Energy-water-food nexus planning considering Co-optimized electric vehicle scheduling

This research presents an integrated Mixed Integer Programming (MIP) formulation that enables long term planning of food, energy, and water (FEW) systems while explicitly modeling electric vehicles (EV) as bi-directional, dynamic storage systems. The model simultaneously co-optimizes hourly EV charging and discharging with water allocation, energy provision, and agricultural land use decisions to minimize total system costs and maximize food production. Utilizing data from Yulin, China, the suggested framework leads to a total system cost reduction of 35% compared to baseline scenario without EVs flexibility (reduced to 2.27 million RMB annually). The model hourly resolution captures both daily and seasonality dynamics providing actionable planning strategies to FEW stakeholders and policymakers. The reported 35% cost savings is consistent with or exceed savings reported in other comparable studies of the FEW nexus without EVs [ 2 , 10 ], or in short term EV-integrated energy systems [ 1 ].