Without main grid support, ensuring the economic and stable operation of islanded microgrids is a key research focus. Integrating advanced communication technologies has gradually shaped cyber-physical microgrids, greatly boosting their observability and controllability. Yet, the impact of cyber and physical disturbances on control performance cannot be overlooked. To address this, a cyber-physical collaborative economic control strategy is proposed, covering three parts: 1) A distributed cooperative control strategy based on virtual cost and the virtual leader-following consensus algorithm is proposed to generate optimal active power output commands for controlled resources; 2) To cope with physical uncertainties like external disturbances, the consensus algorithm is combined with a sliding mode controller to improve anti-perturbation performance; 3) To cope with cyber uncertainties like packet loss, a path update strategy is designed based on data importance evaluation and path reconstruction. Simulation results demonstrate that the proposed cyber-physical collaborative strategy reduces the power generation cost by at least 1.37 %, while significantly improving the system's anti-perturbation capability and data transmission reliability. This research provides a feasible technical solution for the economic and stable operation of microgrids, laying a foundation for cyber-physical collaborative control in islanded scenarios.

Due to the widespread deployment of intelligent acquisition and monitoring equipment in cyber-physical power system (CPPS), more and more multi-source power data tasks need to be transmitted and analyzed. However, limited network resources and a single cloud computing mode hinder the real-time data analysis and control of CPPS. To address the challenging problem that existing task processing scheme can no longer satisfy the real-time processing requirement of power data, in this paper, cooperative relay, mobile edge computing (MEC), and non-orthogonal multiple access (NOMA) technologies are introduced into the CPPS communication architecture, and a joint resource optimization scheme for relay-assisted NOMA-MEC networks based on cloud-edge collaboration is proposed. Specifically, a novel relay-assisted NOMA-MEC system model based on cloud-edge collaboration is proposed. This model allows each relay to serve multiple edge gateways simultaneously to raise resources utilization and adopts NOMA technology to reduce interference impact. Then, based on the proposed model, the tasks processing issue is modeled as a joint multi-objective optimization problem, aiming to allocate network resources reasonably and achieve a trade-off between system delay and energy consumption. For the variable coupling and non-convex characteristics of the proposed optimization problem, a channel performance-based relay selection method and a block coordinate descent (BCD)- based joint optimization algorithm are designed. Furthermore, the optimality conditions of the subproblems are also derived. Finally, extensive simulation results illustrate that the proposed scheme is superior to other baseline schemes.

With the widespread application of renewable energy in microgrids, collaborative optimization scheduling of microgrid clusters has become a key issue in improving energy utilization efficiency and operational economy. To solve this problem, this paper proposes a microgrid cluster optimization scheduling method based on the sparrow search algorithm. Firstly, construct a microgrid cluster model that includes wind turbines, photovoltaics, energy storage batteries, diesel generators, and hydrogen fuel cells. Secondly, the economic optimum is defined as the objective function, and combined with the constraints of equipment and system operation, the sparrow search algorithm is proposed to solve the optimization model of the microgrid cluster. Finally, this method is used to simulate an IEEE 9-node system with 4 microgrids. The simulation results showed that this method has significant advantages over traditional particle swarm optimization algorithms in reducing total costs and new energy utilization efficiency, verifying the feasibility of this method in microgrid cluster optimization scheduling.

With increasing integration of intermittent renewable energy into power systems, effective frequency regulation is more urgent. Virtual power plants (VPPs), aggregating demand-side distributed resources, have great potential for frequency regulation. Nevertheless, deep cyber-physical interactions render VPPs susceptible to diversified cyber attacks, such as denial of service (DoS), false data injection (FDI), and deception attacks, which degrade frequency performance. Notably, existing research on VPPs' frequency regulation largely ignores comprehensive impacts of such attacks. To address this gap, this paper presents a multi-agent system based cooperative power control method for distributed VPPs to ensure frequency regulation under diversified cyber attacks. First, we introduce a novel VPPs cooperative power control strategy to obtain optimal power references for VPPs and mitigate the combined effects of DoS and FDI hybrid attacks. It combines acknowledgment technique, event-triggered mechanism, and trust agents-based state screening and compensation method, enhancing reliability and economic efficiency of VPPs' frequency regulation. Moreover, we propose a unique inverters dynamic power control scheme for accurate reference tracking of each internal unit within VPPs under deception attack. Based on a new event-triggered dynamic power control model with deception attack, it employs a specially designed dynamic event-triggered communication-based control strategy, ensuring robust tracking control and reducing communication burdens. Finally, simulation results demonstrate the validity and superiority of our presented method.

