Headroom based adaptive droop control for regulating DC voltage and active power in MTDC grid with integrated renewable energy.

Control techniques for neural-network-based charging stations (CSs) are attracting attention worldwide. This popularity is due to the emergent need for alternative intelligent and adaptive control solutions for attaining a CS with stabilized power transfer and voltage control at the point of common coupling. This paper demonstrates novel neural-network-based improved virtual synchronous motor (NN-i-VSM) control through the mechanism of the charging voltage feedback in conjunction with a trained neural network model to adaptively produce field excitation (MN) that mimics a virtual flux model. The MN adaptively generates an electromotive force based on the trained NN output to control the rectifying converter response of the CS for power quality enhancement during multiple-CS operation. Simulation results in the scenario of multiple CSs at 750 kW (5 × 150 kW) with varying capacities showed significant improvement in voltage variable tracking capacity of up to 500 V as well as power response overshot reduction and grid voltage response tracking improvement compared with an i-VSM-based CS model. A comprehensive CS efficiency assessment and plant stability analysis, including Bode plot evaluation, further confirmed the superior dynamic response performance and robustness of the NN-i-VSM model over the i-VSM model. The proposed model offers scalable applicability in smart mobility and wireless CS integration, signifying a new control advancement for future generations of multiple-grid-friendly charging infrastructure for penetration of batteries at varying capacities.

Reliable knowledge of power transmission tower locations is fundamental for large-scale inspection and asset management in modern power grids. However, in satellite and aerial remote sensing imagery, towers typically appear as small, slender structures embedded in cluttered backgrounds, which leads to frequent missed and false detections. To address this challenge, we propose SCOPE-YOLO, an integrated super-resolution-plus-detection framework tailored for scalable transmission and distribution tower monitoring. In the first stage, low-resolution image patches are enhanced by a Real-ESRGAN ×4 super-resolution frontend, which restores high-frequency lattice details and sharpens tower boundaries. The reconstructed images are then processed by SCOPE-YOLO, a YOLOv11-based detector that incorporates a Cross-Scale Feature Aggregation (CFA) module, a Gather–Distribute (GD) routing mechanism, and a high-resolution P2 detection head, together with SAT and layered inference strategies to strengthen small-object perception under complex backgrounds. Experiments on the public SRSPTD dataset demonstrate that SCOPE-YOLO improves F1 score by 0.051 and raises mAP@0.5 by 10.2 percentage points over the YOLOv11-s baseline, while maintaining a compact model size. Compared with a broad set of state-of-the-art detectors, SCOPE-YOLO achieves the best overall performance, reaching 82.8% mAP@0.5 for power tower detection. Cross-domain evaluation on the GZ-PTD test set further confirms the effectiveness of the super-resolution–detection pipeline: Real-ESRGAN×4@2048 + SCOPE-YOLO increases Recall from 0.8621 to 0.9278 and mAP@0.5 from 0.8365 to 0.9132 relative to the low-resolution baseline, substantially reducing missed detections of small and weak tower targets in real-world scenes.

With the increasing integration of high-proportion renewable energy, power systems are exhibiting low-inertia and low-damping characteristics, posing severe challenges to frequency stability. This paper proposes a coordinated supplementary frequency regulation strategy utilizing electrolytic aluminum (EA) loads and a hybrid energy storage system (HESS). Firstly, a system frequency response model is established, incorporating EA, electrochemical energy storage, pumped hydro storage, and conventional generation units. Secondly, an improved variable filter time constant controller is designed, supplemented by fuzzy logic, to achieve adaptive power allocation under different disturbance magnitudes. Concurrently, regulation intervals are defined based on the area control error (ACE), enabling a tiered response from source-grid-load resources. Simulation results demonstrate that under a severe disturbance of 0.05 p.u., the proposed strategy reduces the maximum frequency deviation from 0.198 Hz to 0.054 Hz, achieving a 72.7% performance improvement, and shortens the system settling time by 59.5%. Furthermore, the state of charge (SOC) of the electrochemical storage is successfully maintained within the range of [0.482, 0.505], effectively balancing frequency regulation performance and device lifespan. The findings demonstrate the effectiveness of the proposed strategy in enhancing the frequency resilience of low-inertia power grids.

