EAGER: Collaborative Research: Empowering Smart Energy Communities: Connecting Buildings, People, and Power Grids

EAGER：协作研究：赋能智能能源社区：连接建筑物、人员和电网

• 批准号: 1637249
• 负责人: Bing Dong
• 金额: $ 17.34万
• 依托单位: University of Texas at San Antonio
• 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 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637249&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Empowering; Smart

1637258 / 1637249 Yu, Nanpeng / Dong, Bing By 2050, 70% of the world's population is projected to live and work in cities, with buildings as major constituents. Buildings' energy consumption contributes to more than 70% of electricity use, with people spending more than 90% of their time in buildings. Future cities with innovative, optimized building designs and operations have the potential to play a pivotal role in reducing energy consumption, curbing greenhouse gas emissions, and maintaining stable electric-grid operations. Buildings are physically connected to the electric power grid, thus it would be beneficial to understand the coupling of decisions and operations of the two. However, at a community level, there is no holistic framework that buildings and power grids can simultaneously utilize to optimize their performance. The challenge related to establishing such a framework is that building control systems are neither connected to, nor integrated with the power grid, and consequently a unified, global optimal energy control strategy at a smart community level cannot be achieved. Hence, the fundamental knowledge gaps are (a) the lack of a holistic, multi-time scale mathematical framework that couples the decisions of buildings stakeholders and grid stakeholders, and (b) the lack of a computationally-tractable solution methodology amenable to implementation on a large number of connected power grid-nodes and buildings.In this project, a novel mathematical framework that fills the aforementioned knowledge gaps will be investigated, and the following hypothesis will be tested: Connected buildings, people, and grids will achieve significant energy savings and stable operation within a smart city. The envisioned smart city framework will furnish individual buildings and power grid devices with custom demand response signals. The hypothesis will be tested against classical demand response (DR) strategies where (i) the integration of building and power-grid dynamics is lacking and (ii) the DR schemes that buildings implement are independent and individual. By engaging in efficient, decentralized community-scale optimization, energy savings will be demonstrated for participating buildings and enhanced stable operation for the grid are projected, hence empowering smart energy communities. To ensure the potential for broad adoption of the proposed framework, this project will be regularly informed with inputs and feedback from Southern California Edison (SCE). In order to test the hypothesis, the following research products will be developed: (1) An innovative method to model a cluster of buildings--with people's behavior embedded in the cluster's dynamics--and their controls so that they can be integrated with grid operation and services; (2) a novel optimization framework to solve complex control problems for large-scale coupled systems; and (3) a methodology to assess the impacts of connected buildings in terms of (a) the grid's operational stability and safety and (b) buildings' optimized energy consumption. To test the proposed framework, a large-scale simulation of a distribution primary feeder with over 1000 buildings will be conducted within SCE?s Johanna and Santiago substations in Central Orange County.

1637258 / 1637249 Yu，Nanpeng / Dong，Bing预计到2050年，世界上70%的人口将在城市生活和工作，而建筑物将成为主要组成部分。建筑物的能源消耗占用电量的70%以上，人们90%以上的时间都花在建筑物上。具有创新、优化的建筑设计和运营的未来城市有可能在降低能源消耗、遏制温室气体排放和维持稳定的电网运营方面发挥关键作用。建筑物在物理上连接到电网，因此了解两者的决策和操作的耦合将是有益的。然而，在社区层面，没有一个整体框架可以让建筑物和电网同时利用来优化其性能。与建立这种框架相关的挑战是，建筑控制系统既不连接到电网，也不与电网集成，因此无法在智能社区层面实现统一的全局最佳能源控制策略。因此，基本的知识差距是（a）缺乏一个整体的、多时间尺度的数学框架，该数学框架将建筑物利益相关者和电网利益相关者的决策耦合起来，以及（B）缺乏一种适合于在大量连接的电网节点和建筑物上实施的计算易处理的解决方案方法。将研究一种新的数学框架，填补上述知识空白，并测试以下假设：连接的建筑物，人和电网将在智能城市中实现显著的节能和稳定的运行。设想的智慧城市框架将为各个建筑物和电网设备提供定制的需求响应信号。该假设将被测试对经典的需求响应（DR）策略，其中（i）的集成建设和电网动态是缺乏和（ii）的DR计划，建筑物实现是独立的和个人的。通过参与高效、分散的社区规模优化，将为参与的建筑物展示节能效果，并预计将增强电网的稳定运行，从而增强智能能源社区的能力。为确保广泛采用拟议框架的可能性，将定期向本项目通报南加州爱迪生公司（SCE）的意见和反馈。为了验证这一假设，将开发以下研究产品：（1）一种创新的方法来模拟建筑群-将人的行为嵌入建筑群的动态-及其控制，以便它们能够与网格操作和服务相结合;（2）一种新的优化框架，以解决大规模耦合系统的复杂控制问题;以及（3）评估联网建筑物在（a）电网运行稳定性和安全性以及（B）建筑物优化能耗方面的影响的方法。为了测试所提出的框架，一个大规模的模拟超过1000个建筑物的配电主馈线将在SCE？位于中橙子县的约翰娜和圣地亚哥变电站。

项目成果

Impact of Occupancy-Based Buildings-to-Grid Integration on Frequency Regulation in Smart Grids

基于占用的建筑并网一体化对智能电网频率调节的影响

• 发表时间: 2018
• 期刊: American Control Conference
• 作者: Dong, D.;Taha, A.F.;Gatsis, N.;Li, Z.;Pipri, A.
• 通讯作者: Pipri, A.

Market and behavior driven predictive energy management for residential buildings

• DOI: 10.1016/j.scs.2018.01.030
• 发表时间: 2018-04
• 期刊: Sustainable Cities and Society
• 影响因子: 11.7
• 作者: Amin Mirakhorli;B. Dong

Occupancy Driven Buildings-to-grid Integration Framework for Large Commercial Buildings

大型商业建筑的占用驱动建筑到电网集成框架

• 发表时间: 2018
• 期刊: 17th International Refrigeration and Air Conditioning Conference at Purdue
• 作者: Dong, B;Fontenot, H
• 通讯作者: Fontenot, H

Short term predictions of occupancy in commercial buildings—Performance analysis for stochastic models and machine learning approaches

• DOI: 10.1016/j.enbuild.2017.09.052
• 发表时间: 2018
• 期刊: Energy and Buildings
• 影响因子: 6.7
• 作者: Zhaoxuan Li;B. Dong

A new modeling approach for short-term prediction of occupancy in residential buildings

• DOI: 10.1016/j.buildenv.2017.05.005
• 发表时间: 2017-08
• 期刊: Building and Environment
• 影响因子: 7.4
• 作者: Zhaoxuan Li;B. Dong