Abstract

The integration of renewable energy sources (RES) into electrical power systems introduces critical challenges to grid stability, including frequency deviation, voltage fluctuation, and reduced transient performance. This study investigates the impact of increasing RES penetration (15%, 30%, 45%, and 60%) on grid stability using the IEEE 39-bus benchmark system. A coordinated mitigation framework integrating battery energy storage systems (BESS), synthetic inertia, and advanced inverter-based controls is proposed and evaluated. Simulation results demonstrate that at 60% RES penetration without mitigation, the frequency nadir declines to 49.32 Hz, the rate of change of frequency (RoCoF) increases to 1.82 Hz/s, voltage deviations exceed 9.3%, and the critical clearing time (CCT) reduces to 180 ms, indicating significant stability deterioration. The proposed mitigation strategy improves these metrics to 49.76 Hz, 0.94 Hz/s, 4.6%, and 260 ms, respectively, representing improvements of+0.44 Hz, –48.4%, –50.5%, and+44.4%. Benchmarking against recent literature confirms the superior performance of the coordinated approach. These findings provide quantitative guidance for grid planners and operators to maintain reliable operation under high renewable penetration scenarios.OPEN ACCESS Received: 19/01/2026 Accepted: 28/02/2026

Abstract

This study presents a hybrid modelling framework for predicting river water temperature (Tw) by combining advanced boosting algorithms with signal decomposition techniques. Four models were compared: (i) categorical boosting (CatBoost), (ii) extreme gradient boosting (XGBoost), (iii) adaptive boosting (AdaBoost), and (iv) light gradient boosting machine (LightGBM). Each model was developed using air temperature (Ta) as input, both with and without signal decomposition. Three algorithms, empirical mode decomposition (EMD), ensemble EMD (EEMD), and complete EEMD with adaptive noise (CEEMDAN), were applied to decompose Ta into intrinsic mode functions (IMFs), which served as additional predictors. The models were trained and validated using data from four Polish river stations, and their performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Nash–Sutcliffe efficiency (NSE). Tw was modelled as an instantaneous function of Ta, without incorporating a forecasting horizon, to assess the ability of decomposition-boosting hybrids to reproduce observed thermal dynamics at the same time step rather than perform multi-step predictions. Unlike previous single-hybrid studies, this work systematically compares multiple boosting-decomposition combinations under identical settings, isolating the decomposition effect and assessing accuracy, efficiency, and robustness. Results show that integrating signal decomposition substantially improves prediction accuracy. The best performance was achieved by XGBoost_EEMD (RMSE = 0.804, MAE = 0.598, R = 0.993, NSE = 0.987), reflecting a ∼64% improvement over the standalone model. The proposed hybrid modelling framework offers methodological innovation and practical potential for real-time, datadriven river temperature prediction in support of climate-resilient water management.