Multi-objective optimal scheduling of an integrated energy system under the multi-time scale ladder-type carbon trading mechanism

Given the ladder-type carbon tax in the carbon quota market is a relatively macroscopic large-time scale concept, and the volatility and uncertainty of new energy generation output and the load, it is currently difficult to accurately predict and control carbon trading cost in the multi-time scale scheduling of integrated energy system (IES), there is a lack of in-depth research on dynamic adjustment and ground implementation in small time scale. To address the challenges of these problems, this paper proposes a multi-objective optimal scheduling model for IES considering the multi-timescale ladder-type carbon trading mechanism. Optimization models and algorithms were developed for day-ahead, intraday and real-time scheduling time scales, enhancing scheduling accuracy and usability. Day-ahead scheduling minimizes comprehensive cost and pollutant emissions while considering the ladder-type carbon trading mechanism; intraday scheduling dynamically adjusts carbon trading cost functions based on day-ahead results and equipment output; and real-time scheduling establishes new carbon trading ladder-type cost functions according to intraday results and emissions. A novel particle swarm optimization algorithm was introduced, integrating uncertainty models for wind, photovoltaic power, and load, along with electric vehicle charging strategies. The multi-time scale model in ladder-type carbon trading exhibits approximately 5% more economic advantage and 2% more environmental benefit.