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; 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.

这项小型企业创新研究（SBIR）I期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，从而节省电费并降低碳足迹，而不会损害乘员舒适。提出的平台还可以通过允许建筑物参与辅助电力市场并响应电力公用事业的价格或要求响应信号来实现收入的产生。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，这可以帮助电力公司避免/推迟对其电气基础设施的广泛升级，例如发电，传输或分销设施，并且对电力的需求不断增长。提出的软件平台展示了建筑自动化对建筑物/运营商的好处，并为他们提供了与建筑物建筑物的最佳实践交叉引用的能力。它还通过促进双方相互满足，工作和交流体验相互交流，与高等教育的体验之间的知识转移过程桥接。本小型企业创新研究（SBIR）I阶段项目建立在基于代理的基于代理的自动化系统的平台上，用于构建自动化算法，在该平台上，高级算法使软件平台可实现并帮助建立建筑工程。它还可以执行故障检测，诊断和预测性维护，整合新兴的物联网（IoT）设备，并使建筑物能够以智能的方式与网格进行交互。在拟议的项目下，将开发一种增强的机器学习算法，该算法使用历史建筑能源消耗数据和环境条件（包括乘员偏好），以根据建筑物每个区域的主要负载和电源的使用模式来优化建筑物运营。另一个主要的研究贡献在于新型算法，使建筑物能够与网格进行交易。这些使建筑物可以通过自动调整主要负载和内部发电源的运行来应对系统应力状况和电价信号。总体而言，拟议的软件平台允许建筑物优化其运营，在网格压力条件下执行峰值需求管理并参与辅助服务市场。