Optimization of the Smart Water-Energy Nexus

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CrozierBaird Engineering and the Applicant seek to develop a paradigm shifting planning and operations model for water utilities based on smart metering, behavior analytics, environmental forecasting, and emerging energy management principals. The model will be designed to optimize current utility network architectures and will be forward-compatible with proposed micro water-energy grids to be examined in concurrent studies. The immediate region serviced by CrozierBaird is home to a diverse mix of residential, industrial, and commercial clients that include the highest concentration of greenhouses in North America. Utilities from this region have committed an interest in a model with hourly, daily, and weekly demand forecasting capabilities. Such an application will enable the integration of strategic energy management practices that include monitoring, targeting, and load shifting. Such a model will also be critical to facility master plans that attempt to accurately forecast major future infrastructure requirements. The principal objective of the project is to improve overall service and value for the rate-payer. This will be achieved by first assembling the data map for the model. Influence variables will include, customer type, usage behavior, economic, and meteorological conditions. Next, it will be determined how to develop the most representative characterization of the demand base with a finite number of sampling sites. Then, specialized high frequency smart meters will be deployed to select sampling locations. Data from these strategically varied archetypal sites will complement additional feeds from existing in-field meters and billing databases. Concurrently, appropriate climactic indicators and other driving environmental factors will be selected for correlation with the demand data. The ultimate analysis of this data collection will seek the most efficient scheme for water supply subject to the two fundamental constraints of safe uninterruptible supply, and cost minimization.