Grid-Interactive Electric Vehicle and Building Coordination Using Coupled Distributed Control [electronic resource] : Preprint.
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Corporate Author: | |
Format: | Government Document Electronic eBook |
Language: | English |
Published: |
Washington, D.C. : Oak Ridge, Tenn. :
United States. Department of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
2022.
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Subjects: |
Abstract: | As an increasing number of controllable devices are introduced onto the grid, they can individually provide ancillary services in support of grid stability. However, the goals of each device differ due to their type and individual objectives, causing instances where they may conflict. To reduce the chances of these devices contributing to grid instability, these devices must effectively communicate in a cooperative manner to both meet their own needs while providing services to the grid. Previous work demonstrates that the NALD (Network Lasso-ADMM - Limited Communication - DMPC) algorithm allows coordination between two subsystems that use different control algorithms (building and charging stations) to provide services to the grid and individually optimize their performance in a specific scenario. The ideal NALD algorithm should be generalized to allow plug-and-play capabilities across devices of differing characteristics. This paper takes a step toward generalizability by updating the electric vehicle charging objective and re-defining the communication scheme compared to prior work to generalize the coordination and, as a result, improve the performance of the NALD algorithm. |
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Item Description: | Published through Scitech Connect. 07/12/2022. "NREL/CP-5000-82071." "Other: MainId:82844." "UUID:6acbf1fd-6214-48f3-97df-0398c2c4a2ac." "MainAdminID:64844." Wald, Dylan (ORCID:0000000297425288); Johnson, Kathryn (ORCID:0000000197717718); Bay, Christopher J. (ORCID:0000000226585559); King, Jennifer (ORCID:0000000162190098); Chintala, Rohit (ORCID:0000000191439788); |
Physical Description: | Size: 2.69 MB : digital, PDF file. |