The integration of electric vehicles into decentralized energy sharing systems demands innovative parallel computing solutions to address the real-time processing and trust challenges in vehicle-to-vehicle transactions. This study tackles two critical limitations: (1) conventional routing methods’ inability to handle dynamic spatio-temporal constraints, and (2) centralized reputation mechanisms conflicting with vehicle-to-vehicle’s decentralized nature. This paper proposes a parallel distributed computing framework combining spatio-temporal network optimization with blockchain architecture. First, a tensor-based spatio-temporal network model converts dynamic routing into parallelizable static flow allocation, enabling real-time constraint embedding through distributed parallel matrix operations. Second, a blockchain-powered transaction layer implements parallel smart contracts for concurrent verification and social welfare-optimized pricing, achieving Byzantine fault-tolerant consensus through sharded transaction processing. Experimental results demonstrate 23.6% faster computation throughput and 31.2% higher transaction concurrency compared to existing distributed systems, while maintaining 17.8% social welfare improvement. The framework effectively resolves the latency-trust dichotomy in V2V energy exchange through coordinated parallel computing paradigms.

The power control loop in a virtual synchronous generator (VSG) exhibits inherently insufficient damping, making it prone to low-frequency oscillation (LFO) under disturbances. Moreover, the dynamic coupling between active and reactive power can induce unnecessary reactive power fluctuations during active power transfer. This not only affects voltage stability but may also further exacerbate LFO. Leveraging the static VAR compensator (SVC)'s capability for fast reactive power support and supplementary damping, this study proposes an LFO suppression strategy based on the NPOD-SVC-VSG grid-connected system. First, this study develops a small-signal model and state-space representation of the SVC-VSG grid-connected system. Eigenvalue analysis is then employed to investigate the stability influence mechanisms in the SVC-VSG grid-connected system under weak interactions. Furthermore, a Phillips-Heffron model of the SVC-VSG system is developed for the mechanism analysis of LFO. To enhance system damping, nonlinear power oscillation damping (NPOD) is proposed that adaptively adjusts gain based on oscillation amplitude while considering the impact of communication delay between the SVC and VSG. NPOD is incorporated into the voltage control loop of the SVC, and its parameters are designed using the phase compensation method that accounts for communication delay. Finally, MATLAB/Simulink simulations demonstrate that the proposed NPOD-SVC-VSG strategy effectively suppresses LFO, increasing the system damping ratio by 10.93% compared to the VSG strategy. The strategy also rapidly compensates for reactive power deficits during transients, thereby enhancing system voltage stability.

In recent years, renewable energy generation such as wind power has been widely applied in distribution networks. However, its output randomness leads to inaccurate power flow optimization results. To address the reactive power optimization problem of distribution networks under such uncertain operating conditions, a dynamic reactive power optimization method is proposed by incorporating the real-time regulation characteristics and operational constraints of the power system automatic voltage control (AVC) system. A day is discretized into 24 time intervals, and a dynamic probabilistic reactive power optimization model for distribution networks considering multiple uncertain factors is constructed, with the objective of minimizing the sum of expected values of the system's active power loss across all time intervals. For the solution of the established model, an improved grey wolf optimizer is adopted to overcome the premature convergence defect of traditional optimization algorithms, so as to derive the optimal solutions of deterministic control variables and the expected value of system power loss. Simulation calculations on the IEEE 33-bus system verify the feasibility of the proposed model and the effectiveness of the algorithm.

With the rapid development of new energy generation technology, the large-scale grid connection of the new power system with electronic power integration based on virtual synchronous generator (VSG) technology has a significant impact on the system flow, damping and inertia, and also introduces the problem of active power oscillation. According to this, this paper establishes a small signal port characteristic model based on virtual synchronous generator control technology, and analyzes the system stability through the mutual coupling between VSG control parameters. Aiming at the weak damping characteristics of VSG parallel system and the low-frequency oscillation of the main grid, the small signal port characteristic model of multi-VSG parallel system is established, and the stability judgment method of parallel system is proposed. Therefore, the multi-parameter cooperative adaptive control strategy of VSG is designed. Finally, the influence of rotational inertia coefficient and active droop coefficient of the controller on the stability of low low-inertia system under power disturbance is verified by simulation, which makes the grid-connected inverter under the control of virtual synchronous generator have better dynamic response characteristics.

