Multi-objective Optimization Approach to Meet Water, Energy, and Food Needs in an Arid Region Involving Security Assessment

The need to quantitatively measure the security of the water–energy–food (WEF) nexus sectors in a region is of great importance due to the vulnerability of each sector and the associated cost. This paper presents an approach for optimizing the security indices of the WEF nexus by analyzing the availability, accessibility, and sustainability of water, energy, and food resources. To maximize the security indices, a multi-objective mixed integer nonlinear programing model involving multiple stakeholders is presented. Through the model, the dependent variables for the optimization of the indices are described, and these in turn represent new and existing technologies considering the economic and environmental aspects. Furthermore, the optimization model results in different configurations when selecting technologies to satisfy high demands imposed by the population, maximizing the value of the evaluated indices, and improving the performance of the distribution network of the studied area. Different scenarios are designed to observe the behavior of the objective functions (minimizing the total annual cost, fresh water consumption, and greenhouse gas emissions) while maximizing the security of the WEF nexus; the indices are illustrated in radar graphs that allow identifying the interactions between the involved sectors. A case study of an area of Mexico with great development is analyzed due to its economic importance; however, the model is applicable to any region with the corresponding data and similar climatic characteristics. The results show the vulnerability of the distribution network and that it is possible to satisfy the demands of a region with the implementation of new policies that allow maximizing the values of the WEF nexus security indices. In general, the total annual cost and greenhouse gas emissions are dependent on the variations in the indices, and the indices associated with the energy sector fluctuate more in the analyzed scenarios.