Abstract: The flow of energy and water are intrinsically interconnected. This is mainly because water is useful for producing energy as a fluid in thermo-electric cooling systems, and energy is required to treat and distribute water for human use. In the United States, conveyance and treatment of water typically takes 2-3% of annual electricity production and up to 12% of local production. Reducing the cost of the water supply increases the resiliency of both water and energy production, especially when onsite recycling and treatment can be achieved. However, water treatment in thermal power plants not only reduces the water supply but also consumes energy. Hence, there is a trade-off between reducing water cost and increasing energy supply.
In this talk, we tackle this trade-off by solving for optimal operational costs both with and without water treatment while satisfying all the operational constraints. The integrated water-energy nexus is modeled as a nonlinear program where the nonlinear constraints come from the water mass balance and the calculation of the water treatment energy consumption. To solve this nonlinear program, a McCormick relaxation, which is a mixed-integer linear program, is formulated and recognized as a lower bound for the original nonlinear program. A process is designed to solve the original nonlinear program by using AMPL/Knitro. Finally, we compare the total costs of the model with and without water treatment to demonstrate the efficiency of using water treatment.
Bio: Dan Hu is pursuing her Ph.D. degree in industrial engineering with a minor in statistics at Iowa State University. She received her B.E. degree in energy and resources engineering and B.A. degree in economics from Peking University. Her research interests are the application of stochastic models and the analysis of the integration of natural gas and power systems.