Selected Proposals from Previous EmPOWER Air Data Challenges
4th EmPOWER Air Data Challenge Winner: 2022-2023 academic year
Ohio University: The Effect of Thermal Electric Generator Ramping on Emissions, Regulatory Compliance, and the Duck Curve
Daniel Karney and Khyati Malik
- Researchers from Ohio University (Daniel H. Karney) and The Ohio State University (Khyati Malik) will use CAPD’s Power Sector Emissions Data to study the ramping behavior of fossil fuel-fired power plants and the subsequent impact on emissions and regulatory compliance. Utility-scale power plants traditionally ramp generation up and down to balance grid-level electricity demand and supply, but the increased deployment of intermittent and distributed renewables may impact the need for ramping. The researchers will also evaluate the potential effect of increased work-from-home employment during the pandemic on ramping and "duck curve" phenomenon.
3rd EmPOWER Air Data Challenge Winners: 2021-2022 academic year
Lehigh University: Risk in Electricity Systems via ABSCoRES
Alberto J. Lamadrid L.
- Researchers from Lehigh, Massachusetts Institute of Technology, Lawrence Livermore National Laboratory, and Argonne National Laboratory used CAMD’s Power Sector Emissions Data and other datasets to develop risk and environmental metrics and scores for different components of the electricity grid.
State University of New York at Albany/University of Pennsylvania: Information Provision and Price Formation in Environmental Markets
Cuicui Chen and Jose Miguel Abito
- Researchers from State University of New York at Albany and University of Pennsylvania used CAMD’s Power Sector Emissions Data as well as allowance data from CAMD to evaluate how the availability of market information affects the efficiency of environmental markets using the Acid Rain Program as a case study.
2nd EmPOWER Air Data Challenge Winners: 2020-2021 academic year
George Mason University: An accessible education tool presenting 20 years of monthly source impacts from individual coal power plants in the CAMD database
Lucas Henneman
- Researchers from George Mason, Georgia Institute of Technology, University of Texas-Austin, and ETH Zurich analyzed 20 years of CAMD's Power Sector Emissions Data and CASTNET data to assess the health benefits of the installation of pollution controls and shutdowns at coal-fired electric generating units (EGUs).
- Researchers also created a public-facing data visualization tool to understand these air quality and health benefits.
University of California-Davis: Hourly Average Emissions Factors for the Emissions & Generation Resource Integrated Database (eGRID)
Greg Miller
- Researchers from UC-Davis and Catalyst Cooperative used CAMD's Power Sector Emissions Data and other resources to create a database of hourly average emissions factors for eGRID and are analyzing how hourly emissions factors impact greenhouse gas inventory calculations.
1st EmPOWER Air Data Challenge Winners: 2019-2020 academic year
Cornell University: Predicting the Environmental Performance of Power Plants Using Machine Learning
Max Zhang, Ye Jiang, and Jeff Sward
- Researchers applied machine learning models to CAMD's Power Sector Emissions Data to predict power plant emissions rates and identify anomalies in the data to enhance data quality.
Georgia Institute of Technology: EmPOWERing Classroom Data Engagement
Jennifer Kaiser, Ximin Mi, Lisa Rosenstein, and CEE 430 students
- Students created data-driven “air pollution story” websites for select facilities using CAMD's Power Sector Emissions Data.
- Professors created a “teaching toolkit” so future classes and classes at other institutions may replicate the activity.
University of California-Berkeley/University of Oregon: Bringing Satellite Data Down to Earth: Estimating the Health Impacts of the Remarkable Decline in Coal Plant Emissions
Meredith Fowlie and Edward Rubin
- Researchers worked to improve estimates of the health impacts of pollution from coal-fired power plants by using multiple data sets, including CAMD's Power Sector Emissions Data and satellite data, to causally connect pollution with health outcomes.