Multi-objective optimization for comprehensive water, energy, food nexus modeling

Abstract

The precarious circumstances surrounding the depletion of resources stress the need for efficient resource management in a manner that prevents the improvement of one resource sector without a potential expense of another. The objective of this work is to provide a holistic water energy food nexus (WEFN) model that accounts for the three sectors and their interlinkages, while securing a healthy and sustainable diet within environmental planetary boundary limits and economic budgets. Multi-objective optimization is applied in the Python software, where the GEKKO package optimizer is used. In addition to optimization, a Pareto analysis yields a frontier of optimal solutions for varying levels of objective function priorities. Excel is used to import case study data and export model results. Different case study scenarios showcase the model and demonstrate how resource availability and imposed self-sufficiency impact resource consumption (energy: 184–428 MWh, water: 7–64 km3) and allocation across sectors. The Pareto analysis illustrates the trade-offs between nexus sectors and reflects how priorities drive interactions between conflicting objectives. A Sustainable Development Goal (SDG) analysis reveals the role of imposed constraints on energy resource use and resource supply in achieving SDG targets (achievement rates ranging from 0.43 to 93 %). Thus, the contribution of this work is a holistic WEFN model that includes interlinkage, dietary/nutritional, environmental, and economic constraints. The design of this work, combining Excel with Python, serves as a platform for facilitated case study generation in a manner that will improve the deficient understanding of WEFN interlinkages and their interactions. Results shed light on interactions between sectors and the complex process of balancing the various factors in determining optimal resource allocation and healthy diet composition. These results also highlight the potential of this work to serve as a decision support tool. © 2023 Institution of Chemical Engineers