ABSTRACT In recent years, extreme cold disasters have occurred frequently worldwide. The security of rural distribution network is threatened. The current research on enhancing the resilience of distribution network fails to consider the changes in the operating characteristics and efficiency of equipment under extreme cold disasters. To address this, this paper uses hydrogen‐integrated energy systems (HIES) to enhance the resilience of rural distribution network under extreme cold disasters. The method considers ensuring the power supply of rural lifeline load. A power support capability evaluation model of HIES is established. The model takes into account the impacts of low‐temperature conditions on the operational characteristics and efficiency of hydrogen energy equipment and photovoltaic equipment. An energy demand model considering livelihood security lifeline load, public service lifeline load and cultivation‐breeding lifeline load under extreme cold disasters is established. A rural distribution network resilience enhancement model is developed. Its goal is to maximise the restored energy demand of lifeline load. The example shows that the lifeline load recovery rate is increased by 38.98% and the primary load recovery rate is increased by 5.44% using the method proposed in this paper.

The tightening of global environmental regulations is accelerating the adoption of Electric Trucks (ETs) in logistics; however, the integrated optimization of their routing, charging schedules, and cargo loading presents a formidable challenge. This complex coupling inherently introduces a Generalized Multiple Knapsack Problem and results in a Mixed-Integer Linear Programming model that is typically NP-hard and computationally demanding for standard solvers. To address this, we propose a novel Graph-Benders algorithm based on an exact algorithmic framework that strategically decomposes the problem into an ET routing master problem and a Vehicle-to-Grid (V2G) scheduling subproblem, reformulated within a graph-based modeling approach. This structure ensures all delivery tasks are fulfilled while tightening the feasible region for grid coordination. By synergizing feasibility and optimality cuts from the linearly relaxed subproblem with no-good cuts to address integer infeasibility, the method efficiently eliminates infeasible solutions,yielding a high-quality near-optimal solution, which is at least as good as the optimal V2G schedule for the best feasible routes. Through experiments in various geographic scenarios and split-delivery tasks, the algorithm demonstrates its versatility and achieves a breakthrough in efficiency without sacrificing solution quality compared to Gurobi. In large-scale tests, for instances with 19,585 decision variables, the proposed Graph-Benders decomposition yields a superior, high-quality near-optimal solution in merely 28 seconds, outperforming Gurobi’s 10,000-second time-limit result. Notably, even at a scale of 127,489 variables, the algorithm yields high-quality near-optimal solutions in only 130 seconds.Consequently, the proposed algorithm effectively tackles the strong coupling of large-scale ET routing and V2G coordination, offering a powerful tool for practical low-carbon logistics deployment.

Independent metering control (IMC) hydraulic systems in excavators are characterized by strong nonlinearity, time-varying parameters, and significant uncertainties due to oil temperature variations, viscosity changes, and load disturbances. Conventional Type-1 fuzzy controllers often struggle to achieve a satisfactory trade-off between rapid response and pressure stability, particularly during mode switching operations. This paper proposes a hierarchical Interval Type-2 Fuzzy Variable Universe Fuzzy Controller (IT2-VUFC) for the boom and arm IMC hydraulic system of a 6-ton excavator. The upper layer employs an Interval Type-2 fuzzy system to dynamically adjust the universe scaling factors, while the lower layer utilizes a Type-1 Mamdani fuzzy controller to generate command signals for two independent proportional valves. By incorporating the Footprint of Uncertainty (FOU), the proposed controller effectively enhances robustness against system uncertainties. Lyapunov stability analysis demonstrates that the closed-loop system is uniformly ultimately bounded. Co-simulation results using AMESim and Simulink show that, compared with conventional outlet-throttling PID and Type-1 variable universe fuzzy controllers, the IT2-VUFC achieves faster response speed and significantly better pressure stability. Notably, during boom lifting and lowering phases, the IT2-VUFC reduces pressure fluctuations by up to 48.43% (rodless chamber) and 74.44% (rod chamber) compared to the PID controller. The results confirm that the integration of Interval Type-2 fuzzy logic with variable universe strategy provides an effective solution for high-performance control of complex IMC hydraulic systems in construction machinery.

