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

  1. Monteiro, C. 3
  2. Ramirez-Rosado, I.J. 2
  3. Fernandez-Jimenez, L.A. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad de Zaragoza
    info

    Universidad de Zaragoza

    Zaragoza, España

    ROR https://ror.org/012a91z28

  3. 3 Universidade Do Porto
    info

    Universidade Do Porto

    Oporto, Portugal

    ROR https://ror.org/043pwc612

Revista:
Renewable Energy

ISSN: 0960-1481

Año de publicación: 2013

Volumen: 50

Páginas: 387-394

Tipo: Artículo

DOI: 10.1016/J.RENENE.2012.06.061 SCOPUS: 2-s2.0-84864340581 WoS: WOS:000311865900046 GOOGLE SCHOLAR

Otras publicaciones en: Renewable Energy

Resumen

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. © 2012 Elsevier Ltd.