SBIR-STTR Award

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.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to advance energy sustainability, especially regarding estimation, understanding, and optimization of real-time grid carbon emissions. Decarbonizing the grid means reducing its carbon emissions by decreasing the emissions per unit of electricity generated. In-depth, accurate knowledge of real-time grid emissions will facilitate various grid decarbonization use cases and applications ranging from corporate carbon accounting, carbon-aware energy management, and new emission tracking standards to future electricity and carbon market design. More broadly, the prposed platform may demonstrate that achieving near-optimal carbon emissions reductions via co-optimization for costs and carbon is possible and provide a commercially viable, scalable path for policy makers, regulators, and corporations to meet their ambitious sustainability commitments.This Small Business Innovation Research (SBIR) Phase II project seeks to develop novel and practical approaches to estimate, learn, and optimize real-time grid emissions. The Phase II project will optimize carbon estimation and forecast models, develop decision-making algorithms, implement the proposed methods as software, and validate the software platform through pilots. The team seeks to will build a user-friendly, cloud-based software platform with multiple applications that will enable climate regulators and sustainability directors to meet their public commitments and stay compliant with new regulations. Integrating real-time carbon emissions with energy and sustainability management is a critical challenge. Little is known about real-time carbon signals from the grid and the proposed project aims to demystify grid emissions to enable accurate, actionable, and transparent carbon tracking, reporting, and management.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.