The aggregation of heterogeneous and flexible distributed energy resources on the demand side by virtual power plants (VPPs) to provide frequency regulation for power systems has emerged as a novel paradigm. Nevertheless, the frequency regulation of VPPs is challenged by physical network constraints, uncertainties, and communication transmission issues. To address these obstacles, this paper proposes an integrated design method of coordinated control and communication transmission to facilitate VPPs participation in frequency regulation. First, a coordinated control strategy is devised, taking into account power flow constraints and source-load uncertainties. This strategy employs a physical power flow model and uncertainties handling based on the conditional value-at-risk to circumvent grid security issues and mitigate the impacts of uncertainties, thereby enhancing the reliability of VPPs frequency regulation. Moreover, a novel joint design scheme is developed for delay and synchronous control. A distributed biased min-consensus-based routing strategy is proposed to minimize delay. Meanwhile, a waiting mechanism is designed to ensure the synchronous execution of control commands, thus improving the real-time performance and robustness of VPPs frequency regulation. Finally, simulation results validate the efficiency and superiority of the proposed method.

The degree of active power fluctuation is a key indicator for assessing the stability of active distribution networks. However, with the increasing clustering of distributed resources within these networks and the deepening integration of cyber-physical systems, uncertainties arising from cyber and physical domains, e.g., load variations and transmission congestion, will compound and exacerbate power fluctuations. Unlike existing methods that use cyber-physical cut-off control or firewall-based passive defenses, this paper proposes a bi-level active power control method based on a cyber-physical cooperation perspective to address these issues. At the upper level, which encompasses source-grid-storage clusters: in the physical layer, an active power support approach is proposed, which incorporates multi-factor matching while considering flow constraints to achieve multi-objective optimization regulation. In the cyber layer, we propose data sensitivity calculations along with demand-driven path planning techniques to ensure that planned paths align with regulatory requirements. At the lower level, focusing on in-cluster resources: in the physical layer, a multi-resource distributed control method based on fault-tolerance principles and a virtual leader-following consensus algorithm is proposed, which enables flexible responses to cluster commands while defending against light congestion interference. In the cyber layer, an eventtriggered path reconstruction method is proposed to defend against heavy congestion interference. The proposed methodology effectively harnesses the aggregation control capabilities of massive resources and facilitates an active defense against network congestion issues. Case studies show that these methods can generate optimal control commands for aggregators and internal resources within seconds to mitigate power fluctuations while ensuring reliable network performance in both planning and operational dimensions.

This article proposes a novel control framework for hydraulic excavators based on a high-order fully actuated (HOFA) system approach. First, a comprehensive HOFA model is established, which describes the excavator dynamics in task space, joint space, and drive space. The proposed control algorithm systematically addresses multisource uncertainties, including kinematic calibration errors, as well as structured and unstructured parameter uncertainties in the dynamic and actuator models, via physically derived adaptive neural network compensation integrated into the controller. By decoupling the kinematic and dynamic loops, the algorithm simplifies controller design and theoretical analysis. Furthermore, by integrating the HOFA approach with task-space sensory feedback, the controller enables direct task specification and high-precision control of the excavator bucket tip. Finally, Lyapunov-based theoretical analysis proves asymptotic convergence of task-space tracking errors, and both simulation and experimental results validate the effectiveness of the proposed algorithm.

