The increasing penetrations of renewable energy and electric vehicles bring more uncertainties and challenges to the existing power grid. The coordinated networked microgrids (MGs) contain renewable distributed generations (DGs) and nonrenewable DGs, which will be an important component in the future. We formulate an optimization problem based on a transactive energy (TE) framework for the energy schedule of upstream network and networked MGs to minimize the operation cost. The energy management between MGs and upstream network is operated by the distribution system operator (DSO), which is different from the direct control signal and fixed pricing mechanism in the traditional power system. We develop a distributionally robust optimization algorithm with ambiguity set based on Wasserstein distance (DROW) to solve the optimization problem with the uncertainties from real-time electricity price, renewable energy, loads, and electric vehicles. We carry out case studies about the energy schedule of the modified IEEE 33-bus and IEEE 118-bus power system with networked MGs. Numerical results indicate that the TE framework is conducive to schedule the energy of upstream network and networked MGs efficiently with the dynamic pricing scheme and the proposed DROW algorithm can seek a robust energy schedule of DSO and networked MGs with uncertainties.

The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. Unmanned aerial vehicles (UAVs) present a safer, autonomous, and efficient way to examine the power system components without closing the power distribution system. In this work, a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. A deep Laplacian pyramid-based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low-light images, a low-light image enhancement technique is used for the robust exposure correction of the training images. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. Several flight path strategies are proposed to overcome the shuttering effect of insulators, along with providing a less complex and time- and energy-efficient approach for capturing a video stream of the power system components. The performance of different object detection models is presented for selecting the most suitable one for fine-tuning on the specific faulty insulator dataset. For the detection of damaged insulators, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets and presents a simple and more efficient flight strategy. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust fault recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat, Pakistan.

UAV object detection is crucial in open water search and rescue missions. Due to varying perspectives and altitudes of UAV images, the apparent size of objects varies significantly. Challenges such as insufficient feature representation and background confusion make open water object detection particularly difficult. Currently, deep learning-based detection methods rely on convolution to extract features at a fixed spatial scale. This limited receptive field leads to insufficient feature representation, causing false detections and missed detections, which severely impact detection accuracy. This paper proposes an efficient, feature-enhanced real-time detection network based on transformer architecture, called OWRT-DETR, to address the challenges of diverse UAV image detection in open water. To the best of our knowledge, a Transformer-based detection network has not yet been explored for open water UAV images. OWRT-DETR incorporates a cross-scale feature pyramid module (CFPIM), multi scale sensing fusion (MSSF), and small object enhancement module (SOEM). These modules enhance cross-scale interaction, cross-channel spatial global association, and local perception of the network, while avoiding increased complexity, improving weak feature representation of small targets, and suppressing easily confused backgrounds. Three public datasets are used to validate the effectiveness of OWRT-DETR. OWRT-DETR achieves an averaged precision (AP) of 51.5%, 45.9%, 50.6% on the SeaDronesSee, Aerial Dataset of Floating Objects, and Aerialbus Ship datasets, exceeding the performance of several state-of-the-art models. To ensure efficiency and reduce computational resources, OWRT-DETR is optimized by reconstructing the backbone network using PConv and Rep, resulting in Light-OWRT-DETR. Compared with OWRT-DETR, Light-OWRT-DETR is faster, uses fewer parameters, requires less computational power, and achieves higher accuracy. The code will be available at https://github.com/mshauima/OWRT-DETR.

Traffic congestion control is becoming a popular field of research due to the rapid development of the automotive market. Vehicular ad hoc networks (VANETs) have become the core research technology for numerous application possibilities related to road safety. Road congestions have been a serious issue of all time since the nodes have high mobility and transmission range is limited, resulting in an interruption of communication. One of the significant technical challenges faced in implementing VANET is the design of the routing protocol, providing adequate information and a reliable source for the destination. We proposed a novel mechanism unmanned aerial vehicle (UAV)-assisted ad hoc on-demand distance vector (AODV) routing protocol (UAVa) for current-time traffic information accumulation and sharing to the entire traffic network and to control congestions before it happens. The UAV-assisted (UAVa) protocol is dedicated to urban environments, and its primary goal is to enhance the performance of routing protocols based on intersections. We compared the overall performance of existing routing protocols, namely ad hoc on-demand distance vector. The simulations were done by using OpenStreetMap (OSM), Network Simulator (NS-2.35), Simulation of Urban Mobility (SUMO), and VanetMobiSim. Furthermore, we compared the simulation results with AODV, and it shows that UAV-assisted (UAVa) AODV has significantly enhanced the packet delivery ratio, reduced the end-to-end delay, improved the average and instant throughput, and saved more energy. The results show that the UAVa is more robust and effective and we can conclude that UAVa is more suitable for VANETs.

The availability of conventional mobile-edge computing (MEC) for vehicles is often hindered by signal interference and attenuation, limiting its efficiency in supporting computationally intensive and latency-sensitive applications. To address these challenges, we propose a novel blockchain-enabled vehicular MEC (VMEC) system that enhances resource sharing and energy efficiency in electric vehicle (EV)-centric services. The system employs an improved RAFT-based consensus mechanism (mRAFT), which dynamically evaluates the reputation of access point (AP) nodes based on their available resources, ensuring fair leader election and enhancing consensus reliability and efficiency. Furthermore, a probabilistic model is introduced to describe AP behaviors, improving the security of the consensus process. To minimize overall energy consumption, we develop a decentralized optimization framework using the alternating direction method of multipliers (ADMM). This framework jointly optimizes AP clustering, computation resource allocation, and bandwidth scheduling to achieve energy-efficient task offloading and consensus. Simulation results demonstrate that the proposed VMEC system reduces latency by 29.53% and energy consumption by 43.43% compared to benchmark schemes, showcasing its effectiveness in delivering low-latency, energy-efficient services for advanced vehicular applications.

A high-performance broadband photodetector based on a Zn-doped NiO/p-Si isotype heterojunction was successfully fabricated, which consisted of a Zn-doped NiO film on a Si substrate via a facile sol–gel method. The as-fabricated isotype heterojunction device displays an excellent broad-band (350–650 nm) detection performance with an outstanding external quantum efficiency (EQE) of 89.5% at a small reverse bias of −1 V and as high as ∼184% at −4 V. Moreover, the maximum photo–dark current ratio (switching ratio) of 1793% was achieved at −1 V under illumination with 650 nm light (0.5 mW cm⁻²); additionally, the response time of the as-fabricated device was less than 0.3 s. Such a high-performance of the Zn-doped NiO/p-Si isotype heterojunction photodetector guarantees its potential use in UV-visible low-voltage optoelectronic devices, especially for weak-signal detection and portable equipment.