Project Grant NA23OAR0210332
- This three-year, $290,000 National Science Foundation project grant supports research at The Pennsylvania State University to develop new mathematical methods, computer models, and algorithms for electric grid operational planning under non-Gaussian uncertainties in renewable energy forecasts. Funded through NSF's Mathematical and Physical Sciences program (CFDA 47.049), the research directly addresses challenges in integrating intermittent renewable resources like wind and solar power into...
- This Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) provides $275,000 to Sunairio Inc. to develop a high-fidelity climate simulation engine powered by generative adversarial networks. The goal is to create a broad (1,000 outcomes), hyperlocal (less than 3 km) climate simulation archive that can be used by power grid planners and energy investors to better understand forward-looking risks to grid...
- This three-year, $283,995 Project Grant from the National Science Foundation's Division of Mathematical Sciences will support the development of robust methods for grid operational planning that account for non-Gaussian uncertainties in renewable energy forecasts. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the grantee—Northwestern University—will work with its sub-awardee to create new mathematical models, theory, algorithms, and computer implementations to...
- The National Science Foundation's Division of Atmospheric and Geospace Sciences awarded University of California, Davis a $570,041 Project Grant effective September 1, 2025, through August 31, 2028, to develop a theoretical framework for assessing the value of climate and weather information in risk management and adaptation decision-making. This collaborative research initiative will produce a generalizable economic framework for evaluating how climate model outputs improve decision outcomes...
- Altitude Grid, LLC received a $200,000 Project Grant award from the Department of Energy Office of Science on June 27, 2022 to develop a Reinforced Hierarchical Probabilistic Solar Forecasting Tool Based on Dynamic Multi-Model Machine Learning. The tool will utilize machine learning techniques to provide probabilistic solar power forecasting through June 26, 2023. This aligns with the Office of Science Financial Assistance Program's mission under the 81.049 CFDA to transform understanding of...
- This $199,339 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program to the California State University San Marcos Corp (CSUSM) will develop novel AI and machine learning models for supervisory control of wind farm connections to the electric grid for stability monitoring. The project aims to create innovative AI/ML models that can directly analyze raw power data to enable accurate fault prediction and detection, which...
- This Project Grant from the National Science Foundation Division of Mathematical Sciences provides $429,158 to develop computational tools for modeling, prediction and control of distributed and reconfigurable renewable energy systems. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), key outcomes include noise-resilient identification methods for transient dynamics, stochastic models integrating statistical closure with topology-aware data, and optimal control...
- This Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of machine learning methods for estimating the risk of adverse weather and climate events, such as rapid changes in solar power generation and flooding. The University of Hawaii at Manoa, as the prime contractor, will apply advances in generative artificial intelligence...
- Wayne State University received a $224,999 project grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems on August 1, 2021 to support research titled "COLLABORATIVE RESEARCH: GENERATION VERSUS DEGRADATION: STRIKING THE OPTIMAL BALANCE FOR WIND FARM PROFITABILITY VIA DIGITIZATION, PREDICTIVE AND PRESCRIPTIVE ANALYTICS." The research is funded under the National Science Foundation's Engineering program (CFDA 47.041), which supports...
- This Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) provides $750,000 in funding to The Regents of the University of California, doing business as the University of California, Berkeley (UC Berkeley), to investigate the impacts of economic incentives, planning paradigms, and conservation concerns on electricity infrastructure investments. The project aims to improve the effectiveness and social impact of...
THIS RESEARCH AIMS TO DEVELOP DECISION SUPPORT TOOLS FOR BETTER MAINTENANCE AND PLANNING OF RENEWABLE ENERGY GENERATION AND STORAGE ASSETS USING ATMOSPHERIC, LOCAL WEATHER, AND ON-GROUND ENVIRONMENTAL DATA BLENDED WITH SITE-, REGIONAL- AND NATIONAL-GENERATION DATA. THE STUDY WILL USE A COMBINATION OF THESE DATA TO UNDERSTAND THE RELATIONSHIP BETWEEN NON-CLOUD COVER BASED ATMOSPHERIC CONDITIONS AND THE IMPACT ON RENEWABLE ENERGY GENERATION AND STORAGE WHICH WILL THEN BE USED TO DEVELOP MODELS FOR PLANNING AND DECISION SUPPORT FOR OPERATIONS. RESEARCH WILL BE CONDUCTED ACROSS MULTIPLE RENEWABLE GENERATION AND STORAGE SITES WITH DIVERSE ENVIRONMENTAL CONDITIONS TO VALIDATE AND VERIFY THE EXISTING MODEL DEVELOPED DURING PHASE I OF THIS RESEARCH. THE EXPECTED RESULTS OF THIS RESEARCH INCLUDE A BETTER UNDERSTANDING OF THE RELATIONSHIP BETWEEN ATMOSPHERIC CONDITIONS AND THE IMPACT ON RENEWABLE ENERGY GENERATION AND STORAGE, THE DEVELOPMENT OF A DECISION SUPPORT TOOL FOR PROACTIVE AND REACTION
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 8/19/25 | ||
| Not listed | $0 | 7/9/25 | ||
| Not listed | $0 | 7/9/25 | ||
| Not listed | $0 | 1/29/24 | ||
| Not listed | $650.0k | 7/12/23 |