Overcoming modeling and computational complexity challenges in food–energy–water nexus optimization

The food–energy–water nexus (FEWN) postulates that sustainable decision-making regarding the interconnected resources food, energy and water must consider all involved resources holistically. Due to its multi-scale complexity, modeling challenges and computational intractability regarding the interconnected FEWN optimization remain. To overcome these challenges, this work proposes employing surrogate models based on data-driven and model optimization techniques, while quantifying the introduced errors due to both the selected approximation and optimization methods. In turn, we derive a mixed-integer linear FEWN planning and scheduling optimization model based on a greenhouse farming, a renewable energy and a reverse osmosis desalination water supply system, which is initially computationally intractable. This computational complexity is first discussed and overcome for the energy–water nexus supply system, before solving the complete FEWN supply system by utilizing strategies such as relaxation, modularization and convex hull reformulation.