SBIR Phase I: A Data-driven Demand Response Recommendation System

SBIR第一阶段：数据驱动的需求响应推荐系统

• 批准号: 1648320
• 负责人: Rahul Mangharam
• 金额: $ 22.49万
• 依托单位: Expresso Logic, 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: 2016-12-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1648320&HistoricalAwards=false
• 关键词: SBIR; Phase; Data; driven; Demand

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to provide a data intelligence layer for buildings and electric grid operators, and help remove any guesswork from the implementation of optimized electricity demand response. Better demand response means that less efficient, and often more expensive, forms of electricity generation do not need to come online during times of high electricity demand. Demand response optimization reduces the stress on transmission and distribution systems, making them less likely to fail. A well implemented demand response program leads to competitive and efficient wholesale electricity market operations, and helps in keeping electricity prices in check. This Small Business Innovation Research (SBIR) Phase I project aims to develop, deploy, and evaluate innovative algorithms and software for control-oriented predictive modeling for energy systems, with specific application to electricity demand response. There are four significant technical hurdles that will be addressed by the proposed effort: (a) modeling complexity and heterogeneity, (b) limitations of rule-based demand response, (c) control complexity and scalability, and (d) interpretability of modeling and control. The goal of the proposed research is to address each of the technological hurdles, and in doing so develop (i) data-driven control oriented modeling tools for demand response and for large scale energy systems, and (ii) a methodology to preserve interpretability of our data-driven control-oriented models for providing energy analytics to the facilities manager based on real-world data. The proposed project will include pilot deployments on real buildings to evaluate the performance of the demand response software.

这项小型企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力将是为建筑物和电网运营商提供数据智能层，并帮助从实施优化的电力需求响应中消除任何猜测。更好的需求响应意味着在高电力需求时，发电的效率降低，通常更昂贵的形式不需要上网。需求响应优化减轻了传输和分配系统的压力，从而使它们失败的可能性降低。实施良好的需求响应计划可导致竞争性和高效的批发电力市场运营，并有助于控制电价。这项小型企业创新研究（SBIR）I阶段项目旨在开发，部署和评估用于能源系统的以控制为导向的预测建模的创新算法和软件，并针对电力需求响应进行了具体应用。提出的努力将解决四个重大的技术障碍：（a）建模复杂性和异质性，（b）基于规则的需求响应的局限性，（c）控制复杂性和可扩展性，以及（d）建模和控制的解释性。拟议的研究的目的是解决每个技术障碍，并为此开发（i）以数据驱动的控制为需求响应和大规模能源系统的导向控制的建模工具，以及（ii）一种方法，可为基于真实数据提供基于现实数据的设施管理器提供以数据驱动的控制能源分析的能源分析的方法来源。拟议的项目将包括对实际建筑物的试点部署，以评估需求响应软件的性能。