A Physics-Constrained Probabilistic Forecasting Method for Wind–PV–Load Systems Under Extreme Weather Based on Extreme-Event Awareness and Uncertainty Calibration

With the increasing penetration of renewable energy into power systems, wind and photovoltaic (PV) power generation exhibit substantial uncertainty due to meteorological variability, which becomes more pronounced under extreme weather conditions. The scarcity of extreme samples, complex dependencies among wind power, PV power, and load, and degraded prediction-interval reliability further increase the difficulty of probabilistic forecasting. To address these challenges, this study proposes a physics-constrained probabilistic forecasting framework for wind–PV–load systems under extreme weather. First, extreme value theory (EVT) is employed to characterize the tail behavior of meteorological variables, while extreme operating states are identified by jointly considering meteorological anomalies and renewable power responses. Second, a full-covariance mixture density network (MDN) is developed to model the joint probability distribution of wind power, PV power, and load while preserving their cross-variable dependencies. A physics-constrained Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is then employed to generate multi-step wind–PV–load scenarios by incorporating operating, temporal, and cross-variable regularization. The historical and generated scenarios are subsequently used to train a long short-term memory (LSTM)-based quantile forecasting model, followed by conformalized quantile regression (CQR) for prediction-interval calibration. Experiments are conducted using wind power, PV power, load, and meteorological data from the Hai xi region of Qinghai Province, China, during 2021–2024. Results show that the full-covariance MDN effectively preserves the joint dependency structure, achieving a correlation reconstruction mean absolute error (MAE) of 0.032. The physics-constrained WGAN-GP substantially improves extreme-scenario generation quality. Meanwhile, LSTM+CQR achieves overall prediction interval coverage probability at a nominal 90% coverage level (PICP90) values of 0.895 and 0.893 for PV and wind power, respectively, while under extreme weather the corresponding coverage reaches 0.704 and 0.635. The controlled augmentation experiment further shows improvements in extreme-weather forecasting accuracy and interval reliability. These results demonstrate that the proposed framework integrates extreme-state identification, joint uncertainty modeling, physics-constrained scenario generation, and uncertainty calibration for probabilistic renewable-power forecasting under extreme weather.