This paper provides a systematic review on the resilience analysis of active distribution networks (ADNs) against hazardous weather events, considering the underlying cyber–physical interdependencies. As cyber–physical systems, ADNs are characterized by widespread structural and functional interdependencies between cyber (communication, computing, and control) and physical (electric power) subsystems and thus present complex hazardous-weather-related resilience issues. To bridge current research gaps, this paper first classifies diverse hazardous weather events for ADNs according to different time spans and degrees of hazard, with model-based and data-driven methods being utilized to characterize weather evolutions. Then, the adverse impacts of hazardous weather on all aspects of ADNs’ sources, physical/cyber networks, and loads are analyzed. This paper further emphasizes the importance of situational awareness and cyber–physical collaboration throughout hazardous weather events, as these enhance the implementation of preventive dispatches, corrective actions, and coordinated restorations. In addition, a generalized quantitative resilience evaluation process is proposed regarding additional considerations about cyber subsystems and cyber–physical connections. Finally, potential hazardous-weather-related resilience challenges for both physical and cyber subsystems are discussed.
The surge of distributed renewable energy resources has given rise to the emergence of prosumers, facilitating the low-carbon transition of distribution networks. However, flexible prosumers introduce bidirectional power and carbon interaction, increasing the complexity of practical decision-making in distribution networks. To address these challenges, this paper presents a carbon-coupled network charge-guided bi-level interactive optimization method between the distribution system operator and prosumers. In the upper level, a carbon-emission responsibility settlement method that incorporates the impact of peer-to-peer (P2P) trading is proposed, based on a carbon-emission flow model and optimal power flow model, leading to the formulation of carbon-coupled network charges. In the lower level, a decentralized P2P trading mechanism is developed to achieve the clearing of energy and carbon-emission rights. Furthermore, an alternating direction method of multipliers with an adaptive penalty factor is introduced to address the equilibrium of the P2P electricity–carbon coupled market, and an improved bisection method is employed to ensure the convergence of the bi-level interaction. A case study on the modified IEEE 33-bus system demonstrates the effectiveness of the proposed model and methodology.
The dynamics of network power response play a crucial role in system stability. However, the integration of power electronic equipment leads to amplitude and angular frequency (abbreviated as “frequency”) time-varying characteristics of the node voltage during dynamic processes. As a result, traditional calculation methods for and characteristics of the power response of the network based on phasor and impedance lose their validity. Therefore, this paper undertakes mathematical calculations to reveal the power response of a network under excitation by voltage with time-varying amplitude and frequency (TVAF), relying on the original mathematical relationships and superimposed step response. Then, the multi-timescale characteristics of both the active and reactive power of the network are explored physically. Additionally, this paper reveals a new phenomenon of storing and releasing the active and reactive power of the network. To meet practical engineering requirements, a simplified power expression is presented. Finally, the theoretical analysis is validated through time-domain simulations.
As the number of distributed power supplies increases on the user side, smart grids are becoming larger and more complex. These changes bring new security challenges, especially with the widespread adoption of data-driven control methods. This paper introduces a novel black-box false data injection attack (FDIA) method that exploits the measurement modules of distributed power supplies within smart grids, highlighting its effectiveness in bypassing conventional security measures. Unlike traditional methods that focus on data manipulation within communication networks, this approach directly injects false data at the point of measurement, using a generative adversarial network (GAN) to generate stealthy attack vectors. This method requires no detailed knowledge of the target system, making it practical for real-world attacks. The attack’s impact on power system stability is demonstrated through experiments, highlighting the significant cybersecurity risks introduced by data-driven algorithms in smart grids.
