Abstract
The increasing penetration of power electronics in power grids significantly raises the computing requirements in a real-time (and/or fast) simulation of the power grid. The real-time simulation is an enabler for evaluating controllers, protection systems, new equipment, and twinning. In this paper, emerging computing architectures such as tensor processing units (TPU), neural/neuromorphic processing units (NPU), and quantum processing units (QPU) are introduced and characterized for the real-time (and/or fast) simulation of power electronics-dominated power grids. The metrics and the process to characterize emerging computing architectures to perform real-time (and/or fast) simulations of future power grids with power electronics are discussed. Three of the emerging computing units are characterized based on these metrics and the process developed. This characterization will enable identification and comparison of emerging computing architectures that can perform real-time (and/or fast) simulation of future power grids.
| Original language | English |
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| Title of host publication | 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728193878 |
| DOIs | |
| State | Published - 2022 |
| Event | 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 - Detroit, United States Duration: Oct 9 2022 → Oct 13 2022 |
Publication series
| Name | 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 |
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Conference
| Conference | 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 |
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| Country/Territory | United States |
| City | Detroit |
| Period | 10/9/22 → 10/13/22 |
Funding
ACKNOWLEDGMENT Authors would like to thank Alireza Ghassemian from Department of Energy (DOE) Office of Electricity (OE) Advanced Grid Modeling (AGM) Office for overseeing the project developments and providing guidance. Research sponsored by Office of Electricity of U.S. Department of Energy. This material is based upon work supported by the Advanced Grid Modeling (AGM) Office. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Research sponsored by Office of Electricity of U.S. Department of Energy. This material is based upon work supported by the Advanced Grid Modeling (AGM) Office. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Authors would like to thank Alireza Ghassemian from Department of Energy (DOE) Office of Electricity (OE) Advanced Grid Modeling (AGM) Office for overseeing the project developments and providing guidance. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- NPU
- Power electronics
- QPU
- TPU
- emerging computing
- real-time simulations