从日内订单簿到不平衡价格：理解跨市场交互
From Intraday Orderbook to Imbalance Price: Understanding Cross-Market Interaction
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中文总结 AI 辅助
本文通过概率建模研究德国和奥地利日内订单簿信息对不平衡价格形成的影响，发现VWAP与相邻产品组合效果最佳，且跨市场交互存在国家依赖性。
中文摘要 AI 辅助
随着可变可再生能源发电占比的不断增加，电力系统在调度和平衡方面面临更大的不确定性。日内和平衡电力市场促进了接近交割时的头寸调整和实时平衡。随着交割时间的临近，面临不平衡结算的连续日内市场参与者通过交易额外电量来调整其头寸，以减少不平衡敞口。我们推测，日内交易后仍保持未平仓的头寸，连同影响物理市场参与者的供需不确定性，会影响平衡市场的价格形成。然而，这种跨市场交互很少被研究。为理解这种交互，本文采用概率建模方法，研究德国和奥地利的日内订单簿信息如何反映随后的不平衡价格形成。我们比较了基于开盘价、最高价、最低价、收盘价和成交量、成交量加权平均价格（VWAP）以及最后中间价的订单簿表示，跨越多个时间范围。每种表示都使用自身产品、相邻产品以及邻国产品进行评估。然后，我们将最佳订单簿设置与基础特征集及其组合进行比较，并对可用训练历史进行消融研究。结果表明，在两国中，VWAP与相邻产品的组合提供了最佳性能。结合订单簿和基础信息在德国降低了测试损失，但在奥地利却增加了损失。使用所有可用观测值提供了整体最佳性能，而排除2022年可降低极端价格样本的损失。这些结果揭示并有助于解释日内市场和平衡市场之间依赖于国家的交互。
英文摘要
Power systems with increasing variable renewable generation face greater uncertainty in scheduling and balancing. Intraday and balancing electricity markets facilitate position adjustments and real-time balancing close to delivery. As delivery approaches, continuous intraday market participants exposed to imbalance settlement adjust their positions by trading additional volumes to reduce their imbalance exposure. We conjecture that positions remaining open after intraday trading, together with demand and supply uncertainties affecting physical market participants, influence price formation in the balancing market. This cross-market interaction is, however, rarely studied. To understand this interaction, this paper uses probabilistic modeling to examine how intraday orderbook information reflects subsequent imbalance price formation in Germany and Austria. We compare orderbook representations based on open, high, low, close, and volume, Volume-Weighted Average Price (VWAP), and last mid price across multiple horizons. Each representation is evaluated using the self product, neighboring products, and the product from the neighboring country. We then compare the best orderbook setting with fundamental feature sets and their combinations, followed by an ablation study of the available training history. We show that VWAP with neighboring products provides the best performance in both countries. Combining orderbook and fundamental information reduces testing loss in Germany but increases it in Austria. Using all available observations provides the best overall performance, while excluding 2022 can reduce loss for extreme price samples. These results reveal and help explain country-dependent interactions between the intraday and balancing markets.
发表机构
- London Business School（伦敦商学院）
- Delft University of Technology（代尔夫特理工大学）
- Austrian Institute of Technology（奥地利技术研究所）
- Imperial College London（帝国理工学院）
- Technical University of Denmark（丹麦技术大学）
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
- Institute of Science Tokyo（东京科学大学）
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