Yunyang Xu

High penetration of wind and photovoltaic generation reshapes power system net-load profiles and increases the computational burden of repeatedly solving security-constrained unit commitment problems under multiple renewable scenarios. This paper proposes a fast schedule generation method based on historical schedule transfer. A library is constructed by pairing feasible unit commitment schedules with their corresponding 24 h net-load trajectories. For a target day, hour-wise, standardized net-load trajectories are compared using Euclidean distance, and the Top-K most similar historical schedules are selected as candidates. Each candidate is verified under the target-day operating conditions. When no candidate satisfies the feasibility or economic requirements, a limited-perturbation model is applied to repair the retrieved schedule with minimal changes in unit statuses. Case studies show that the proposed method effectively transfers historical commitment patterns, maintains operational feasibility and near-optimal economic performance, and reduces the computational effort required for unit commitment schedule generation.

The large-scale integration of inverter-interfaced renewable generation has made the steady-state short-circuit current of renewable energy stations increasingly important for setting protections, planning, and fault analysis. Traditional single-unit multiplication methods are computationally efficient but often ignore the effects of internal electrical distance and active power output dispersion among generation units, which can lead to non-negligible errors at the station level. To address this issue, this paper proposes an improved single-unit multiplication method and a corresponding dimension-reduction framework for centralized renewable energy stations considering short-circuit current fitting characteristics. A unified three-segment positive-sequence current control model is first adopted to represent the low-voltage ride-through behavior of photovoltaic (PV), direct-drive wind turbine, and battery energy storage system (BESS) stations. The upper and lower voltage breakpoints are selected as 0.9 p.u. and 0.2 p.u., respectively, and the linear support coefficient was determined as 1.5 according to Chinese national standards. On this basis, the effects of electrical distance and active power output dispersion on the calculation error of the traditional single-unit multiplication method are analyzed. A grouping criterion based on the average access-point voltage and critical active power is then established, and the resulting two-group equivalent method is used to estimate the total short-circuit current of renewable energy stations. Electromagnetic transient simulations in PSCAD are conducted for PV, direct-drive wind, and BESS stations. The results show that, compared with the traditional single-unit multiplication method, the proposed method more accurately captures the steady-state short-circuit current characteristics under different voltage dips and output-dispersion conditions while retaining high engineering practicality.

The rapid growth of wind and solar energy poses new challenges to safe and reliable system operation. Effectively characterizing and generating high-risk wind and photovoltaic (PV) power output scenarios is therefore essential for system risk assessment and preventive dispatch and control. However, existing scenario generation methods either rely on predefined probability distributions or focus narrowly on extreme output levels, failing to comprehensively reflect system-level operational risk induced by renewable energy. To this end, a power system optimal dispatch model and a flexibility indicator system mainly incorporating system ramping and transmission margins are established. Thereafter, analytic hierarchy process (AHP) and the entropy weight method (EWM) are used to fuse indicators into a quantitative operational risk index. Historical wind and PV scenarios are evaluated through the dispatch model to generate risk-labeled samples, based on which a conditional generative adversarial network (cGAN) is trained to produce wind and PV power output scenarios with specified risk levels. Case studies verify that the risk labels constructed can effectively guide the subsequent conditional generation model and scenarios corresponding to a given risk level can be effectively generated by the model.

With the large-scale integration of renewable energy and the increasing use of power electronics, the issue of wide-band oscillations in power grids has become increasingly prominent. The scarcity and uneven distribution of oscillation samples pose significant challenges for training data-driven models, and traditional generative models struggle to ensure fidelity in both time and frequency domains. To address this, this paper proposes a Time–Frequency Fusion Generative Adversarial Network (TFF-GAN) for generating power grid oscillation risk scenarios. The method constructs a dual-path generation and discrimination framework, where the generator decomposes the signal using Short-Time Fourier Transform (STFT), with time-domain features extracted by a convolutional neural network (CNN) and frequency-domain features extracted from the STFT representation by a dedicated spectral network. These features are then fused using a U-Net structure. The discriminator simultaneously evaluates the authenticity of both the time-domain waveform and the frequency-domain spectrum. A composite loss function, incorporating time-domain loss, frequency-domain loss, and adversarial loss, is used for joint optimization. Experimental results demonstrate that the proposed method generates oscillation scenarios with high fidelity in both time-domain waveforms and frequency-domain spectra, effectively supporting power grid oscillation risk assessment and control strategy validation.

