Distributed Model Predictive Control for Building Energy Systems

建筑能源系统的分布式模型预测控制

• 批准号: 1563361
• 负责人: Bryan Rasmussen
• 金额: $ 32.5万
• 依托单位: Texas A&M Engineering Experiment Station
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563361&HistoricalAwards=false
• 关键词: Distributed; Model; Predictive; Control; Building

Building heating, ventilation, and air-conditioning systems are comprised of many different pieces of equipment, which are controlled individually and separately. By coordinating the operation of these systems in real-time, significant energy savings are possible. However, the changing nature of building systems require that this coordinated control strategy be flexible and modular, with the ability to adapt to new equipment or changes in building operations. This award supports both fundamental and applied research to create and demonstrate algorithms capable of coordinating building systems to reduce energy usage, while maintaining or improving the comfort of building occupants. Results of these research will directly benefit the United States by reducing building energy costs and environmental impact. The project will also help broaden participation of underrepresented groups in engineering research.The hierarchy of control strategies investigated in this project includes lower level control strategies that compensate for the inherently nonlinear behavior of building energy subsystems, while preserving the intuitive controller design and tuning methods common to current equipment. The research also investigates a supervisory distributed predictive control algorithm that is capable of determining to the system-level energy optimal reference trajectories for lower-level controllers. The distributed controller requires minimal communication between systems, and is modular and adaptable to changes in building equipment or operational conditions. The coordinated controller minimizes energy consumption while maintaining occupant comfort. By including occupant demands in the predictive controllers, the system can also provide feedback to users regarding the energy costs associated with specific behaviors, and help shape occupant behavior.

建筑物的供暖、通风和空调系统由许多不同的设备组成，这些设备分别进行控制。通过实时协调这些系统的运行，可以显著节省能源。然而，建筑系统不断变化的性质要求这种协调控制策略是灵活和模块化的，能够适应新设备或建筑运行中的变化。该奖项支持基础研究和应用研究，以创建和展示能够协调建筑系统以减少能源消耗的算法，同时保持或改善建筑居住者的舒适性。这些研究的结果将通过降低建筑能源成本和环境影响直接造福美国。该项目还将有助于扩大代表不足的群体在工程研究中的参与。本项目研究的控制策略层次包括较低级别的控制策略，这些控制策略弥补了建筑能源子系统固有的非线性行为，同时保留了当前设备常见的直观控制器设计和调整方法。该研究还研究了一种监督分布式预测控制算法，该算法能够为低级控制器确定系统级能量最优参考轨迹。分布式控制器只需要系统之间的最少通信，并且是模块化的，能够适应建筑设备或运行条件的变化。协调控制器在保持乘员舒适性的同时将能耗降至最低。通过将乘员需求包括在预测控制器中，该系统还可以向用户提供关于与特定行为相关联的能源成本的反馈，并帮助塑造乘员行为。