Satisfaction of energy and water demands has always been a driving force for societal growth. At the same time, natural energy reserves and freshwater resources are becoming severely scarce. The strong interdependencies between energy generation and water treatment give rise to a nexus of interconnected networks. At the regional level, we further need to consider the time-dependent resource availabilities and consumer demands, the selection, location and allocation of the generation plants (sources-sinks), and the potential recycle and reuse from the consumers. There are numerous ways to configure a water-energy nexus (WEN), comprised of energy sources (acting also as water sinks), water sources (acting also as energy sinks), and energy and water consumers . However, not all configurations lead to feasible and/or optimal WEN designs. Furthermore, depending on the kind and quality of the water and energy resources, the choice of treatment/conversion technologies vary. To address these, we consider a superstructure formulation that embeds all plausible WEN configurations for a given nexus design problem. The superstructure is modeled using a mixed-integer nonlinear program (MINLP) which is used to optimize the allocation of appropriate energy and water sources (treatment/conversion technologies), their capacities, connectivity and locations for minimum total cost. The total expenditures include the costs of resource supply, generation, and transportation. The nonlinearities arise from the distance calculations and the variable concentrations and flow rates appearing in the mixing constraints. We illustrate that this problem can be posed as a special case of a generalized multiperiod Capacitated Multi-facility Weber Problem (CMWP) . The applicability of the WEN superstructure for regional planning is demonstrated using case studies for the design of WEN for different Texas Counties.
 Tsolas, S. D., Karim, M. N., & Hasan, M. F. (2018). Systematic Design, Analysis and Optimization of Water-Energy Nexus. Foundations of Computer-Aided Process Design 2019, Paper ID 101
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