With the acceleration of global energy transformation, the penetration rate of distributed generation (DG) in distribution system is increasing, which brings new challenges and opportunities to the operation and scheduling of distribution system. The intermittence, volatility and uncertainty of distributed generation make the scheduling problem of distribution system more complicated, and it is necessary to comprehensively consider the multi-objective balance of operation cost, environmental protection cost and system reliability. In this paper, a multi-objective optimization scheduling method based on Non-Dominated Sorting Dung beetle optimizer (NSDBO) is proposed. By constructing a multi-objective optimization model, considering the operation cost and environmental protection cost, and using the global optimization ability of NSDBO algorithm, the optimal scheduling scheme is found. The results show that the NSDBO algorithm can efficiently solve the multi-objective optimization problem, quickly converge to the Pareto front, and maintain the uniform distribution of the solution set.

With the wide application of information and communication technology in power system, the traditional power system has gradually transformed into a highly coupled cyber physical power system (CPPS). The identification of key nodes in cyber physical power system is an important issue in its vulnerability analysis. An improved power flow betweenness on the grid side and a key node identification method considering information flow on the communication network side are proposed. Firstly, the CPPS interdependent network model is constructed based on the complex network theory. Then, the node criticality is analyzed from the perspective of topology based on the structural hole theory and degree and average neighbor degree. At the same time, from the perspective of operating characteristics, the influence of the maximum available transmission power between the generator and the load on the node criticality is considered on the grid side, and the influence of the information flow of the communication link on the node importance is considered on the communication network side. The node importance index of the grid and communication network is established and the key nodes are identified. Finally, the IEEE39 node system is used to verify the effectiveness of the proposed method. The results show that the proposed key node identification method can effectively reflect the importance of nodes in CPPS.

In the face of the challenges posed by the aging population with a declining birth rate in terms of life care issues, this paper proposes an intelligent smart home based on multi-heterogeneous elderly-care robots and their safety care methods. The aim is to address the shortage of professional caregivers and meet the safety assistance and continuous behavioral care needs of elderly individuals with reduced capabilities. The system incorporates seven heterogeneous elderly-care robots and on-board multiple sensor systems, covering various care tasks from delivery, getting up, transferring, walking, excretion, to indoor rehabilitation training. To ensure the efficiency and timeliness of task allocation in a multi-robot system during execution, a dynamic parallel auction execution algorithm is proposed, effectively handling the allocation of multi-priority tasks and emergency tasks. This algorithm can effectively handle the allocation of multi-priority tasks and emergency tasks, ensuring efficient and timely task execution. Experiments conducted in multi-task care scenarios and real-life home environments have verified the feasibility and adaptability of the proposed system architecture and methods in certain care scenarios, providing an effective solution to improve the quality of life for elderly individuals and alleviate the pressure on caregivers.

The large-scale integration of power electronic equipment, mainly based on virtual synchronous generator (VSG) technology, has a significant impact on power system damping and inertia, which may introduce active power oscillation issue. This paper proposes a method for suppressing electromechanical oscillations in multi-VSG systems, analyzing the impact of parametric coupling on system stability through small-signal modeling. First, a small-signal model of a multi-VSG parallel system is established to analyze the weak damping characteristics and the electromechanical oscillation. Second, an electromechanical oscillation suppression method is proposed for multi-VSG parallel systems, which combines droop control and VPSS. Finally, the influence of the inertia coefficient, damping coefficient and droop coefficient of the proposed method on the system stability is verified by simulations.

