Search Results (103)

Emerging power electronic devices like soft open points (SOPs) and electric springs (ESs) play a vital role in enhancing active distribution network (ADN) efficiency. SOPs enable flexible active/reactive power control, while ESs improve demand-side management and voltage regulation. This paper proposes a two-stage stochastic programming model to optimize ADN’s operation by coordinating these fast-response devices with legacy mechanical equipment. The first stage determines hourly setpoints for conventional devices, while the second stage adjusts SOPs and ESs for intra-hour control. To handle ES nonlinearities, a hybrid data–knowledge approach combines knowledge-based linear constraints with a data-driven multi-layer perceptron, later linearized for computational efficiency. The resulting mixed-integer second-order cone program is solved using commercial solvers. Simulation results show the proposed strategy effectively reduces power loss by 42.5%, avoids voltage unsafety with 22 time slots, and enhances 4.3% PV harvesting. The coordinated use of SOP and ESs significantly improves system efficiency, while the proposed solution methodology ensures both accuracy and over 60% computation time reduction. Full article

The integration of renewable energy into distribution networks has led to voltage violations and increased network losses. Traditional control devices, with slow response, struggle to precisely control power flow in active distribution networks (ADNs). Optimizing from both supply and demand sides, an ADN power flow optimization method is proposed for accurate and dynamic power flow regulation to address these issues. On the demand side, the peak, valley, and flat periods are divided by the fuzzy transitive closure method. Balancing user satisfaction maximization and load fluctuation minimization, time-of-use (TOU) prices are solved by the non-dominated sorting genetic algorithm II (NSGA-II). On the supply side, operating cost and voltage deviation minimization are objectives, with a proposed optimization method coordinating precise continuous regulation devices and low-cost discrete ones. After second-order cone programming and linearization, the multi-objective model is solved via the normalized normal constraint (NNC) algorithm to get a solution set, from which the optimal solution is selected using Entropy Weight and Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS). The results indicate that, in comparison with the proposed method, ADN not implementing demand-side TOU pricing strategies exhibits an increase in operating costs by 13.83% and a rise in voltage deviation by 4.14%. Meanwhile, ADN utilizing only traditional discrete control devices demonstrates more significant increments, with operating costs increasing by 182.40% and voltage deviation rising by 113.02%. Full article

Radio frequency (RF) fingerprinting, as an emerging physical layer security technology, demonstrates significant potential in the field of Internet of Things (IoT) security. However, most existing methods operate under a ‘closed-set’ assumption, failing to effectively address the continuous emergence of unknown devices in real-world scenarios. To tackle this challenge, this paper proposes an open-set radio frequency fingerprint identification (RFFI) method based on Multi-Task Prototype Learning (MTPL). The core of this method is a multi-task learning framework that simultaneously performs discriminative classification, generative reconstruction, and prototype clustering tasks through a deep network that integrates an encoder, a decoder, and a classifier. Specifically, the classification task aims to learn discriminative features with class separability, the generative reconstruction task aims to preserve intrinsic signal characteristics and enhance detection capability for out-of-distribution samples, and the prototype clustering task aims to promote compact intra-class distributions for known classes by minimizing the distance between samples and their class prototypes. This synergistic multi-task optimization mechanism effectively shapes a feature space highly conducive to open-set recognition. After training, instead of relying on direct classifier outputs, we propose to adopt extreme value theory (EVT) to statistically model the tail distribution of the minimum distances between known class samples and their prototypes, thereby adaptively determining a robust open-set discrimination threshold. Comprehensive experiments on a real-world dataset with 16 Wi-Fi devices show that the proposed method outperforms five mainstream open-set recognition methods, including SoftMax thresholding, OpenMax, and MLOSR, achieving a mean AUROC of 0.9918. This result is approximately 1.7 percentage points higher than the second-best method, demonstrating the effectiveness and superiority of the proposed approach for building secure and robust wireless authentication systems. This validates the effectiveness and superiority of our approach in building secure and robust wireless authentication systems. Full article

The rapid growth of electric vehicles (EVs) poses significant challenges to the safe operation of charging stations and distribution networks. Variations in charging power across different EV manufacturers lead to substantial load fluctuations at charging stations. In some tourist cities in China, charging loads can surge at specific times, yet existing research mainly focuses on optimizing station location and basic capacity configuration, neglecting sudden peak load management. To address this, we propose a method that enhances charging station carrying capacity (CSCC) by coordinating multi-flexibility resources. This method optimizes the configuration of soft open points (SOPs) to enable flexible interconnections between feeders and incorporates elastic load scheduling for data centers. An optimization model is developed to coordinate these flexible resources, thereby improving the CSCC. Case studies demonstrate that this approach effectively increases CSCC at lower costs, facilitates the utilization of renewable energy, and enhances the overall system economy. The results validate the feasibility and effectiveness of the proposed approach, offering new insights for urban grid planning and EV charging stations optimization. Full article

