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Summary A scheduling model is a prerequisite for an operation strategy of integrated energy system (IES). Existing scheduling models of IES, however, are typically based on heat‐transfer variables either completely or partially, which oversimplify detailed thermal characteristics. To this end, a novel scheduling model is proposed where all thermal processes are modeled by temperature and flowrate of working fluids. This improvement renders the capability to the scheduling model to incorporate different thermal processes. Furthermore, the nonlinear product terms of temperature and flowrate in the proposed model are linearized by the binary expansion method. Based on the linearized scheduling model, a stochastic model predictive control (SMPC) operation strategy is exploited to optimize the economic performance by energy forecast, scenario reduction, rolling optimization, and feedback correction. Afterwards, four operation modes considering different temperature changes of the devices, networks, and the environment are performed and compared. The results found that thermal characteristics will affect device operation results and the degree of influence varies. The network temperature changes have the broadest influence, followed by the device and the ambient temperature changes. Moreover, system operation costs are also affected by detailed thermal characteristics. The total cost, the gas cost, and the electricity cost under Mode 2 are almost the same to those of Mode 1. However, the first two costs are reduced by 3.4% and 5.3% under Mode 3, and are reduced by 2.7% and 4% under Mode 4, despite that the electricity cost increases by 0.2% under Mode 3 and remains almost the same under Mode 4. These indicate that reliability and economy of an IES are affected by thermal characteristics, and it is thus the necessity to consider detailed thermal characteristics in an operation. Moreover, the results demonstrate the capability of the generalized temperature‐flowrate based scheduling model and the effectiveness of the SMPC operation strategy.

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In the face of the pressing environmental issues, the past decade witnessed the booming development of the distributed energy systems (DESs). A notable problem of DESs is the inevitable uncertainty that may make DESs deviate significantly from the deterministically obtained expectations, in both aspects of optimal design and economic operation. It thus necessitates the sensitivity analysis to quantify the impacts of the massive parametric uncertainties. This paper aims to give a comprehensive quantification, and carries out a multi-stage sensitivity analysis on DESs from the perspectives of evaluation criteria, optimal design and economic operation. First, a mathematical model of a DES is developed to present the solutions to the three stages of the DES. Second, the Monte-Carlo simulation is carried out subject to the probabilistic distributions of the energy, technical and economic parameters. Based on the simulation results, the variance-based Sobol method is applied to calculate the individual importance, interactional importance and total importance of various parameters. The comparison of the multi-stage results shows that only a few parameters play critical roles while the uncertainty of most of the massive parameters has little impact on the system performance. In addition, the influence of parameter interactions in the optimal design stage are much stronger than that in the evaluation criteria and operation strategy stages.