Under the background of the rapid change of energy technology and the deep integration of artificial intelligence into the power system, it is of great significance to study the intelligent decision-making method of security-constrained unit commitment (SCUC) with high adaptability and high accuracy. Thus, in this article, an expanded sequence-to-sequence (E-Seq2Seq)-based data-driven SCUC expert system for dynamic multiple-sequence mapping samples is proposed. First, dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input–output sequence characteristics. Then, an E-Seq2Seq approach with a multiple-encoder–decoder architecture and a fully connected extension layer is proposed. On this basis, the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct deep learning models, and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system in Hunan province in China. The results indicate that the proposed approach could possess strong generality, high solution accuracy, and efficiency over traditional methods.

To adapt to the “dual carbon" goals and effectively guide the charging of electric vehicles (EVs) while reducing issues such as long queueing times and underutilization of charging stations (CSs) due to unreasonable pricing by charging station operators (CSOs), this paper proposes an optimal spatial-temporal pricing strategy for CSs based on the group price response behavior of EVs. Firstly, this study predicts the spatial-temporal distribution of EV load demand using trip chain and probability theory. Then, the Monte Carlo method is employed to simulate the spatial-temporal distribution of EV load demand and group charging behavior. Subsequently, an EV-CSO two-layer pricing demand response model is established, comprising an upper-layer pricing model for CSOs and a lower-layer charging decision model for the EV group. Finally, the model is solved to obtain the optimal pricing strategy. Additionally, the decision behavior of EVs is simplified through node clustering, and the optimal spacing is obtained through the iterative search algorithm. The results show that compared to traditional pricing strategies, the proposed method improves the total profit of CSs and the average utilization rate of charging piles. Furthermore, the node clustering method significantly improves the computational efficiency of the pricing model, providing theoretical guidance for complex traffic network analysis of large-scale EVs.

This letter proposes a control framework for Connected and Automated Vehicles(CAVs) to approach the signalized intersections with good driving-comfortability. Both the velocity plan and longitudinal dynamics control are concerned in this study. Regarding the velocity plan problem, a two-layer framework is designed. First, a scenario identifier method is proposed to identify which scenario the target velocity profile should be according to the current vehicle speed, distance to the intersection, and traffic signal information. Then, an assigned-time velocity planning problem with velocity and acceleration constraints is formulated and solved to obtain a smooth velocity profile with the minimal jerk. For the longitudinal dynamics control, a predictive controller with Online Sequential Extreme Learning Machine(OSELM) is applied to realize the smooth velocity profile tracking. CarMaker-Simulink co-simulation has been conducted to validate the proposed method. The validation results show that the proposed method can identify the scenario in 100% of the time according to the validation results. On the other hand, the OSELM-based predictive control has improved MSE $e_v,e_a,e_{jerk}$ of 33.85%, 27.66%, and 38.03% respectively than PID control.

This paper proposes a statistical framework to assess the risk of passing a non-signalized intersection for vehicles. First, an intensity model of the near-accident event is established by regarding the near-accident event as a non-homogeneous Poisson process. The non-homogeneous Poisson process is defined on the sigma-algebra of the 2-dimension plane of vehicle velocity and distance to the intersection instead of in the time axis. On the other hand, the pedestrian intention is defined as a binary variable with 1 as passing through the crosswalk and 0 as stopping. Logistic function is applied to model the probability of pedestrian intention. The proposed statistical models are evaluated by the residual analysis-based model checking method. Besides, based on the two models, the pedestrian-aware risk model is established to give a predictive risk metric quantitatively when pedestrian appears.