Multi-objective Optimization Method Based on Deterministic and Metaheuristic Approaches in Water-Energy-Food Nexus Under Uncertainty

This paper presents a multi-objective optimization approach for the security assessment of the water-energy-food nexus considering the involved uncertainty. The proposed optimization approach consists of a hybrid strategy that combines deterministic and metaheuristic optimization approaches for solving this complex problem. The deterministic optimization part of the mathematical model is developed in the platform general algebraic modeling system (GAMS). The metaheuristic part is approached using the improved multi-objective optimization differential evolution algorithm programmed in visual basic for applications. The communication between the different software is implemented using GAMS data exchange files and linking routines. The uncertainty associated with the problem is considered using a code that generates random values of uncertain parameters of the mathematical model. The optimization approach is applied to assess the water-energy-food nexus in an arid region. Three objective functions (one economic and two environmental) were incorporated in the proposed formulation. A case study from a region of Mexico is addressed to show the applicability of the proposed approach. The results of the optimization process offer alternatives that conciliate economic and environmental interests considering uncertainty in weather conditions such as rainfall and solar radiation. The obtained results show a set of solution proposals in which different increases are offered in the satisfaction of the demand for energy and water in different sectors and at the same time, the smallest possible increase in the economic and environmental objective functions. In addition, the proposed methodology is replicable to different case studies where approaching the optimal solution becomes a complicated task. [GRAPHICS] .