Evaluation of planning policy scenarios for the water-food and energy nexus through the development of a multi-objective optimization model

This study addresses the increasing interdependence of water, food, and energy systems by developing an integrated simulation–optimization framework for the Sefidroud irrigation and drainage network. Leveraging system dynamics modeling in Simulink and multi-objective genetic algorithm (GA) optimization within MATLAB, the model simultaneously pursues four critical objectives: minimizing water scarcity, maximizing agricultural profit, reducing greenhouse gas emissions through pump electrification, and optimizing hydropower production. Trade-offs among these objectives are resolved using a weighted sum approach, with seven distinct weight combinations tested to capture a range of policy priorities. Simulation outputs, validated against empirical data, demonstrate the model's high accuracy in achieving balanced resource management. Among the scenarios, the fourth weighting combination, emphasizing equal priorities for water scarcity reduction and economic profit, proved the most balanced. Under this configuration, 78.9% of the water demand is met through integrated sources (reservoir dams: 80.4%, groundwater: 7.6%, drainage: 3.4%, rivers: 5.4%, reservoirs: 3.2%). This scenario also improves pump electrification ratios for rice paddies (0.44, a 16.9% increase) and tea gardens (0.74, a 12.5% increase) over current conditions, and boosts hydropower output by 8.4%. The findings highlight that farmers can adjust cultivated areas in line with water availability to optimize profits, managers can customize objective weights to prioritize emission reduction or energy security, and the framework enables adaptive strategies to balance sectoral demands under dynamic hydrological and socio-economic conditions. Overall, this study underscores the value of simulation–optimization tools for nexus management and provides actionable insights for harmonizing water, food, and energy objectives.