Swap charging (SC) technology offers the possibility of swapping the batteries of electric vehicles (EVs), providing a perfect solution for achieving a long-distance freeway trip. Based on SC technology, a shared SC system (SSCS) concept is proposed to overcome the difficulties in optimal swap battery strategies for a large number of EVs with charging requests and to consider the variance in the battery charging rate simultaneously. To realize the optimal SSCS design, a binary integer programming model is developed to balance the tradeoff between the detour travel cost and the total battery recharge cost in the SSCS. The proposed method is verified with a numerical example of the freeway system in Guangdong Province, China, and can obtain an exact solution using off-the-shelf commercial solvers (e.g., Gurobi).

Since emergency vehicles (EMVs) in urban transit systems play a crucial role in responding to time-critical events, the quick response of EMVs is essential for improving the success rate of rescue operations and minimizing property loss. Booming connected vehicle (CV) technology provides a new perspective to further enhance the effectiveness of EMV priority. Based on this CV technology, we propose a cooperative multiple EMV priority model in which the speed, acceleration, and lane changing actions of both the EMVs and surrounding ordinary vehicles (OVs) are set as decision variables. This proposed model is rigorously formulated in integer linear programming to maximize the EMV traffic efficiency and find a trade-off between the interference with normal traffic flows and the smoothness of the EMV driving trajectories. Two customized algorithms are developed to reduce the number of decision variables and constraints to obtain the better feasible solution in an acceptable computational time. A numerical experiment based on real-world data is proposed to further verify the utility and effectiveness of the aforementioned mathematical model. The customized algorithms achieve near-exact solutions with significantly faster computation compared to the benchmark solver. The robustness of the proposed model is tested with different parameter settings in the sensitivity analysis.

In this study, optimal reactive power regulation in distribution networks is achieved through the use of distributed reactive power regulators that can 1) perceive their own voltage magnitude and the P/Q flows in the connected branches, 2) communicate with nearby regulators, and 3) adjust the reactive power injections into the grid to minimize system power losses and maintain the bus voltages of nearby loads. Compared with many existing distributed reactive power regulation strategies, the proposed method can estimate and maintain the bus voltage of unmeasurable load buses within the limitations. Furthermore, this method releases the hardly achieved bus voltage angle requirement, which makes it practical for real-world.

Accurate transfer time prediction and future transfer time information are important for both public transport operators and passengers. However, existing studies cannot effectively manage high-dimensional transfer time data, capture the complex nonlinearity of transfer time, or provide accurate transfer time information. This study provides a reliable prediction model called low-rank Bayesian temporal matrix factorization (LBTMF) to address these challenges. First, on the basis of a high-dimensional spatiotemporal matrix of transfer time data, we develop a low-rank temporal-regularized matrix factorization-based imputation module to capture spatial and temporal characteristics to replace missing transfer time data. Second, to further predict the transfer time with the imputation of missing data, we propose the spatiotemporal-based Bayesian temporal matrix factorization prediction module to recover hourly and daily regular characteristics to predict the transfer time at different metro stations during various periods. Finally, the comprehensive experimental findings suggest that the LBTMF model outperforms other excellent approaches in terms of imputation efficiency, prediction accuracy, and robustness.