Cardiovascular diseases are responsible for a large number of severe disability cases and deaths worldwide. Strong research in this field has been extensively carried out, in particular for the associated complications, such as the occlusion of small-diameter (<6 mm) vessels. Accordingly, in the present research, two random copolyesters of poly(butylene 2,5-furandicarboxylate) (PBF) and poly(butylene isophthalate) (PBI), were successfully synthesized via two-step melt polycondensation and were thoroughly characterized from molecular, thermal, and mechanical perspectives. The copolymeric films displayed a peculiar thermal behavior, being easily processable in the form of films, although amorphous, with T_g close to room temperature. Their thermal stability was high in all cases, and from the mechanical point of view, the materials exhibited a high ultimate strength, together with values of elastic moduli tunable with the chemical composition. The long-term stability of these materials under physiological conditions was also demonstrated. Cytotoxicity was assessed using a direct contact assay with human umbilical vein endothelial cells (HUVECs). In addition, hemocompatibility was tested by evaluating the adhesion of blood components (such as the adsorption of human platelets and fibrinogen). As a result, a proper chemical design and, in turn, both the solid-state and functional properties, are pivotal in regulating cell behavior and opening new frontiers in the tissue engineering of soft tissues, including vascular tissues. Full article

With the development of modern power systems, AC distribution networks face increasing demands for supply flexibility and reliability. Energy storage-based soft open points (E-SOPs), which integrate energy storage systems into the DC side of traditional SOP connecting AC distribution networks, not only maintain power flow control capabilities but also enhance system supply performance, providing a novel approach to AC distribution network fault recovery. To fully leverage the advantages of E-SOPs in handling faults in flexible interconnected AC distribution networks (FIDNs), this paper proposes an E-SOP-based FIDN islanding recovery method. First, the basic structure and control modes of SOPs for AC distribution networks are elaborated, and the E-SOP-based AC distribution network structure is analyzed. Second, with maximizing total load recovery as the objective function, the constraints of E-SOPs are comprehensively considered, and recovery priorities are established based on load importance classification. Then, a multi-dimensional improvement of the dung beetle optimizer (DBO) algorithm is implemented through Logistic chaotic mapping, adaptive parameter adjustment, elite learning mechanisms, and local search strategies, resulting in an efficient solution for AC distribution network power supply restoration. Finally, the proposed FIDN islanding partitioning and fault recovery methods are validated on a double-ended AC distribution network structure. Simulation results demonstrate that the improved DBO (IDBO) algorithm exhibits a superior optimization performance and the proposed method effectively enhances the load recovery capability of AC distribution networks, significantly improving the self-healing ability and operational reliability of AC distribution systems. Full article

The paper introduces a new hybrid optimization algorithm, HWCAPSO, for optimal distributed generator (DG) placement and soft-open point (SOP) size determination along with network reconfiguration. The hierarchical algorithm combining the Water Cycle Algorithm (WCA) and Particle Swarm Optimization (PSO) is introduced to solve this nonconvex problem. WCA excels in global exploration due to its water-cycle-inspired diversification, while PSO’s velocity-based update mechanism ensures rapid local convergence. Their hybrid synergy mitigates premature convergence in challenging problems. The proposed HWCAPSO algorithm uniquely integrates the global exploration capability of WCA with the local exploitation strength of PSO in a hierarchical framework, addressing the mixed-integer nonlinear programming (MINLP) challenges of simultaneous DG/SOP allocation and reconfiguration gap in existing hybrid methods. It aims to optimize total active power losses while fulfilling operational constraints such as voltage limits, thermal capacities, and radiality. The efficiency of the HWCAPSO is confirmed by exhaustive case studies from the 33-bus test system and the 69-bus test system, where its performance is compared with that of individual WCA and PSO. Findings show that HWCAPSO yields better loss reduction (up to 92.4% for the 33-bus network as and 92.7% for the 69-bus network), enhanced voltage profiles, as well as satisfactory convergence characteristics. Results are statistically validated over 30 independent runs, with 95% confidence intervals confirming robustness. The versatility of the algorithm to deal with intricate, multi-objective optimization applications make it an efficient option for real distribution network planning and operation. Full article

