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Modular Design of Community-Scale Photovoltaic Reverse Osmosis Systems Under Uncertainty

[+] Author Affiliations
Amy Bilton

University of Toronto, Toronto, ON, Canada

Steven Dubowsky

Massachusetts Institute of Technology, Cambridge, MA

Paper No. DETC2014-35038, pp. V02AT03A025; 10 pages
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 40th Design Automation Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4631-5
  • Copyright © 2014 by ASME


Photovoltaic reverse osmosis (PVRO) systems can provide a viable clean water source for many remote communities. To be cost-effective, PVRO systems need to be custom-tailored for the local water demand, solar insolation, and water characteristics. Designing a custom system composed of modular components is not simple due to the large number of design choices and the variations in the sunlight and demand. This paper presents a modular design architecture, which when implemented on a low-cost PC, would enable users to configure systems from inventories of modular components. The method uses a hierarchy of filters or design rules, which can be provided in the form of an expert system, to limit the design space. The architecture then configures a system from the reduced design space using a genetic algorithm to minimize the system lifetime cost subject to system constraints. The genetic algorithm uses a detailed cost model and physics-based PVRO system model which determines the ability of the system to meet demand. Determining the ability to meet demand is challenging due to variations in water demand and solar radiation. Here, the community’s historical water demand, solar radiation history, and PVRO system physics are used in a Markov model to quantify the ability of a system to meet demand or the loss-of-water probability (LOWP). Case studies demonstrate the approach and the cost-reliability trade-off for community-scale PVRO systems. In addition, long-duration simulations are used to demonstrate the Markov model appropriately captures the uncertainty.

Copyright © 2014 by ASME



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