Cybersees Type 2: Cyber-Enabled Water and Energy Systems Sustainability Utilizing Climate Information

Cyber​​sees 类型 2：利用气候信息实现网络支持的水和能源系统可持续性

• 批准号: 1442909
• 负责人: Sankarasubraman Arumugam
• 金额: $ 120万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1442909&HistoricalAwards=false
• 关键词: Cybersees; Type; Cyber; Enabled; Water

Continually increasing water demand (due to population growth) and fuel costs threaten the reliability of water and energy systems and also increase operational costs. In addition, climatic variability and long-term change increase the vulnerability of these two systems. For instance, reservoir systems primarily depend on monthly to seasonal precipitation; whereas power systems demand depend on variations in diurnal temperature over the season. Currently, both systems consider climatological averages for their short-term (0-3 months) management, which ignores uncertainty in the climate resulting in reduced hydropower (i.e., increased spill) from reservoirs and increased operational costs for power systems from excessive fuel stockpiling. While seasonal climate forecasts contain appreciable levels of skill over parts of the US in both winter and summer, the uptake of these forecasts for co-optimization of water and power systems has been limited due to lack of a framework to assimilate, visualize and communicate probabilistic forecasts into management models. The project will develop a prototype visual analytic simulation-optimization framework for better communication and visualization of probabilistic information on the decision space for improving water and power systems sustainability utilizing monthly to seasonal multimodel climate forecasts. The primary goal of this poject is to develop an integrated cyber-enabled approach for improved water and energy sustainability utilizing the monthly to seasonal climate forecasts by: A. Developing a cyberinfrastructure framework using Optimus-PRIME that co-optimizes water and power system allocations by incorporating precipitation, power demand and wind power ensembles. B. Developing a visual analytic framework for interactive exploration of multi-objective decision space and interaction with the optimization engine by leveraging recent developments in visual analytics. C. Developing fine-grained and coarse-grained resource utilization strategies for targeting different parts of the computation on heterogeneous many-core/accelerator based architectures. D. Analyzing risk management strategies that include improved fuel stockpiling, scheduled plant maintenance and reservoir operational schedules to minimize operational costs utilizing the multimodel electricity demand, wind power, and streamflow forecasts available for the pilot basin. F. Performing scenario analyses that consider increased renewable energy availability -- hydropower and wind -- in the power generation mix, reduced CO2 emission scenarios under current and increased water demand potentials contingent on multimodel climate forecasts. F. Synthesizing the findings from objectives (A-F) by considering various virtual water and power system configurations that are typical across the country for generalization and broader application. The fundamental contribution of the proposal lies in developing a prototype cyberinfrastructure for improving water and power systems management utilizing climate forecasts. Using the framework, the study proposes to minimize the operational costs of these two interdependent systems by considering various scenarios for maximizing the renewable energy potential utilizing multimodel climate (precipitation, temperature and wind) forecasts. By utilizing the streamflow and power demand forecasts, a paradigm shift in water and power systems management is targeted that promotes various proactive strategies such as forward purchasing of fuels, reducing emissions and meeting target reservoir storage to ensure both systems reliability. To overcome the computational challenges arising from probabilistic forecasts, an HPC-enabled cyberinfrastructure will parallelize the computations across ensembles and provide a seamless interaction between water and power stochastic optimization models. For interactive exploration of multi-objective decision spaces in the water and power sectors, new developments in visual analytics will be employed that can lead to improved stakeholder facilitated solutions for the pilot basin and promote learning and understanding through virtual systems.Developed research modules and findings will be incorporated into various advanced graduate courses at NCSU in the water resources and environmental engineering, computer-aided engineering and power system engineering curricula. The developed climate information based risk-management tools will also be used in the NCSU's Climate Change and Society Professional Science Master's program, which exclusively draws professionals with interdisciplinary background. The investigators will also work with the State Climate Office of NC and federal agencies, PNNL and ORNL, to implement the algorithms developed in the project for broader applications. The investigators will also develop customized podcasts and modules on climate-water-energy nexus and their significance to sustainability for high school students by collaborating with the engineering place at NCSU.

不断增加的水需求（由于人口增长）和燃料成本威胁到水和能源系统的可靠性，也增加了运营成本。此外，气候多变性和长期变化增加了这两个系统的脆弱性。例如，水库系统主要取决于每月到季节性降水量;而电力系统的需求则取决于整个季节的昼夜温度变化。目前，这两个系统都考虑了短期（0-3个月）管理的气候平均值，这忽略了气候的不确定性导致水电减少（即，增加的溢出）以及由于过量的燃料储存而增加的电力系统的操作成本。虽然季节性气候预测包含相当水平的技能在美国部分地区在冬季和夏季，这些预测的水电系统的共同优化的吸收一直是有限的，由于缺乏一个框架来同化，可视化和沟通概率预测到管理模型。该项目将开发一个原型可视化分析模拟优化框架，以便更好地交流和可视化决策空间上的概率信息，从而利用月度至季度多模型气候预测改善水电系统的可持续性。 该项目的主要目标是开发一种综合的网络支持方法，利用每月到季节的气候预测来改善水和能源的可持续性。使用Optimus-PRIME开发网络基础设施框架，通过整合降水，电力需求和风力发电整体来共同优化水和电力系统分配。B。通过利用可视化分析的最新发展，开发一个可视化分析框架，用于多目标决策空间的交互式探索和与优化引擎的交互。C.开发细粒度和粗粒度的资源利用策略，以针对基于异构众核/加速器架构的计算的不同部分。D.分析风险管理策略，包括改善燃料储备，定期工厂维护和水库运营计划，以尽量减少运营成本，利用多模型电力需求，风力发电，径流预测可用于试点流域。F.进行情景分析，考虑在发电组合中增加可再生能源-水电和风能-的可用性，在目前情况下减少二氧化碳排放的情景，以及根据多模式气候预测增加水需求的潜力。F.通过考虑全国各地典型的各种虚拟水电系统配置，综合目标（A-F）的结果，以进行推广和更广泛的应用。该提案的基本贡献在于开发一个原型网络基础设施，利用气候预测改善水电系统管理。使用该框架，该研究提出了最大限度地减少这两个相互依赖的系统的运营成本，考虑各种方案，利用多模式气候（降水，温度和风）预测，最大限度地提高可再生能源的潜力。通过利用径流和电力需求预测，水和电力系统管理的范式转变是有针对性的，促进各种积极主动的战略，如提前购买燃料，减少排放和满足目标水库存储，以确保两个系统的可靠性。为了克服概率预测带来的计算挑战，HPC支持的网络基础设施将跨集合并行计算，并提供水电随机优化模型之间的无缝交互。对于水电部门多目标决策空间的交互式探索，将采用可视化分析的新发展，这可以改善试点流域的利益相关者便利的解决方案，并通过虚拟系统促进学习和理解。开发的研究模块和研究结果将被纳入NCSU水资源和环境工程的各种高级研究生课程，计算机辅助工程和电力系统工程课程。开发的基于气候信息的风险管理工具也将用于NCSU的气候变化和社会专业科学硕士课程，该课程专门吸引具有跨学科背景的专业人士。调查人员还将与北卡罗来纳州气候办公室和联邦机构PNNL和ORNL合作，实施该项目中开发的算法，以获得更广泛的应用。调查人员还将通过与NCSU的工程部门合作，开发定制的播客和模块，介绍气候-水-能源关系及其对高中生可持续发展的意义。