Fast and accurate transient stability analysis is crucial to power system operation. With high penetration level of wind power resources, practical dynamic security region (PDSR) with hyper plane expression has outstanding advantages in situational awareness and series of optimization problems. The precondition of obtaining accurate PDSR boundary is to locate sufficient points around the boundary (critical points). Therefore, this paper proposes a space division and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based fast generation method of PDSR boundary. First, the typical differential topological characterizations of dynamic security region (DSR) provide strong theoretical foundation that the interior of DSR is hole-free and the boundaries of DSR are tight and knot-free. Then, the space division method is proposed to calculate critical operation area where the PDSR boundary is located, tremendously compressing the search space to locate critical points and improving the confidence level of boundary fitting result. Furthermore, the WGAN-GP model is utilized to fast obtain large number of critical points based on learning the data distribution of the small training set aforementioned. Finally, the PDSR boundary with hyperplanes is fitted by the least square method. The case study is tested on the Institute of Electrical and Electronics Engineers (IEEE) 39-bus system and the results verify the accuracy and efficiency of the proposed method.
Establishing power systems with a high share of renewable energy sources is a pivotal step toward achieving a globally sustainable transition to green and low-carbon energy. This study focuses on low-output wind power that affects the generation capacity of power systems with a high share of renewable energy sources. Utilizing the Coupled Model Intercomparison Project Phase 6 datasets, a predictive model for low-output wind power was employed to investigate regional trends worldwide. The frequency and duration of low-output wind-power events exhibited increasing trends globally, particularly in East Asia and South America, but not in North America. By 2060, the annual total days with low-output wind power in East Asia and South America could rise to 13 and 5 d, and the maximum continuous duration of low-output wind power could reach 5 and 2 d, respectively. As wind power becomes a primary electricity source, such low output could lead to shortages in energy supply within the power system, triggering large-scale power outages. This issue calls for critical attention when establishing power systems with a high share of renewable energy sources. The conclusions provide a basis for analyzing power supply risks and configuring flexible power sources for scenarios with a high share of renewable energy.
The continuously increasing renewable energy sources (RES) and demand response (DR) are becoming crucial sources of system flexibility. Consequently, decision-dependent uncertainties (DDUs), interchangeably referred to as endogenous uncertainties, impose new characteristics on power system dispatch. The DDUs faced by system operators originate from uncertain dispatchable resources such as RES units or DR, while reserve providers encounter DDUs from the uncertain reserve deployment. Thus, a systematic framework was established in this study to address robust dispatch problems with DDUs. The main contributions are drawn as follows. ① The robust characterization of DDUs was unfolded with a dependency decomposition structure. ② A generic DDU coping mechanism was manifested as the bilateral matching between uncertainty and flexibility. ③ The influence of DDU incorporation on the convexity/non-convexity of robust dispatch problems was analyzed. ④ Generic solution algorithms adaptive for DDUs were proposed. Under this framework, the inherent distinctions and correlations between DDUs and decision-independent uncertainties (DIUs) were revealed, laying a fundamental theoretical foundation for the economic and reliable operation of RES-dominated power systems. Illustrative applications in the source and demand sides are provided to show the significance of considering DDUs and demonstrate the proposed theoretical results.
In recent years, renewable energy (RE) penetration has become an important target in power systems. However, RE power is affected by climate change and has strong randomness and volatility. Adequate transmission capacity and energy storage systems (ESSs) are conducive to the integration of RE. Therefore, coordinated transmission renewable–storage expansion planning (TRSEP) is an effective decision-making approach to cope with the impacts of climate change and achieve the development target of RE penetration. Electricity trading between different systems is common; therefore, in addition to the penetration of RE into the internal loads of the system, the proportion of RE generation in tie lines is gaining attention, making analyses of the RE transmission path necessary. Referring to the flow of carbon emissions, this paper defines the RE power flow density to track the transmission path of RE. Next, a TRSEP model is proposed that can clearly distinguish the RE transmission path into internal loads, external loads, and energy losses. To address the presence of bilinear terms in the proposed model, the McCormick method is applied, and a customized feasibility correction strategy is designed to obtain a good feasible solution. Numerical results from case studies are provided to verify the rationality and effectiveness of the approach proposed in this paper.
