FMRG: Cyber: Manufacturing USA: Exploiting Spatio-Temporal Interdependency Between Electrochemical Manufacturing and Power Grid to Optimize Flexibility and Sustainability
FMRG:网络:美国制造:利用电化学制造和电网之间的时空相互依赖性来优化灵活性和可持续性
基本信息
- 批准号:2328160
- 负责人:
- 金额:$ 299.95万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-01 至 2027-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The chemical manufacturing sector and the power grid are becoming increasingly coupled. This is driven by their shared interest in decarbonizing operations: the chemical sector is aiming to decarbonize via electrification of key technologies, while the grid is aiming to decarbonize via adoption of renewable (wind/solar) power. As such, mitigating the inherent intermittency of renewable power is a grand challenge that needs to be overcome to achieve decarbonization of both sectors. This will require the design of new and flexible technologies that can shift power demands from the seconds to seasons timescale and from the local to the national level. The chemical sector is uniquely positioned to help provide these unprecedented levels of flexibility. This can be achieved via modular electrochemical (EC) technologies that use highly intermittent power to produce chemicals in a distributed manner and via the efficient production of energy carriers (e.g., hydrogen, ammonia, formic acid) that can be used to store, transport, and re-generate power. To realize this vision, a paradigm shift under which EC technologies are co-designed with electricity markets is needed; specifically, EC technologies need to be designed as flexibility providers that actively participate in electricity markets and such markets need to properly remunerate flexibility services. More broadly, EC technologies are vital sector-coupling assets that can provide flexibility to enable risk mitigation (e.g., extreme weather, cyber-attacks) and can play a key role in ensuring that the power grid and chemical supply chains operate in a reliable, sustainable, and economic manner. Accordingly, This Future CyberManufacturing research grant will boost American chemical manufacturing and advance the prosperity and welfare of the United States. The project will also lead to new design principles, simulation models, data, and technologies that enhance the training of the next-generation workforce of chemical engineers, electrical engineers, and chemists to leverage multiscale thinking to develop technologies and solutions. The project will also lead to new business practices that foster coordination between chemical and power sectors with the goal of helping accelerate the adoption of new technologies and decarbonization efforts. The goal of this Future Manufacturing project is to conduct fundamental computational and experimental research to design new and flexible electrochemical (EC) technologies that best integrate with the power grid and with chemical supply chains, to identify key aspects that limit flexibility of these technologies, and to determine how to best exploit their flexibility to achieve economic and sustainability goals at a societal scale. Specifically, an interdisciplinary team will: (i) develop infrastructure-level modeling techniques that capture the multiscale coupling between the chemical and power sectors that capture different types of EC technologies and help identify amounts and types of flexibility needed; (ii) develop device-level models for EC technologies that capture the interplay between flexibility and design (e.g., cell capacity, ramping capacity, efficiency, durability); (iii) develop density functional theory models that help identify electrode materials to meet design specifications; and (iv) develop experimental procedures to evaluate different reaction chemistries, materials, and EC configurations (e.g., coupled, decoupled, tandem, cascade), and to collect key data that informs modeling and economic/environmental assessments. These capabilities will be combined via convergent studies that will answer questions of societal and industrial relevance. The convergent research approach will lead