用改进扩散模型生成风电光伏日间输出场景，提升可解释性与准确性。
The day-ahead scenario generation method for new energy based on an improved conditional generative diffusion model
- 基于马尔可夫链与变分推断，通过去噪过程生成条件场景。
- 采用余弦噪声表改进，生成场景质量显著提升。
- 适合电力系统调度人员用于新能源出力预测与规划决策。
随着新能源发电占比上升，准确生成新能源出力场景对日前电力系统调度至关重要。基于深度学习的场景生成方法虽能满足需求，但其黑箱特性引发可解释性担忧。本文提出一种基于改进条件生成扩散模型的日前新能源场景生成方法，该方法建立在马尔可夫链与变分推断理论框架之上。首先通过扩散过程将历史数据转化为纯噪声，再利用条件信息引导去噪过程，最终生成满足条件分布的场景。此外，将噪声表改进为余弦形式，提升了生成场景的质量。在真实风电与光伏出力数据上的实验表明，该方法能有效生成具有良好适应性的新能源出力场景。
原文摘要 · Abstract (English)
In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability.
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