With the rapid energy transition in power systems

the growing penetration of renewable energy and diversified loads has increased the complexity of grid operating scenarios. The deep coupling of multiple influencing factors among energy sources

loads

and environmental conditions further complicates scenario feature extraction. To address these challenges

this paper proposes an integrated wind-solar-load scenario generation method incorporating complex environmental coupling and multi-dimensional temporal features. First

considering strong temporal coupling and nonlinear characteristics of wind

solar

and load profiles

a bidirectional gated recurrent unit (Bi-GRU) is used to extract initial features from environmental factors and historical data

with feature vectors refined via error iteration. Subsequently

to accurately capture the probabilistic dependencies among wind

solar

load

and environmental variables

the extracted initial features are fed into a conditional variational autoencoder (CVAE) to construct a wind-solar-load scenario generation model. Then

to address the issue of unequal sequence lengths in wind-solar-load power curves

the dynamic time warping (DTW) algorithm is employed to reduce the generated scenarios

ensuring temporal consistency across the final output. Finally

the proposed method is validated using real-world wind-solar-load data from a region in China. The results demonstrate that the method can generate joint scenarios that more closely reflect real-world operating conditions.