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This paper presents two variable time-step algorithms for time-domain simulation of electromagnetic transients. The first one has been created to simulate basic switching transients and the second one is intended to simulate power-electronic systems. Each algorithm is tested in a dedicated circuit. Simulation results yield excellent accuracy and significant improvements in computation time when compared to fixed time-step algorithm.

A variety of numerical integration methods are currently being used for simulating electromagnetic transients in commercial software and experimental codes. This paper intends to provide a review and comparison of these methods for time-domain integration, including 2S-DIRK, ARTEMiS art5 solver, and a semi-analytical matrix exponential-based method. The possibility of utilizing these methods is explored for reliable and efficient treatment of discontinuity, which is known to give rise to numerical oscillations for the widely used trapezoidal method. Case studies are conducted to show the strengths and limitations of these methods and their combinations.

In order to meet the demand of fast electromagnetic transient simulation for large-scale interconnected power systems, a new method of GPU based parallel matrix exponential algorithm for power system electromagnetic transient simulation is presented in this paper. Firstly, the hardware structure and programming model of GPU are introduced. Then the high data parallelism based on matrix exponential integration algorithm is proposed. Then, a simulation test is carried out for a wind farm system including 17 WTGs. The accuracy and efficiency of the proposed method are verified. The results show that the speed of parallel computing based on GPU is about 2 times faster than that of CPU and Matlab/SimPowerSystems.

Large-scale wind farm integration poses challenges to the modeling and simulation of power system electromagnetic transients. In this paper, state space modeling of typical induction generator wind farms is demonstrated using a subsystem assembly process, and a novel matrix exponential based simulation algorithm is applied, which is efficient, stable, and especially suitable for high dimensional nonlinear problems like wind farms. Numerical performance of the proposed algorithm is demonstrated through case studies.

The ever-increasing scale of system interconnection and diverse dynamic time constants of power equipment pose challenges to power system transient simulation algorithms. In this paper, an adaptive numerical integration method for DAE systems based on matrix exponential operator is presented. Krylov subspace technique is utilized to effectively reduce the problem dimension. A novel step adaption scheme is established to enable the method adjusting the computation step size according to error budget while maintaining the output step size unchanged. Acceleration is accomplished without perceived degradation in the simulation results. Numerical performance and computation efficiency is demonstrated through numerical case studies.

Model reduction is a popular trend in large-scale system simulations. It is also an effective way to improve the efficiency of distribution system simulations. In this paper, the realization of state-space-based model reduction methods in EMTP-type programs is presented. The state-space model of linear time-invariant distribution network is reduced and simulated with the detailed nodal model in EMTP-type programs. The combined state-space nodal analysis algorithm is adopted as the interface between the state-space models and the nodal models. Simulations are performed to show the feasibility and validity of the proposed method, and the improvement in efficiency with the application of model reduction.

The ever-increasing scale of system interconnection and diverse dynamic time constants of power equipment pose challenges to power system electromagnetic transients simulation algorithm. In this paper, an advanced numerical integration method based on matrix exponential operator is proposed. Krylov subspace approximation is utilized to effectively reduce the problem dimension, and an accurate dense output formula is established to enable the method adopting larger simulation step size and revealing higher frequency dynamic details simultaneously. Accuracy, numerical stability and computation efficiency is demonstrated through numerical case studies.

This paper proposes a coordinated control method of the diesel generators (DGs) and battery storage system (BSS) in AC island microgrid. When the DGs run as the main power source, an auxiliary power control signal is added to the conventional droop control for BSS to prevent system collapses caused by long-time over-current of the DGs, which can improve the stability of the microgrid. To avoid short-term outage when the main power source in the micro-grid changes from the DGs to BSS or from the BSS to the DGs, a seamless transition control strategy for the DGs and BSS is presented. Finally, the control methods previously mentioned had been validated with the PSCAD simulation software.

The integration of distributed energy resources into low and medium voltage networks will have profound impact on distribution network reconfiguration. In this paper, the impact of DG on distribution network reconfiguration is analyzed in both normal operation and post-fault scenarios with different load levels. Distribution system simulation is performed with the Open Distribution Simulator Software (OpenDSS). Due to the intrinsic nonlinear and non-differentiable mixed-integer nature of the problem, simulated annealing is used as the optimization algorithm. Using the proposed method, a case study of IEEE 33-bus system gives positive conclusions: in normal operation, the integration of DG increases the number of distribution network reconfiguration feasible solutions and reduces the total network loss of the optimal solution; while in post-fault condition, feasible solution can emerge with the integration of DG where there isn't any feasible solution originally. The integration of DG helps to ensure network power supply.