Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks.

Distribution networks (DNs) now operate under tighter conditions due to rising penetration of renewables, active prosumers, and exposure to transmission-level contingencies. Distribution Network Reconfiguration (DNR) has proven effective for reducing losses, improving voltage profiles, and enhancing the resiliency of the grid. This paper introduces a three-stage optimization strategy for DNR, combining topological reconfiguration with reactive power support. The first stage, Reconfiguration of Tie-Line Switches (RTLS), utilizes a Particle Swarm Optimization (PSO) algorithm augmented with a Depth-First Search (DFS) mechanism to identify optimal radial structures that minimize active power losses. Once a viable configuration is established, the process proceeds to the second stage, Shunt Capacitor Sizing (SCS), wherein PSO is again employed to determine optimal capacitor sizing across predefined bus locations. The third stage reexecutes the RTLS process using the updated reactive power profile to assess whether further improvements in loss reduction can be achieved. If a superior topology is discovered, it is adopted as the final configuration; otherwise, the SCS solution is retained. This iterative and feedback-based architecture ensures an effective balance between network efficiency and voltage stability using a heuristic approach. The proposed methodology is validated on the IEEE 33-bus and IEEE 123-bus benchmark systems, as well as a custom 7-bus test case. Comprehensive scenario-based analysis, including normal, heavily, and lightly loaded conditions and varying power factor (PF) cases (good and poor PF), confirms the robustness and effectiveness of the approach in achieving considerable loss minimization and voltage profile improvement. For instance, in heavy-load conditions, active-power losses dropped by 39% and 70% for 33-bus and 123-bus cases, respectively.