scheduling methods not meeting expectations. Therefore, a multi-time-scale low-carbon scheduling method for regional integrated energy systems under chance constrained planning is proposed. Firstly, it uses the power of wind and solar power generation at different time periods as random variables, and introduces confidence level quantification constraints, an improved particle swarm algorithm is used to determine the optimal decision variables. Secondly, it introduces carbon capture power plants to capture, store, and reuse CO₂, constructs a carbon cycle system, and designs a tiered carbon trading mechanism and user demand response mechanism. Finally, it designs a multi-time-scale real-time rolling control plan, constructs real-time scheduling objective functions and constraints, and achieves low-carbon scheduling of regional integrated energy systems at multiple time scales. The experimental results show that the designed scheduling method reduces carbon emissions by 4570.1 kg compared to the no strategy scenario, and the actual carbon emissions are slightly lower than the free quota by 5%. It can effectively utilize low-carbon resources while meeting the requirements of system carbon emission constraints.