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Integration in the European electricity market: A machine learning-based convergence analysis for the Central Western Europe region

机译：欧洲电力市场的整合：中西欧地区基于机器学习的融合分析

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摘要

The European electricity market is immersed in an integration process that requires a fundamental transformation. In this process, Flow-Based Market Coupling, which was employed for the first time in the Central Western Europe electricity market in 2015 as a means to manage cross-border capacity allocation, is a crucial cornerstone. The novelty of this paper lies in the analysis of the price convergence or congestion across the Central Western Europe region since the Flow-Based Market Coupling was implemented. We propose using random forests to build learning models that are trained and tested with features from connected markets of this region during 2016 and 2017. These machine learning models are used for mining knowledge about our target variable, price equalization. To search for robust predictive patterns that decision-makers can use to understand congestion situations, we have tested different combinations of learning schemes, several estimators and different model parameters. The results of all implemented models are robust and reveal that promoting renewable energy can contradict the integration of the electricity market if the grid network and, in particular, the transmission lines are not adapted to the new paradigm.
机译：欧洲电力市场沉浸在需要根本转变的整合过程中。在此过程中，基于流量的市场耦合是至关重要的基石，该市场耦合于2015年在中西欧电力市场中首次用作管理跨境容量分配的手段。本文的新颖之处在于自实施基于流量的市场耦合以来，中西欧地区的价格趋同或拥堵。我们建议使用随机森林来构建学习模型，并在2016年和2017年之间对该区域的关联市场的功能进行训练和测试。这些机器学习模型用于挖掘有关我们的目标变量（价格均衡）的知识。为了寻找决策者可以用来理解拥塞情况的可靠的预测模式，我们测试了学习方案，几种估计器和不同模型参数的不同组合。所有已实施模型的结果都是可靠的，并且表明，如果电网网络（尤其是输电线路）不适应新的范式，那么促进可再生能源可能会与电力市场的整合相矛盾。

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