With the focus on the growing concern of voltage instability and its inherent risks connected to blackouts, this study addresses the importance of Volt/VAR control (VVC) in maintaining voltage stability, optimizing power factor, and reducing losses. As such, this scientific article presents a review of the methodologies used to estimate the quantity of reactive power required to restore voltage in power grids. Although reviews exist on classical methods, optimization, and machine learning, a study unifying these approaches is lacking. This gap hinders an integrated comparison of methodologies and constitutes the main motivation for this study in 2025. This absence of a consolidated and up-to-date review limits both academic progress and practical decision-making in modern power systems, especially as DER penetration accelerates. This research was conducted using the Scopus database through the selection of articles that address reactive power estimation methods. The results indicate that traditional numerical and optimization methods, although accurate, demonstrate high computational costs for real-time application. In contrast, techniques such as Deep Reinforcement Learning (DRL) and hybrid models show greater potential for dealing with uncertainties and dynamic topologies. The conclusion reached is that the solution for reactive power management lies in hybrid approaches, which combine machine learning with numerical methods, supported by an intelligent and robust data infrastructure. The comparative analysis shows that numerical methods offer high precision but are computationally expensive for real-time use; optimization techniques provide good robustness but depend on detailed models that are sensitive to system conditions; and machine learning-based approaches offer greater adaptability under uncertainty, although they require large datasets and careful training. Given these complementary limitations, hybrid approaches emerge as the most promising alternative, combining the reliability of classical methods with the flexibility of intelligent models, especially in smart grids with dynamic topologies and high penetration of Distributed Energy Resources (DERs).

The fault of the power grid transmission line itself or the foreign matter caught in it will pose a potential threat to the power system. Efficient anomaly detection is the key to maintain the stability of modern transmission systems. At present, the increasing demand for edge computing equipment makes it a trend to develop lightweight and efficient power grid anomaly detection methods. To deal with the practical demands of power grid anomaly detection, this paper brings the LPGANet, a lightweight model designed to achieve high accuracy while enhancing detection efficiency. The model is integrated with dynamic snake convolution (DSConv) and spatial-channel reconstruction convolution (SCConv) to increase multi-scale feature extraction and fusion and cut computational cost. In addition, An EMA method is adopted to enhance the concentration on foreground and reduce the impact of background. We release a new dataset containing 6,200 images of typical power grid anomalies such as broken strands, scattered strands, and other floating suspensions. The experimental results on the dataset demonstrate that LPGANet achieves the best accuracy, highest efficiency, and best comprehensive performance compared with other object detection methods. In addition, the effectiveness of the system under computational resource limitation is also verified in the deployment on Jetson AGX Orin edge devices.

The integration of new energy into the grid has significantly intensified power grid operational pressure, posing higher demands on hydropower system regulation. As a key unit for power grid load tracking and stability maintenance, parameter mismatch of the PID governor is prone to inducing system bifurcation, thus leading to oscillatory instability, which has emerged as a critical challenge affecting the reliable consumption and sustainable supply of new energy. To address this challenge, a hydroelectric power generation system (HPGS) model in the infinite-bus power system is established. Bifurcation analysis is employed to quantitatively identify the critical thresholds of PID parameters that cause HPGS instability. Based on this, system dynamic response processes under critical thresholds are clarified using time-domain analysis. Furthermore, the potential oscillation instability mechanism is revealed using eigenvalue analysis, and suggestions for PID parameter selection are provided. Key quantitative results indicate that variations in proportional gain, kp, induce five limit point bifurcations. The system enters an unstable region when kp exceeds 2.467, whereas operation within the range below 0.891 is conducive to system stability. A supercritical Hopf bifurcation arises when integral gain ki reaches 0.925, so strict restrictions should be imposed on ki to avoid operating around this critical value. Two supercritical Hopf bifurcations that may trigger system oscillatory instability are identified during differential gain kd changing, and it should be regulated to a level below 5.188 to ensure system stability. By integrating bifurcation analysis, time-domain analysis, and eigenvalue analysis, this study effectively improves the accuracy of characterizing system dynamic behaviors, providing a clear quantitative basis for PID parameter optimization and bifurcation suppression, as well as laying a theoretical foundation for hydropower system stable operation and the efficient absorption of new energy.

