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Tytuł artykułu

Selected aspects of generating short-term electricity demand forecasts, taking into account renewable energy sources

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The risk of a decline in the quality of electricity demand forecasts in the short term increases due to the increase in the installed capacity of renewable energy sources (RES). This is mainly due to the high daily variability of electricity production from renewable sources, which is strongly dependent on local weather conditions. Production from renewable energy sources is a very complex time series, additionally reinforced by a significant increase in its share in total production. This applies in particular to photovoltaic sources in low-voltage networks. There is therefore an urgent need to improve the quality of forecasts in this area. The main goal of the research was to verify statistical models that often achieve good results in the complex problem of forecasting electricity demand. The main objective, regarding daily forecasts of consumers' demand for electricity, was achieved through the implementa-tion of intermediate objectives, including the development of a methodology for estimating electricity generated by photovoltaic installations.
Rocznik
Strony
21--34
Opis fizyczny
Bibliogr. 25 poz., rys., tab., wykr.
Twórcy
  • MSc Eng., PhD student, Silesian University of Technology, Department of Energy Ma-chines and Devices, ul. Konarskiego 18, 44-100 Gliwice
  • Silesian University of Technology, Department of Energy Machines and Devices, ul. Konarskiego 18, 44-100 Gliwice
  • Director of the Electricity and Gas Market Department, TAURON Sprzedaż sp. z oo, ul. Łagiewnicka 60, 30-417 Kraków
Bibliografia
  • 1. P. Machał, L. Remiorz and D. Bukowiec, "Preliminray analysis of selected aspects of short-term electricity demand," Rynek Energii, 2022.
  • 2. "TAURON Dystrybucja SA," 15/05/2023. [Online]. Available: https://www.tauron-dystrybucja.pl/kontakt/oddzialy.
  • 3. D. Paul, G. De Michele, B. Najafi and S. Avesani, "Benchmarking clear sky and transposition models for solar irradiance estimation on vertical planes to facilitate glazed facade design," Energy and Buildings, 15 1 2022.
  • 4. D. a. LE a. GM Heinemann, "Forecasting of solar radiation," Solar energy resource management, pp. 83-94, 2006.
  • 5. L. Hontoria, J. Aguilera and P. Zufiria, "Generation of hourly irradiation synthetic series using the neural network multilayer perceptron," Solar Energy, Vol. 72, No. 5, pp. 441-446, 2002.
  • 6. P. Lauret, M. David, E. Fock, A. Bastide, and C. Riviere, "Bayesian and Sensitivity Analysis Approaches to Modeling the Direct Solar Irradiance," Solar Energy, Vol. 128, No. 3, pp. 394-405, 2006.
  • 7. M. Paulescu, R. Blaga, C. Dughir, N. Stefu, A. Sabadus, D. Calinoiu and V. Badescu, "Intra-hour PV power forecasting based on sky imagery," Energy, Vol. 279, 2023.
  • 8. F. Lin, Y. Zhang and J. Wang, "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Vol. 39, No. 1, pp. 244-265, 2023.
  • 9. A. Hammer, D. Heinemann and E. Lorenz, "Short-term forecasting of solar radiation: a statistical approach using satellite data," Solar Energy, pp. 139-150, July 1999.
  • 10. Y. Liwei, X. Gao, J. Hua, P. Wu, Z. li, and D. Jia, "Very Short-Term Surface Solar Irradiance Forecasting Based On FengYun-4 Geostationary Satellite," Sensors, Vol. 20, 2020 .
  • 11. A. Razagui, K. Abdeladim, S. Semaoui, A. Hadj Arab and BS, "Modeling the forecasted power of a photovoltaic generator using numerical weather prediction and radiative transfer models coupled with a behavioral electrical model," Energy Reports, Vol. 6 , No. 1, pp. 57-62, 2020.
  • 12. R. Deo, M. Ahmed, D. Casillas-Pérez, A. Pourmousavi, G. Segal, Y. Yu and Salcedo-Sanz, Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression ”, Renewable Energy, vol. 203, pp. 113-130, 2023.
  • 13. MJ Mayer, B. Biró, B. Szücs and A. Aszódi, "Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning," Applied Energy, Vol. 336, 2023.
  • 14. J. Lemos-Vinasco, P. Bacher and JK Møller, "Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households' electrical load," Applied Energy, Vol. 303, No. 1, 2021.
  • 15. X. Zhao, W. Gao, F. Qian and J. Ge, “Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system,” Energy, Vol. 229, No. 15, 2021.
  • 16. H. Wu, A. Pratt, P. Munankarmi, M. Lunacek, SP Balamurugan, X. Liu and P. Spitsen, "Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics," Advances in Applied Energy, Volume 6, 2022.
  • 17. "Distribution Network Operation and Operation Manual," May 15, 2023. [Online]. Available: https://www.tauron-dystrybucja.pl/-/media/offer-documents/dystrybucja/uslugi-dystrybucyjne/iriesd/2023-02-20-iriesd_tauron-dystrybucja-tekst- Jednolity.ashx.
  • 18. “Power Grid,” May 15, 2023. [Online]. Available: https://www.power-grid.com/executive-insight/load-forecasting-weather-anomalies-data-access-are-key-to-managing-the-grid-of-the-future/#gref .
  • 19. O. Rubasinghe, T. Zhang, X. Zhang, SS Choi, TK Chau, Y. Chow, T. Fernando, and H. Ho-Ching Iu, “Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration,” Applied Energy, volume 333, 2023.
  • 20. ZN Qingchun Hou, E. Du, M. Miao, F. Peng, and C. Kang, "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Vol. 242, pp . . 205-215, 2019.
  • 21. J. Kolańska-Płuska and A. Gallus, "Statistical analysis of data for forecasting electricity production in a selected photovoltaic system," POZNAN UNIVERSITY OF TECHNOLOGY ACADEMIC JOURNALS, 2022.
  • 22. J. Nowotarski and R. Weron, "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, pp. 228-235, 2016.
  • 23. S. Koohi-Kamali, N. Abd. Rahim and S. Sobri, "Solar photovoltaic generation forecasting methods: A review," Energy Conversion and Management, pp. 459-497, 2018.
  • 24. "Mathworks," [Online]. Available: https://uk.mathworks.com/help/stats/choose-regression-model-options.html. [Access date: 2023].
  • 25. D. Gong, N. chen, Q. Ji, Y. Tang, and Y. Zhou, "Multi-scale regional photovoltaic power generation forecasting method based on sequence coding reconstruction," Energy Reports, vol. 9, pp. 135-143, 2023.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e7f9178a-5441-4167-aa2b-e37893010857
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