Optimization of the Smart Water-Energy Nexus

CrozierBaird Engineering和申请人寻求开发一种基于智能计量、行为分析、环境预测和新兴能源管理原则的水务公司转型规划和运营模型。该模型旨在优化目前的公用事业网络结构，并将与拟议的微型水能电网向前兼容，将在同时进行的研究中进行审查。由CrozierBaird提供服务的邻近地区是各种住宅、工业和商业客户的所在地，其中包括北美最集中的温室。该地区的公用事业公司对具有每小时、每天和每周需求预测功能的模型感兴趣。这样的应用程序将能够集成战略能源管理实践，包括监测、目标确定和负载转移。这样的模型对于试图准确预测未来主要基础设施需求的设施总体规划也将至关重要。这项计划的主要目的，是改善差饷缴纳人的整体服务和价值。这将通过首先组装模型的数据地图来实现。影响变量将包括客户类型、使用行为、经济和气象条件。接下来，将确定如何利用有限数量的采样点来开发需求基础的最具代表性的表征。然后，将部署专门的高频智能电表来选择采样位置。来自这些战略上不同的原型站点的数据将补充来自现有现场仪表和计费数据库的额外馈送。同时，将选择适当的气候指标和其他驱动环境因素与需求数据进行关联。对这一数据收集的最终分析将寻求最有效的供水方案，以满足两个基本限制：安全、不间断供水和最低成本。

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.