Interconnection planning involving bi-directional converters (BdCs) is crucial for enhancing the reliability and robustness of hybrid alternating current (AC)/direct current (DC) microgrid clusters with high penetrations of renewable energy resources (RESs). However, challenges such as the non-convex nature of BdC efficiency and renewable energy uncertainty complicate the planning process. To address these issues, this paper proposes a tri-level BdC-based planning framework that incorporates dynamic BdC efficiency and a data-correlated uncertainty set (DcUS) derived from historical data patterns. The proposed framework employs a least-squares approximation to linearize BdC efficiency and constructs the DcUS to balance computational efficiency and solution robustness. Additionally, a fully parallel column and constraint generation algorithm is developed to solve the model efficiently. Numerical simulations on a practical hybrid AC/DC microgrid system demonstrate that the proposed method reduces interconnection costs by up to 21.8% compared to conventional uncertainty sets while ensuring robust operation under all considered scenarios. These results highlight the computational efficiency, robustness, and practicality of the proposed approach, making it a promising solution for modern power systems.
Renewable energy sources (RES) have strong uncertainties, which significantly increase the risks of power imbalance and load shedding in composite power systems. It is thus necessary to evaluate the operational reliability for guiding economic dispatch and reducing the risks. Current methods cannot meet the requirement for the operational timeliness of reliability evaluations due to the high computational complexity of the optimal power flow (OPF) calculations of massive contingencies. This paper proposes a fully analytical approach to construct fast-to-run analytical functions of reliability indices and avoid reassessments when the load and RES change. The approach consists of uniform design (UD)-based contingency screening and a modified stochastic response surface method (mSRSM). The contingency screening method is used to select critical contingencies while considering the uncertainties. The mSRSM is used to construct the analytical functions of the load shedding to the load and RES generation for the selected contingencies. An analytical function of a smooth virtual variable that maps to the load shedding is established in such a way that, when the load and RES vary, the reliability can be assessed within a very short time rather than using laborious OPF calculations. Case studies illustrate the excellent performance of the proposed method for real-time reliability evaluation.
In wind and solar renewable-dominant hybrid alternating current/direct current (AC/DC) power systems, the active power of high-voltage direct current (HVDC) system is significantly limited by the security and stability events caused by cascading failures. To identify critical lines in cascading failures, a rapid risk assessment method is proposed based on the gradient boosting decision tree (GBDT) and frequent pattern growth (FP-Growth) algorithms. First, security and stability events triggered by cascading failures are analyzed to explain the impact of cascading failures on the maximum DC power. Then, a cascading failure risk index is defined, focusing on the DC power being limited. To handle the strong nonlinear relationship between the maximum DC power and cascading failures, a GBDT with an update strategy is utilized to rapidly predict the maximum DC power under uncertain operating conditions. Finally, the FP-Growth algorithm is improved to mine frequent patterns in cascading failures. The importance index for each fault in a frequent pattern is defined by evaluating its impact on cascading failures, enabling the identification of critical lines. Simulation results of a modified Ningxia–Shandong hybrid AC/DC system in China demonstrate that the proposed method can rapidly assess the risk of cascading failures and effectively identify critical lines.
Electromagnetic devices have been widely used in the fields of information communication, medical treatment, electrical engineering, and national defense, and their properties are strongly dependent on the constituent electromagnetic materials. Conversely, electromagnetic metamaterials (EMMs), which are artificially engineered with distinctive electromagnetic properties, can overcome the limitations of natural materials owing to their structural advantages. Three-dimensional (3D) printing is the most effective technique for fabricating EMM devices with different geometric parameters and associated properties. However, conventional 3D-printed EMM devices may lack manufacturing flexibility and environmental adaptability to different physical stimuli, such as electric and magnetic fields. Four-dimensional (4D) printing is an ideal technique for schemes to integrate structural design with intelligent materials environmentally adaptive to external fields, for example, the printed components can change shape under electric stimulation. Given the rapid advancements in the EMM field, this paper first reviews typical EMM devices, their design theories, and underlying principles. Subsequently, it presents various EMM structural topologies and manufacturing technologies, emphasizing the feasibility of combining 3D and 4D printing. In addition, we highlight the important applications of EMMs and their future trends and the challenges associated with functional EMMs and additive manufacturing.
