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
基本信息
- 批准号:1238097
- 负责人:
- 金额:$ 63.84万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
该项目的基础是史蒂夫·波特(Steve Potter)和Venyagamoorthy团队之间新的深度合作,前者是体外神经回路功能研究(“活神经网络,LNN”)的世界先驱,后者领导了自适应、预期优化在电网组件中的应用。这两个团队正在联合起来应对空间复杂性的挑战。以前关于LNN的工作主要集中在管理单个控制变量等挑战上,但电网需要数千个相互关联的变量,这些变量必须实时管理。这项新的体外研究将探索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。新的数学概念,如何科普复杂性也将在解决同样的预测挑战,并在尝试应用自适应,预期控制的第一次大规模电网控制模拟测试。商业电网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。由于缺乏预期优化(以及最佳时移,如Venayagamoorthy等人的工作所示),使用风力发电替代煤炭并减少二氧化碳排放量目前仅限于约20%。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),从而能够将发电过程中的二氧化碳排放量降至零。 目前看来,美国和中国都有足够的陆上风力资源来实现这一目标。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Ganesh Venayagamoorthy其他文献
Ganesh Venayagamoorthy的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Ganesh Venayagamoorthy', 18)}}的其他基金
Collaborative Research: MoDL: Graph-Optimized Cellular Connectionism via Artificial Neural Networks for Data-Driven Modeling and Optimization of Complex Systems
合作研究:MoDL:通过人工神经网络进行图优化的细胞连接,用于复杂系统的数据驱动建模和优化
- 批准号:
2234032 - 财政年份:2023
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
Collaborative Research: CISE-MSI: DP: IIS RI: Research Capacity Expansion via Development of AI Based Algorithms for Optimal Management of Electric Vehicle Transactions with Grid
合作研究:CISE-MSI:DP:IIS RI:通过开发基于人工智能的算法来扩展研究能力,以实现电动汽车与电网交易的优化管理
- 批准号:
2318612 - 财政年份:2023
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
Collaborative Research: CISE-MSI: DP: CCF: SHF: MSI/HSI Research Capacity Building via Secure and Efficient Hardware Implementation of Cellular Computational Networks
合作研究:CISE-MSI:DP:CCF:SHF:通过安全高效的蜂窝计算网络硬件实现进行 MSI/HSI 研究能力建设
- 批准号:
2131070 - 财政年份:2021
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
Collaborative Research: Planning Grant: I/UCRC for Real-Time Intelligence for Smart Electric Grid Operations (RISE)
合作研究:规划资助:I/UCRC 智能电网运营实时智能 (RISE)
- 批准号:
1464637 - 财政年份:2015
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
Collaborative Research: An Intelligent Restoration System for a Self-healing Smart Grid (IRS-SG)
合作研究:用于自愈智能电网的智能恢复系统(IRS-SG)
- 批准号:
1408141 - 财政年份:2014
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
Scalable Intelligent Power Monitoring and Optimal Control of Distributed Energy Systems Using Adaptive Critics
使用自适应批评的分布式能源系统的可扩展智能电力监控和优化控制
- 批准号:
1308192 - 财政年份:2013
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
AIR Option 2: Research Alliance Situational Intelligence for Smart Grid Optimization and Intelligent Control
AIR选项2:智能电网优化和智能控制研究联盟态势智能
- 批准号:
1312260 - 财政年份:2013
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
Collaborative Research: Computational Intelligence Methods for Dynamic Stochastic Optimization of Smart Grid Operation with High Penetration of Renewable Energy
合作研究:可再生能源高渗透智能电网运行动态随机优化的计算智能方法
- 批准号:
1232070 - 财政年份:2012
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
RAPID: Impact of Earthquakes on the Electricity Infrastructure
RAPID:地震对电力基础设施的影响
- 批准号:
1216298 - 财政年份:2012
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems
职业:利用智能技术和神经网络进行可扩展的学习和适应,以实现复杂系统的重新配置和生存能力
- 批准号:
1231820 - 财政年份:2012
- 资助金额:
$ 63.84万 - 项目类别:
Continuing Grant
相似海外基金
CopN mechanism as a key to understanding Type Three Secretion in bacteria
CopN 机制是理解细菌三型分泌的关键
- 批准号:
8759663 - 财政年份:2014
- 资助金额:
$ 63.84万 - 项目类别:
CopN mechanism as a key to understanding Type Three Secretion in bacteria
CopN 机制是理解细菌三型分泌的关键
- 批准号:
9305827 - 财政年份:2014
- 资助金额:
$ 63.84万 - 项目类别:
CopN mechanism as a key to understanding Type Three Secretion in bacteria
CopN 机制是理解细菌三型分泌的关键
- 批准号:
9093685 - 财政年份:2014
- 资助金额:
$ 63.84万 - 项目类别:
EFRI-COPN: Dynamics of Neural Networks on a Planar Patch-Clamp Array: Training, Identification, and Control
EFRI-COPN:平面膜片钳阵列上的神经网络动力学:训练、识别和控制
- 批准号:
0835947 - 财政年份:2008
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
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:
0836017 - 财政年份:2008
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
EFRI-COPN Deep Learning in the Mammalian Visual Cortex
EFRI-COPN 哺乳动物视觉皮层深度学习
- 批准号:
0835878 - 财政年份:2008
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant
EFRI-COPN: Reverse-engineering the Human Brain's Ability to Control the Hand
EFRI-COPN:对人脑控制手的能力进行逆向工程
- 批准号:
0836042 - 财政年份:2008
- 资助金额:
$ 63.84万 - 项目类别:
Standard Grant