Economic optimization scheduling of integrated energy systems considering multiple agents at multiple time scales

Under the "dual carbon" goal, an economic optimization scheduling model is proposed for integrated energy systems that incorporates multiple agent interaction and multiple energy demand response to address challenges such as source-load uncertainty and insufficient grid flexibility, thereby achieving coordinated economic and low-carbon operation. Based on the model predictive control method, considering the green certificate reward and punishment tiered carbon trading mechanism at multiple time scales, an optimization model is established with the goal of maximizing the daily net profit of the integrated energy system operators. The nonlinear model is reformulated as a mixed integer linear programming model and extended to the intraday and real-time stages, which is then efficiently solved using the Gurobi solver via the MATLAB platform. Meanwhile, Latin hypercube sampling method and Kantorovich scenario reduction method are used to deal with the uncertainty of new energy output. Comparative analysis of different models shows that under the proposed strategy, the expected daily operating profits of the integrated energy system operator and the demand response aggregator are well-defined, effectively promoting new energy consumption, reducing carbon emissions, and increasing operator revenue. For example, the net profit of scenario (1) is 3382.97 yuan higher than that of scenario (2), while carbon trading costs decrease by approximately 17.2%. Additionally, the solution time in the real-time stage is shortened, and higher revenues are achieved during peak load demand periods. Multiple time scales scheduling can cope with forecasting errors of new energy and load. The scheduling strategy based on model predictive control can explore demand response resources. The optimal dispatch considering the green certificate reward and punishment tiered carbon trading mechanism and integrated demand response can balance economic efficiency and low-carbon performance, effectively smooth source load fluctuations, and improve the benefits of all agents.