Photovoltaic power generation as a green energy source is often used in power systems, but the volatility of PV output and randomness of the problem affect the stability of the power-grid power supply; so, for the problem of low prediction accuracy of photovoltaic power generation under different weather conditions, this paper proposes a Variational Mode Decomposition (VMD), combined with a Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) secondary decomposition method for the original signal decomposition, to reduce the signal volatility and reduce the complexity of feature mapping the PV data, followed by the use of a BiLSTM model to model the timing information of the decomposed IMF. Simultaneously, the Informer model predicts the components obtained from the secondary decomposition, and finally, the subsequence is reconstructed and superimposed to obtain the PV power prediction value. The results show that the RMSE and MAE of the proposed model are improved by up to 10.91% and 17.33% on the annual PV dataset, with high prediction accuracy and stability, which can effectively predict the ultra-short-term power of PV power plants.

The accuracy of monthly runoff predictions is crucial for decision-making and efficiency in various areas, such as water resources management, flood control and disaster mitigation, hydraulic engineering scheduling, and agricultural irrigation. Therefore, in order to further improve the accuracy of monthly runoff prediction, aiming at the problem that the traditional Stacking ensemble method ignores (the base model correlation between different folds in the prediction process), this paper proposes a novel Stacking multi-scale ensemble learning model (SWA–FWWS) based on random weight averaging and a K-fold cross-validation weighted ensemble. Then, it is evaluated and compared with base models and other multi-model ensemble models in the runoff prediction of two upstream and downstream reservoirs in a certain river. The results show that the proposed model exhibits excellent performance and adaptability in monthly runoff prediction, with an average RMSE reduction of 6.44% compared to traditional Stacking models. This provides a new research direction for the application of ensemble models in reservoir monthly runoff prediction.

Accurate and reliable short-term electricity load forecasting plays an important role in ensuring the healthy operation of the power grid and promoting sustainable socio-economic development. This research proposes a novel hybrid load probability prediction model, BiGRU-GAM-GPR, which combines a bidirectional gated recurrent unit (BiGRU), global attention mechanism (GAM), and Gaussian process regression (GPR). Firstly, BiGRU-GAM is used to predict the sequence to obtain preliminary prediction results, and then these results are input into GPR to obtain more accurate deterministic and probabilistic prediction results. To verify the effectiveness of the proposed model, a series of experiments are conducted on three real-world power load datasets. The experimental results show the following: (1) BiGRU has the optimal forecasting ability compared with the other basic models. (2) The global attention mechanism improves the model’s perception ability of the spatial features of multi-feature sequences and plays a positive role in enhancing the model’s forecasting performance. (3) The GPR model further explores the internal relationships of the data by expanding the deterministic prediction results into probabilistic results, thus improving the forecasting effect. (4) The proposed model BiGRU-GAM-GPR exhibits the best performance in both deterministic and probabilistic forecasting and has good robustness.

In renewable energy generation, uncertainties mainly refer to power output fluctuations caused by the intermittency, variability, and forecasting errors of wind and photovoltaic power. These uncertainties have adverse effects on the secure operation of the power systems. Probabilistic load supply capability (LSC) serves as an effective perspective for evaluating power system security under uncertainties. Therefore, this paper studies the influence of renewable energy generation on probabilistic LSC to quantify the impact of these uncertainties on the secure operation of the power systems. Global sensitivity analysis (GSA) is introduced for the first time into probabilistic LSC evaluation. It can quantify the impact of renewable energy generation on the system’s LSC and rank the importance of renewable energy power stations based on GSA indices. GSA necessitates multiple rounds of probabilistic LSC evaluation, which is computationally intensive. To address it, this paper introduces a novel probabilistic repeated power flow (PRPF) algorithm, which employs a basis-adaptive sparse polynomial chaos expansion (BASPCE) model as a surrogate model for the original repeated power flow model, thereby accelerating the probabilistic LSC evaluation. Finally, the effectiveness of the proposed methods is verified through case studies on the IEEE 39-bus system. This study provides a practical approach for analyzing the impact of renewable generation uncertainties on power system security, contributing to more informed planning and operational decisions.

The uncertainty of natural inflows and market behavior challenges ensuring a reliable power balance in hydropower-dominated electricity markets. This study proposes a novel framework integrating hourly load balancing on typical days into a monthly scheduling model solved with Gurobi11.0.1 to evaluate demand-met reliability across storage and inflow states. By employing total storage as a system state to reduce dimensional complexity and simulating future runoff scenarios based on current inflows, the method performs multi-year statistical simulations to assess reliability over the following year. Applied to a system of 39 hydropower reservoirs in China, the case studies of present models and procedures suggest: (1) controlling reservoir storage levels during the dry season is crucial for ensuring the power demand-met rate in the following year, with May being the most critical month; (2) the power demand-met rate does not monotonically increase with higher storage levels—there is an optimal storage level that maximizes the demand-met rate; and (3) June and October offer the greatest flexibility in storage adjustment to achieve the highest demand-met reliability.