An optimal placement problem on Battery Energy Storage Systems (BESSs) integrating the optimization on BESS controller parameters is formulated for power system oscillation damping improvement in this paper. First, a BESS model is established based on the power output characteristics of its power converter interface considering the converter power limit, State of Charge (SOC) limit, and the time constant of converters. Then the objective function and constraints are introduced to establish a black-box mixed-integer optimization problem tackled by interfacing time-domain simulations with a Mixed-Integer Particle Swarm Optimization (PSO) algorithm. Finally, the proposed optimization procedure is tested on the New England 39-bus system and the Nordic test system. The optimal results are also verified by time-domain simulations. Finally, the applicability of the proposed method is investigated including: consideration of seasonal load change; minimum number of BESS units to be placed; improvement of the PSO efficiency and performance comparison of different controllers. Results demonstrate the effectiveness of the proposed approach for optimization of the battery energy storage placement.

To overcome the slow-running drawback of the Monte Carlo simulation method for bulk power system reliability assessment, this paper develops an end-to-end machine learning approach to directly predict the targeted reliability index considering grid topology changes caused by emergent line maintenance. Three representative machine learning models, i.e., Support Vector Machine, Boosting Trees, and Graph Neural Network, are considered and compared. The grid topology information is embedded into the above models via two different feature engineering schemes. Details regarding dataset creation and data preprocessing are also described. Then, two case studies with different experimental settings and prediction targets are performed on the IEEE RTS-79 system to inspect the proposed approach’s adaptability. Results demonstrate the proposed approach’s effectiveness and speed improvement. Finally, an analysis is presented regarding the generalizability of the adopted machine learning model with respect to the varying dataset size based on the empirical-risk theory from the machine learning community.

This letter derives an analytical formula to estimate the electromechanical oscillation frequencies in multi-machine systems. The proof of this analytical formula is given via matrix determinant simplification and matrix decomposition considering line power losses. A half size system matrix is obtained, which brings the advantage in solving eigenvalues faster. The accuracy of the derived solution is evaluated by comparing with the PSAT toolbox on two power systems: the NPCC (Northeast Power Coordinating Council) system and the WECC (Western Electricity Coordinating Council) system in different operating conditions. Numerical results in the study cases demonstrate the accuracy and highlight the speeding effect on computation.

This paper proposes a data-driven framework for user–feeder topology identification in low-voltage residential power networks using ambient (current and voltage) measurements from smart meters. The framework first prepossesses the raw dataset via wavelet-based denoising, principal component analysis-based dimensionality reduction, and deep learning-based temporal feature extraction. In addition, a deep learning-based anomaly detection approach is also applied. Seven clustering algorithms are adopted for user–feeder relationship identification, and then the results are fused via a result-fusion strategy to enhance the identification accuracy further. Experiments on three real-world residential power networks demonstrate that the proposed approach significantly outperforms the results obtained by a single clustering method and the results obtained by simple voting-based fusion. The proposed approach achieves up to 88% identification accuracy in the considered case studies. Ablation studies are also conducted to validate the importance of each module in the proposed framework.

This letter develops a fast analytical method for uncertainty quantification of electromechanical oscillation frequency due to varying generator dampings. By employing the techniques of matrix determinant reduction, two types of uncertainty analysis are investigated to quantify the impact of the generator damping on electromechanical oscillation frequency, i.e., interval analysis and probabilistic analysis. The proposed analytical frequency estimation formula is verified against conventional methods on two transmission system models. Then, Monte Carlo experiments and interval analysis are respectively conducted to verify the established lower/upper bound formulae and probability distribution formulae. Results demonstrate the accuracy and speed of the proposed method.

This paper presents a study on using different offline reinforcement learning algorithms for microgrid voltage regulation with solar power penetration. When environment interaction is unviable due to technical or safety reasons, the proposed approach can still obtain an applicable model through offline-style training on a previously collected dataset, lowering the negative impact of lacking online environment interactions. Experiment results on the IEEE 33-bus system demonstrate the feasibility and effectiveness of the proposed approach on different offline datasets, including the one with merely low-quality experience.