Selected Aspects of Generating Short-Term Electricity Demand Forecasts, Taking into Account Renewable Energy Sources

• Autorzy: Paweł Machał , Leszek Remiorz , Dariusz Bukowiec
• 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.(original abstract)

Słowa kluczowe

EN

Photovoltaic systems; Renewable energy sources; Prosumer

PL

Systemy fotowoltaiczne; Odnawialne źródła energii; Prosument

• Czasopismo: Energy Policy Studies
• Rocznik: 2023
• Numer: nr 1(12)
• Strony: 21--34

Twórcy

autor

Paweł Machał

• Silesian University of Technology

autor

Leszek Remiorz

• Silesian University of Technology

autor

Dariusz Bukowiec

• TAU-RON Sprzedaż sp. z oo

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 de- sign," 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-dys- trybucyjne/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/