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Net load disaggregation at secondary substation level

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  • Toro-Cárdenas, Mateo
  • Moreira, Inês
  • Morais, Hugo
  • Carvalho, Pedro M.S.
  • Ferreira, Luis A.F.M.

Abstract

With more and more micro-generation being connected to the low-voltage network, it becomes increasingly difficult for operators to anticipate the loading condition of their networks. Several facts concur with such difficulty, one being the frequent lack of knowledge about the generation component of the measured net load. The generation component is typically more volatile than the natural load and is impossible to predict without reliable information on the grid’s renewable installed capacity. In this context, the objective of this paper is to propose a non-intrusive methodology to estimate distributed generation’ installed capacity. Relying upon net-load historical data obtained at the secondary substation level and on historical meteorological data, the paper presents algorithms that proved effective in determining an approximation of the renewable embedded capacity. The accuracy and effectiveness of the proposed algorithms are illustrated in the paper with real data, for real use cases in the distribution systems.

Suggested Citation

  • Toro-Cárdenas, Mateo & Moreira, Inês & Morais, Hugo & Carvalho, Pedro M.S. & Ferreira, Luis A.F.M., 2023. "Net load disaggregation at secondary substation level," Renewable Energy, Elsevier, vol. 207(C), pages 765-771.
    • Handle: RePEc:eee:renene:v:207:y:2023:i:c:p:765-771
    DOI: 10.1016/j.renene.2022.11.034
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    1. Pierro, Marco & Gentili, Damiano & Liolli, Fabio Romano & Cornaro, Cristina & Moser, David & Betti, Alessandro & Moschella, Michela & Collino, Elena & Ronzio, Dario & van der Meer, Dennis, 2022. "Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study," Renewable Energy, Elsevier, vol. 189(C), pages 983-996.
    2. Kryonidis, Georgios C. & Kontis, Eleftherios O. & Papadopoulos, Theofilos A. & Pippi, Kalliopi D. & Nousdilis, Angelos I. & Barzegkar-Ntovom, Georgios A. & Boubaris, Alexandros D. & Papanikolaou, Nick, 2021. "Ancillary services in active distribution networks: A review of technological trends from operational and online analysis perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    3. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    4. Kotarela, F. & Kyritsis, A. & Papanikolaou, N. & Kalogirou, S.A., 2021. "Enhanced nZEB concept incorporating a sustainable Grid Support Scheme," Renewable Energy, Elsevier, vol. 169(C), pages 714-725.
    5. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    6. Hosseini, Sayed Saeed & Agbossou, Kodjo & Kelouwani, Sousso & Cardenas, Alben, 2017. "Non-intrusive load monitoring through home energy management systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1266-1274.
    7. Sevdari, Kristian & Calearo, Lisa & Andersen, Peter Bach & Marinelli, Mattia, 2022. "Ancillary services and electric vehicles: An overview from charging clusters and chargers technology perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    8. Hyeongpil Bang & Dwi Riana Aryani & Hwachang Song, 2021. "Application of Battery Energy Storage Systems for Relief of Generation Curtailment in Terms of Transient Stability," Energies, MDPI, vol. 14(13), pages 1-14, June.
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    1. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).

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