Underwater robots have emerged as key tools for marine exploration because of their unique ability to traverse and navigate underwater regions, which pose challenges or dangers to human expeditions. Miniature underwater robots are widely employed in marine science, resource surveys, seabed geological investigations, and marine life observations, owing to their compact size, minimal noise, and agile movement. In recent years, researchers have developed diverse miniature underwater robots inspired by bionics and other disciplines, leading to many landmark achievements such as centimeter-level wireless control, movement speeds up to hundreds of millimeters per second, underwater three-dimensional motion capabilities, robot swarms, and underwater operation robots. This article offers an overview of the actuation methods and locomotion patterns utilized by miniature underwater robots and assesses the advantages and disadvantages of each method. Furthermore, the challenges confronting currently available miniature underwater robots are summarized, and future development trends are explored.
In-memory computing (IMC) based on spin-logic devices is regarded as an advantageous way to optimize the Von Neumann bottleneck. However, performing complete Boolean logic with spintronic devices typically requires an initialization operation, which can reduce processing speed. In this work, we conceptualize and experimentally demonstrate a programmable and initialization-free spin-logic gate, leveraging spin-orbit torque (SOT) to effectuate magnetization switching, assisted by in-plane Oersted field generated by an integrated bias-field Au line. This spin-logic gate, fabricated as a Hall bar, allows complete Boolean logic operations without initialization. A current flowing through the bias-field line, which is electrically isolated from the device by a dielectric, generates an in-plane magnetic field that can invert the SOT-induced switching chirality, enabling on-the-fly complete Boolean logic operations. Additionally, the device demonstrated good reliability, repeatability, and reproducibility during logic operations. Our work demonstrates programmable and scalable spin-logic functions in a single device, offering a new approach for spin-logic operations in an IMC architecture.
Spray-drying is a widely used industrial technique to achieve the scale-up fabrication of functional powders. In this work, we report the spray-drying fabrication of perovskite quantum dot (PQD) microspheres from a precursor solution at a scale of 2000 kg∙a−1. The obtained PQDs are embedded in polymer microspheres, resulting in a high photoluminescence quantum yield and enhanced stability. By controlling the precursor concentration, the average size of the polymer microspheres can be tuned from 40.97 to 0.44 μm. The as-prepared PQD-embedded polymer microspheres are mixed with ultraviolet adhesive to fabricate PQD-enhanced optical films for liquid crystal display (LCD) backlights. These films exhibit long-term operational stability under heat, humidity, and blue light irradiation (remaining at more than 90% initial photoluminescence intensity after a 1000 h aging test at 60 °C with 90% relative humidity and 70 °C with 455 nm 150 W∙m−2 blue light irradiation). In addition, we demonstrate the use of PQD-embedded polymer microspheres as patterned color converters for micro light-emitting diode applications. Overall, this work demonstrates the scale-up fabrication of PQDs toward industrialization in display technology.
Sequential-modular-based process flowsheeting software remains an indispensable tool for process design, control, and optimization. Yet, as the process industry advances in intelligent operation and maintenance, conventional sequential-modular-based process-simulation techniques present challenges regarding computationally intensive calculations and significant central processing unit (CPU) time requirements, particularly in large-scale design and optimization tasks. To address these challenges, this paper proposes a novel process-simulation parallel computing framework (PSPCF). This framework achieves layered parallelism in recycling processes at the unit operation level. Notably, PSPCF introduces a groundbreaking concept of formulating simulation problems as task graphs and utilizes Taskflow, an advanced task graph computing system, for hierarchical parallel scheduling and the execution of unit operation tasks. PSPCF also integrates an advanced work-stealing scheme to automatically balance thread resources with the demanding workload of unit operation tasks. For evaluation, both a simpler parallel column process and a more complex cracked gas separation process were simulated on a flowsheeting platform using PSPCF. The framework demonstrates significant time savings, achieving over 60% reduction in processing time for the simpler process and a 35%–40% speed-up for the more complex separation process.