Since massive distributed resources have been aggregated into active distribution network (ADN), the operation of ADN faces serious challenges. One of these challenges is the significant power fluctuations in physical-space due to the uncoordinated integration of multi-aggregator generation; another one is the network congestion in cyber-space due to the quick growth of power data. Both of them will seriously affect the operational performance of ADN. To address these issues, a co-design approach for active power control and two-stage emergency communication scheme is first proposed with the idea of cyber-physical cross-space understanding and cooperation. In physical-space, considering the flow correlation among aggregators, a centralized power control is studied to achieve optimal regulation. In cyber-space, a two-stage communication architecture coordinated with uncrewed aerial vehicles (UAVs) is studied to relieve congestion. In Stage I, through understanding the demand for power control regarding transmission performance, a demand-driven path reconfiguration strategy is proposed to restore transmission and gain time for UAV movement. In Stage II, considering the uncertainty of UAV flight altitude, a flight method based on stochastic planning is proposed, which ensures reliable communication via UAVs in case the reconfigured paths become severely congested again. Case studies are presented at the end.

This paper investigates the prescribed performance tracking control problem for electro-hydraulic systems with hydraulic force constraints. First, a barrier function-based method is employed to confine the error variables within predefined performance bounds. Subsequently, a nonlinear state-dependent function is constructed to enforce the hydraulic force constraints. However, under adverse operating conditions, the presence of such rigid hydraulic force constraints inevitably degrades control performance. This conflict makes it difficult to maintain the tracking error within the original prescribed bounds. To address this issue, a reference modification module is incorporated to achieve an appropriate balance between the prescribed tracking performance and the hydraulic force constraints. It is demonstrated that the proposed control framework can effectively prevent both cavitation and over-pressure. Simulation and experimental results are presented to illustrate the effectiveness and superiority of the proposed controller.

Dear Editor, The integration of distributed energy resources(DERs)and com-munication infrastructures makes distribution networks increasingly cyber-physical,requiring resilient and real-time voltage regulation.Network partitioning enables scalable control,yet existing methods often ignore communication and security constraints or rely on costly optimization,limiting practicality under dynamic and adversarial conditions.

Forward uncertain difference equations and backward stochastic difference equations are two distinct types of dynamic systems. The former is based on uncertainty theory, while the latter is formulated within probability theory. This paper investigates optimal control problems involving both types of equations. By relationships among uncertain expectation, probabilistic expectation, and chance expectation, we develop the framework of chance theory to handle uncertain stochastic optimal control problems of such hybrid systems. Using an equivalent transformation method, we convert the problems into deterministic difference equations solving problems. We then derive analytical solutions for three types of optimal control problems through backward induction. Furthermore, we explore the impact of different computation orders of uncertain and probabilistic expectations. Numerical examples are provided to illustrate key differences and demonstrate the effectiveness of the proposed method.

Achieving deep decarbonization of power systems requires large-scale integration of distributed renewables, yet such integration is increasingly constrained by the limited capacity and flexibility of existing distribution networks. Besides, the energy transition has triggered significant load growth, necessitating the capacity expansion and upgrade of numerous distribution assets. Flexible interconnection technology offers a promising solution by enabling coordinated power exchange among neighboring distribution systems, but its deployment has been hindered by high upfront costs and the lack of large-scale assessments. This study develops a nationwide expansion framework that explicitly incorporates scale-dependent cost evolution of flexible interconnection technology and regional heterogeneity in electricity demand across rural, urban, and industrial networks in China. Results show that large-scale deployment can reduce unit costs of interconnection device by more than 50%, reaching approximately 135.81 $/kVA, while enabling annual carbon emission reductions of up to 247 million tons. These findings highlight the critical role of scale-cost interactions in shaping the techno-economic viability and decarbonization potential of distribution-level flexibility technologies.

