Collaborative Research: CyberSEES: Climate-Aware Renewable Hydropower Generation and Disaster Avoidance

合作研究：Cyber​​SEES：气候感知型可再生水力发电和防灾

• 批准号: 1331804
• 负责人: Diana Marculescu
• 金额: $ 45.6万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1331804&HistoricalAwards=false
• 关键词: Collaborative; Research; CyberSEES; Climate; Aware

Water, solar and wind are essential for a sustainable transformation of our energy systems. Distributed solar and wind farms proliferate, but energy harvesting from water is trapped in a century-old damming paradigm with high up-front costs and ecological impacts. And yet, as a river runs down to the ocean, there is enormous amount of kinetic energy that could be sustainably harvested, if done without impoundments that break up the run of the river. An environmentally friendly alternative, known as hydrokinetic or run-of-the-river power extraction, harvests a portion of the kinetic energy in the river at relatively small, local scales at multiple places along the river. However, these projects are characterized by uncertainty in generated output and strong weather/climate dependence. They are typically developed in an ad-hoc manner without prior large-scale analysis of determining optimum locations, online analysis of the produced output, or effective decentralized control of distributed hydrokinetic generators. Furthermore, climate change introduces highly variable weather patterns that alternate benign conditions with catastrophic levels of wind, precipitation, temperature extremes, and droughts.This project investigates climate-aware modeling, analysis, and control for large-scale sustainable energy harvesting in river networks. The project goals are: (i) modeling of time-space varying river network flow conditions and water levels both with and without multiple hydrokinetic generators; (ii) determining optimum locations for hydrokinetic units in a distributed river network based on economic, reliability-driven, and environmental criteria; (iii) evaluating environmental sensor requirements for demand/response or environmental disaster avoidance; (iv) predictive matching of hydrokinetic power generation and time-varying demand in complex river networks with geospatial and temporal dependencies; and (iv) climate-aware planning for distributed hydrokinetic power generation resources and avoiding catastrophic events.The potential societal impacts of this research include flood disaster avoidance and guidance on small footprint hydropower project development. Given that the estimated hydrokinetic resource potential in the United States is roughly four times the amount of hydroelectricity currently produced each year, small footprint hydroelectric projects could create enough low-carbon energy to power an economy the size of Virginia's while minimizing the impact to the surrounding environment and nearby communities. While the main goal of the research is determining in a climate-dependent manner the production output and identifying any imbalance between production and demand or the control mechanisms to match one by the other, the approach is sufficiently general to be applied to a more diverse body of applications where deterministic small-scale processes need to be upscaled for effective feedback response and control. The educational and mentoring component incorporates multidisciplinary collaboration across electrical, civil and energy/public policy engineering in creating large-scale complex system models that cross traditional boundaries of both academia and industry. Through its educational component the project involves a diverse student body, while expanding the project's outreach beyond the typical undergraduate and graduate demographics to high-school and middle-school student population.

水、太阳能和风能对我们能源系统的可持续转型至关重要。分布式太阳能和风力发电场数量激增，但从水中获取能量却被一个世纪以来的水坝模式所束缚，前期成本高，对生态影响大。然而，当一条河流流入海洋时，有大量的动能可以被可持续地收集，如果没有截流的水库的话。一种环保的替代方案，被称为水动力或河流动力提取，在河流沿线的多个地方以相对较小的局部规模收集河流中的一部分动能。然而，这些项目的特点是产出的不确定性和强烈的天气/气候依赖性。它们通常以一种特别的方式开发，没有事先对确定最佳位置进行大规模分析，没有对生产输出进行在线分析，也没有对分布式水动力发电机进行有效的分散控制。此外，气候变化带来了高度多变的天气模式，在温和的条件下交替出现灾难性的风、降水、极端温度和干旱。该项目研究了气候感知模型、分析和控制在河流网络中的大规模可持续能源收集。该项目的目标是：(i)模拟时空变化的河网流量条件和水位，包括有和没有多个水动力发电机；（ii）根据经济、可靠性驱动和环境标准，在分布式河网中确定水动力单元的最佳位置；（iii）评估对环境传感器的需求/反应或避免环境灾害的要求；（iv）具有地理空间和时间依赖性的复杂河网中，水动力发电和时变需求的预测匹配；（四）分布式水动力发电资源的气候意识规划，避免灾难性事件的发生。本研究的潜在社会影响包括预防洪水灾害和指导小足迹水电项目发展。考虑到美国估计的水动力资源潜力大约是目前每年水力发电量的四倍，小足迹的水力发电项目可以创造足够的低碳能源，为弗吉尼亚州的经济规模提供动力，同时最大限度地减少对周围环境和附近社区的影响。虽然该研究的主要目标是以气候依赖的方式确定生产产出，并确定生产与需求之间的任何不平衡，或相互匹配的控制机制，但该方法足够普遍，可以应用于更多样化的应用领域，在这些领域，确定性的小规模过程需要扩大规模，以获得有效的反馈响应和控制。教育和指导组件结合了跨电气、民用和能源/公共政策工程的多学科协作，以创建跨越学术界和工业界传统边界的大型复杂系统模型。通过其教育组成部分，该项目涉及多元化的学生群体，同时将项目的扩展范围从典型的本科和研究生人口统计扩展到高中和初中学生人口。