EFRI-RESIN: A Multi-Scale Design and Control Framework for Dynamically Coupled Sustainable and Resilient Infrastructures, with Application to Vehicle-to-Grid Integration

EFRI-RESIN：动态耦合可持续和弹性基础设施的多尺度设计和控制框架，应用于车辆到电网集成

• 批准号: 0835995
• 负责人: Jeffrey Stein
• 金额: $ 200万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835995&HistoricalAwards=false
• 关键词: EFRI; RESIN; Multi; Scale; Design

PI: Jeffrey SteinInstitution: University of Michigan Ann ArborProposal Number: 0835995EFRI-RESIN: A Multi-Scale Design and Control Framework for Dynamically Coupled Sustainable and Resilient Infrastructures, with Application to Vehicle-to-Grid Integration This award is an outcome of the competition as part of the Emerging Frontiers in Research and Innovation (NSF 07-579) program solicitation under the subtopic Resilient and Sustainable Infrastructures (RESIN). The goal of this research is to formulate a fundamental system science for interdependent, dynamically coupled civil infrastructures via multi-role intermediaries (MRIs), with the objective of improving their collective performance for resiliency and sustainability. This project examines the flow of resources and commodities between infrastructures via devices and junctions referred to as MRIs. Examples of MRIs include building energy systems that couple the built environment with electricity infrastructures, GPS-enabled mobile phones that couple transportation and telecommunications infrastructures to manage traffic congestion, and plug-in hybrid electric vehicles (PHEVs) that couple transportation and electricity infrastructures by using grid electricity for transportation, possibly sending stored electricity to the grid (vehicle to grid, or V2G) when appropriate. This project will formulate a framework to design and control MRIs whose functions are influenced by stochastic processes, interactions across space and time scales, and human decision-making. The research includes the following tasks: (1) combine agent-based modeling and life cycle assessment into a framework for evaluating the long-term sustainability of dynamically coupled infrastructures; (2) develop hybrid state diffusion approximation methods to model the dynamics of resiliency in coupled infrastructures with stochastically available resources and MRIs; (3) create a fundamental hierarchical (multi-scale) framework for optimizing the design and configuration of MRIs for resiliency and sustainability within each infrastructure; (4) use stochastic dynamic programming and Poincaré map techniques to optimally control intermediaries in light of their stochastic dynamic switching between different infrastructures; (5) construct Lyapunov energy functions for MRI-coupled systems to control for stability and resiliency; and (6) develop statistical, energy-based model reduction techniques to reduce complexity in infrastructure and intermediary models to facilitate analysis and design of coupled infrastructures. The research outcomes will provide fundamental theoretical contributions to engineering disciplines that are rooted in dynamics and controls, as well as to the social sciences and the field of industrial ecology. The research will use plug-in hybridization and V2G integration as the test bed application. With respect to PHEVs, the research will (1) quantify their impact on sustainability and resiliency of the transportation and electricity generation infrastructures, (2) design and configure PHEV powertrains that balance conflicting needs of the transportation and electricity generation infrastructures, (3) formulate power and energy management strategies in PHEVs, taking into account their transportation role, their role in providing distributed storage to the electrical grid, and the switching between such roles, and (4) investigate grid power and energy management methods that capitalize on the distributed capacity provided by V2G integration and the resulting ability to accommodate renewable resource intermittency and prevent and recover from catastrophic failures. The framework developed in this research will enable practical and efficient identification of infrastructure configurations that are globally sustainable and resilient. The research team includes investigators from the University of Michigan and the Missouri University of Science and Technology in the fields of electrical and mechanical engineering, economics and public policy, natural resources and environment, and transportation research. The methods and tools will be disseminated to industry via an external advisory and technical publications. The research results (including a sustainability simulation tool) will be incorporated into classroom instruction in the new graduate level Engineering Sustainable Systems program at the University of Michigan and through education and outreach programs that target underrepresented students at the high school and undergraduate level.

主要研究者：Jeffrey Stein机构：密歇根大学安娜堡分校提案编号：0835995 EFRI-树脂：动态耦合可持续和弹性结构的多尺度设计和控制框架，应用于车辆到电网集成该奖项是竞争的结果，作为研究和创新新兴前沿（NSF 07-579）计划征集的一部分，子主题为弹性和可持续结构（树脂）。 本研究的目标是制定一个基本的系统科学的相互依存，动态耦合的民用基础设施，通过多角色中介（MRI），提高他们的弹性和可持续性的集体表现的目标。 该项目通过被称为MRI的设备和连接点检查基础设施之间的资源和商品流动。MRI的示例包括将建筑环境与电力基础设施耦合的建筑物能量系统、将交通和电信基础设施耦合以管理交通拥堵的具有GPS功能的移动的电话、以及通过使用电网电力进行交通而将交通和电力基础设施耦合的插电式混合动力电动车辆（PHEV（车辆到电网，或V2 G）。 该项目将制定一个框架来设计和控制MRI，其功能受到随机过程，跨空间和时间尺度的相互作用以及人类决策的影响。 研究内容包括：（1）将联合收割机建模与生命周期评估相结合，建立动态耦合基础设施长期可持续性评估框架：（2）发展混合状态扩散近似方法，对具有随机可用资源和MRI的耦合基础设施的弹性动态进行建模;（3）建立基本的等级制度（多尺度）框架，用于优化MRI的设计和配置，以实现每个基础设施内的弹性和可持续性;（4）利用随机动态规划和Poincaré映射技术，根据中介在不同基础设施之间的随机动态切换，对中介进行最优控制;（5）为MRI耦合系统构建李雅普诺夫能量函数，以控制稳定性和弹性;以及（6）开发统计的、基于能量的模型简化技术，以降低基础设施和中间模型的复杂性，从而促进耦合基础设施的分析和设计。研究成果将为植根于动力学和控制的工程学科以及社会科学和工业生态学领域提供基础理论贡献。 研究将使用插件混合和V2 G集成作为测试床应用。 关于PHEV，研究将（1）量化其对交通和发电基础设施的可持续性和弹性的影响，（2）设计和配置PHEV动力系统，以平衡交通和发电基础设施的冲突需求，（3）制定PHEV中的电力和能源管理策略，考虑其运输作用，它们在向电网提供分布式存储方面的作用，以及这些作用之间的切换，以及（4）研究利用V2 G集成提供的分布式容量的电网电力和能源管理方法，以及由此产生的适应可再生资源不稳定性并防止灾难性故障并从灾难性故障中恢复的能力。在这项研究中开发的框架将使实际和有效的基础设施配置，是全球可持续和弹性的识别。 研究团队包括来自密歇根大学和密苏里州科技大学的电气和机械工程、经济和公共政策、自然资源和环境以及交通研究等领域的调查人员。 这些方法和工具将通过外部咨询和技术出版物向业界传播。 研究结果（包括可持续性模拟工具）将被纳入密歇根大学新的研究生水平工程可持续系统计划的课堂教学中，并通过针对高中和本科阶段代表性不足的学生的教育和推广计划。