Energy–Water Management System Based on Predictive Control Applied to the Water–Food–Energy Nexus in Rural Communities

Abstract

Generating strategies and techniques to feed the increasing world population is a significant challenge under climate change effects such as drought. Rural areas are especially sensitive to such effects as they are unable to overcome the lack of water with new agricultural production techniques. In developing countries, rural communities commonly do not have access to high-quality electricity supplies. In some cases, these communities lack electricity in their homes, which affects the opportunity to improve food production through the incorporation of new technologies. This work proposes an integrated optimizer based on model predictive control (MPC) that combines a water management system, which handles the medium-term water requirements for irrigation, with an energy management system, which handles short-term energy requirements. The proposed approach is based on predictive phenomenological models of evapotranspiration and electricity consumption considering climate conditions such as temperature, precipitation, solar radiation, and wind speed, and aims to optimize the use of energy and water and the relative yields of crops. The integrated energy-water management system (EWMS) improves water resource sustainability according to energy availability/costs and water use requirements. Simulation results using real data from a rural community in southern Chile show that the integrated EWMS based on an MPC optimizer successfully determines and satisfies the water and energy requirements under aquifer sustainability constraints.