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Abstract Under the influence of environmental pollution and energy scarcity, integrated energy systems (IES) have received extensive attention in the field of energy supply due to their ability to consume renewable energy and enhance energy utilization. In the context of low‐carbon scheduling for IES, numerous studies calculate the system's carbon emissions based on the carbon emission coefficients of energy devices. However, IES is a multi‐energy coupling system in which a device's energy input can originate from multiple sources with varying degrees of carbon emissions, making it difficult to accurately calculate the resulting carbon emissions using fixed coefficients. Consequently, a carbon emission flow (CEF) model is constructed for the system to calculate carbon emissions. In addition to the basic input–output CEF model, the CEF model for energy storage devices is considered, and carbon emission constraints during system operation are formulated based on the CEF model. Furthermore, many studies on low‐carbon scheduling of IES overlook the uncertainties associated with load and renewable energy. Therefore, a two‐stage scheduling model consisting of day‐ahead stage and intra‐day stage is developed to achieve reliable energy supply. Finally, through experiments, the low‐carbon performance and reliability of the model are validated.

Reasonable capacity configurations of distributed energy system are issues which need to be discussed. Determinate design without considering variations in energy load and energy prices can result in non-achievement of project targets during its service life. Therefore, a design method that takes into account uncertain factors takes precedence over other methods. In this paper, a three-stage optimization method is proposed to provide theoretical guidance on the optimization of combined cooling, heating, and power system configurations. The first two stages link the optimization of the operation strategy and equipment capacities simultaneously under current load and energy prices. The Monte-Carlo simulation is applied in the third stage to fully consider the effects of various possible scenarios, and the Tabu search algorithm was introduced for system optimization. The comprehensive benefits include energy consumption, economy, and emission level. These were taken into consideration in the objective function. Moreover, a detailed design process was presented to illustrate the application of the proposed method. In conclusion, the proposed method is not only suitable for the design of combined cooling, heating, and power system, but could easily extend to other energy system easily. [ABSTRACT FROM AUTHOR]