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

SBIR 第二阶段：估计、学习和优化实时电网排放

• 批准号: 2051953
• 负责人: Wenbo Shi
• 金额: $ 100万
• 依托单位: SINGULARITY ENERGY, INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051953&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Estimating; Learning; SBIR; Phase; II; Estimating; Learning

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.

这项小型企业创新研究（SBIR）II期项目的更广泛的影响/商业潜力是提高能源可持续性，尤其是在实时网格碳排放的估计，理解和优化方面。脱氧网格意味着通过减少产生的电力排放来减少其碳排放。深入的，准确的实时网格排放知识将有助于各种网格脱碳用例和应用程序，从公司碳会计，碳感知能源管理以及新的排放跟踪标准到未来的电力和碳市场设计。更广泛地说，该平台可能表明，通过优化成本和碳实现近乎最佳的碳排放减少是可能的，并为政策制定者，监管机构和公司提供一条可靠的可行，可扩展的途径，以实现雄心勃勃的可持续性承诺，以满足其雄心勃勃的可持续性。排放。第二阶段项目将优化碳估计和预测模型，开发决策算法，将提出的方法实现为软件，并通过飞行员验证软件平台。该团队试图建立一个具有多个应用程序的用户友好，基于云的软件平台，这将使气候监管机构和可持续发展董事能够满足其公开承诺，并遵守新法规。将实时碳排放与能源和可持续性管理整合是一个至关重要的挑战。关于电网的实时碳信号知之甚少，该项目旨在使网格排放脱落，以实现准确，可行和透明的碳跟踪，报告和管理。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是值得的。