In large wind farms, uneven voltage distribution caused by feeder impedance and turbine spacing may pose safety hazards and reduce operational efficiency. This paper proposes a two-layer voltage coordination optimal control method for wind farms that balances both grid-connection point voltage levels and turbine-end voltage safety. The outer layer tracks voltage commands issued by the AVC master station at the point of common coupling (PCC), while the inner layer establishes a global optimization model considering generator terminal voltage safety. The second-order cone relaxation method converts nonlinear constraints into solvable convex forms. Through a two-layer iterative solution, it achieves optimal allocation of active and reactive power between wind turbines and static var compensators (SVGs) within the field, thereby enhancing the active power output at the wind farm port and increasing the system’s reactive power margin. Simulation results demonstrate that compared to conventional unified power factor control, the proposed method effectively enhances terminal voltage security, increases wind farm power generation, and boosts system reactive power reserve capacity while stably tracking PCC voltage commands.

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S_VDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between S_VDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids.

This paper presents a dynamic model for full-power converter permanent magnet synchronous wind turbines based on Physics-Informed Neural Networks (PINNs). The model integrates the physical dynamics of the wind turbine directly into the loss function, enabling high-accuracy equivalent modeling with limited data and overcoming the typical “black-box” constraints and large data requirements of traditional data-driven approaches. To enhance the model’s real-time adaptability, we introduce an online update mechanism leveraging Bayesian Neural Networks (BNNs) combined with a clustering-guided strategy. This mechanism estimates uncertainty in the neural network weights in real-time, accurately identifies error sources, and performs local fine-tuning on clustered data. This improves the model’s ability to track real-time errors and addresses the challenge of parameter-specific adjustments. Finally, the data-driven model is integrated into the CloudPSS platform, and its multi-scenario modeling accuracy is validated across various typical cases, demonstrating the robustness of the proposed approach.

This paper addresses the challenges of broadband impedance identification in wind farms connected to the power grid, where broadband oscillations can compromise grid stability. Traditional impedance modeling approaches, including white-box and black/grey-box methods, face limitations in real-world applications, particularly when dealing with commercial new energy units with unknown control structures. To overcome these challenges, a novel real-time impedance identification method is proposed for PMSGs(Permanent Magnet Synchronous Generators). The method, called ASSA (Attention-based Shared and Specific Architecture), utilizes a multi-task neural network model combined with an attention mechanism to improve the accuracy of impedance fitting across different frequency bands. A broadband impedance dataset is constructed offline under various operating conditions, incorporating uncertainties like wind speed. The proposed approach offers an efficient solution for impedance identification, enhancing the stability and reliability of grid-connected renewable energy systems.

Decentralized control offers a more effective way to avoid the complexities and coordination challenges encountered in centralized control. In the decentralized design of additional controllers for multi-infeed DC transmission systems, it is crucial to focus on the interactions among the controllers. To solve this problem, this paper primarily discusses an enhanced damping characteristic method for hybrid AC-DC systems based on an improved Effective Relative Gain Array (ERGA) index. Initially, feedback signals with better control effects on existing oscillation modes are pre-screened. Subsequently, the ERGA-based interaction index is utilized to pair these feedback signals with control locations, aiming to identify the optimal pairing scheme with the minimum index value, indicating the least interaction effect. This approach minimizes the mutual influence between loops and reduces adverse interactions among controllers. Simulations of multi-DC additional damping controllers designed using the multi-stage Linear Quadratic Regulator (LQR) method in a multi-DC system demonstrate that the optimal pairing scheme significantly outperforms both uncontrolled and poorly paired schemes in controlling low-frequency oscillations, thereby validating the optimality of the proposed method. Furthermore, various disturbances are introduced to verify the effectiveness and robustness of the proposed control strategy against low-frequency oscillations.

It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters.