Stochastic Co-Optimization of Hybrid Renewable Energy Systems for Climate-Resilient Precision Irrigation

This paper develops a stochastic approach for designing and operating a hybrid renewable energy system (HRES) through stochastic co-optimization. A hybrid renewable energy system consisting of solar photovoltaic power, a battery, and a grid is considered as the energy source to supply irrigation demands. Wind generation is retained in the formulation as a candidate technology, and the optimization returns an effectively zero optimal wind capacity under the conditions examined. A multi-scenario approach addresses uncertainty in solar output and demand. The methodology optimizes HRES components for future uncertain climate scenarios. Irrigation demand has a strong daily cycle. For example, irrigation demand rises from 4–6 kW at night and early morning, reaching a maximum of 88 kW at hour 13. The PV system’s peak power matches the maximum irrigation demand. In total, the PV system provides around 430–440 kWh of energy. The grid imports 35–40 kWh of energy, yielding a PV-to-grid ratio of more than 10:1. Therefore, solar-dominant systems can reliably supply energy to support agricultural activities, particularly irrigation. This methodology can serve as a basis for sustainable irrigation planning under uncertainty.