Quantification of river flood risks is a prerequisite for floodplain management and development. The lower Yellow River (LYR) is characterized by a complex channel–floodplain system, which is prone to flooding but inhabits a large population on the floodplains. Many floodplain management modes have been presented, but implementation effects of these management modes have not been evaluated correctly. An integrated model was first proposed to evaluate the flood risks to people’s life and property, covering an improved module of two-dimensional (2D) morphodynamic processes and a module of flood risk evaluation for people, buildings and crops on the floodplains. Two simulation cases were then conducted to validate the model accuracy, including the hyperconcentrated flood event and dike-breach induced flood event occurring in the LYR. Finally, the integrated model was applied to key floodplains in the LYR, and the effects of different floodplain management modes were quantified on the risks to people’s life and property under an extreme flood event. Results indicate that: ① Satisfactory accuracy was achieved in the simulation of these two flood events. The maximum sediment concentration was just underestimated by 9%, and the simulated inundation depth agreed well with the field record; ② severe inundation was predicted to occur in most domains under the current topography (Scheme I), which would be alleviated after implementing different floodplain management modes, with the area in slight inundation degree accounting for a large proportion under the mode of “construction of protection embankment” (Scheme II) and the area in medium inundation degree occupying a high ratio under the mode of “floodplain partition harnessing” (Scheme III); and ③ compared with Scheme I, the high-risk area for people’s life and property would reduce by 21%–49% under Scheme II, and by 35%–93% under Scheme III.
Engineered water systems such as wastewater treatment plants (WWTPs) are potential reservoirs of various biological risk factors (BRFs), including pathogens, antibiotic resistance genes (ARGs), and virulence factors (VFs). Currently, a BRF database relevant to engineered water systems on a global geographic scale is lacking. Here, we present the Global Wastewater Pathogen Database (GWPD), an online database that provides information on the diversity, abundance, and distribution of BRFs from 1302 metagenome samples obtained from 186 cities, 68 countries, and six continents. We sorted these samples into six types: sewer networks, influent, anoxic activated sludge, oxic activated sludge, effluent, and receiving/natural waters. In total, 476 pathogens, 442 ARGs, and 246 VFs were identified. As a multifunctional database, GWPD provides an interactive visualization of these BRFs in a world map, an information retrieval interface, and an online one-click service for BRF annotation from metagenome sequencing data. GWPD is built on a web service framework, which can be readily extended to future versions of GWPD by adding more functional modules and connecting to other data sources, such as epidemic databases, to support risk assessment and control in the context of “One Health.”
To address the rising global agricultural labor costs, there is an urgent need for robots to accomplish some complex agronomic tasks and break through the limitations of traditional machinery. Thus, robots are considered an essential support for the future smart agriculture. Given that agronomic targets, such as plants and animals, are living organisms with diverse growth patterns and physical characteristics, effective hand–eye coordination is crucial for robots to interact with these targets proficiently. This paper reviews the developments in hand–eye coordination technology for agricultural robots, focusing on its configuration, principles, and applications in target detection and manipulation, based on a review of research literature and technical specifications of commercial products. Furthermore, the ongoing challenges in hand–eye coordination for robotic operations in complex agronomic tasks are analyzed and summarized, and the potential trends for overcoming these challenges are predicted. Finally, this review aims to deepen understanding of agricultural robots’ capabilities and inspire ongoing innovation to further their agricultural applications.
Ovarian cancer (OC), a common malignancy of the female reproductive system, has the highest mortality rate among gynecological cancers. A distinguishing feature of OC cells (OCCs) is their reduced autophagic flux compared with normal cells. This phenomenon indicates that excessive autophagy activation or impaired autophagosome–lysosome fusion may lead to OCC death. This study investigated the anti-OC effects of dihydrotanshinone I (DHT), a tanshinone compound from Salvia miltiorrhiza. Proteomic analysis suggested that DHT suppressed OC growth via the autophagy–lysosome pathway, with sortilin 1 (SORT1) identified as a critical target. In vitro, DHT promoted autophagosome formation mediated by microtubule-associated protein 1 light chain 3-II (LC3-II), while inhibiting autophagosome–lysosome fusion. The results of an orthotopic OC model corroborated these findings, showing that DHT induced autophagic cell death (ACD) and suppressed SORT1 expression in tumors. Further RNA interference experiments confirmed that SORT1 depletion caused autophagosomes to accumulate in OCCs. Notably, we found that SORT1 interacted with autophagy-related gene (ATG)-encoded proteins ATG5 and ATG16L1, and that depleting SORT1 increased the levels of these proteins. Co-immunoprecipitation, ubiquitination, and cellular thermal shift assay analyses revealed that DHT directly targeted and promoted ubiquitin-dependent degradation of SORT1. By degrading SORT1, ATG5 and ATG16L1 were released, which enhanced autophagosome formation and disrupted the autophagic flux. These findings identified DHT as a novel autophagosome inducer that induced ACD by targeting SORT1, making it a promising therapeutic candidate for OC.
