The carbon footprint and energy cost of irrigation are increasing due to the modernization of irrigation systems, which also necessitates highly efficient use of water resources. Alternatives to conventional energy sources to power irrigation systems are renewable sources, primarily photovoltaic energy. Photovoltaic energy has the main disadvantage of producing a highly variable amount of energy, which affects the irrigation uniformity. Modeling irrigation systems in an integrated manner generates useful information about system performance for technicians that helps in the decision-making process. The EVASOR (EVAluation of SOlar iRrigation systems) model integrates different modules to simulate the whole solar irrigation system using a holistic approach: (1) I-Solar, which simulates the instantaneous power generated by the photovoltaic system, (2) AS-Solar, which simulates the variable speed pumping system, (3) Solar-Net, which simulates the hydraulic performance of the water distribution network, and (4) PRESUD-Irregular, which determines the discharge and pressure of all the emitters of the subunits together with irrigation quality parameters (coefficient of uniformity (CU), emission uniformity (EU), and coefficient of variation of the emitter discharge in the subunit (CVq) for any pressure at the subunit inlet. The integrated model EVASOR determines the irrigation quality parameters of complex irrigation systems with information on irradiance, air temperature, wind speed, and water table level for any combination of open subunits. To validate the model, results are presented regarding a case study located in southeast Spain.

Global energy consumption and costs have increased exponentially in recent years, accelerating the search for viable, profitable, and sustainable alternatives. Renewable energy is currently one of the most suitable alternatives. The high variability of meteorological conditions (irradiance, ambient temperature, and wind speed) requires the development of complex and accurate management models for the optimal performance of photovoltaic systems. The simplification of photovoltaic models can be useful in the sizing of photovoltaic systems, but not for their management in real time. To solve this problem, we developed the I-Solar model, which considers all the elements that comprise the photovoltaic system, the meteorologic conditions, and the energy demand. We have validated it on a solar pumping system, but it can be applied to any other system. The I-Solar model was compared with a simplified model and a machine learning model calibrated in a high-power and complex photovoltaic pumping system located in Albacete, Spain. The results show that the I-Solar model estimates the generated power with a relative error of 7.5%, while the relative error of machine learning models was 5.8%. However, models based on machine learning are specific to the system evaluated, while the I-Solar model can be applied to any system.

Photovoltaic solar energy is becoming very important globally due the benefits of their use. Climate change is resulting in frequent climatic variations that have a direct effect on the energy production in photovoltaic installations, so their good management is essential. This can be a big problem, for example, in photovoltaic pumping systems where irrigated crops can be affected due to lack of water. In this work, a PREPOSOL (PREdiction of POwer in SOLar installations) model was developed in MATLAB® software, which allowed to predict the power generated in the photovoltaic installations up to 3 h in advance using Artificial Neural Networks (ANNs) in a Bayesian framework with Genetic Algorithms. Despite that the PREPOSOL model can be implemented for other activities with photovoltaic solar energy, in this case, it was applied to photovoltaic pumping systems. The results showed that the model estimated the generated power with a relative error (RE) and R2 of 8.10 and 0.9157, respectively. Moreover, a representative example concerning irrigation programming is presented, which allowed adequate management. The methodology was calibrated and validated in a high-power and complex photovoltaic pumping system in Albacete, Spain.

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Resumen: Uno de los aspectos clave que condicionan el buen funcionamiento de los sistemas de bombeo solar es la gestión que se hace de ellos. Esta gestión debe englobar tanto a la instalación fotovoltaica, siendo capaz de transformar la gran variabilidad de la radiación solar en potencia útil disponible, como a la instalación hidráulica, para poder conseguir una adecuada gestión de riego en cuanto a uniformidad y dosis a aplicar. Por tanto, el presente estudio tiene como objetivo el desarrollo de una herramienta de análisis inteligente de bombeos solares, tanto en sistemas de inyección directa como en almacenamiento a embalse, compuesta por un modelo fotovoltaico de alta precisión que permita obtener la potencia generada en tiempo real, integrado con un modelo hidráulico, para poder reproducir el comportamiento del sistema de riego frente a las posibles variaciones de presión y caudal descargado según la potencia eléctrica disponible.Palabras clave: riego, energías renovables, gestión, optimización.