The proliferation of next-generation renewable energy systems has driven widespread adoption of electronic devices and nonlinear loads, causing grid distortion that degrades waveform quality in grid-forming (GFM) converters. Additionally, unbalanced grid faults exacerbate overcurrent risks and transient stability challenges when employing conventional virtual impedance strategies. While existing studies have separately examined these challenges, few have comprehensively addressed non-ideal grid conditions. To bridge this gap, a novel control strategy is proposed that reshapes the output current waveforms and enhances transient stability in GFM converters under such conditions. First, a sliding mode controller with an improved composite reaching law to achieve rapid reference tracking while eliminating chattering is designed. Second, a multi-quasi-resonance controller incorporating phase compensation is introduced to suppress harmonic distortion in the converter output current. Third, an individual-phase fuzzy adaptive virtual impedance strategy dynamically reshapes the current amplitude during unbalanced faults and improves the system’s transient stability. Validated through PSCAD/EMTDC simulations and hardware-in-the-loop experiments, the proposed strategy demonstrates superior transient stability and fault ride-through capability compared to state-of-the-art methods, ensuring reliable GFM converter operation under severe harmonic and unbalanced grid conditions.

Aiming at the problem of long-timescale prediction deviation in the distribution network, a fast regulation strategy of cloud-edge coordination based on an intelligent transformer supply zones edge consistency algorithm is proposed. The cloud makes the global initial optimal allocation to the edge transformer supply zone clusters, and then the cluster makes the secondary collaborative optimal allocation to the edge zones. A fast power interaction model within a cluster based on a consensus algorithm is established, and the micro-increase rate of dispatching cost is used as the consistency variable so that the cluster adjustment amount is optimally allocated to each transformer supply zone, and the total dispatch cost of all transformer supply zones is minimized. The simulation example verifies the effectiveness of the cloud-edge collaborative fast control strategy based on the intelligent station cluster edge consensus algorithm in this article.

Compared with the traditional fixed relay assisted communication network, the mobile relay assisted wireless communication network has greater communication coverage and better flexibility. It enables to extend the lifetime of a communication system and improve the throughput of uses. Due to the time-varying nature of the actual communication environment and the existence of various interferences, there are uncertain factors in the communication channel that affect the quality of service of users. In this paper, we study the energy efficiency maximization problem based on power control, mobile relay selection, and subcarrier allocation under uncertain channel. The proposed mobile relay selection and subcarrier allocation algorithm improves the energy efficiency of the system while ensuring the normal communication of mobile users. Probabilistic constraints are introduced into the optimization problem to describe the uncertain channel, and after the mathematical transformation, it becomes a solvable form. The subcarrier allocation algorithm and distributed robust power control algorithm are also presented. Simulation results show that the improved system energy efficiency has been achieved and the proposed algorithm behaves better scalability and robustness under dynamic communication environment.

To address the challenges of real-time control in power systems with high renewable penetration, identifying historical transmission sections similar to future scenarios enables efficient reuse of mature control strategies. However, existing data-driven identification methods exhibit two primary limitations: they typically rely on static Total Transfer Capacity (TTC), ignoring the rapid regulation capability of Energy Storage Systems (ESS) in alleviating congestion; and they employ fixed weights for similarity measurement, failing to distinguish the varying importance of different features (e.g., critical line flows vs. ordinary voltages). To overcome these issues, this paper proposes a similarity identification method for transmission sections considering ESS regulation capabilities and adaptive feature weights. First, a hierarchical decision model is utilized to screen basic grid features. An optimization model incorporating ESS charge/discharge constraints and emergency power support potential is established to calculate the Dynamic TTC, constructing a multi-scale feature set that reflects the real-time safety margin of the grid. Second, a Dispersion-Weighted Fuzzy C-Means (DW-FCM) clustering algorithm is proposed. By introducing a dispersion-weighting mechanism, the algorithm utilizes data distribution characteristics to automatically learn and assign higher weights to key features with high distinguishability during the iteration process, overcoming the subjectivity of manual weighting. Furthermore, fuzzy validity indices (XB, PC, FS) are introduced to adaptively determine the optimal number of clusters. Finally, case studies on the IEEE 39-bus system verify that the proposed method significantly improves identification accuracy compared to traditional methods and provides more reliable references for dispatching decisions.

Extreme scenarios involving abnormal load fluctuations pose serious challenges to the safe and stable operation of power systems. To address these challenges, an ultra-short-term load forecasting model is proposed, specifically designed for extreme conditions. The model combines density-based spatial clustering of applications with noise (DBSCAN), random search Bayesian optimization (RSBO), bidirectional gated recurrent units (BiGRUs), k-nearest neighbor (KNN), and an attention mechanism, enhanced by a fine-tuning strategy to improve forecasting accuracy. Firstly, the original load data are reconstructed weekly, and extreme scenarios are identified using the DBSCAN. Secondly, the RSBO is employed to optimize model parameters within the high-dimensional search space. To further refine performance, the final fully connected layer is fine-tuned to adapt to extreme conditions. Finally, case studies demonstrate that the proposed approach reduces the root mean square error (RMSE) by 12.37% and the mean absolute error (MAE) by 6.73% compared to benchmark models, achieving superior accuracy under all tested extreme scenarios.