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Analysis of institutional authors

Riyahi, MiladCorresponding AuthorMartin, Alvaro GutierrezAuthor

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April 22, 2025
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Optimizing capacity expansion modeling with a novel hierarchical clustering and systematic elbow method: A case study on power and storage units in Spain

Publicated to: ENERGY. 323 135788- - 2025-05-15 323(), DOI: 10.1016/j.energy.2025.135788

Authors:

Riyahi, M; Martín, AG
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Affiliations

Univ Politecn Madrid, Higher Tech Sch Engineers Telecommun, Madrid, Spain - Author

Abstract

To reduce the computational complexity of Capacity Expansion Models, the planning horizon must be simplified into representative time-periods. Also, to accurately model the expansion of power and storage units, these representative time periods must reveal the mid-term dynamics of the planning horizon. In this paper, a novel hierarchical clustering algorithm is presented that retains the chronology of the original data in creating representative time periods. The proposed algorithm, first, determines the optimal number of clusters with a modified elbow method, enhanced with a stopping criterion to prevent it from running uselessly. The designed stopping criterion works based on percentage variance and runtime to determine the number of clusters systematically. Then, the proposed clustering algorithm employs a novel selection strategy based on the Euclidean distance, k-Medoid, and k-Means to determine the most proper representative vector in each cluster. In this way, it reduces the computational time of capacity expansion models while maintaining the accuracy of final answers. To evaluate its performance, the proposed algorithm is tested on energy data, including demand, photovoltaic, wind, and hydrogen generation, across hourly, daily, and weekly time periods. Also, the performance of the proposed clustering algorithm in selecting the number of clusters and clustering is compared with the results of some well-known methods on accuracy and runtime metrics. Numerical results show that the proposed clustering method selects a more appropriate number of clusters in less computational time than other systematic approaches. Moreover, findings on clustering show that the proposed algorithm achieves the highest accuracy on weekly and daily time periods compared to well-known clustering methods, with the error rate of 118 % and 52 %, respectively. Furthermore, implementation results show that the proposed clustering reduces the computational time of capacity expansion models by 84.81 % and 55.91 % on weekly and daily time periods. Additionally, this study assesses the robustness of the clustering methods through a sensitivity analysis, which shows that the proposed algorithm outperforms the others in this metric, as well.
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Keywords

AlgorithmsCapacity expansion modelElbow methodEuclidean distanceGenerationHierarchical clusteringImpacK -meanK-meansK-medoidsPeriodsStopping criterionTime-series aggregation

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal ENERGY due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 3/79, thus managing to position itself as a Q1 (Primer Cuartil), in the category Thermodynamics.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-26:

  • WoS: 3
  • Scopus: 3
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-26:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 18.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 12 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.
  • The number of mentions on Wikipedia: 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/91958/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 56
  • Downloads: 38
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (RIYAHI, MILAD) and Last Author (GUTIERREZ MARTIN, ALVARO).

the author responsible for correspondence tasks has been RIYAHI, MILAD.

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Awards linked to the item

This work has been funded by the Universidad Politecnica de Madrid Project "SDGine for Healthy People and Cities" which received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 945139 and from REPSOL, S.A.
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