To accurately identify critical nodes that exert significant influence on the overall performance of power grids, this paper proposes a critical node identification method that explicitly accounts for the impact of line impedance and power losses on energy transmission paths. First, the coupling between line electrical characteristics (impedance and power loss) and system energy transmission paths is quantified by constructing an edge-betweenness weighted matrix and an electrical node betweenness index. Then, considering the fault transition probability of power nodes, an improved PageRank-based node importance evaluation method is developed by integrating the edge-betweenness weighted matrix with the electrical node betweenness index, thereby enabling the identification of critical nodes. Finally, the proposed method is validated on the IEEE 57 and IEEE 118 test systems, by simulating deliberate attacks and analyzing the resulting variations in grid transmission efficiency, and by comparing with the entropy weight method and the entropy-weighted TOPSIS method.

To address the obstacle avoidance problem in multi-agent systems, this study proposes a reinforcement learning-based approach for multi-agent collaborative obstacle avoidance. First, a mathematical model for multi-agent obstacle avoidance is established, defining optimization objectives and constraints. Second, a joint state-action space is designed, and an optimal reciprocal collision avoidance (ORCA) algorithm is incorporated to construct the reward function, balancing avoidance efficiency and safety. Finally, simulation experiments compare the proposed method with the velocity obstacle (VO) method and ORCA. The results demonstrate that the proposed method outperforms traditional velocity obstacle and ORCA approaches in terms of path length, runtime, and motion smoothness. Moreover, it maintains algorithmic stability and generalization capability even as the number of agents increases.

Live working robots in distribution networks not only reduce the labor intensity of operators but also keep them away from environments with numerous safety hazards. These robots are key equipment for intelligent operation and maintenance in the power maintenance field, and their dexterous manipulation capabilities are crucial for disassembling equipment in complex scenarios. Current research on live working robots in distribution networks primarily focuses on tasks such as lead wire disconnection and reconnection as well as conductor stripping operations, with limited studies on arrester disassembly. During live disassembly of arresters, challenges include insufficient nut positioning accuracy and poor grasping stability. This paper conducts research on planning methods for dual-arm robots to disassemble arresters. First, a grasping strategy integrating YOLOv11-Seg instance segmentation and a centroid-constrained GPD algorithm is proposed. Instance segmentation is used to obtain the arrester mask and centroid coordinates, and a cylindrical range filter is applied to enhance grasping stability. Second, a two-stage visual positioning framework is designed, combining coarse positioning by a global camera and fine positioning by an end-effector camera to achieve millimeter-level nut positioning (median error ≤ 3 mm). An orderly path for nut removal is planned to avoid collision risks. Finally, experiments verify that the proposed method increases the grasping success rate within the centroid-defined range to 85

With the increasing penetration of power electronic devices in power systems, virtual synchronous generator (VSG) technology has garnered widespread attention for its ability to provide inertia and damping support to the grid. However, while simulating the external characteristics of synchronous generators, this technology also introduces inherent rotor oscillation issues. Particularly in multi-machine parallel operation, insufficient system damping can easily lead to low-frequency oscillations, threatening system stability and equipment safety. To address this issue, this paper first establishes a small-signal model for multi-VSG parallel grid connection. Subsequently, a Phillips-Heffron model tailored for multi-VSG parallel structures is constructed, revealing the fundamental cause of system oscillations under disturbances. Building upon this foundation and drawing inspiration from traditional power system stabilizer design principles, a virtual power system stabilizer control strategy tailored for multi-VSG parallel grid-connected systems is proposed. Finally, the proposed control strategy is validated through MATLAB/Simulink simulations. The simulation results demonstrate that, compared to conventional control methods, the proposed VPSS control strategy effectively suppresses low-frequency oscillations in the system, significantly enhancing overall stability.

