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A data-driven analysis of unscheduled flows in the European power system
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Cross-border electricity trading is becoming increasingly important in the European power system. Unscheduled power flows induce additional costs and may lead to congestion and impair power grid operation. In this contribution we provide a data-centric analysis of unscheduled flows in the Central European power grid. Using methods from explainable machine learning, we identify the main driving factors for unscheduled flows and quantify their impact. Unscheduled flows in the meshed part of the grid can be mainly attributed to transit or loop flows and well described by a linear model. The performance is substantially worse for unscheduled flows on bridges, with forecasting errors being the most important drivers. The loss of performance is probably due to data quality issues.
Title: A data-driven analysis of unscheduled flows in the European power system
Description:
Cross-border electricity trading is becoming increasingly important in the European power system.
Unscheduled power flows induce additional costs and may lead to congestion and impair power grid operation.
In this contribution we provide a data-centric analysis of unscheduled flows in the Central European power grid.
Using methods from explainable machine learning, we identify the main driving factors for unscheduled flows and quantify their impact.
Unscheduled flows in the meshed part of the grid can be mainly attributed to transit or loop flows and well described by a linear model.
The performance is substantially worse for unscheduled flows on bridges, with forecasting errors being the most important drivers.
The loss of performance is probably due to data quality issues.
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