STTR Phase I: An Agent-based Self-learning Technology for Efficient Building Operations and Automated Participation in Electricity Markets

STTR 第一阶段：基于代理的自学习技术，用于高效建筑运营和自动参与电力市场

• 批准号: 1622867
• 负责人: Manisa Pipattanasomporn
• 金额: $ 22.47万
• 依托单位: BEMCONTROLS, LLC
• 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: 2016-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622867&HistoricalAwards=false
• 关键词: STTR; Phase; Agent; based; Self

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the capability to enable small- and medium-sized commercial buildings to operate more efficiently and facilitate their participation in electricity markets. It will result in energy consumption reduction in buildings, thus providing savings in electricity bills and lowering carbon footprints, without compromising occupant comfort. The proposed platform also enables revenue generation by allowing buildings to participate in ancillary electricity markets and respond to pricing or demand response signals from electric utilities. By integrating intelligent energy management and automatic demand response features to the traditional building operation, this helps electric utility companies to avoid/defer extensive upgrades of their electrical infrastructures, such as generation, transmission or distribution facilities, with the growing demand for electricity. The proposed software platform demonstrates the benefits of building automation to building owners/operators, and provides them the ability to cross-reference their buildings with best practices in building operations. It also bridges the knowledge transfer process between a small business and an institution of higher education by facilitating both sides to meet, work and exchange experiences with each other.This Small Business Innovation Research (SBIR) Phase I project is built on an agent-based platform for building automation systems where advanced algorithms enable the software platform to be proactive and help optimize building operations. It can also perform fault detection, diagnostics and predictive maintenance, integrate emerging Internet of Things (IoT) devices and enable buildings to interact with the grid in an intelligent manner. Under the proposed project, a reinforced machine-learning algorithm will be developed that uses historical building energy consumption data and ambient conditions - including occupant preferences - to optimize building operations based on usage patterns of major loads and power sources in each zone of the building. Another major research contribution lies in novel algorithms that enable buildings to transact with the grid. These allow buildings to respond to system stress conditions and electricity price signals by automatically adjusting the operation of major loads and internal generating sources. Overall, the proposed software platform allows a building to optimize its operation, perform peak demand management during grid stress conditions, and participate in ancillary service markets.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是使中小型商业建筑能够更有效地运营并促进其参与电力市场的能力。这将导致建筑物的能源消耗减少，从而节省电费和降低碳足迹，而不会影响居住者的舒适度。拟议的平台还通过允许建筑物参与辅助电力市场并响应来自电力公司的定价或需求响应信号来实现创收。通过将智能能源管理和自动需求响应功能集成到传统的建筑运营中，这有助于电力公司避免/推迟其电力基础设施的大规模升级，例如发电，输电或配电设施，以满足不断增长的电力需求。拟议的软件平台向建筑物业主/运营商展示了楼宇自动化的好处，并使他们能够将其建筑物与建筑物运营的最佳做法进行交叉参考。它还通过促进双方会面，工作和交流经验，在小型企业和高等教育机构之间建立知识转移过程的桥梁。这个小型企业创新研究（SBIR）第一期项目建立在基于智能体的楼宇自动化系统平台上，先进的算法使软件平台能够主动帮助优化楼宇运营。它还可以执行故障检测，诊断和预测性维护，集成新兴的物联网（IoT）设备，并使建筑物能够以智能方式与电网进行交互。根据拟议的项目，将开发一种强化的机器学习算法，该算法使用历史建筑能耗数据和环境条件（包括居住者偏好），根据建筑物每个区域的主要负载和电源的使用模式优化建筑运营。另一个主要的研究贡献在于新的算法，使建筑物与网格进行交易。这使得建筑物能够通过自动调整主要负载和内部发电源的运行来响应系统压力条件和电价信号。总体而言，所提出的软件平台允许建筑物优化其运行，在电网压力条件下执行峰值需求管理，并参与辅助服务市场。