EFRI-COPN: Neuroscience and Neural Networks for Engineering the Future Intelligent Electric Power Grid

EFRI-COPN：用于设计未来智能电网的神经科学和神经网络

• 批准号: 1238097
• 负责人: Ganesh Venayagamoorthy
• 金额: $ 63.84万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-02 至 2014-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1238097&HistoricalAwards=false
• 关键词: EFRI; COPN; Neuroscience; Neural; Networks

The basis of this project is a new and deep partnership between Steve Potter, a world pioneer in searching for functional capabilities of neural circuits in vitro ("living neural networks, LNN"), and the Venyagamoorthy team, which has led the application of adaptive, anticipatory optimization to components of the electric power grid.The two groups are combining together to address the challenge of spatial complexity. Previous work on LNNs has focused on challenges like managing a single control variable, but electric power grids entail thousands of interconnected variables which must be managed in real-time. The new work in vitro will probe the ability of LNNs made up of thousands of neurons and glia to predict the behavior of a complicated power grid simulator, and test the ability of new biological learning models to explain their capabilities. New mathematical concepts for how to cope with complexity will also be tested in addressing the same prediction challenge, and in attempting to apply adaptive, anticipatory control for the first time to large scale power grid control in simulation. Testing on commercial electric power grids will mainly occur through their collaborations with Mexico, Brazil, China, Nigeria, Singapore and South Africa.The use of wind power to displace coal and reduce CO2 emissions is currently limited to about 20%, because of the lack of anticipatory optimization (and optimal time-shifting, as demonstrated in the work of Venayagamoorthy et al.) and storage. If combined with adequate storage, the new algorithms aimed at here should make it possible for both China and the US to assimilate enough wind (or solar) power to be able to zero out their emissions of CO2 in power generation. It currently appears that the US and China both have enough onshore wind resources to make this possible.

该项目的基础是 Steve Potter 与 Venyagamoorthy 团队之间的新的深度合作，Steve Potter 是寻找体外神经回路功能（“活神经网络，LNN”）的世界先驱，Venyagamoorthy 团队领导了自适应神经网络的应用。 ，对电网组件的预期优化。两个小组正在联合起来解决空间复杂性的挑战。先前有关 LNN 的工作主要集中于管理单个控制变量等挑战，但电网需要数千个必须实时管理的互连变量。这项新的体外工作将探讨由数千个神经元和神经胶质细胞组成的 LNN 预测复杂电网模拟器行为的能力，并测试新的生物学习模型解释其能力的能力。如何应对复杂性的新数学概念也将在解决相同的预测挑战时得到测试，并尝试首次将自适应预期控制应用于仿真中的大规模电网控制。商业电网的测试将主要通过与墨西哥、巴西、中国、尼日利亚、新加坡和南非的合作进行。利用风电替代煤炭并减少二氧化碳排放量目前限制在20%左右，因为缺乏足够的资源。预期优化（以及最佳时移，如 Venayagamoorthy 等人的工作所示）和存储。如果与足够的存储相结合，针对这里的新算法应该使中国和美国能够吸收足够的风能（或太阳能），从而能够将发电中的二氧化碳排放量归零。 目前看来，美国和中国都拥有足够的陆上风能资源来实现这一目标。