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

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是使中小型商业建筑能够更有效地运营，并促进它们参与电力市场。它将减少建筑物的能源消耗，从而节省电费并降低碳足迹，而不会影响乘员的舒适度。拟议的平台还允许建筑物参与辅助电力市场，并对电力公用事业公司的定价或需求响应信号做出反应，从而实现创收。通过将智能能源管理和自动需求响应功能集成到传统建筑运营中，这有助于电力公用事业公司避免/推迟发电、输电或配电设施等电力基础设施的大规模升级，以满足日益增长的电力需求。拟议的软件平台向楼宇业主/营运者展示楼宇自动化的好处，并使他们能够将楼宇与楼宇运作的最佳做法互相参照。它还通过促进双方见面、工作和交流经验，在小企业和高等教育机构之间架起了知识转移的桥梁。这个小企业创新研究(SBIR)第一阶段项目建立在一个基于代理的平台上，用于建筑自动化系统，其中先进的算法使软件平台能够积极主动，帮助优化建筑运营。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交互。在拟议的项目中，将开发一种增强的机器学习算法，该算法使用建筑能耗历史数据和环境条件-包括居住者偏好-来根据建筑每个区域的主要负荷和电源的使用模式来优化建筑运营。另一项主要的研究贡献在于使建筑物能够与电网进行交易的新颖算法。这使建筑物能够通过自动调整主要负荷和内部发电源的运行来对系统压力条件和电价信号做出反应。总体而言，拟议的软件平台使建筑物能够优化其运营，在电网压力条件下执行高峰需求管理，并参与辅助服务市场。

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.