电力系统监管变化的可解释人工智能探索
Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence
AI总结：
本文利用可解释人工智能（XAI）模型区分并理解德国电力市场监管变化（竞价区拆分和平衡市场价格方案调整）的影响，通过提升树和神经网络模型及Shapley解释识别关键特征，展示了XAI在电力系统变化研究中的应用。
摘要：
稳定的电能供应对于社会的正常运转至关重要。因此，电网的运行以及能源市场和平衡市场都受到严格监管。随着外部技术、经济或社会因素对电网的影响发生变化，这些监管规定也必须不断调整。然而，这些监管变化是否达到了预期效果并不容易评估。可解释人工智能（XAI）模型能否区分监管设置并帮助理解这些变化的影响？在本文中，我们探讨了德国大宗电力市场和备用电力市场中的两个监管变化实例。我们研究了德国-奥地利竞价区的拆分以及德国平衡能源市场价格方案的变化。我们发现，在监管变化前后，提升树模型和前馈神经网络的参数化存在差异。利用Shapley加性解释，我们揭示了模型差异，例如在特征重要性方面，并识别了这些不同模型的关键特征。通过这项研究，我们展示了如何应用XAI来研究电力系统中的系统变化。
英文摘要：
A stable supply of electrical energy is essential for the functioning of our society. Therefore, the electrical power grid's operation and energy and balancing markets are subject to strict regulations. As the external technical, economic, or social influences on the power grid change, these regulations must also be constantly adapted. However, whether these regulatory changes lead to the intended results is not easy to assess. Could eXplainable Artificial Intelligence (XAI) models distinguish regulatory settings and support the understanding of the effects of these changes? In this article, we explore two examples of regulatory changes in the German energy markets for bulk electricity and for reserve power. We explore the splitting of the German-Austrian bidding zone and changes in the pricing schemes of the German balancing energy market. We find that boosted tree models and feedforward neural networks before and after a regulatory change differ in their respective parametrizations. Using Shapley additive explanations, we reveal model differences, e.g. in terms of feature importances, and identify key features of these distinct models. With this study, we demonstrate how XAI can be applied to investigate system changes in power systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
