Under the current context of the large-scale integration of wind and solar power, the coupling of hydropower with wind and solar energy brings significant impacts on grid stability. To fully leverage the regulatory capacity of hydropower, this paper develops a multi-objective optimization scheduling model for hydropower, wind, and solar that balances generation-side power generation benefit and grid-side peak-regulation requirements, with the latter quantified by the mean square error of the residual load. To efficiently solve this model, Latin hypercube initialization, hybrid distance framework, and adaptive mutation mechanism are introduced into the Strength Pareto Evolutionary Algorithm II (SPEAII), yielding an improved algorithm named LHS-Mutate Strength Pareto Evolutionary Algorithm II (LMSPEAII). Its efficiency is validated on benchmark test functions and a reservoir model. Typical extreme scenarios—months with strong wind and solar in the dry season and months with weak wind and solar in the flood season—are selected to derive scheduling strategies and to further verify the effectiveness of the proposed model and algorithm. Finally, K-medoids clustering is applied to the Pareto front solutions; from the perspective of representative solutions, this reveals the evolutionary trends of different objective trade-off schemes and overall distribution characteristics, providing deeper insight into the solution set’s distribution features.

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.

The large-scale integration of wind and solar photovoltaic (PV) power is a cornerstone of low-carbon, sustainable energy systems. However, the uncertainty of the output brings great challenges to the operation and dispatching of power systems. To clearly describe the fluctuation characteristics of wind–PV power output and the spatial–temporal coupling relationship, a two-stage wind–PV scenario-generation method is proposed. This method is based on Difference-Constrained Variational Autoencoder and Mixture of Factor Analyzers (DVAE-MFA). In the first stage, a differential constraint term is added to the reconstruction loss of the Variational Autoencoder (VAE) to build the Difference-Constrained Variational Autoencoder (DVAE) model. This helps the model better learn the fluctuation characteristics of output sequences. In the second stage, to solve the problem of the posterior distribution of the DVAE latent variables deviating from the standard normal prior, the Mixture of Factor Analyzers (MFA) model is introduced for secondary probability modeling of the latent space. The simulation experiment results show that the proposed DVAE-MFA model outperforms comparison models in terms of the scenario temporal fluctuation characteristics, spatial–temporal correlations, and statistical distribution similarity. The generated output scenarios can reproduce the features of historical data, providing high-quality data support for the stochastic optimization scheduling of sustainable power systems.

Accurate reservoir inflow forecasting is essential for sustainable watershed management and low-carbon hydropower operation. Traditional fixed-weight ensemble models lack adaptability under non-stationary hydrological conditions, limiting their reliability in reservoir operation. This study proposes a Context-Aware Localized Weighting Ensemble (CALWE) framework for reservoir inflow forecasting. The framework constructs a predictive response space from heterogeneous model outputs, enabling context identification based on similarities in model prediction behaviors. A localized weighting strategy is then employed to adaptively determine model contributions across contexts. The framework was evaluated using daily reservoir inflow data from Xiaowan Hydropower Station in the Lancang River Basin and monthly inflow data from Xiluodu Hydropower Station in the Jinsha River Basin. Results demonstrate that CALWE outperforms individual models and conventional ensemble approaches in both cases. Compared with the best-performing individual benchmark model for each basin (i.e., SVR for Xiaowan and XGBoost for Xiluodu), CALWE achieved a relative RMSE reduction of 4.93% and an absolute NSE improvement of 0.007 for daily inflow forecasting at Xiaowan, while achieving a relative RMSE reduction of 5.32% and an absolute NSE improvement of 0.027 for monthly inflow forecasting at Xiluodu. SHAP analysis revealed scale-dependent feature contributions, with daily forecasts dominated by antecedent inflow information and monthly forecasts influenced by meteorological, land surface, and hydrological factors. These findings demonstrate that CALWE captures context-dependent inflow responses while providing interpretable insights into model predictions, thereby supporting sustainable watershed management and reservoir operation.

Toward low-carbon and sustainable power systems, hydro–wind–solar complementarity provides an important pathway for enhancing renewable energy accommodation and operational flexibility, while accurate characterization of uncertainty and cross-energy dependencies is essential for system optimization, operational risk assessment, and sustainable utilization of renewable resources. This study proposes a conditional Wasserstein generative adversarial network with gradient penalty integrating one-dimensional multi-scale channel attention (MSA) and an exponential moving average (EMA) mechanism (MSA-cWGAN-GP) for joint runoff–wind–photovoltaic (PV) scenario generation. The generator employs parallel depthwise 1D convolutions with multiple temporal receptive fields to capture multi-timescale variations, while an EMA shadow generator is used for model validation and scenario generation. Conditional labels are obtained by clustering joint 24 h runoff–wind–PV profiles, enabling generation under typical resource states. Case studies using historical runoff observations from Shuibuya Hydropower Station and wind and PV power series derived from ERA5 reanalysis data show overall absolute errors of the autocorrelation function (ACF) and Kendall coefficient of 0.0113 and 0.0495, respectively. The proposed model achieves the best average performance among the evaluated models in preserving intraday temporal dependence, cross-energy dependencies, and distributional characteristics, providing representative scenarios for uncertainty analysis and subsequent optimization of hydro–wind–solar complementary systems.