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

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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.