ABSTRACT Accurately screening the characteristic factors that influence short‐term power load forecasting is an effective means to improve prediction accuracy. Non‐critical features in multidimensional datasets can make it difficult for the prediction model to distinguish electrical loads, thereby reducing model accuracy. To tackle this challenge, a novel Non‐Intrusive Load Monitoring (NILM) framework for appliance recognition is proposed to overcome the problem of distinguishable electrical load features, which combines a Variational Mode Decomposition (VMD) module, a convolutional Neural Network (CNN), and a Bidirectional Long Short‐Term Memory (BiLSTM) network. First, the original electrical power data is first decomposed by the VMD module, which excellently achieves noise reduction and stationary processing of non‐stationary load data, effectively separating valid load features from interference components and laying a high‐quality data foundation for subsequent feature extraction and forecasting. Second, CNN is adopted to extract the local spatial features of the decomposed load data for accurate appliance recognition and classification, which excels in automatically mining hidden spatial features of electrical loads and greatly improves the distinguishability of load features among different electrical equipment. Meanwhile, BiLSTM is used to capture the bidirectional temporal dependencies in the load data for short‐term power load forecasting, which surpasses traditional unidirectional time‐series models in mining long‐term and bidirectional temporal correlation of load data and makes the forecasting results more consistent with the actual operation law of electrical loads. To further exploit the model's potential, an optimised strategy based on the sparrow search algorithm (SSA) is developed to optimise the key parameters of the CNN‐BiLSTM model, which optimises the model's parameter configuration efficiently and adaptively and avoids the accuracy loss caused by manual parameter adjustment. The results show that the proposed method effectively improves the accuracy of short‐term power load forecasting.

Inverters controlled by a virtual synchronous generator (VSG) can provide inertial support for power systems with renewable energy. However, when the power is disturbed, the dynamic process of the output power and frequency response of the system will be seriously affected. To solve this problem, this paper proposes an improved VSG control strategy of adaptive coordination control strategy of inertia-damping dynamics with additional frequency response control. The influence of inertia and damping parameters on the steady-state and dynamic performance of the system is analyzed by the time domain analysis method. In addition, the additional frequency response control is introduced in the control loop to suppress the frequency fluctuation within the specified frequency deviation when the grid-connected system has a large disturbance. Through the adaptive coordination control of virtual inertia, virtual damping, and frequency regulation, the dynamic performance of VSG output power and frequency regulation can be significantly improved. The effectiveness of the proposed control strategy is verified by simulation experiments .

The increasing integration of renewable energy sources has fundamentally altered power system dynamics, resulting in reduced system inertia. Traditional estimation approaches overlook the temporal variability of inertia, leading to inaccurate inertia estimation and potential instability under dynamic conditions. In this paper, a hierarchical Koopman-based framework for real-time inertia estimation is proposed, which distinguishes between long-term global inertia trends and short-term fluctuations. By integrating global and local learning modules, the approach captures long-term dynamics while estimating rapid inertia variations, effectively reducing inertia deviations and modal errors compared to traditional methods. Additionally, temporal neighborhood segmentation enables direct mapping of operational data to system modes, facilitating identification of participation factors links to specific grid components. These capabilities provide crucial support for timevarying inertia estimation using ambient data. Validation encompasses real-time simulations on the IEEE 39bus system and modified NETS-NYPS 68-bus system, along with real-world measurements from Lausanne.

