用可解释AI分析欧洲39个电力市场的价格驱动因素。
Analysing drivers and interdependencies in European electricity markets using XAI
- 结合深度神经网络与SHAP方法，量化各因素对电价影响。
- 太阳能虽发电占比低，却显著影响电价形成。
- 适合能源政策研究者和电力市场分析师参考。
电力市场是具有强非线性、高维交互及区域间日益紧密依赖的复杂系统。尽管深度神经网络（DNN）在预测电价方面表现出色，但其不可解释性限制了对价格形成机制的理解。本文通过将DNN与可解释人工智能（XAI）技术结合，分析39个欧洲竞价区域的电价决定因素。采用SHAP量化特征贡献，并扩展SSHAP框架以提升高维场景下的可解释性。结果显示，可再生能源（尤其是太阳能）虽在总发电量中占比不高，但在电价形成中起着不成比例的重要作用；天然气价格在各市场中始终是主导且持续的驱动因素；电网互联显著影响价格动态，凸显欧洲电力系统的强关联性。此外，构建了一个合成的欧盟统一电力市场，探讨完全一体化市场下单一电价的反事实情景。
原文摘要 · Abstract (English)
Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions. While deep neural networks (DNNs) have demonstrated strong predictive capabilities for electricity prices, their lack of interpretability limits their usefulness for understanding the underlying drivers of price formation. This paper addresses this gap by combining DNN models with explainable artificial intelligence (XAI) techniques to analyse the determinants of electricity prices across 39 European bidding zones. We employ SHAP (SHapley Additive exPlanations) to quantify feature contributions and apply and extend SSHAP, an aggregation framework to improve interpretability in high-dimensional settings. The analysis identifies that renewable energy sources, particularly solar, play a disproportionately important role in price formation despite their lower share in total power generation. Gas prices remain a dominant and consistent driver across electricity markets, while interconnections significantly shape price dynamics, highlighting the strong interdependence of European electricity systems. In addition, a synthetic EU-wide electricity market is constructed to explore the counterfactual scenario of a fully integrated market with a single price.
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