EAGER: Connecting Smart Communities with Intelligent Transportation Systems - Energy Management of Predictive Occupancy Information

EAGER：连接智能社区与智能交通系统 - 预测占用信息的能源管理

• 批准号: 1637340
• 负责人: Yaoyu Li
• 金额: $ 15万
• 依托单位: University of Texas at Dallas
• 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=1637340&HistoricalAwards=false
• 关键词: EAGER; Connecting; Smart; Communities; Intelligent

Building and transportation are two essential and interactive elements for the society. Various means of transportation convey occupants among different buildings/communities via modern transportation networks. Occupants are central to operation of buildings and communities, especially in terms of energy use. Predictive information on building occupancy can be dramatically beneficial for improving the efficiency of building energy management. As occupants are transported to buildings/communities, the occupant arrival information can be predicted to through Intelligent Transportation Systems (ITS), enabled by vehicle-to-infrastructure communication and data analytics. This is expected to significantly benefit the optimization of energy management for buildings and communities. For occupant-carrying plug-in electric vehicles (PEVs), such predictive arrival information can also help optimize the charging management and vehicle-to-grid operation. This EArly-concept Grant for Exploratory Research (EAGER) award supports an exploratory research on integrating the ITS to energy management of Smart Communities, i.e. enhancing the building/community energy management with ITS predicted occupancy information. The results of this research will address major fundamental issues relevant to the integration of ITS and Smart Community, which will benefit the technology development. The findings of the project will be disseminated to a broader community via a dedicated website showing animations and video clips based on simulation results. In addition, seminars and workshops for government, K-16 faculty and students, and general public are planned to illustrate and disseminate the results of the research.Two scenarios of community level energy management are used to demonstrate the potential benefits brought by such community-ITS integration: 1) predictive energy management of community/district cooling system under demand response, enhanced with aggregated stochastic estimation of arrival time of upcoming building occupants; 2) decentralized charging management of parking-lot PEV enhanced with stochastic estimation of arrival time and arrival battery state-of-charge (SOC) of upcoming PEVs. With the moving-horizon prediction of in-coming occupant/vehicle arrival information enabled by ITS integration, stochastic prediction of arrival time will be performed, as well as the PEV arrival SOCs. Through aggregation of the stochastic arrival time estimation, dynamic data-driven modeling of occupant-to-load relations, and weather information, a stochastic model predictive control (MPC) strategy is applied for district cooling of community buildings with a central chilled-water plant, under demand response operation. An important issue of uncertainty propagation will be addressed in order to gain the understanding on how the stochastic estimate of occupant arrival information would affect the ultimate performance of the stochastic MPC and stochastic optimization for the energy management problems.

建筑和交通是社会的两个基本要素和相互作用的要素。各种交通工具通过现代交通网络在不同的建筑物/社区之间运送居住者。居住者是建筑物和社区运营的核心，特别是在能源使用方面。建筑占有率的预测信息对于提高建筑能源管理的效率有很大的帮助。随着乘员被运送到建筑物/社区，乘员到达信息可以通过智能交通系统(ITS)预测到，这是通过车辆到基础设施的通信和数据分析实现的。预计这将大大有利于建筑物和社区的能源管理的优化。对于乘员携带的插电式电动汽车(PEV)来说，这种预测到达信息也可以帮助优化充电管理和车辆并网运营。这一早期概念探索性研究补助金(AGER)奖支持一项关于将智能交通系统与智能社区的能源管理相结合的探索性研究，即利用其预测的入住率信息来增强建筑/社区的能源管理。这项研究的成果将解决智能交通系统与智能社区融合的重大基础性问题，将有利于技术的发展。该项目的结果将通过一个专门的网站向更广泛的社区传播，展示基于模拟结果的动画和视频剪辑。此外，还计划为政府、K-16教师和学生以及公众举办研讨会和研讨会，以说明和传播研究成果。两个社区级能源管理场景被用来展示这种社区-其集成带来的潜在好处：1)需求响应下社区/区域制冷系统的预测能量管理，增强了对即将到来的建筑居住者到达时间的聚合随机估计；2)停车场电动汽车的分散充电管理，通过随机估计到达时间和即将到来的电动汽车的到达电池充电状态(SOC)来增强。通过其集成实现对进站乘员/车辆到达信息的滚动预测，对到达时间以及电动汽车到达的SOC进行随机预测。通过聚合随机到达时间估计、动态数据驱动的人-负荷关系建模和天气信息，将随机模型预测控制(MPC)策略应用于需求响应运行下带有中央冷水厂的社区建筑的区域供冷。为了理解乘员到达信息的随机估计如何影响随机预测控制和能量管理问题的随机优化的最终性能，将讨论不确定性传播的一个重要问题。

项目成果

ENENGY MANAGEMENT OF SMART COMMUNITY WITH EV CHARGING USING DISTRIBUTED MODEL PREDICTIVE CONTROL

使用分布式模型预测控制进行电动汽车充电智能社区的能源管理

• 发表时间: 2018
• 期刊: Proceedings of ASME 2018 Dynamic Systems and Control Conference
• 作者: Fenglin Zhou, Yaoyu Li