Abstract
Despite the value of energy optimization in desalination processes, modeling dynamic operations for monthly billing periods has remained a computational challenge. This work proposes a framework for energy flexibility optimization, which includes new modeling features for independent operation of parallel skids, start-up delays associated with chemical stabilization, the consideration of industrial energy tariff structures, and inclusion of hourly electrical carbon intensities. This is done using a modular and computationally efficient formulation that guarantees a globally optimal solution with standard optimization solvers. The approach is demonstrated in two distinct case studies: a seawater desalination plant in Santa Barbara, CA, and an indirect potable reuse facility in San Jose, CA. Trends predicted from the model are validated against operational facility measurements from a demand response shutdown event. Preliminary results show that optimizing energy flexibility can result in 18.51% monthly cost savings over energy efficiency-optimized operation. The value extracted from a facility-wide shutdown during peak electricity price hours is hampered by start-up delays in post-treatment chemical stabilization. In cases in which a facility does not have much excess capacity, using a flow equalization tank or operating over a wide recovery range may be cost-effective.
Original language | American English |
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Pages (from-to) | 15696-15704 |
Number of pages | 9 |
Journal | ACS Sustainable Chemistry and Engineering |
Volume | 12 |
Issue number | 42 |
DOIs | |
State | Published - 2024 |
NREL Publication Number
- NREL/JA-2C00-90919
Keywords
- convex optimization
- desalination
- energy flexibility
- mixed-integer programming