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: 1331768
• 负责人: Ben Hodges
• 金额: $ 22.78万
• 依托单位: University of Texas at Austin
• 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=1331768&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、太阳能和风能对于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激增，但从水中收集能源被困在一个世纪的筑坝模式中，前期成本高，对生态造成影响。然而，当一条河流流向海洋时，如果没有蓄水池来破坏河流的运行，那么可以可持续地收集大量的动能。一种环境友好的替代方案，被称为水力发电或径流发电，在河流沿着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，这些项目的特点是发电量的不确定性和强烈的天气/气候依赖性。它们通常是在没有事先确定最佳位置的大规模分析、所产生的输出的在线分析或分布式水力发电机的有效分散控制的情况下以特设方式开发的。此外，气候变化引入了高度多变的天气模式，使良性条件与灾难性的风、降水、极端温度和干旱交替出现。本项目研究了河流网络中大规模可持续能源采集的气候感知建模、分析和控制。该项目的目标是：（i）在有和没有多个水力发电机的情况下，对时空变化的河流网络流动条件和水位进行建模;（ii）基于经济、可靠性驱动和环境标准，确定分布式河流网络中水力单元的最佳位置;（iii）评估用于需求/响应或环境灾难避免的环境传感器要求;（iv）具有地理空间和时间依赖性的复杂河流网络中水力发电和时变需求的预测匹配;及（iv）气候-分布式水力发电资源的规划和避免灾难性事件。这项研究的潜在社会影响包括洪水灾害避免和指导小足迹水电项目的开发。鉴于美国水力资源潜力估计约为目前每年水力发电量的四倍，小型水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。虽然研究的主要目标是以一种依赖气候的方式确定产量，并确定生产和需求之间的任何不平衡或控制机制，以匹配一个由另一个，该方法是足够普遍的应用到一个更多样化的机构，确定性的小规模的过程需要扩大有效的反馈响应和控制。教育和指导部分包括跨电气，民用和能源/公共政策工程的多学科合作，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。通过其教育部分，该项目涉及不同的学生群体，同时将项目的推广范围从典型的本科生和研究生人口扩大到高中和初中学生。