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Most existing data-driven power system short-term voltage stability assessment (STVSA) approaches presume class-balanced input data. However, in practical applications, the occurrence of short-term voltage instability following a disturbance is minimal, leading to a significant class imbalance problem and a consequent decline in classifier performance. This work proposes a Transformer-based STVSA method to address this challenge. By utilizing the basic Transformer architecture, a stability assessment Transformer (StaaT) is developed as a classification model to reflect the correlation between the operational states of the system and the resulting stability outcomes. To combat the negative impact of imbalanced datasets, this work employs a conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for synthetic data generation, aiding in the creation of a balanced, representative training set for the classifier. Semi-supervised clustering learning is implemented to enhance clustering quality, addressing the lack of a unified quantitative criterion for short-term voltage stability. Numerical tests on the IEEE 39-bus test system extensively demonstrate that the proposed method exhibits robust performance under class imbalances up to 100:1 and noisy environments, and maintains consistent effectiveness even with an increased penetration of renewable energy. Comparative results reveal that the CWGAN-GP generates more balanced datasets than traditional oversampling methods and that the StaaT outperforms other deep learning algorithms. This study presents a compelling solution for real-world STVSA applications that often face class imbalance and data noise challenges. [Display omitted] • The class imbalance issue is addressed using balanced samples generated by CWGAN-GP. • StaaT with multi-head self-attention mechanisms learns important features. • SFCM labels samples undeterminable directly by domain knowledge. • StaaT exceeds other methods, showing robustness amid class imbalances and noises. [ABSTRACT FROM AUTHOR]

Levelized cost of electricity (LCOE) is widely used to compare the cost of different electricity generation technologies. However, with the increasing penetration of variable renewable energy (VRE), it is inappropriate to use traditional equations to calculate the LCOE for non-dispatchable VRE due to its intermittent nature. Therefore, this paper reviews LCOE for renewable generation considering aspects such as improving traditional methods for evaluating and reducing the LCOE of VRE. The existing methods for improving the accuracy of the traditional LCOE have been divided into four aspects: investment-related cost, operating-related cost, expressions for plant performance and the uncertainty and risk-related costs. The review summarizes the existing studies from academic articles and technical reports and proposes suggestions for future research studies. • Comprehensive Levelized cost of electricity (LCOE) methods review. • LCOE evaluation from cost, plant performance, uncertainty and risk. • Summary of existing methods for improving the LCOE estimation accuracy of variable renewable energy (VRE). • Compare and summarize the existing VRE LCOE methods in academic papers and technical reports. [ABSTRACT FROM AUTHOR]