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Riquelme-Dominguez, JmCorresponding Author

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A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case

Publicated to:International Journal Of Electrical Power & Energy Systems. 156 109730- - 2024-02-01 156(), DOI: 10.1016/j.ijepes.2023.109730

Authors: Riquelme-Dominguez, JM; Carranza-García, M; Lara-Benítez, P; González-Longatt, FM

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Abstract

The progressive substitution of conventional synchronous generation for renewable-based generation imposes a series of challenges in many aspects of modern power systems, among which are the issues related to the low rotational inertia systems. Rotational inertia and the kinetic energy stored in the rotating masses in the power system play a fundamental role in the operation of power systems as it represents in some sort the ability of the system to withstand imbalances between generation and demand. Therefore, transmission system operators (TSOs) need tools to forecast the inertia or the kinetic energy available in the systems in the very short term (from minutes to hours) in order to take appropriate actions if the values fall below the one that ensures secure operation. This paper proposes a methodology based on machine learning (ML) techniques for short-term kinetic energy forecasting available in power systems; it focuses on the length of the moving window, which allows for obtaining a balance between the historical information needed and the horizon of forecasting. The proposed methodology aims to be as flexible as possible to apply to any power system, regardless of the data available and the software used. To illustrate the proposed methodology, time series of the kinetic energy recorded in the Nordic Power System (NPS) has been used as a case study. The results show that Linear Regression (LR) is the most suitable method for a time horizon of one hour due to its high accuracy to-simplicity ratio, while Long Short-Term Memory (LSTM) is the most accurate for a forecasting horizon of four hours. Experimental assessment has been carried out using Typhoon HIL-404 simulator, verifying that both algorithms are suitable for real-time simulation.

Keywords

ControllerData seriesForecastingInertiaKinetic energyMachine learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal International Journal Of Electrical Power & Energy Systems 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, 2024 there are still no calculated indicators, but in 2023, it was in position 65/353, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Electrical & Electronic.

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 2025-06-01:

  • WoS: 4
  • Scopus: 6
  • OpenCitations: 2

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 2025-06-01:

  • 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: 32 (PlumX).

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/86470/

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: 47
  • Downloads: 10

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United Kingdom.

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 (RIQUELME DOMINGUEZ, JOSE MIGUEL) .

the author responsible for correspondence tasks has been RIQUELME DOMINGUEZ, JOSE MIGUEL.