Pathological cardiac hypertrophy contributes to the development of heart failure (HF). NOL1/NOP2/Sun domain family member 2 (NSUN2) is implicated in pathophysiological processes of many diseases. However, the function and operation of NSUN2 in cardiac hypertrophy and HF remain unclear. Here, we observed a significant increase in the levels of NSUN2 expression in both human hearts with HF and in mouse hearts with hypertrophy induced by transverse aortic constriction (TAC) and angiotensin II (Ang II) treatment. Cardiomyocyte-specific knockout of NSUN2 attenuated the reduced cardiac ejection fraction (EF) and fractional shortening (FS) and the increased heart weight to tibial length (HW/TL) upon either TAC or Ang II infusion. Conversely, cardiac-specific overexpression of NSUN2 resulted in cardiac remodeling as indicated by a prominent increase in hypertrophic growth and cardiac fibrosis and a robust decline in cardiac EF and FS. Mechanistically, NSUN2 induces 5-methylcytosine (m5C) modification of La-related protein 1 (LARP1) to enhance its messenger RNA (mRNA) stability, which is mediated by Y-box binding protein 1 (YBX1). Increased LARP1 further interacts with GATA binding protein 4 (GATA4) mRNA and prevents its degradation. LARP1 silencing partially attenuates TAC and NSUN2 induced cardiac hypertrophy and HF. Collectively, this study provides a new insight into the central role of NSUN2 in cardiac hypertrophy, indicating that NSUN2 may serve as a novel therapeutic target for HF.
Deep learning techniques have recently been the most popular method for automatically detecting bridge damage captured by unmanned aerial vehicles (UAVs). However, their wider application to real-world scenarios is hindered by three challenges: ① defect scale variance, motion blur, and strong illumination significantly affect the accuracy and reliability of damage detectors; ② existing commonly used anchor-based damage detectors struggle to effectively generalize to harsh real-world scenarios; and ③ convolutional neural networks (CNNs) lack the capability to model long-range dependencies across the entire image. This paper presents an efficient Vision Transformer-enhanced anchor-free YOLO (you only look once) method to address these challenges. First, a concrete bridge damage dataset was established, augmented by motion blur and varying brightness. Four key enhancements were then applied to an anchor-based YOLO method: ① Four detection heads were introduced to alleviate the multi-scale damage detection issue; ② decoupled heads were employed to address the conflict between classification and bounding box regression tasks inherent in the original coupled head design; ③ an anchor-free mechanism was incorporated to reduce the computational complexity and improve generalization to real-world scenarios; and ④ a novel Vision Transformer block, C3MaxViT, was added to enable CNNs to model long-range dependencies. These enhancements were integrated into an advanced anchor-based YOLOv5l algorithm, and the proposed Vision Transformer-enhanced anchor-free YOLO method was then compared against cutting-edge damage detection methods. The experimental results demonstrated the effectiveness of the proposed method, with an increase of 8.1% in mean average precision at intersection over union threshold of 0.5 (mAP50) and an improvement of 8.4% in mAP@[0.5:.05:.95] respectively. Furthermore, extensive ablation studies revealed that the four detection heads, decoupled head design, anchor-free mechanism, and C3MaxViT contributed improvements of 2.4%, 1.2%, 2.6%, and 1.9% in mAP50, respectively.