A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

Jaka Rober, Leon Maruša and Miloš Beković ()
Additional contact information
Jaka Rober: Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
Leon Maruša: Elektro Celje d.d, Vrunčeva ulica 2a, 3000 Celje, Slovenia
Miloš Beković: Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia

Energies, 2023, vol. 16, issue 17, 1-20

Abstract: This paper presents a step-by-step approach to assess the energy flexibility potential of residential consumers to manage congestion in the distribution network. A case study is presented where a selected transformer station exhibits signs of overloading. An analysis has been performed to evaluate the magnitude of the overloading and the timing of the overload occurrence based on their historical load data. Based on the historical load data, the four most prominent consumers have been chosen for the flexibility assessment. Temperature load dependency has been evaluated for the selected consumers. The paper’s novel approach focuses on selecting individual consumers with the highest energy flexibility potential, and analysing their load patterns to address transformer overloading. To achieve this, machine learning algorithms, specifically, multiple linear regression and support vector machines, were used for load profile forecasting during the overload occurrences. Based on the forecast and measured load patterns, flexibility scenarios were created for each consumer. The generated models were evaluated and compared with the forecasting based on the average load of the past days. In the results, three potential consumers were identified who could resolve the transformer overloading problem. The machine learning models outperformed the average-based forecasting method, providing more realistic estimates of flexibility potential. The proposed approach can be applied to other overloaded transformer stations, but with a limited number of consumers.

Keywords: flexibility; baseline; demand response; distribution transformer; congestion management; power flow control; peak shaving; load shifting; predictive models; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/17/6168/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/17/6168/ (text/html)

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:gam:jeners:v:16:y:2023:i:17:p:6168-:d:1224668

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().