In the DC distribution network, massive distributed resources are connected as networked microgrids to exploit the regulation potential of resources under cyber-physical fusion. However, it cannot be ignored that physical uncertainties, such as generation fluctuations, and cyber uncertainties, such as communication congestion, will easily be superimposed to cause severe active power fluctuations. To address these issues, active power control based on cyber-physical cross-space understanding and cooperation is studied for microgrids and resources, respectively. For microgrids: 1. Considering the multifaceted factors in regulation, the power flow constraints-dependent centralized control method is studied to generate the optimal control commands; 2. Considering the need to build a wired network for microgrids to ensure communication quality, a demand-driven network matching method is designed to provide reliable network support. For resources: 1. Considering the presence of external disturbances in the regulation process, a combinatorial fault-tolerant control strategy is proposed to tolerate faults actively and achieve a flexible response of resources to microgrids' commands; 2. Considering the need to build a wireless network for flexible communication under plug-and-play of resources, a dual optimization strategy is proposed to provide reliable network support for multi-scenario regulation. Related case studies are presented in the final.

Understanding the risk factors for hematoma expansion (HE) in different regions of intracerebral hemorrhage (ICH) can help in the development of more accurate HE prediction tools and in implementing more effective clinical treatment interventions. This study aims to investigate the risk factors for HE in patients with lobar and deep ICH. A retrospective analysis was conducted on 558 cases of primary supratentorial ICH from Tongji Hospital Affiliated to Tongji University. Patients were categorized into lobar ICH and deep ICH groups. Differential analysis of ICH characteristics at different locations was performed, followed by subgroup analysis based on HE occurrence. Binary logistic regression was used to identify independent risk factors for HE in each group. Among the 404 patients with ICH who underwent follow-up noncontrast computed tomography (NCCT) scans, the proportion with HE was similar in the deep ICH group (23.2

Driven by advanced Internet technologies, virtual power plants (VPPs) demonstrate significant potential for providing frequency regulation services to power systems by aggregating heterogeneous demand side distributed resources. However, the frequency regulation performance of VPPs is highly susceptible to the stochastic nature of renewable energy sources (RESs) output on the physical side and to network delay on the cyber side, yet these two aspects are usually considered separately. To overcome these challenges, this paper simultaneously consider cyber and physical uncertainties and develop a novel coordinated power control method to facilitate VPP frequency regulation. Specifically, this paper first design a coordinated control strategy that includes RESs output uncertainty. This strategy integrates the advantages of model predictive control (MPC) and robust optimization (RO), enabling it to effectively mitigate the impact of RESs output uncertainty while enhancing the reliability of VPP frequency regulation. Furthermore, this paper design a dynamic control strategy that accounts for network delay uncertainty. Based on the established dynamic power control model with network delay, a H∞–linear quadratic regulator (LQR) controller is proposed to regulate the output of distributed resources within VPP, enabling them to quickly follow the targeted dynamics in the presence of delay, thereby ensuring the real time performance of VPP frequency regulation. Simulation results validate the feasibility and superiority of the proposed method.

The virtual power plants (VPPs) formed by aggregating distributed energy resources on the demand side have high potential in frequency regulation of power systems. However, due to the complexity of real communication network, diverse communication issues, such as random packets loss and malicious cyber-attacks, may cause undesirable power dispatch and inaccurate dynamic control. This brings great challenges to the participation of VPPs in frequency regulation. To bypass these hurdles, we propose a cyber-physical integration design to facilitate VPPs frequency regulation in a real-time and reliable manner. First, we suggest a joint design scheme of VPPs power dispatch and communication resources allocation for packets loss under cloud-edge collaboration. That is, in the physical-layer, we propose the VPPs power dispatch under packets loss and in the cyber-layer, we develop the communication resources allocation strategy. This scheme aims to eliminate the packets loss impacts and thus determine accurate power dispatch. Second, we present a high reliable information transmission approach for preventing cyber-attacks under edge-end collaboration. Specifically, in the physical-layer, we implement dynamic control for responding to received regulation power and in the cyber-layer, we execute digital signcryption algorithm. Its objective is to improve system information transmission against cyber-attacks and thus ensure the accurate dynamic control. Finally, simulation results validate the feasibility and superiority of our proposed integration design.