用生成模型预测电力市场曲线，提升储能收益表现。
Parametric and Generative Forecasts of EPEX Day-Ahead Energy Market Curves
- 分参数与生成两类方法建模电力供需曲线。
- 生成模型在储能优化中实现更高利润与更小误差。
- 适合能源金融与电力市场研究者参考。
本文提出两种建模EPEX SPOT日前电力市场聚合供需曲线的方法，强调生成模型对分布变异性恢复的重要性。第一种为低维参数化表示，输出确定性点预测；第二种为高维订单级表示，从条件分布中采样生成可能的曲线。两者均建模完整曲线结构，支持价格敏感性、容量敏感性与价格冲击分析。参数化方法使用平台水平、弹性区域边界及多项式系数，通过梯度提升算法预测。主要贡献在于生成表示，采用价格到达与成交量增量标记，并以条件去噪扩散概率模型实现。基于2021至2024年法国EPEX数据，通过曲线重构与价格制定型储能优化问题评估两种方法。参数化版本提供确定性基准，扩散模型版本生成曲线分布，在储能应用中实现更高实际利润且与理想基准差距更小。
原文摘要 · Abstract (English)
We propose two methodologies for modelling aggregated supply and demand curves in the EPEX SPOT Day-Ahead market, emphasizing generative models as a way to recover distributional variability. The first is a low-dimensional parametric representation that yields deterministic point forecasts; the second is a high-dimensional order-level representation that samples from a conditional distribution of plausible curves. Both model the full curve structure, enabling the analysis of price sensitivity, volume sensitivity, and price impact. The parametric representation uses plateau levels, elastic-region boundaries, and polynomial coefficients, forecast with eXtreme Gradient Boosting. The main contribution is the generative representation, which uses price arrivals and volume-increment marks and is implemented with conditional Denoising Diffusion Probabilistic Models. Using French EPEX data from 2021 to 2024, we evaluate both approaches through curve reconstruction and a price-maker storage optimization problem. The parametric implementation provides a deterministic reference, while the diffusion-based implementation produces distributions of plausible curves and achieves higher realized profits and smaller gaps to an oracle benchmark in the storage application.
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