  The energy-water demands of metropolitan regions and agricultural ecosystems
are ever-increasing. To tackle this challenge efficiently and sustainably, the
interdependence of these interconnected resources has to be considered. In this
work, we present a holistic decision-making framework which takes into account
simultaneously a water and energy supply system with the capability of
satisfying metropolitan and agricultural resource demands. The framework
features: (i) a generic large-scale planning and scheduling optimization model
to minimize the annualized cost of the design and operation of the energy-water
supply system, (ii) a mixed-integer linear optimization formulation, which
relies on the development of surrogate models based on feedforward artificial
neural networks and first-order Taylor expansions, and (iii) constraints for
land and water utilization enabling multi-objective optimization. The framework
provides the operational profiles of all energy-water system elements over a
given time horizon, which uncover potential synergies between the essential
food, energy, and water resource supply systems.
