With the increasing integration of renewable generation, many power grids have gradually formed AC–DC hybrid systems. Abnormal operations, such as DC blocking faults and generation trips, have led to several incidents of large frequency deviations. However, current simulation methods result in large errors when estimating the frequency regulation capacity of the system. This paper proposes a generic system frequency-response (SFR) model that can be used to estimate the dynamic frequency behavior of modern large-scale power systems. The limitations of the classical SFR model is first analyzed. Second, a generic SFR model with a more reasonable structure is presented, and the parameter-determination strategy is proposed using both the dynamic and steady-state data. Then, the generic SFR model is built and verified by a simulation case. Finally, a generic SFR model with satisfactory accuracy is established for the power grid in East China based on the measured disturbance data. The results show that the proposed model is promising for broad potential applications.

The wind speeds of each wind turbine generator (WTG) are the basis for establishing an aggregated model of a wind farm for power system transient simulation. However, the commonly obtained operation data of the wind farm do not include the wind speeds of each WTG. Thus, a modeling method for the wind speeds of the WTGs of same moment inside the wind farm was proposed. A wind speed combination model (WSCM) is established via K-means clustering from the sorted field-measured wind speed data, which can provide reasonable wind speeds for each WTG when aggregate modeling the wind farm. Subsequently, an improved two-step clustering method of the WTGs was proposed, by which the WTGs are initially clustered according to whether they enter low voltage ride through (LVRT) mode or not. An “N-1 VS One” aggregated model and the binary search algorithm were used to quickly and correctly predict whether a WTG in the wind farm enters LVRT mode under a grid fault. The case study demonstrates the satisfactory performance of the WSCM and the necessity of clustering the WTGs according to whether they enter or do not enter LVRT mode for obtaining an accurate aggregated model of the wind farm. All the data, models, and the programs used in this paper can be found at DOI: 10.21227/dqyk-t852.

This paper proposes a novel method to analyze impacts of uncertain variability on power system dynamics. There is considerable interest in integrating intermittent renewable energy and plug-in electric vehicles into power systems. Therefore, power systems are in an environment with increasing uncertain variability. In this paper, the uncertain variability is described as a continuous-time stochastic process, and thus the power system under uncertain variability is modeled by stochastic differential equations. To quantify impacts of uncertain variability on power system dynamics, an intra-region probability index is presented. Based on the stochastic averaging method, an analytical method is proposed to calculate the intra-region probability. Compared to Monte Carlo simulation, the proposed method is many orders of magnitude faster without sacrificing the result accuracy. Furthermore, several insights are given to improve the power system dynamics under uncertain variability.

In order to meet the challenges brought by the high penetration of intermittent and fluctuating wind and solar power, a short-term optimal scheduling model for wind-solar-hydro hybrid generation system with cascade hydropower is established with the objective of minimizing the amount of abandoned wind, solar and hydro power and maximizing the stored energy of hydro stations. Cascade hydropower is considered to provide spinning reserve and regulation reserve to ensure the security of system. Mixed Integer Linear Programming (MILP) method is used for the short-term optimal schedule of wind-solar-hydro hybrid generation system. The case studies show that spinning reserve and regulation reserve are beneficial to the hybrid generation system, and verify the practical applicability of the proposed model.

Photovoltaic (PV) generation has high impact on the decarbonization pathways of power systems. Accuracy of day‐ahead PV power forecasting has become crucial in the operation and control of power system with high PV penetration. This paper develops a hybrid approach based on generative adversarial network (GAN) combined with convolutional autoencoder (CAE) to improve PV power forecasting accuracy. Self‐organizing map method is first utilized as data pre‐processing to classify target days into different weather types based on solar irradiance. With the ability of GAN to reduce the burden of loss and the advantages of CAE to extract multi‐scale effective features from the weather and PV power, PV power forecasting model consisting of GAN and CAE is proposed. The developed method has been tested on a real dataset in a Chinese PV station and compared with base reference PV forecasting methods. Numerical testing results demonstrate the effectiveness of our method with high accuracy.