Impact of orderly energy consumption on coordinated optimization between industrial microgrid and power grid

Array multi-frequency piezoelectric vibration energy harvesters powered wireless sensing in power grid transformer

The integration of renewable energy sources into electrical grids introduces significant challenges, particularly in ensuring stability and reliability in dynamic, nonlinear environments. These sources create fluctuations, uncertainties, and voltage regulation issues that traditional control systems struggle to manage, compromising the grid’s ability to deliver a stable power supply. Addressing these challenges requires advanced, adaptive control solutions capable of responding to the variable nature of renewable energy. This article explores advanced techniques, focusing on reinforcement learning and Neuroevolution, to develop innovative control strategies for electrical systems. Neuroevolution, which combines neural networks with evolutionary algorithms, optimizes control without relying on gradient-based methods, making it suitable for complex, unpredictable scenarios. These approaches enhance grid stability, improve response times, and enable real time anomaly detection and corrective actions, offering a resilient and efficient solution to the limitations of traditional control methods. Key words. Renewable energy integration, Reinforcement learning, Neuroevolution, Electrical grid stability, Control systems.

Optimization design and full life cycle feasibility analysis of energy system in power grid interactive building

Operational risk assessment of electric power grids exposed to straight-line winds

Abstract Accurately predicting the charging load of residential electric vehicles (EVs) is crucial for optimizing the power grid and infrastructure planning. In order to reduce the impact of high fluctuation of charging load on prediction results, this paper proposes a hybrid prediction model that integrates enhanced decomposition technology and Conv-Informer network. Firstly, identify key features through Pearson correlation analysis. Then, the fully integrated empirical mode decomposition (CEEMDAN) with adaptive noise is used to perform initial decomposition on the charging load data, obtaining multiple intrinsic mode functions (IMFs). These IMFs are divided into high, medium, and low frequency components based on sample entropy, with the high frequency components further decomposed using variational mode decomposition (VMD) and their modulus optimized using the center frequency method. Input all components into the Conv-Informer model for prediction, where the convolutional layer extracts local load features, the Informer module captures long-term load dependencies, and finally weights and superimposes the predicted values of each subsequence to obtain the final result. Based on real data validation experiments in a residential area in Zhejiang Province, China, the proposed model achieved an RMSE of 5.74 kW and a MAE of 4.03 kW. Compared with other common models, its prediction error has been reduced by about 30% (e.g. LSTM: RMSE=9.28 kW, MAE=6.64 kW; Single Informer: RMSE=7.61 kW, MAE=4.54 kW). It can be seen that the method proposed in this article has good accuracy and stability, and is suitable for predicting the charging load of residential electric vehicles.

A probabilistic analysis method for the impact of traction load and wind power on power grid voltage quality based on Shapley value incorporating spatial correlation and uncertainty

Integrated planning of source-grid-load-storage for regional power grids considering large-scale renewable energy integration

Harnessing battery energy storage systems with CO2 time-shift strategies for power grid emissions control

A voltage-clamped bidirectional fault current limiter based on a three-winding coupled inductor for DC power grids

Power grid stability prediction using stacked machine learning based classification and regression models

Taking the accounts payable of A Power Grid Enterprise as the research object and combining value chain theory, this study selects the Analytic Hierarchy Process (AHP) as the method to determine the weight of factors influencing accounts payable payment. It constructs a payment decision model for accounts payable to provide risk warning references for accounts payable management. The model's operability is tested through a practical case application, aiming to offer ideas and references for improving accounts payable management in similar enterprises.

Grid-scale energy storage technologies are critical for stabilizing power grids increasingly reliant on intermittent renewable energy sources. Among these technologies, aqueous alkaline zinc-ferricyanide flow batteries (AZFFBs) are promising candidates due to their low cost, high safety, and rapid kinetics. However, their practical deployment is hindered by the formation of "dead zinc", which can cause irreversible capacity loss and block flow channels. Herein, we identify the growth of irreversible crystal buds as the primary cause of macroscopic zinc pulverization, which leads to "dead zinc" formation in AZFFBs. To address this issue, we introduce a multi-coordination electrolyte additive, panthenol (PAN), which simultaneously coordinates with Zn(OH)4 2- to homogenize ion transport at the electrode interface and promotes the formation and re-atomization of reversible crystal buds. This strategy results in a 15-fold increase in the cycling life of Zn||K4[Fe(CN)6] full batteries, along with 2.1%, 3.1%, and 1.2% improvements in Coulombic efficiency, energy efficiency, and voltage efficiency, respectively. This work clarifies the formation mechanism of "dead zinc" in AZFFBs and proposes an efficient inhibition of zinc pulverization approach, bridging the gap between renewable energy generation and grid-scale storage.