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

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。分布式太阳能和风电场激增，但从水中收集能源受困于已有百年历史的筑坝模式，前期成本高昂，并对生态造成影响。然而，当一条河流流向海洋时，如果没有蓄水破坏河流的流动，就有大量的动能可以可持续地获得。一种环境友好的替代方案，称为水动力或径流发电，在河流沿线的多个地方以相对较小的当地规模获取河流中的一部分动能。然而，这些项目的特点是发电量不确定，对天气/气候的依赖性很强。它们通常是以一种特别的方式开发的，事先没有对确定最佳位置的大规模分析、对所产生的产出的在线分析，或者对分布式流体动力发电机的有效分散控制。此外，气候变化引入了高度可变的天气模式，使良性条件与风、降水、极端温度和干旱的灾难性水平交替出现。本项目研究河网大规模可持续能源收集的气候感知建模、分析和控制。该项目的目标是：(1)在有和没有多台水动力发电机的情况下模拟时空变化的河网水流条件和水位；(2)根据经济、可靠性驱动和环境标准，确定分布式河网中水动力单元的最佳位置；(3)评估环境传感器对需求/响应或环境灾害避免的要求；(4)在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性匹配；以及(Iv)分布式水动力发电资源的气候感知规划和避免灾难事件。这项研究的潜在社会影响包括避免洪水灾害和指导小型水电项目开发。鉴于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，小规模的水电项目可以创造足够的低碳能源，为弗吉尼亚州这样大的经济提供动力，同时将对周围环境和附近社区的影响降至最低。虽然研究的主要目标是以依赖气候的方式确定生产产出，并确定生产和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，可应用于更多样化的应用，在这些应用中，确定性的小规模过程需要扩大规模，以实现有效的反馈反应和控制。教育和指导部分结合了电力、民用和能源/公共政策工程领域的多学科协作，以创建跨越学术界和工业界传统界限的大型复杂系统模型。通过其教育组成部分，该项目涉及不同的学生群体，同时将该项目的范围从典型的本科生和研究生人口扩大到高中和中学生群体。

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.