Machine Learning for Natural Gas to Electric Power System Design

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Program:
DIFFERENTIATE
Award:
$1,800,000
Location:
Richland, Washington
Status:
ALUMNI
Project Term:
03/26/2020 - 11/30/2022
Website:

Technology Description:

Pacific Northwest National Laboratory (PNNL) will apply multiple machine learning tools to develop next-generation natural gas to electric power conversion system designs. The project leverages a physics-informed machine learning tool for automated reduced order model (ROM) construction. This will significantly reduce prediction errors compared to traditional approaches. Machine learning will also leverage a superstructure-based mathematical optimization tools combined with reinforcement learning and graph network methods to explore and optimize component connections in fuel to electric power conversion systems.

Potential Impact:

DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:

Security:

Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.

Environment:

Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.

Economy:

Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.

Contact

ARPA-E Program Director:
Dr. David Tew
Project Contact:
Dr. Jie Bao
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
jie.bao@pnnl.gov

Partners

National Energy Technology Laboratory
University of Washington

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Release Date:
04/05/2019