SBIR Phase II: Estimating, Learning, and Optimizing Real-Time Grid Emissions

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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.