With the improvement of the energy density and sensing accuracy of wearable devices, there is increasing interest in applying wearable electronics in daily life. However, traditional rigid plate-structured wearable devices cannot meet the human body’s wearing habits and make users may feel uncomfortable after wearing them for a long time. Fabric-type wearable electronics can be conformably coated on human skin without discomfort from mismatches in mechanical properties between the human body and electronics. Although state-of-the-art textile-based wearable devices have shown unique advantages in the field of e-textiles, real-world scenarios often involve stretching, bending, and wetting. Further efforts should be made to achieve “comfortable wearing” due to the great challenge of achieving both promising electrical properties and comfort in a single device. This review presents a comprehensive overview of the advances in smart fabric-based wearable electronics toward comfortable wearing, emphasizing their stretchability, hydrophobicity, air permeability, stability, and color-change abilities. Through addressing the challenges that persist in fabric-type wearable electronics, we are optimistic that these will be soon ubiquitous in our daily lives, offering exceptionally comfortable wearing experiences for health monitoring, sports performance tracking, and even fashion, paving the way for a more comfortable and technologically advanced future.

By implementing a proactive reserve scheduling mechanism, distribution networks (DNs) can optimize emergency resource deployment to improve fault recovery resilience. To address the limitations of existing pre-disaster preparation strategies that only consider limited resources, this paper proposes a proactive scheduling strategy that integrates mobile resources and field personnel in a coordinated manner for DNs. By establishing a two-stage stochastic mixed-integer programming (SMIP) model for coordinated control of emergency resources in DNs, the first stage determines the quantity and location of mobile energy storage systems (MESSs), repair crews (RCs), and switching crews (SCs). In the second stage, the emergency resources rapidly reach the affected sites to participate in sequential restoration of the DN. Finally, the model is validated using standard IEEE test systems. The results from the experiments demonstrate that the proposed method reduces load shedding cost by 19.0% and 19.9% on 33-node and 123-node systems. Empirical simulations confirm that the introduced framework enables efficient emergency resource orchestration, thereby enhancing the pre-disaster preventive response capability and post-disaster real-time restoration capability of the DN, significantly mitigating the impact of disruptive events.

Multi-energy coupled low-carbon distribution networks (MEC-LCDNs) face growing risks from extreme weather and high-order contingencies. Traditional deterministic criteria (e.g., N-1) often overlook these low-probability, high-impact events, while existing simulation-based probabilistic methods suffer from excessive computational burdens and a lack of intuitive visualization. To address these challenges, this paper proposes an explicit representation method for MEC-LCDN operational reliability constraints based on the probabilistic reliability region (PRR). This approach transforms the abstract probabilistic reliability criterion—loss of load probability (LOLP)—into a visualizable geometric space. Specifically, a fast contingency screening technique (FCST) is developed to identify a minimal set of boundary scenarios that anchor the target reliability threshold. Subsequently, complex probabilistic constraints are decoupled into deterministic N-k security constraints under these boundary scenarios, enabling the analytical construction of the PRR boundary. A case study demonstrates that the proposed method reduces the number of required contingency scenarios by over 90% and slashes computation time from 78.8 s to 3.1 s compared to traditional N-k truncation methods. Furthermore, the method accurately quantifies the system’s total supply capability (TSC) at 44.501 MW while providing intuitive visualizations of reliability boundaries that satisfy stringent LOLP criterion.

In low-voltage distribution networks, load switching, induction motor start-up, photovoltaic output variations, and short-circuit faults may produce highly overlapping electrical characteristics, which can lead to maloperation or failure to operate in conventional protection. To address this problem, this paper proposes an adaptive protection method integrating physically guided and cost-sensitive learning. First, an incremental topology-constraint deviation and a voltage-current trajectory curvature are constructed based on the fault-superimposed network constraint and the variation characteristics of system equivalent impedance, enabling the discrimination of short-circuit faults from non-fault transient disturbances. Then, a cost-sensitive physically guided extreme gradient boosting (XGBoost) model is developed, in which a fault-current-increment-based weight is introduced into the objective function to enhance the learning capability for weak-fault samples. Furthermore, a temporal-consistency-based protection operation logic is designed using sliding-window confirmation and majority voting to suppress isolated abnormal predictions. Simulation and RTDS-based real-time validation results on a 0.4-kV low-voltage distribution network with distributed photovoltaic generation show that the proposed method improves weak-fault detection sensitivity and reduces maloperation under complex source–load disturbances. The method relies only on local measurements and has potential for deployment in low-voltage intelligent protection terminals.