Wind Power Scenario Generation Based on Denoising Diffusion Probabilistic Model

The intermittency and randomness of wind power output have a negative impact on the stable operation of the power grid. Accurately modeling the uncertainty of wind power output is essential, and the primary method to achieve this is through scenario generation. Traditional scenario generation methods suffer from limitations such as low accuracy and high computational complexity. In this paper, a novel generation framework based on the denoising diffusion probabilistic model is presented and proposed for scenario generation of wind power. This method can overcome the limitations of traditional methods and learn the distribution of real data to generate reliable wind power scenarios. Compared to a homogeneous generative model, the proposed method shows improved performance in precisely capturing features of wind power scenarios.

I. Introduction

The increasing penetration rate of wind power has caused serious problems in the stable operation of the power system due to its intermittency and randomness. Therefore, modeling the uncertainty of wind power has become a hot topic in the analysis of power systems with wind power. Currently, researchers mainly use the scenario analysis approach to describe and analyze the uncertainty of the wind power output. This approach involves constructing a set of scenarios that is then used to transform the stochastic optimization problem into a deterministic problem to be processed.