The Ubiquitous Power Internet of Things (UPIoT) is a concrete manifestation of the Internet of things (IoT) in the power industry, which is a deep integration of the interconnected power network and communication network, realizing full perception of the system status and full business penetration in all links of power production, transmission, and consumption. The introduction of edge computing in UPIoT fully meets the requirements of rapid response, real-time perception, and to some extent, privacy protection. However, there is currently no comprehensive investigation on the application of edge computing technology in UPIoT. First, this paper introduces the development background and construction of UPIoT and its technical architecture. Then the challenges faced by UPIoT in the process of construction are analyzed. Furthermore, the paper elaborates on the functions and features of edge computing, proposes that the support of edge computing technology can solve the challenges of efficient, fast, and secure processing of massive edge data faced by the traditional cloud-based centralized big data processing technology of UPIoT, and analyzes the architecture of the edge computing-assisted UPIoT. For the three typical scenarios of UPIoT, namely power monitoring system, smart energy system and power metering system, the edge computing architecture of the three scenarios are analyzed, and the specific application methods and roles played by edge computing in the three scenarios are also elaborated. Finally, we discuss the challenges of edge computing in UPIoT, in terms of policy challenges, market challenges, and technical challenges, as well as outline the outlooks of the technical challenges.

This paper proposed a optimal strategy for coordinated operation of electric vehicles (EVs) charging and discharging with wind-thermal system. By aggregating a large number of EVs, the huge total battery capacity is sufficient to stabilize the disturbance of the transmission grid. Hence, a dynamic environmental dispatch model which coordinates a cluster of charging and discharging controllable EV units with wind farms and thermal plants is proposed. A multi-objective particle swarm optimization (MOPSO) algorithm and a fuzzy decision maker are put forward for the simultaneous optimization of grid operating cost, CO₂ emissions, wind curtailment, and EV users’ cost. Simulations are done in a 30 node system containing three traditional thermal plants, two carbon capture and storage (CCS) thermal plants, two wind farms, and six EV aggregations. Contrast of strategies under different EV charging/discharging price is also discussed. The results are presented to prove the effectiveness of the proposed strategy.

The single current sensor operation (SCSO) has attracted widespread attention in the motor drive system because of its ability to reduce cost and volume. However, when the SCSO is used with the conventional space vector puleswidth modulation (SVPWM) technique, the current reconstruction unobservable area in the sector boundary and low modulation area will degrade the accuracy of sampling data. In this article, a mixed SVPWM (MSVPWM) control scheme is proposed to eliminate the unobservable area. With the proposed scheme, the zero vectors ( ${{{\bm V}}_{{\rm {0}}}}$ and ${{{\bm V}}_{{\rm {7}}}}$ ) are replaced by the complementary effective vectors to achieve the same effect in the sector boundary and low modulation area, and the SVPWM is applied to the remaining space vector plane. These inserted effective vectors make the current observation window time prolonged to ensure that the dc-link current data can be accurately acquired in the entire space vector plane. Hence, the high-quality reconstruction of the phase current can be realized with less impact on the output of current ripples and reconstruction errors. Moreover, the relationship between the rotation speed and the increase in switching losses under different switching frequencies is quantitatively analyzed. The effectiveness of the MSVPWM reconstruction algorithm was verified by experimental results obtained from a permanent-magnet synchronous servo motor.

The toluene and mesitylene dynamic adsorption capacity as well as the VOC adsorption bed efficiency and cycle efficiency were improved in HZ. The diffusion of toluene and hexane was enhanced by introducing mesopores.

In this paper, a control method for electric vehicles (EVs) participating in grid Frequency regulation is proposed. Firstly, considering dispatching large-scale electric vehicles, the K-means clustering algorithm is applied to cluster EVs with different battery state of charge and with different average vehicle daily travel miles. Then, for each class of electric vehicle group, a multi-objective optimization model considering reducing power imbalance and feeding the driving power demand for electric vehicles is proposed. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to solve the optimization model and obtain the best control parameters for “virtual synchronous machine”, which is functioned as the power controller between EVs and the power grid. At last, based on a Monte Carlo sampling, the simulation analysis of 50 EVs with the normal distribution of battery state of charge and average vehicle daily travel miles is carried out by using the proposed method. The results show that the proposed method can effectively classify the electric vehicles with different battery state of charge and different average vehicle daily travel miles. The parameters of the power converter controller for different classes of electric vehicles are optimized considering power grid frequency, their battery state of charge and their average daily travel miles, so as to maintain the balance of power grid frequency, and to meet the power needs of EV daily drive.