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Low-frequency oscillation is usually regarded as a small disturbance problem. Traditional methods describe oscillation through eigenvalues obtained by linearized equations at the equilibrium point. Obviously, nonlinear time-varying factors are ignored. This article introduces a trajectory section eigenvalue method that can reflect the influences of complex models and fault scenarios. Based on this theory, a trajectory reconstruction method is proposed to accomplish the decoupling of multi-eigenmodes in the time domain. On the premise that the numerical integration is accurate, the trajectory section eigenvalue and trajectory reconstruction methods can be used to study the dynamics of the oscillation and interaction of multi-eigenmodes. Simulation test results show the validity of the proposed methods. The mechanism of low-frequency oscillation is illustrated by trajectory section eigenvalues and trajectory reconstruction methods from a new perspective. These methods can be used as effective tools for theoretical research and engineering applications in power systems.

Cascading failure analyses are mainly based on simultaneous or quasi-steady-state (QSS) assumptions, which are inappropriate in cases of rapid cascading failure analysis and control. Fault interval is considered in the stability analysis of cascading failure for the first time in this article. Based on the extended equal area criterion (EEAC), a transient stability analysis method is proposed for cascading failure in consideration of intervals. The results show the periodicity of stable margin variations with intervals. The conclusions are validated in a Hamiltonian system, and the effects of time-varying factors are analyzed. The OMIB case shows that it may be over-optimistic to assess stability levels under simultaneous or QSS assumptions. The validity of all conclusions in this paper is verified with the New England system and with an actual power system.

Finding an optimal black-start scheme plays an important role in speeding up the restoration procedure of a power system after a blackout or a local outage. In a practical black-start decision-making procedure, uncertainties are inevitable. Some factors such as index values and indexes’ weights can be better described as uncertain interval values. So far, the black-start decision-making problem with uncertainties has not yet been systematically investigated. Given this background, a new approach for black-start decision-making based on interval values is developed. First, a decision-making matrix with interval values is normalized by using the error propagation theory. Then, a linear goal programming model is developed to seek the ideal vector of index weights and the evaluation values of all candidate black-start schemes can be obtained. A risk attitude factor based method is presented to sort the schemes. Finally, a sample example is served for demonstrating the essential feature of the proposed method, and comparisons with three existing methods are also carried out. Simulation and comparison results show that the proposed method could not only take different kinds of uncertainties into account, but also overcome several shortcomings of the existing methods.

The ever-increasing penetration of wind power integration into a power system can produce significant impacts on the operation of an interconnected power system. As the major energy conversion technology for large wind turbines, the doubly fed induction generator (DFIG) will play an important role in future power systems. Hence, the impacts of the DFIG on the low-frequency oscillations of interconnected power systems have become an important issue with extensive concerns. This paper examines the impacts of several factors, including the DFIG transmission distance, tie-line power of the interconnected system, DFIG capacity, with/without a power system stabilizer (PSS), on the low frequency oscillation characteristic of an interconnected power system using both eigenvalue analysis and dynamic simulations. To investigate the effects of these factors on the interarea oscillation mode, case studies are carried out on two two-area interconnected power systems, and some conclusions are obtained.

The quantitative analysis of the low-carbon energy transition is crucial for providing decision-support for energy planning and policy-making. This study presented an approach for quantitative analysis of energy transition, involving dynamic simulation model, mathematical methods of describing the energy transition targets and pathways, boundary condition, and evaluation indicators. The transition targets of Baseline scenario were set following the Energy Production and Consumption Revolution Strategy (2016–2030) (hereinafter referred to as the Energy Strategy) promulgated by the Chinese government. Four comparative scenarios were generated by adjusting the targets of the total primary energy consumption and the proportion of power generation. The carbon emissions and economic costs were calculated based on the quantitative dynamic simulation model. In the study, an attempt was made to explain whether China’s cumulative carbon emissions following the Energy Strategy match the emission quotas under the global 2 °C temperature rise target. Furthermore, the future research direction for low-carbon transition was also analyzed.

Fast restoration of critical loads and non-black-start generators can significantly reduce the economic losses caused by power system blackouts. In a parallel power system restoration scenario, the sectionalization of restoration subsystems plays a very important role in determining the pickup of critical loads before synchronization. Most existing research mainly focuses on the startup of non-black-start generators. The restoration of critical loads, especially the loads with cold load characteristics, has not yet been addressed in optimizing the subsystem divisions. As a result, sectionalized restoration subsystems cannot achieve the best coordination between the pickup of loads and the ramping of generators. In order to generate sectionalizing strategies considering the pickup of critical loads in parallel power system restoration scenarios, an optimization model considering power system constraints, the characteristics of the cold load pickup and the features of generator startup is proposed in this paper. A bi-level programming approach is employed to solve the proposed sectionalizing model. In the upper level the optimal sectionalizing problem for the restoration subsystems is addressed, while in the lower level the objective is to minimize the outage durations of critical loads. The proposed sectionalizing model has been validated by the New-England 39-bus system and the IEEE 118-bus system. Further comparisons with some existing methods are carried out as well.

Traditionally, power system contingency analysis involves massive power flow calculations, which are usually based on linear approximation methods, such as dc load flow model or distribution factors based method, for fast speed but with compromised accuracy. Particularly, the accuracy of power flow results can deteriorate with the increase of k given N – k contingency analysis. Consequently, the obtained results may provide misleading information by under estimating the impact of some severe contingencies. In order to effectively implement online N – k contingency analysis, we propose a flexible framework of power flow estimation, where generalized line outage distribution factors ( GLODF s) and ac power flow model are integrated together to formulate a two-stage scheme. At first stage, the Monte Carlo sampling technique is used to generate tables of computing errors for the hybrid ac -( GLODF s/ ac ) method. The achieved error information can then provide useful references in the second stage to select either ac - GLODF s based method or ac power flow model for N – k contingency analysis. Theoretically, the framework can provide significantly enhanced accuracy as well as satisfied efficiency. Finally, comprehensive case studies with the IEEE -118 bus system are given to validate the proposed framework.

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.

To ensure the small-signal stability of a power system, power system stabilizers (PSSs) are extensively applied for damping low frequency power oscillations through modulating the excitation supplied to synchronous machines, and increasing interest has been focused on developing different PSS schemes to tackle the threat of damping oscillations to power system stability. This paper examines four different PSS models and investigates their performances on damping power system dynamics using both small-signal eigenvalue analysis and large-signal dynamic simulations. The four kinds of PSSs examined include the Conventional PSS (CPSS), Single Neuron based PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). A steep descent parameter optimization algorithm is employed to seek the optimal PSS design parameters. To evaluate the effects of these PSSs on improving power system dynamic behaviors, case studies are carried out on an 8-unit 24-bus power system through both small-signal eigenvalue analysis and large-signal time-domain simulations.