Security-constrained unit commitment (SCUC) has been extensively studied as a key decision-making tool to determine optimal power generation schedules in the operation of electricity market. With the development of emerging power grids, fruitful research results on SCUC have been obtained. Therefore, it is essential to review current work and propose future directions for SCUC to meet the needs of developing power systems. In this paper, the basic mathematical model of the standard SCUC is summarized, and the characteristics and application scopes of common solution algorithms are presented. Customized models focusing on diverse mathematical properties are then categorized and the corresponding solving methodologies are discussed. Finally, research trends in the field are prospected based on a summary of the state-of-the-art and latest studies. It is hoped that this paper can be a useful reference to support theoretical research and practical applications of SCUC in the future. 

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A Comprehensive Review of Security-constrained Unit Commitment

A multi-agent game based joint planning approach for electricity-gas integrated energy systems considering wind power uncertainty

A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings

With the rapid development of microgrids, the types of users and loads in microgrids are increasing. Comprehensively reflecting the value of loads in load shedding decisions is one of the main challenges for the intelligent control of an islanded microgrid. However, the existing load shedding schemes ignore the user’s subjective preference for multiple attributes of loads, which are insufficient for reflecting the comprehensive value of loads. To address the aforementioned problem, an underfrequency load shedding (UFLS) scheme based on the comprehensive weight of loads is proposed. This scheme evaluates the multiple attributes of loads and the user’s preference for load attributes based on the entropy method with high adaptability (EMHA) and the analytical hierarchy process (AHP) to obtain the objective and subjective weights of loads. Then, the comprehensive weight of loads is calculated based on the relative entropy to determine the sequence of load shedding. Furthermore, the optimal objective function of the comprehensive cost of load shedding is constructed, and gray wolf optimization (GWO) is used to solve the objective function and obtain the optimal load shedding scheme. Ultimately, the effectiveness of the proposed scheme is verified based on a modified standard IEEE 37-bus system. 

Underfrequency Load Shedding Scheme for Islanded Microgrids Considering Objective and Subjective Weight of Loads

Research on Leakage Current Waveform Spectrum Characteristics of Artificial Pollution Porcelain Insulator

An expanded sequence-to-sequence (E- Seq2Seq) based data-driven security-constrained unit commitment (SCUC) expert system for dynamic multiple-sequence mapping samples is proposed in this paper. First,the 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 the deep learning model,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 from Hunan province in China. The results indicate that the proposed approach possesses strong generality,high solution accuracy,and efficiency over traditional methods.

Intelligent Data-Driven Decision-making Method for Dynamic Multi-Sequence: An E-Seq2Seq Based SCUC Expert System

Deep learning-based SCUC decision-making: An intelligent data-driven approach with self-learning capabilities

The fault of power systems introduces a severe challenge in terms of fault recording analysis, and the traditional Prony method cannot perform satisfactorily in the process of signal recordings fitting caused by faults. Therefore, an improved adaptive Prony algorithm is proposed in this article to study the characteristics of fault recordings. Specifically, the search step size is taken as an adaptive variable, and the mean square relative fitting error (MSRFE) is set as the criterion. Then, a large step is employed to rapidly determine an approximate segmentation point in the initial stage of the searching process, and its horizon is gradually reduced to establish an accurate subsegment point. Finally, the Prony algorithm is deployed to analyze the subsegment fitting original signal. The proposed approach has been simulated on an assumed fault signal, and the results validate the accuracy and efficiency of the method.

https://www.frontiersin.org/articles/10.3389/fenrg.2021.831347/pdf

A Fault Signal Processing Method Based on An Improved Prony Algorithm

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Intelligent Data-Driven Decision-making Method for Dynamic Multi-Sequence: An E-Seq2Seq Based SCUC Expert System

Deep learning-based SCUC decision-making: An intelligent data-driven approach with self-learning capabilities

A Fault Signal Processing Method Based on An Improved Prony Algorithm