Multi-Scale Analysis and Control of Smart Energy Systems

智能能源系统的多尺度分析与控制

• 批准号: 1611349
• 负责人: Marco Levorato
• 金额: $ 26.03万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1611349&HistoricalAwards=false
• 关键词: Multi; Scale; Analysis; Control; Smart

One of the principal aspects of the SmartGrid is the introduction of intrinsically stochastic and distributed renewable energy production, which decreases the ability of semi-centralized controllers to effectively control the system. To compensate for this effect, distributed communication, monitoring, and control systems are introduced. The microgrid is the logical unit of operations and interaction of these components, as well as the interface to the utility grid. The complexity and multifaceted nature of the system even at the local scale makes the analysis, prediction and control of the microgrid's dynamics a challenging task. The project will conjugate power system control, design and simulation with advanced theoretical analysis and control to an innovative framework. The envisioned methodology will enable a number of novel applications in smart energy systems ranging from system adaptation to user behavior, user classification, and feedback to the consumer. The project will engage undergraduate and graduate students in the research effort, and will create new curriculum opportunities for UCI?s students.The complexity of the problem addressed in the project originated from the large number of interconnected heterogeneous sub-systems interact and contribute to the overall system behavior. This interaction manifests at different topological scales and abstraction levels. The sub-systems interact at the physical level, where the electrical signals travel through the physical interconnections. However, significant temporal and inter-component interdependencies exist at the logical state level, that is, a set of variables describing the current state of microgrid components and influential factors. Control of the logical level typically takes the form of scheduling and management frameworks. Most prior work considers either the physical or the logical domain, without providing a clear methodology to bridge these two interdependent domains. The project will use advanced Dynamic Programming, graph theory, and estimation theory to create a bidirectional flow of information and control between the physical and logical systems, where learning algorithms are designed to map physical and logical signals to higher level logical states.

智能电网的主要方面之一是引入本质随机和分布式的可再生能源生产，这降低了半集中式控制器有效控制系统的能力。为了补偿这种影响，分布式通信，监测和控制系统被引入。微电网是这些组件的操作和交互的逻辑单元，也是公用电网的接口。即使在局部范围内，系统的复杂性和多面性也使得微电网动态的分析、预测和控制成为一项具有挑战性的任务。该项目将结合电力系统控制，设计和模拟与先进的理论分析和控制到一个创新的框架。设想的方法将使智能能源系统中的一些新的应用，从系统适应用户行为，用户分类，并反馈给消费者。该项目将从事本科生和研究生的研究工作，并将创造新的课程机会UCI？该项目中所解决的问题的复杂性源于大量相互关联的异构子系统的交互作用，并有助于整体系统的行为。这种相互作用表现在不同的拓扑尺度和抽象层次。子系统在物理层面上相互作用，其中电信号通过物理互连进行传输。然而，显着的时间和组件间的相互依赖性存在于逻辑状态级，即，一组变量描述微电网组件的当前状态和影响因素。逻辑层的控制通常采取调度和管理框架的形式。大多数先前的工作考虑物理或逻辑域，没有提供一个明确的方法来桥接这两个相互依赖的域。该项目将使用先进的动态规划，图论和估计理论来创建物理和逻辑系统之间的双向信息流和控制，其中学习算法旨在将物理和逻辑信号映射到更高级别的逻辑状态。