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Short-term forecasting model for electric power production of small-hydro power plants

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  • Monteiro, Claudio
  • Ramirez-Rosado, Ignacio J.
  • Fernandez-Jimenez, L. Alfredo

Abstract

This paper presents an original short-term forecasting model for hourly average electric power production of small-hydro power plants (SHPPs). The model consists of three modules: the first one gives an estimation of the “daily average” power production; the second one provides the final forecast of the hourly average power production taking into account operation strategies of the SHPPs; and the third one allows a dynamic adjustment of the first module estimation by assimilating recent historical production data. The model uses, as inputs, forecasted precipitation values from Numerical Weather Prediction tools and past recorded values of hourly electric power production in the SHPPs. The structure of the model avoids crossed-influences between the adjustments of such model due to meteorological effects and those due to the operation strategies of the SHPPs. The forecast horizon of the proposed model is seven days, which allows the use of the final forecast of the power production in Power System operations, in electricity markets, and in maintenance scheduling of SHPPs. The model has been applied in the forecasting of the aggregated hourly average power production for a real-life set of 130 SHPPs in Portugal achieving satisfactory results, maintaining the forecasting errors delimited in a narrow band with low values.

Suggested Citation

  • Monteiro, Claudio & Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo, 2013. "Short-term forecasting model for electric power production of small-hydro power plants," Renewable Energy, Elsevier, vol. 50(C), pages 387-394.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:387-394
    DOI: 10.1016/j.renene.2012.06.061
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    1. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    2. Peña, Rafael & Medina, Aurelio & Anaya-Lara, Olimpo & McDonald, James R., 2009. "Capacity estimation of a minihydro plant based on time series forecasting," Renewable Energy, Elsevier, vol. 34(5), pages 1204-1209.
    3. Moreno, Julián, 2009. "Hydraulic plant generation forecasting in Colombian power market using ANFIS," Energy Economics, Elsevier, vol. 31(3), pages 450-455, May.
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    1. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    2. Gang Li & Bao-Jian Li & Xu-Guang Yu & Chun-Tian Cheng, 2015. "Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants," Energies, MDPI, vol. 8(10), pages 1-14, October.
    3. Sitzenfrei, Robert & von Leon, Judith, 2014. "Long-time simulation of water distribution systems for the design of small hydropower systems," Renewable Energy, Elsevier, vol. 72(C), pages 182-187.
    4. Manzano-Agugliaro, Francisco & Taher, Myriam & Zapata-Sierra, Antonio & Juaidi, Adel & Montoya, Francisco G., 2017. "An overview of research and energy evolution for small hydropower in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 476-489.
    5. Emanuele Ogliari & Alfredo Nespoli & Marco Mussetta & Silvia Pretto & Andrea Zimbardo & Nicholas Bonfanti & Manuele Aufiero, 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network," Forecasting, MDPI, vol. 2(4), pages 1-19, October.
    6. Nedaei, Mojtaba & Walsh, Philip R., 2022. "Technical performance evaluation and optimization of a run-of-river hydropower facility," Renewable Energy, Elsevier, vol. 182(C), pages 343-362.

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    Keywords

    Hydro power plants; Short-term forecasting;

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