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This paper considers locating congested fast charging stations (FCSs) and deploying chargers in a stochastic environment, while the related studies have predominantly focused on problems in deterministic environments. Reducing the inconvenience caused by congestion at FCSs is an important challenge for FCS service provider. This is the underlying motivation for this study to consider a problem for FCS network design with the congestion restriction in a stochastic environment. We proposed a maximal coverage problem subject to budget constraints and a congestion restriction in order to maximize the demand coverage. With the derivation of the congestion restriction in the considered stochastic environment, the problem is formulated into an integer programming model. A real-life case study is conducted and managerial implications are drawn from its results.

This paper discusses a case of truck sharing as an application of the sharing economy. This case study examines a real mixed feed company with multiple factories. In this company’s operation, bulk trucks located in a factory had not previously been shared for delivery with other factories to their pre-assigned customers of stock farms. Therefore, this paper suggests a new delivery system that facilitates truck-sharing and analyzes its effects on the transport cost and trucks’ CO₂ emissions. To this end, this paper develops vehicle routing models to represent the current delivery practice and the new truck-shared delivery (TSD). In addition, models are developed for a carbon control policy of an emission trading scheme (ETS) and the effects of the ETS on truck-sharing are investigated. Numerical analysis is conducted to identify the effects of the TSD and the carbon control policy and draw practical implications.

The purpose of this paper is to improve the online monitoring level of low-frequency oscillation in the power system. A modal identification method of discrete Fourier transform (DFT) curve fitting based on ambient data is proposed in this study.

An autoregressive moving average mathematical model of ambient data was established, parameters of low-frequency oscillation were designed and parameters of low-frequency oscillation were estimated via DFT curve fitting. The variational modal decomposition method is used to filter direct current components in ambient data signals to improve the accuracy of identification. Simulation phasor measurement unit data and measured data of the power grid proved the correctness of this method.

Compared with the modified extended Yule-Walker method, the proposed approach demonstrates the advantages of fast calculation speed and high accuracy.

Modal identification method of low-frequency oscillation based on ambient data demonstrated high precision and short running time for small interference patterns. This study provides a new research idea for low-frequency oscillation analysis and early warning of power systems.