A Two-Stage Generative Architecture for Renewable Scenario Generation Based on Temporal Scenario Representation and Diffusion Models

Scenario generation proves to be an effective approach for addressing uncertainties in stochastic programming for power systems with integrated renewable resources. In recent years, numerous studies have explored the application of deep generative models to scenario generation. Considering the challenge of characterizing renewable resource uncertainty, in this paper, we propose a novel two-stage generative architecture for renewable scenario generation using diffusion models. Specifically, in the first stage the temporal features of the renewable energy output are learned and encoded into the hidden space by means of a representational model with an encoder–decoder structure, which provides the inductive bias of the scenario for generation. In the second stage, the real distribution of vectors in the hidden space is learned based on the conditional diffusion model, and the generated scenario is obtained through decoder mapping. The case study demonstrates the effectiveness of this architecture in generating high-quality renewable scenarios. In comparison to advanced deep generative models, the proposed method exhibits superior performance in a comprehensive evaluation.