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.