With the acceleration of the energy transition process, the Electricity-Gas-Heat Interconnection System (EGHIS) has become a key carrier for improving energy utilization efficiency owing to its multi-energy complementary characteristics. However, the heterogeneous energy coupling problem existing in the interconnection system leads to low utilization rates of various energy sources and poor transmission security, which has an impact on the stability of the system. Therefore, in this paper, for the multi-energy flow coupling characteristics and safe operation requirements in the EGHIS, an economic optimal scheduling method for the EGHIS considering safety indicators is proposed. By constructing a multi-energy flow coordinated optimization model that takes into account safety margin, safety balance equation and safety constraints, the collaborative improvement of the economic operation and safety of the system is achieved. The effectiveness of this method was verified through the system simulation of YALMIP and CPLEX. Compared with the adoption of traditional methods, the line blocking rate is reduced by 33

The large-scale integration of renewable energy sources has amplified the challenge of low-frequency oscillations (LFO) in power systems. This paper addresses the low-frequency oscillation suppression needs of virtual synchronous generators (VSG) and traditional synchronous generators (SG) in interconnected systems. A modular model of the VSG-SG interconnected system is established to reveal its electromechanical oscillation mechanisms. Additionally, an optimized control strategy based on a power-enhanced Virtual Power System Stabilizer (VPSS2B) is proposed. This stabilizer uses a dual-input design (frequency deviation and power deviation) to generate an additional power signal through dynamic phase compensation. This design overcomes the limitations of traditional single-input stabilizers in responding to rapid disturbances and effectively enhances the system’s damping capability. Simulation results demonstrate that the VPSS2B significantly improves the damping coefficient of oscillation modes, reduces the oscillation amplitude of the VSG’s active power and virtual power angle, and shortens the system’s recovery time. This strategy provides technical support for the stable operation of high-penetration renewable energy grids.

Faced with the dual challenges of energy transition and renewable energy consumption, achieving low-carbon, efficient, and clean energy is the mainstream trend for future energy use. Based on the current park integrated energy system (PIES) model and multi-level thermal energy coupling, this paper proposes a multi-park integrated energy system (MPIES) collaborative scheduling model. In MPIES, each sub-park cooperates with each other to interact between electrical energy and multi-level thermal energy. To protect the privacy of each sub-zone, the ADMM algorithm is employed to decompose the optimization problem into two sub-problems: maximizing the joint operation total benefit of MPIES and maximizing the transaction benefit of PIES. The joint operation benefits after energy sharing are then allocated reasonably according to the Shapley value method based on the marginal contribution of each PIES to MPIES. Simulation results indicate that the operational costs of each PIES are reduced compared to independent operation, and this model is beneficial for enhancing the willingness of MPIES members to participate in energy sharing.

BackgroundPhysiotherapy robots offer a feasible and promising solution for achieving safe and efficient treatment. Among these, acupoint recognition is the core component that ensures the precision of physiotherapy robots. Although the research on the acupoint recognition such as hand and ear has been extensive, the accurate location of acupoints on the back of the human body still faces great challenges due to the lack of significant external features.MethodsThis paper designs a two-stage acupoint recognition method, which is achieved through the cooperation of two detection networks. First, a lightweight RTMDet network is used to extract the effective back range from the image, and then the acupoint coordinates are inferred from the extracted back range, reducing the inference consumption caused by invalid information. In addition, the RTMPose network based on the SimCC framework converts the acupoint coordinate regression problem into a classification problem of sub-pixel block subregions on the X and Y axes by performing sub-pixel-level segmentation of images, significantly improving detection speed and accuracy. Meanwhile, the multi-layer feature fusion of CSPNeXt enhances feature extraction capabilities. Then, we designed a physiotherapy interaction interface. Through the three-dimensional coordinates of the acupoints, we independently planned the physiotherapy task path of the physiotherapy robot.ResultsWe conducted performance tests on the acupoint recognition system and physiotherapy task planning in the physiotherapy robot system. The experiments have proven our effectiveness, achieving a recall of 90.17% on human datasets, with a detection error of around 5.78 mm. At the same time, it can accurately identify different back postures and achieve an inference speed of 30 FPS on a 4070Ti GPU. Finally, we conducted continuous physiotherapy tasks on multiple acupoints for the user.ConclusionThe experimental results demonstrate the significant advantages and broad application potential of this method in improving the accuracy and reliability of autonomous acupoint recognition by physiotherapy robots.