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

PI：Jeffrey SteinInstitution：密歇根大学AnnArborProposal编号：0835995EFRI-RESPESS：适用于车辆到电网集成的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该奖项是作为新兴前沿研究和创新(NSF 07-579)项目征集的一部分，在副主题弹性和可持续基础设施(REST)下的竞争结果。这项研究的目标是通过多角色中间人为相互依赖、动态耦合的民用基础设施制定一项基本的系统科学，目的是改善它们在复原力和可持续性方面的集体表现。该项目通过称为磁共振成像的设备和连接点检查基础设施之间的资源和商品流动。核磁共振成像的例子包括将建成环境与电力基础设施相结合的建筑能源系统，将交通和电信基础设施相结合以管理交通拥堵的具有GPS功能的手机，以及插电式混合动力电动汽车(PHEV)，它通过使用电网电力进行运输来将交通和电力基础设施相结合，可能在适当的时候将存储的电力发送到电网(车辆对电网，或V2G)。该项目将制定一个框架，以设计和控制核磁共振成像设备，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。研究包括以下任务：(1)将基于代理的建模和生命周期评估结合到一个框架中，用于评估动态耦合基础设施的长期可持续性；(2)发展混合状态扩散近似方法，以模拟耦合基础设施中随机可用的资源和MRI的弹性动力学；(3)创建一个基本的分层(多尺度)框架，以优化每个基础设施内弹性和可持续性的MRI的设计和配置；(4)根据中介机构在不同基础设施之间的随机动态切换，使用随机动态规划和Poincarémap技术来优化控制中介机构；(5)构造用于磁共振成像耦合系统的Lyapunov能量函数以控制稳定性和弹性；以及(6)开发基于统计的、基于能量的模型简化技术，以降低基础设施和中介模型的复杂性，以便于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。本研究将使用插件杂交和V2G集成作为试验床应用。关于混合动力电动汽车，研究将(1)量化它们对交通和发电基础设施的可持续性和弹性的影响，(2)设计和配置混合动力电动汽车动力系统，以平衡交通和发电基础设施的相互冲突的需求，(3)制定混合动力电动汽车的电力和能源管理战略，考虑它们的运输角色，它们在向电网提供分布式存储方面的作用，以及这些角色之间的切换，以及(4)研究电网电力和能源管理方法，以充分利用V2G集成提供的分布式容量和由此产生的适应可再生资源间歇性和防止灾难性故障并从灾难性故障中恢复的能力。这项研究开发的框架将能够切实有效地确定全球可持续和有弹性的基础设施配置。研究团队包括来自密歇根大学和密苏里科技大学的研究人员，他们涉及机电工程、经济和公共政策、自然资源和环境以及交通研究等领域。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。研究成果(包括可持续发展模拟工具)将被纳入密歇根大学新的研究生水平工程可持续系统项目的课堂教学，并通过针对高中和本科水平代表不足的学生的教育和推广计划。

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.