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.

DC short-circuit faults are one of the challenges for modular multilevel converter (MMC) based DC grid. It is vital for proper design of protection system to estimate the fault currents and voltages. The existing calculation methods based on RLC equivalent model of MMC have enough accuracy in estimating the branch currents but suffer from poor accuracy in estimating the node voltages. To better reflect the dynamics of MMC control during the fault, MMC is equivalent to a RLC series circuit in parallel with a variable controlled current source. This model not only considers the discharge of sub-module capacitors but also the AC active power and MMC control. Then, based on the discrete adjoint model of the equivalent MMC model and the RL series equivalent model of DC lines, the fault voltages and currents for the pre-fault and faulted DC grids could be easily obtained. From the aspect of power balance, the importance of AC active power on estimating the fault currents and voltages is discussed then. At last, based on the Zhangbei bipolar DC grid, comparisons are conducted between the simulations on PSCAD, the numerical calculation under the proposed method and the existing methods. The results show that the proposed method and the existing methods are both able to accurately estimate the fault currents within a relative error of 1%. However, compared with the error of the existing methods in calculating the fault voltages, the relative error for the proposed method is limited to less than 5% for the whole DC gird.

Due to the complex operation characteristics of AC/DC hybrid power grids, it is a great challenge to comprehensively evaluate their stability and formulate appropriate strengthening schemes for them. To address this challenge, the following studies are carried out in this paper. First, an evaluation system including six indicators is established for AC/DC hybrid power grids. Next, aiming at the problems that may be revealed by the comprehensive evaluation, strengthening measures that can be utilized are introduced. Then, a comprehensive evaluation method for AC/DC hybrid power grids and their potential strengthening schemes is proposed. This method can deal with three issues, including normalization of the indicators, weighting of the indicators, and the trade-off of technology and cost. Finally, in the case study of the Qujing Power Grid, the main problems faced by regional power grids are pointed out, and four feasible strengthening schemes are formulated and evaluated.

The flue gas desulfurization system is vulnerable to various unknown or unmeasured disturbances. In reality, the type of disturbance is usually unknown or multiple disturbances occur simultaneously in the system. To cope with this issue, this paper proposes the disturbance rejection predictive control strategy based on the new augmented state space model. The new state space model is firstly augmented to reduce the modeling error and be convenient for practical use. To detect the type of disturbance, the disturbance model bank is then established and state estimation is applied to estimate the unknown disturbance model. Furthermore, the method of disturbance model weighting is provided. Finally, the objective function is modified to cope with the continuous effects of the disturbances. Based on the above procedure, a model predictive controller is designed. The validity of the strategy is demonstrated by simulation results.