Intelligent driving has advanced significantly in recent decades, paving the path for the transportation of the future. Digital twin (DT) technology, which can bridge the physical and virtual space gaps in real time, plays an important role in the collaboration of vehicles and roads for intelligent driving. In this paper, we design the Digital Twin empowered Cloud-native Vehicular Networks (DT-CVN) architecture as well as the workflow for task execution aiming at executing intelligent driving tasks efficiently and reliably. In DT-CVN, we propose a design and implementation scheme for digital twins, in which different modules of the same digital twin entity can be deployed in different network locations in a distributed manner by taking advantage of the distributed features of microservices based on cloud-native technology. Furthermore, we design the modules reuse and requests aggregation mechanisms of digital twins invocation for task scheduling in DT-CVN, which can improve its efficiency even further. Then we model the task scheduling in DT-CVN into a combinational optimization problem and propose a deep reinforcement learning (DRL) based adaptive task scheduling algorithm. Simulation results show that the proposed scheme can improve the efficiency of task scheduling while reducing energy consumption.

As a key technology in intelligent driving, Cooperative Vehicle Infrastructure System is an advanced communication framework that enables cooperative and information exchange between vehicles and infrastructure. However, this system encounters challenges in meeting the low latency, ultra reliability, and high efficiency requirements of task execution of intelligent driving applications. Meanwhile, the deployment and combination of some key technologies supporting this system are not well-established. To address these issues, this article proposes a Digital Twin-based Cloud-native Vehicular Networks (DT-CVN) architecture to enhance the efficiency of virtual-reality integration in real-world vehicle traffic scenarios. In DT-CVN, the digital twins, which can bridge the physical space and cyberspace gaps in real-time, are implemented and deployed in a distributed manner by leveraging the distributed features of microservices based on cloud-native technology. DT-CVN employs the cybertwin as a smart communication agent in cloud-native vehicular networks, enabling efficient communication between cyberspace and physical spaces. Moreover, a case study is presented to demonstrate the effectiveness of DT-CVN. Simulation result shows the potential to address the challenges of integrating resources in vehicular networks with our proposed DT-CVN.

The wind-thermal-bundled(WTB) transmission has become an important way of wind power generation delivery. This paper proposes a coordinated design method for power system stabilizer(PSS) and unified power flow controller with power oscillation damping(UPFC-POD) controller to enhance the damping of low-frequency oscillation(LFO) in WTB system. The installation location of UPFC affects the power flow distribution of the system and the active network losses of the system. If installed in an inappropriate location, it will result in large active network losses and high system operating costs. This paper adopts the multi-objective salp swarm algorithm(MSSA) to transform controller coordination optimization and UPFC location optimization into multi-objective optimization problem, in order to enhance the stability and operational economy of the WTB system. The proposed method is evaluated on an IEEE 4-machine 2-area system. The analysis of the results shows that the optimized controller has good ability to damp LFO and reduce network losses in WTB system.