SBIR Phase I: Estimating, Learning, and Optimizing Real-Time Carbon Emissions for Energy and Sustainability Management

SBIR 第一阶段：估算、学习和优化实时碳排放，以实现能源和可持续发展管理

• 批准号: 1938082
• 负责人: Wenbo Shi
• 金额: $ 22.5万
• 依托单位: SINGULARITY ENERGY, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938082&HistoricalAwards=false
• 关键词: SBIR; Phase; Estimating; Learning; Optimizing

The broader impact/commercial potential of this Small Business Innovation Research project is that it will advance the area of energy sustainability with proactive monitoring and management of carbon emissions in real time. The knowledge of real-time grid carbon data will provide a new perspective for various applications ranging from smart grid controls to future electricity market design. More broadly, the demonstration of achieving near-optimal carbon emissions reduction without sacrificing economics will provide a commercially viable, scalable path to proactively manage carbon emissions under existing market mechanisms.This Small Business Innovation Research (SBIR) Phase I project will: 1) estimate real-time carbon emissions and analyze grid carbon intensity models from historical data using statistical (offline) learning approaches, 2) design real-time/online optimization and control approaches to reduce carbon emissions while maximizing economic benefits under uncertainties, and 3) develop a proof-of-concept software platform, implement the models and strategies, integrate with hardware, and validate the methods through a pilot. A major theme is to integrate real-time carbon emissions with energy and sustainability management to estimate real-time carbon emissions, develop grid carbon intensity models, leverage real-time data to improve sustainability, and make real-time control decisions under uncertainties of the ambient environment and customer behaviors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究项目的更广泛的影响/商业潜力是，它将通过真实的时间对碳排放进行主动监测和管理来推进能源可持续性领域。实时电网碳数据的知识将为从智能电网控制到未来电力市场设计的各种应用提供新的视角。 更广泛地说，在不牺牲经济性的情况下实现接近最佳的碳减排的示范将提供一条商业上可行且可扩展的路径，以在现有市场机制下主动管理碳排放。小企业创新研究（SBIR）第一阶段项目将：1）使用统计（离线）学习方法从历史数据估计实时碳排放并分析电网碳强度模型，2）设计实时/在线优化和控制方法，以减少碳排放，同时在不确定性下最大限度地提高经济效益，3）开发概念验证软件平台，实施模型和策略，与硬件集成，并通过试点验证方法。一个主要的主题是将实时碳排放与能源和可持续性管理相结合，以估计实时碳排放，开发电网碳强度模型，利用实时数据提高可持续性，让真实的-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。