The most cost-effective measure for reducing energy costs in direct pumping irrigation systems is demand management through irrigation scheduling.

 

This involves:

 

1) Supplying the requested irrigation times within the requested timeframe.

 

2) Preferably using hours with the most economical electricity rates to minimize costs. That is,

 

3) Ensuring that there is sufficient pressure in all hydrants when they are needed to irrigate.

 

4) Modulating the pressure, if possible, and limiting the contracted power in each period.

 

This strategy is especially recommended for the months of June, July, and August, when the highest irrigation needs coincide with the most expensive rates.

 

This optimal scheduling represents an enormously complex problem, with many decision variables, that an operator cannot satisfactorily solve through experience or trial and error alone.

 

Artificial Intelligence computational algorithms and applications have been developed that successfully solve this challenge of obtaining truly optimal irrigation schedules, without pressure deficits, enabling savings of between 16 and 39% in operational costs in the cases analyzed.