With the growing integration of renewable sources on a large scale into modern power systems, the operation of distribution networks faces significant challenges under fluctuating renewable energy outputs. Therefore, achieving multi-objective optimization over multiple time periods, including minimizing energy losses and maximizing renewable energy utilization, has become a pressing issue. This paper proposes a Collaborative Intelligent Optimization Reconfiguration Strategy (CIORS) based on a dual-agent framework to achieve a global collaborative optimization of distribution networks in a multi-time period environment. CIORS addresses goal conflicts in multi-objective optimization by designing a collaborative reward mechanism. The discrete agent and continuous agent are responsible for optimizing the switch states within the distribution grid while coordinating the control of both active and reactive power flows through Soft Open Points (SOPs), respectively. To respond to the dynamic fluctuations of loads and renewable energy outputs, CIORS incorporates a dynamic weighting mechanism into the comprehensive reward function, allowing the flexible adjustment of the priority of each optimization objective. Furthermore, CIORS introduces a prioritized experience replay (PER) mechanism, which improves sample utilization efficiency and accelerates model convergence. Simulation results based on an actual distribution network in a specific area demonstrate that CIORS is effective under high-penetration clean energy scenarios. Full article

This paper proposes a flexible distribution network operation optimization strategy considering mobile energy storage system (MESS) integration. With the increasing penetration of renewable energy in power systems, its stochastic and intermittent characteristics pose significant challenges to grid stability. This study introduces an MESS, which has both spatial and temporal controllability, and soft open point (SOP) technology to build a co-scheduling framework. The aim is to achieve rational power distribution across spatial and temporal scales. In this paper, a case study uses a regional road network in Chengdu coupled with an IEEE 33-node standard grid, and the model is solved using the non-dominated sorting genetic algorithm III (NSGA-III) algorithm. The simulation results show that the use of the MESS and SOP co-dispatch in the grid not only reduces the net loss and total voltage deviation but also obtains considerable economic benefits. In particular, the net load peak-to-valley difference is reduced by 20.1% and the total voltage deviation is reduced by 52.9%. This demonstrates the effectiveness of the proposed model in improving the stability and economy of the grid. Full article

By using a soft normal open point (SNOP) to connect multiple distribution networks to form a flexible interconnected distribution system (FIDS), the power distribution can be flexibly and controllably regulated among distribution stations, but it is also necessary to ensure the system’s operational efficiency and maintain voltage quality when carrying out optimal scheduling. In this paper, a FIDS optimal scheduling strategy considering dynamic weight grading is proposed. By considering the voltage overrun status of each distribution station area, the voltage level of each distribution station area is divided into three voltage overrun situations, including normal operation, safe boundary, and protection boundary levels, and an optimal scheduling model applicable to the multi-level operation of the FIDS is constructed. In order to adapt to the coordinated optimal operation objectives under different overrun levels, an optimal operation strategy considering the dynamic weights of system operation cost, voltage deviation, customer satisfaction, and SNOP regulation capability is proposed and finally simulated and verified using the improved IEEE33 node arithmetic case. The results verify the effectiveness of the method proposed in this paper in improving the system’s operational efficiency and node voltage quality. Full article

The penetration rate of distributed photovoltaic (PV) in mountainous distribution networks is increasing year by year, and the assessment of distributed PV hosting capacity (PVHC) in distribution networks in mountainous areas is also becoming more and more important. To this end, this paper proposes a robust assessment method for distributed PVHC of flexible distribution networks in mountainous areas. The method utilizes soft open point (SOP) and energy storage to realize the flexible interconnection of distribution networks in mountainous areas, connecting the low-voltage nodes at the end of distribution networks in mountainous areas and improving the overall power quality of distribution networks. Secondly, the output curves of distributed PV output and load demand are analyzed and the distributed PV uncertainty model is drawn, so as to construct a two-layer robust assessment model of distributed PVHC for mountainous flexible distribution networks. Finally, the dual-layer robust assessment model, which cannot be solved directly, is transformed into a solvable mixed-integer linear programming model using the pairwise method, and the effectiveness of this paper’s method is verified by the simulation results of the IEEE 33-node distribution network system. Full article

This paper presents a comprehensive optimization framework for modern distribution systems, integrating distribution system reconfiguration (DSR), soft open point (SOP) operation, photovoltaic (PV) allocation, and energy storage system (ESS) management to minimize daily active power losses. The proposed approach employs a novel hybrid metaheuristic algorithm, the Cheetah-Grey Wolf Optimizer (CGWO), which synergizes the global exploration capabilities of the Cheetah Optimizer (CO) with the local exploitation strengths of Grey Wolf Optimization (GWO). The optimization model addresses time-varying loads, renewable generation profiles, and dynamic network topology while rigorously enforcing operational constraints, including radiality, voltage limits, ESS state-of-charge dynamics, and SOP capacity. Simulations on a 33-bus distribution system demonstrate the effectiveness of the framework across eight case studies, with the full DER integration case (DSR + PV + ESS + SOP) achieving a 67.2% reduction in energy losses compared to the base configuration. By combining the global exploration of CO with the local exploitation of GWO, the hybrid CGWO algorithm outperforms traditional techniques (such as PSO and GWO) and avoids premature convergence while preserving computational efficiency—two major drawbacks of standalone metaheuristics. Comparative analysis highlights CGWO’s superiority over standalone algorithms, yielding the lowest energy losses (997.41 kWh), balanced ESS utilization, and stable voltage profiles. The results underscore the transformative potential of coordinated DER optimization in enhancing grid efficiency and reliability. Full article

