The energy crisis and increasing concerns about the environment in modern society have fostered the development of the integrated energy system (IES) in smart grid. The energy hub (EH) is a minimum construction of the IES. The chief purpose of this paper is to propose a Lyapunov optimizationbased energy management strategy for the energy hub with an energy router. First, this paper describes a configuration of the EH with an energy router and sets the basic operating principles of the energy router. Second, a method is proposed to analyze the energy balance equations of the EH. Furthermore, the problem of energy management is formulated considering the constraints of energy balance equations, energy storage, flexible loads, and other constraints of operation. Finally, energy storage and flexible electric loads are modeled as stochastic processes, and three virtual queues are constructed to relax these constraints. The Lyapunov drift-plus-penalty function is applied to formulate a relaxed form of the energy management problem. Case studies of a single EH and two-connected EHs are performed to test the effectiveness of the proposed strategy, and the results are compared with a greedy algorithm.

In wireless power transfer system, not only the mutual inductance variation but also the self-inductance variation can be occurred under the misalignment condition. In light of the Unmanned Aerial Vehicles, the variation of the secondary self-inductance and mutual inductance are significantly under the misalignment condition. In order to stabilize the output voltage and current and achieve near-ZPA (near-zero phase angle) of the input impedance. A variable inductor control is presented in this paper, which regulate the inductance of the secondary side by adjusting the dc bias current under large k variation. The proposed control method does not need any auxiliary circuit, and constant current-constant voltage (CC-CV) charging can be achieved by controlling the duty cycle of the boost converter. To verify the proposed control method, a 500-W prototype was set up in the laboratory, and the variable inductor control method ensures a stable output voltage and current with horizontal and vertical offsets of 200 mm and 92 mm, respectively. Additionally, communication is not need between primary and secondary side, and the results show that the maximum efficiency is 92.9%, which is 2.3% higher than the conventional dc-dc regulation method.

The geophysical effects are the main factor that causes the nonlinear motion of the station, and a comprehensive analysis of the relationship between the GRACE seasonal load deformation and the GPS station coordinates is helpful to study the physical mechanism that causes the nonlinear motion of the station. Aiming at the continuous GPS coordinate time series in Greenland, this paper comprehensively analyzes the correlation between GRACE seasonal load deformation and GPS station coordinates. First, in order to improve the accuracy of GPS station coordinates, the principle component analysis (PCA) method was used to eliminate the common mode error (CME) of the station coordinates. The results show that this method effectively reduces the uncertainty of the station coordinates time series. Secondly, when extracting seasonal signals, it is found that the singular spectrum (SSA) method can effectively obtain the time-varying part of seasonal signals, and its extraction effect is better than that of the least square fitting (LSF) method. Finally, the seasonal relationship between GRACE load deformation and GPS station coordinates is analyzed from the aspects of time series change, correlation, and WRMS reduction. It is found that there are differences in the amplitude and phase parts of the time series. The mean value of correlation is 0.73, the maximum reduction of WRMS is 55.20% (QAQ1 station), and the minimum is −22.69% (KMJP station), indicating that most stations mainly exhibit seasonal load deformation, while individual stations cannot effectively reflect. In addition, the influence of GRACE seasonal load deformation on the station coordinate parameters is quantitatively analyzed. The results show that the best noise model of the station is mainly WN + FN, which effectively reduces the velocity uncertainty of the station coordinate, and weakens the seasonal term oscillation.

Missing data in precipitable water vapor derived from global navigation satellite systems (GNSS-PWV) is commonly a large hurdle in climatical applications, since continuous PWV is an important prerequisite. Interpolation using principal component analysis (PCA) is typically used to resolve this problem. However, the popular PCA-based interpolating methods, e.g., rank-deficient least squares PCA (RDPCA) and data interpolating empirical orthogonal function (DINEOF), often lead to unsatisfactory results. This study analyzes the relationship between missing data and PCA-based interpolation results and proposes an improved interpolation-based RDPCA (IRDPCA) that can take into account the PWV derived from ERA5 (ERA-PWV) as an additional aid. Three key steps are involved in the IRDPCA: initially interpolating missing data, estimating principal components through a functional model and optimizing the interpolation through an iterative process. Using a 6-year GNSS-PWV over 26 stations and ERA-PWV in Yunnan, China, the performance of the IRDPCA is compared with the RDPCA and DINEOF using simulation experiments based on both homogeneous data (i.e., interpolating ERA-PWV using available ERA-PWV) and heterogeneous data (i.e., interpolating GNSS-PWV using ERA-PWV). In the case of using homogeneous data, the root mean square (RMS) values of the interpolation errors are 3.45, 1.18 and 1.17 mm for the RDPCA, DINEOF and IRDPCA, respectively; while the values are 3.50, 2.50 and 1.55 mm in the heterogeneous case. These results demonstrate the superior performance of the IRDPCA in both the heterogeneous and homogeneous cases. Moreover, these methods are also applied to the interpolation of the real GNSS-PWV. The RMS, absolute bias and correlation of the GNSS-PWV are calculated by comparison with ERA-PWV. The results reveal that the interpolated GNSS-PWV using the IRDPCA is not impacted by the systematic discrepancies in the ERA-PWV and agrees well with the original data.