Multi-chronological hierarchical clustering to solve capacity expansion problems with renewable sources

Renewable sources are increasing their presence in power systems to achieve the goal of decarbonizing the generation of electricity. However, the variability of these resources introduces high uncertainty in the operation of a power system and, hence, fast generating and storage units must be incorporated in the system. To obtain economically and technically efficient solutions in the capacity expansion problem, an adequate representation of the variability of the renewable resource is crucial, but this may increase the size of the problem up to computational intractability. The most suitable representation is often selected through clustering techniques. The clustering algorithms proposed in the literature suffer from a significant trade-off between their ability to capture the inter-temporal correlation of the clustered elements and the representativeness of the clusters when only very few are needed. This paper proposes an innovative clustering technique that overcomes this trade-off. A case study based on the European power system is solved. The outcomes given by the new clustering algorithm and other well-known techniques are investigated through in-sample and out-of-sample analyses applied to multiple cases that differ in to the number of clusters and the minimum renewable production. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Renewable sources are increasingly integrated into power systems for decarbonization, but their variability introduces uncertainty, requiring fast generating and storage units. Adequate representation of renewable variability in capacity expansion problems is crucial, often selected through clustering techniques. Trade-offs exist in current clustering algorithms, highlighting the need for innovative approaches to accurately capture inter-temporal correlation and representativeness with minimal clusters.