The impact of temporal clustering on long-term energy system models

Matteo Catania, Giuseppe Muliere, Fabrizio Fattori and Paolo Colbertaldo

Applied Energy, 2025, vol. 399, issue C, No S0306261925010840

Abstract: The field of energy system modelling is experiencing significant development, driven by the urgent need to redesign the national energy systems to achieve carbon neutrality. A growing interest regards long-term energy system models, which enable tracking the pathway and not only the final need for installations. The increase in complexity may easily lead them to face computational limits. Therefore, modelling approaches are required that cluster data to reduce the size of the problem while limiting errors and inaccuracies. This article studies the impact of temporal clustering on the performances of a sector-integrated energy system model, considering the double-layer clustering scheme operating on two distinct temporal scales: intra-year and inter-year. The former is addressed through typical-day clustering (k-means and k-medoids), while the latter introduces multi-year gaps between representative years. This methodology is implemented in the open-source framework oemof, which is customized to the dual clustering approach. The study addresses a sector-integrated energy system, built on the Italian structure, with a multi-vector and multi-sector perspective along the 2020–2050 horizon. The impact is investigated by comparing multiple options with varying number of typical days and multi-year gap, comparing each configuration with a benchmark without clustering. The approach yields coherent representations of the energy system evolution, simultaneously reducing the memory usage down to 4 %. The outcomes show the benefits of balancing the number of representative years with the number of representative days, suggesting that such a trade-off leads to significant computational advantages. Although literature shows that time-series reduction is case-dependent, the double-layer clustering scheme appears promising for application on even more complex models, where a full-hour resolution would be computationally intractable.

Keywords: Clustering; Energy system modelling; Oemof; Time series; Temporal clustering; Energy transition (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925010840
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:399:y:2025:i:c:s0306261925010840

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.126354

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().