to new design principles, simulation models, data, and technologies that will serve as the backbone of a workforce development plan that will train a new generation of chemical engineers, electrical engineers, and chemists that leverage multiscale thinking to develop technologies and solutions. The project will lead to new business practices that foster coordination between the chemical and power sectors with the goal of helping accelerate the adoption of EC technologies and decarbonization efforts. This Future Manufacturing award was supported by the Divisions of Civil, Mechanical and Manufacturing Innovation (CMMI), Chemical, Bioengineering, Environmental and Transport Systems (CBET), Engineering Education and Centers (EEC), Chemistry (CHE), and the Division of Mathematical Sciences (DMS).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
化学制造行业和电网的耦合日益紧密,这是由于它们对脱碳运营的共同兴趣所驱动:化学行业的目标是通过关键技术的电气化来脱碳,而电网的目标是通过采用可再生能源(风能)来脱碳。因此,减轻可再生能源固有的间歇性是实现这两个行业脱碳的一个巨大挑战,这需要设计新的灵活技术,将电力需求从秒级转变为秒级。化学行业具有独特的优势,可以帮助提供前所未有的灵活性,这可以通过使用高度间歇性电力以分布式方式生产化学品的模块化电化学(EC)技术来实现。通过高效生产可用于存储、运输和再生电力的能源载体(例如氢、氨、甲酸),为了实现这一愿景,需要将 EC 技术与电力共同设计。市场是具体而言,EC 技术需要被设计为积极参与电力市场的灵活性提供者,并且此类市场需要对灵活性服务进行适当的补偿。更广泛地说,EC 技术是重要的部门耦合资产,可以提供灵活性以实现风险缓解。 、极端天气、网络攻击),并且可以在确保电网和化学品供应链以可靠、可持续和经济的方式运行方面发挥关键作用。因此,这项未来网络制造研究拨款将促进美国化学品制造业并推动化学工业的发展。繁荣该项目还将带来新的设计原则、模拟模型、数据和技术,以加强对下一代化学工程师、电气工程师和化学家的培训,以利用多尺度思维来开发技术。该项目还将带来新的商业实践,促进化学和电力行业之间的协调,以帮助加速新技术的采用和脱碳工作。该未来制造项目的目标是进行基础计算和实验研究。设计新的、灵活的电化学 (EC) 技术,最适合与电网和化学品供应链整合,以确定限制这些技术灵活性的关键方面,并确定如何最好地利用其灵活性以实现社会规模的经济和可持续发展目标。具体而言,跨学科团队将:( i) 开发基础设施级建模技术,捕获化学和电力行业之间的多尺度耦合,捕获不同类型的 EC 技术,并帮助确定所需的灵活性数量和类型; (ii) 为捕获 EC 技术的设备级模型开发捕获灵活性和设计之间的相互作用(例如,细胞容量、升温容量、效率、耐久性);(iii)开发密度泛函理论模型,帮助识别满足设计规范的电极材料;以及(iv)开发实验程序来评估不同的反应化学、材料和 EC 配置(例如,耦合) 、解耦、串联、级联),并收集为建模和经济/环境评估提供信息的关键数据。这些能力将通过聚合研究结合起来,从而回答社会和工业相关的问题。新的设计原则、模拟模型、数据和技术将成为劳动力发展计划的支柱,该计划将培训新一代化学工程师、电气工程师和化学家,利用多尺度思维来开发新的技术和解决方案。促进化学和电力行业之间协调的商业实践,旨在帮助加速 EC 技术的采用和脱碳工作。该未来制造奖得到了土木、机械和制造创新 (CMMI)、化学、生物工程、环境和运输系统 (CBET)、工程教育和中心 (EEC)、化学 (CHE) 和数学科学部 (DMS)。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力和能力进行评估,被认为值得支持。更广泛的影响审查标准。
项目成果
期刊论文数量(0)
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Victor Zavala Tejeda其他文献
Victor Zavala Tejeda的其他文献
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{{ truncateString('Victor Zavala Tejeda', 18)}}的其他基金
NEW AND SCALABLE PARADIGMS FOR DATA-DRIVEN MODEL PREDICTIVE CONTROL
数据驱动模型预测控制的新的、可扩展的范式
- 批准号:
2315963 - 财政年份:2023
- 资助金额:
$ 299.95万 - 项目类别:
Standard Grant
EFRI DCheM: Distributed Photosynthetic Recovery of Livestock Waste Nutrients for Sustainable Production of Fertilizers
EFRI DCheM:畜牧废物养分的分布式光合回收用于肥料的可持续生产
- 批准号:
2132036 - 财政年份:2021
- 资助金额:
$ 299.95万 - 项目类别:
Standard Grant
CAREER: OPTIMIZATION FORMULATIONS AND ALGORITHMS FOR THE ANALYSIS AND DESIGN OF HIERARCHICAL MODULAR SYSTEMS
职业:分层模块化系统分析和设计的优化公式和算法
- 批准号:
1748516 - 财政年份:2018
- 资助金额:
$ 299.95万 - 项目类别:
Standard Grant
CRISP 2.0 Type 2: Collaborative Research: Exploiting Interdependencies Between Computing and Electrical Power Infrastructures to Maximize Resilience and Flexibility
CRISP 2.0 类型 2:协作研究:利用计算和电力基础设施之间的相互依赖性来最大限度地提高弹性和灵活性
- 批准号:
1832208 - 财政年份:2018
- 资助金额:
$ 299.95万 - 项目类别:
Standard Grant
CRISP 2.0 Type 2: Collaborative Research: Exploiting Interdependencies Between Computing and Electrical Power Infrastructures to Maximize Resilience and Flexibility
CRISP 2.0 类型 2:协作研究:利用计算和电力基础设施之间的相互依赖性来最大限度地提高弹性和灵活性
- 批准号:
1832208 - 财政年份:2018
- 资助金额:
$ 299.95万 - 项目类别:
Standard Grant
BIGDATA: IA: Collaborative Research: Data-Driven, Multi-Scale Design of Liquid-Crystals for Wearable Sensors for Monitoring Human Exposure and Air Quality
大数据:IA:协作研究:用于监测人体暴露和空气质量的可穿戴传感器的数据驱动、多尺度液晶设计
- 批准号:
1837812 - 财政年份:2018
- 资助金额:
$ 299.95万 - 项目类别:
Standard Grant
Multi-Stakeholder Decision-Making for the Development of Livestock Waste-to-Biogas Systems
畜牧废物转化沼气系统发展的多方利益相关者决策
- 批准号:
1604374 - 财政年份:2016
- 资助金额:
$ 299.95万 - 项目类别:
Standard Grant
Multi-Scale Predictive Control of Coupled Energy Networks
耦合能源网络的多尺度预测控制
- 批准号:
1609183 - 财政年份:2016
- 资助金额:
$ 299.95万 - 项目类别:
Standard Grant
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