Sizing Optimization and Experimental Verification of a Hybrid Generation Water Pumping System in a Greenhouse

In remote agricultural areas, electrical energy is usually deficient for pumping water into greenhouses. Photovoltaic (PV) panels and wind generators are considered suitable options for power supply. The reliability of hybrid generation water pumping depends primarily on the number of system components, which should be adapted to the local climatic conditions and crop irrigation schedule. In this study, a universal size optimization model is established to optimize the configuration of a hybrid PV-wind-battery (PWB) generation system. The climatic conditions and crop irrigation schedule are parameterized in the model. Minimization of the annual cost of the hybrid PWB system is the objective function. The constraints include the battery state of charge (SOC) and the power supply reliability, which consists of the loss of power supply (δLPS) and the excess energy (δEX). The numbers of PV panels and batteries, as well as the rated power of the wind generator, are the decision variables. The optimization model of the PWB generation system is solved using a particle swarm optimization (PSO) algorithm based on penalty function. The model is then applied to determine the optimal configuration of a water pumping system for a greenhouse used to grow tomatoes. Measured climatic data are used in the optimization process, which is conducted in the month of maximum irrigation water requirement (August). The optimal results for this greenhouse are two PV panels and two batteries, and the rated power of the wind generator is 375 W. Furthermore, field experiments are performed to validate the optimization model. The field experiment results show that the total output power of the PV panels and wind generator during 15 d are 41.478 kW and 6.235 kW, respectively. The total load power of the pump is 36.965 kW. The field experiments demonstrate that the optimal results are able to meet the power requirements of the water pumping system and the sizing optimization model is appropriate.