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Estimating regional potential for micro-hydropower energy recovery in irrigation networks on a large geographical scale

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  • Crespo Chacón, Miguel
  • Rodríguez Díaz, Juan Antonio
  • García Morillo, Jorge
  • McNabola, Aonghus

Abstract

Micro-hydropower has been highlighted as a potential technology suitable for installation in irrigation networks to reduce system overpressures and to reduce the net energy consumption of the irrigation process. However, the full impact of this technology on a large regional scale is unknown. Artificial Neural Networks and regression models were used in this research to predict the energy recovery potential for micro-hydropower in on-demand pressurised irrigation networks across a large spatial scale. Predictors of energy recovery potential across spatial unit areas included: Irrigated land surface area, irrigation crop water requirements, rainfall, evapotranspiration, and mean topographical slope. The model was used to predict the energy recovery potential across the 164,000 ha of the Spanish provinces of Seville and Cordoba in the absence of hydraulic models. A total of 21.05 GWh was identified as the energy potential which could have been recovered using micro-hydropower during the 2018 irrigation season. This amount of energy would have potentially reduced the energy consumption of the irrigation process in this region by approximately 12.8%. A reduction in energy consumption in the agriculture sector of this magnitude could have significant impacts on food production and climate change. The main novelty of this paper lies in the assessment of micro hydropower resources in operating irrigation networks on a large geographical scale, in areas where no information is available. It provides an approximation of the existing potential using computational methods.

Suggested Citation

  • Crespo Chacón, Miguel & Rodríguez Díaz, Juan Antonio & García Morillo, Jorge & McNabola, Aonghus, 2020. "Estimating regional potential for micro-hydropower energy recovery in irrigation networks on a large geographical scale," Renewable Energy, Elsevier, vol. 155(C), pages 396-406.
    • Handle: RePEc:eee:renene:v:155:y:2020:i:c:p:396-406
    DOI: 10.1016/j.renene.2020.03.143
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    References listed on IDEAS

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    1. R. Perea & E. Poyato & P. Montesinos & J. Díaz, 2015. "Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5551-5567, December.
    2. Fernández García, I. & Rodríguez Díaz, J.A. & Camacho Poyato, E. & Montesinos, P. & Berbel, J., 2014. "Effects of modernization and medium term perspectives on water and energy use in irrigation districts," Agricultural Systems, Elsevier, vol. 131(C), pages 56-63.
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    4. Crespo Chacón, Miguel & Rodríguez Díaz, Juan Antonio & García Morillo, Jorge & McNabola, Aonghus, 2020. "Hydropower energy recovery in irrigation networks: Validation of a methodology for flow prediction and pump as turbine selection," Renewable Energy, Elsevier, vol. 147(P1), pages 1728-1738.
    5. Gallagher, J. & Harris, I.M. & Packwood, A.J. & McNabola, A. & Williams, A.P., 2015. "A strategic assessment of micro-hydropower in the UK and Irish water industry: Identifying technical and economic constraints," Renewable Energy, Elsevier, vol. 81(C), pages 808-815.
    6. Rodriguez-Diaz, J.A. & Camacho-Poyato, E. & Lopez-Luque, R. & Perez-Urrestarazu, L., 2008. "Benchmarking and multivariate data analysis techniques for improving the efficiency of irrigation districts: An application in spain," Agricultural Systems, Elsevier, vol. 96(1-3), pages 250-259, March.
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