Representative Domains for Flow-Based Market Coupling Through One-Step Clustering

Market simulations are used to analyze various aspects of the European electricity market. Flow-based market coupling is one relevant component in this formulation. This computationally complex component models commercial exchange while considering restrictions imposed by the underlying transmission grid. If combined with frameworks requiring multiple market simulations itself, e.g. to assess uncertainties through Monte Carlo Simulations, the resulting computational complexity is increased to a point where it is no longer practically feasible. As current approaches to reduce complexity depend on reference data, limiting uncertainty assessments, this paper proposes a new one-step clustering methodology. By performing clustering and flow-based domain computation as part of a Monte Carlo iteration, this methodology can be used to model uncertainties such as topological changes and power plant portfolio variations. Direct clustering during the market simulation is achieved using input features. Choosing a suitable input feature is justified by analyzing the similarity to a clustering based on an improved variant of currently known distance measures.