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 paper proposes an intelligent Deep Learning (DL) based approach for Data‐Driven Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed approach includes data pre‐processing and a two‐stage decision‐making process. Firstly, historical data is accumulated and pre‐processed. Then, the DD‐SCUC model is created based on the Gated Recurrent Unit‐Neural Network (GRU‐NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two‐stage decision‐making process outputs the decision results based on various applications and scenarios. This approach has self‐learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118‐bus test system and a real power system from China showed that compared with deterministic Physical‐Model‐Driven (PMD)‐SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.

The uncertainty of wind power brings many challenges to the operation and control of power systems, especially for the joint operation of multiple wind farms. Therefore, the study of the joint probability density function (JPDF) of multiple wind farms plays a significant role in the operation and control of power systems with multiple wind farms. This research was innovative in two ways. One, an adaptive bandwidth improvement strategy was proposed. It replaced the traditional fixed bandwidth of multivariate nonparametric kernel density estimation (MNKDE) with an adaptive bandwidth. Two, based on the above strategy, an adaptive multi-variable non-parametric kernel density estimation (AMNKDE) approach was proposed and applied to the JPDF modeling for multiple wind farms. The specific steps of AMNKDE were as follows: First, the model of AMNKDE was constructed using the optimal bandwidth. Second, an optimal model of bandwidth based on Euclidean distance and maximum distance was constructed, and the comprehensive minimum of these distances was used as a measure of optimal bandwidth. Finally, the ordinal optimization (OO) algorithm was used to solve this model. The scenario results indicated that the overall fitness error of the AMNKDE method was 8.81% and 11.6% lower than that of the traditional MNKDE method and the Copula-based parameter estimation method, respectively. After replacing the modeling object the overall fitness error of the comprehensive Copula method increased by as much as 1.94 times that of AMNKDE. In summary, the proposed approach not only possesses higher accuracy and better applicability but also solved the local adaptability problem of the traditional MNKDE.

As maritime technology advances, multi-energy ship microgrids (MESMs) are widely used in large cruise tourism. In this context, studying cost-effective and highly reliable energy system planning methods for MESMs in their whole lifespan becomes paramount. Therefore, this paper proposes a joint planning method for a MESM during its lifespan. Firstly, a long timescale coordinated planning and operation scheme is formulated with the aim of maximizing the Net Present Value (NPV) value, thereby reducing both project investment and energy supply cost. In addition, this paper introduces novel operation models that incorporate customer thermal comfort levels, considering thermal inertia, and ship navigation, accounting for the effects of waves and wind. These models enhance the flexibility and practicality of the planning process. Finally, to ensure the safe operation of vessel and alleviate the negative effects of uncertain wind and waves during ship navigation, a robust optimization (RO) approach is employed. A case study demonstrates the effectiveness of the proposed method, with several comparison analyses further highlighting its advantages.