通过深度聚类分析德国燃气机组实际灵活性差异。
Revealing the empirical flexibility of gas units through deep clustering
- 用5年小时级发电数据，通过深度聚类提取机组灵活性特征。
- 半数非调峰机组灵活性低，与燃煤机组相当，平均在低价时段发电1.3GWh。
- 工业和市政企业持有的机组响应市场波动能力弱，需政策激励释放潜力。
发电机组的灵活性决定了其快速启停的能力。能源模型通常基于技术参数（如装机容量或涡轮技术）假设其灵活性。本文通过分析49个德国燃气机组（总装机容量100MWp）2019-2023年5年的单位级小时发电数据，采用新型深度聚类方法，将数据转化为低维嵌入，揭示了实际运行中灵活性的差异。无监督方法识别出两类调峰机组（高灵活性）和两类非调峰机组（低灵活性）。非调峰机组占样本一半，其实际爬坡率低，灵活性接近燃煤机组。这些主要由工业及市政公用事业持有，对低剩余负荷和负电价响应有限，平均在该类时段发电1.3 GWh。随着可再生能源占比提升，市场波动加剧，需政策调整以释放此类机组的灵活性潜力。
原文摘要 · Abstract (English)
The flexibility of a power generation unit determines how quickly and often it can ramp up or down. In energy models, it depends on assumptions on the technical characteristics of the unit, such as its installed capacity or turbine technology. In this paper, we learn the empirical flexibility of gas units from their electricity generation, revealing how real-world limitations can lead to substantial differences between units with similar technical characteristics. Using a novel deep clustering approach, we transform 5 years (2019-2023) of unit-level hourly generation data for 49 German units from 100 MWp of installed capacity into low-dimensional embeddings. Our unsupervised approach identifies two clusters of peaker units (high flexibility) and two clusters of non-peaker units (low flexibility). The estimated ramp rates of non-peakers, which constitute half of the sample, display a low empirical flexibility, comparable to coal units. Non-peakers, predominantly owned by industry and municipal utilities, show limited response to low residual load and negative prices, generating on average 1.3 GWh during those hours. As the transition to renewables increases market variability, regulatory changes will be needed to unlock this flexibility potential.
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