With the increasing penetration of renewable energy in the power system, how to ensure the normal operation of urban microgrids is gradually receiving attention. It is necessary to evaluate the overall active support capability and provide optimal operation strategies for urban microgrids. The paper proposes an active–reactive power coordinated optimization method for urban microgrids with a high proportion of renewable energy. Firstly, a quantification model of the active support capability is established to evaluate the active support capacity and reactive support capacity of urban microgrids, respectively. Then, an active–reactive power collaborative optimization model, which considers multiple types of distributed resources, is established to provide optimal scheduling strategies for urban microgrids. Consequently, a platform integrating evaluation and regulation functions is constructed to enable the evaluation of the active support capability for distributed resources in urban microgrids and the scheduling of distributed resource operations. This paper aims to solve the key technical challenges of the safe operation of new urban microgrids. The simulation results demonstrate that the proposed optimal scheduling method can reduce the comprehensive operating costs of urban microgrids with high renewable energy penetration by up to 19.86% and decrease the voltage deviation rate by up to 7.25%, simultaneously improving both economic efficiency and operational security. Full article

With the increasing penetration of high-proportion renewable energy sources and large-scale integration of power electronic devices, distribution networks are evolving towards power-electronized systems. The integration of high-proportion renewable energy introduces challenges such as bidirectional power flow and voltage violations. Unlike traditional voltage regulation devices with slow and discrete adjustment characteristics, power electronic devices can continuously and rapidly respond to voltage fluctuations in distribution networks. However, the integration of power electronic devices alters the operational paradigm of distribution networks, necessitating adaptive voltage-reactive power control methods tailored to the regulation characteristics of both power electronic devices and discrete equipment. To fully exploit the real-time regulation capabilities of power electronic devices, this paper established a hierarchical coordinated control model for power-electronized distribution networks to achieve optimal voltage-reactive power control. A three-stage hierarchical coordinated control architecture is proposed based on the distinct response speeds of different devices. A variable-slope linear droop control method based on voltage boundary parameter optimization is employed for real-time adjustment of soft open point (SOP) and inverter outputs. To address uncertainties in PV generation and load demand, a rolling optimization strategy is implemented for centralized control, supplemented by probabilistic modeling to generate multiple representative scenarios for hierarchical coordinated control. Case studies demonstrate optimized operational results across centralized and local control stages, with comparative analyses against existing voltage-reactive power control methods confirming the superiority of the proposed hierarchical coordinated control framework. Full article

The joint of a shield tunnel segment is the weak part of tunnel, and the opening amount of the joint seriously affects the watertightness of the internal structure of the tunnel. In this experiment, a model was created with ANSYS, the fluid–solid coupling effect of the seawater and seabed was considered using the SuperFLUSH/2D 6.0 software, and the local site effect was considered by free-field seismic response analysis. Considering the structure and stress characteristics of the shield tunnel in conjunction with the marine area, earthquake research on shield tunnel culverts was conducted using lateral and longitudinal beam–spring models. The main focus of this article is to study the earthquake resistance of shield tunnel joints under extreme seismic excitation (SL-2) in complex marine environments. The results indicated that in the lateral analysis, under varying soil layer conditions, the diameter deformation rates for sections 1 and 2 using high-strength bolts were 1.752% and 1.334%, respectively, while the joint-opening amounts were 0.515 mm and 0.387 mm, respectively. This suggests that locations with thicker silt layers exhibit larger joint-opening amounts and are more susceptible to deformation. In the longitudinal analysis, when bolt strength varied, the maximum joint-opening ranged from 4.706 mm to 6.507 mm, and the maximum dislocation ranged from 0.625 mm to 1.326 mm. The deformation rule of the joint bolts followed the pattern that higher stiffness led to smaller deformation, whereas poorer geological conditions resulted in larger deformation. Therefore, the interface between soft and hard strata is a weak point in the longitudinal seismic resistance of the shield tunnel structure. The conclusions of this study can supplement the seismic research on shield tunnels in the marine areas of nuclear power plants. Full article