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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jaka Rober & Leon Maruša & Miloš Beković, 2023. "A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers," Energies, MDPI, vol. 16(17), pages 1-20, August.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6168-:d:1224668
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    References listed on IDEAS

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    1. Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
    2. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    3. Cátia Silva & Pedro Faria & Zita Vale, 2023. "Demand Response Implementation: Overview of Europe and United States Status," Energies, MDPI, vol. 16(10), pages 1-20, May.
    4. Sebastian Pater, 2023. "Increasing Energy Self-Consumption in Residential Photovoltaic Systems with Heat Pumps in Poland," Energies, MDPI, vol. 16(10), pages 1-14, May.
    5. Ottavia Valentini & Nikoleta Andreadou & Paolo Bertoldi & Alexandre Lucas & Iolanda Saviuc & Evangelos Kotsakis, 2022. "Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load," Energies, MDPI, vol. 15(14), pages 1-36, July.
    6. Klyapovskiy, Sergey & You, Shi & Michiorri, Andrea & Kariniotakis, George & Bindner, Henrik W., 2019. "Incorporating flexibility options into distribution grid reinforcement planning: A techno-economic framework approach," Applied Energy, Elsevier, vol. 254(C).
    7. Sridhar, Araavind & Honkapuro, Samuli & Ruiz, Fredy & Stoklasa, Jan & Annala, Salla & Wolff, Annika & Rautiainen, Antti, 2023. "Toward residential flexibility—Consumer willingness to enroll household loads in demand response," Applied Energy, Elsevier, vol. 342(C).
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