Bulk Electric Power System Risks From Coordinated Edge Devices
As smart load adoption grows on the electric power system, potential for losing load diversity increases, possibly in ways that impact system stability. Cloud computing resources are able to coordinate large amounts of behind-the-meter loads and resources. Inadvertent or malicious actions could pote...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9328163/ |
| _version_ | 1818971407671885824 |
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| author | Richard Kenyon Jeff Maguire Elaina Present Dane Christensen Bri-Mathias Hodge |
| author_facet | Richard Kenyon Jeff Maguire Elaina Present Dane Christensen Bri-Mathias Hodge |
| author_sort | Richard Kenyon |
| collection | DOAJ |
| description | As smart load adoption grows on the electric power system, potential for losing load diversity increases, possibly in ways that impact system stability. Cloud computing resources are able to coordinate large amounts of behind-the-meter loads and resources. Inadvertent or malicious actions could potentially result in gigawatts of load, distributed across large regions, acting nearly simultaneously. We study the resulting impacts of such a perturbation, which were previously recognized, with improved fidelity and granularity using a physically-based power system and demand model. The ResStock tool was used to calculate residential air conditioning load at more than 3,000 locations across the Western Interconnection, corresponding in time to heavy summer and light spring loading. Under an assumption that one cloud platform managed smart thermostats controlling 10%, 15%, or 20% of residential air-conditioning, calculated load steps could be injected into Positive Sequence Load Flow dynamic simulations. These load-driven effects were coupled with two classes of distributed generation ride-through to evaluate the potential for cascading outages. We found frequency deviations in the spring case far exceed the credible contingency event, leading to widespread distributed generation loss, while voltage depressions during the summer loading lead to widespread distributed generation loss and system separation. |
| first_indexed | 2024-12-20T14:51:53Z |
| format | Article |
| id | doaj.art-86efb183e4954ca288d128241fd90f5a |
| institution | Directory Open Access Journal |
| issn | 2687-7910 |
| language | English |
| last_indexed | 2024-12-20T14:51:53Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj.art-86efb183e4954ca288d128241fd90f5a2022-12-21T19:36:56ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102021-01-018354410.1109/OAJPE.2021.30524339328163Bulk Electric Power System Risks From Coordinated Edge DevicesRichard Kenyon0Jeff Maguire1Elaina Present2Dane Christensen3https://orcid.org/0000-0002-4539-2402Bri-Mathias Hodge4https://orcid.org/0000-0001-8684-0534National Renewable Energy Laboratory (NREL), Golden, CO, USADepartment of Electrical, Computer and Energy Engineering, University of Colorado, Boulder, CO, USANational Renewable Energy Laboratory (NREL), Golden, CO, USANational Renewable Energy Laboratory (NREL), Golden, CO, USANational Renewable Energy Laboratory (NREL), Golden, CO, USAAs smart load adoption grows on the electric power system, potential for losing load diversity increases, possibly in ways that impact system stability. Cloud computing resources are able to coordinate large amounts of behind-the-meter loads and resources. Inadvertent or malicious actions could potentially result in gigawatts of load, distributed across large regions, acting nearly simultaneously. We study the resulting impacts of such a perturbation, which were previously recognized, with improved fidelity and granularity using a physically-based power system and demand model. The ResStock tool was used to calculate residential air conditioning load at more than 3,000 locations across the Western Interconnection, corresponding in time to heavy summer and light spring loading. Under an assumption that one cloud platform managed smart thermostats controlling 10%, 15%, or 20% of residential air-conditioning, calculated load steps could be injected into Positive Sequence Load Flow dynamic simulations. These load-driven effects were coupled with two classes of distributed generation ride-through to evaluate the potential for cascading outages. We found frequency deviations in the spring case far exceed the credible contingency event, leading to widespread distributed generation loss, while voltage depressions during the summer loading lead to widespread distributed generation loss and system separation.https://ieeexplore.ieee.org/document/9328163/Bulk power systemresidential air conditioningcoordinated command and controlcloud connecteddistributed generation |
| spellingShingle | Richard Kenyon Jeff Maguire Elaina Present Dane Christensen Bri-Mathias Hodge Bulk Electric Power System Risks From Coordinated Edge Devices IEEE Open Access Journal of Power and Energy Bulk power system residential air conditioning coordinated command and control cloud connected distributed generation |
| title | Bulk Electric Power System Risks From Coordinated Edge Devices |
| title_full | Bulk Electric Power System Risks From Coordinated Edge Devices |
| title_fullStr | Bulk Electric Power System Risks From Coordinated Edge Devices |
| title_full_unstemmed | Bulk Electric Power System Risks From Coordinated Edge Devices |
| title_short | Bulk Electric Power System Risks From Coordinated Edge Devices |
| title_sort | bulk electric power system risks from coordinated edge devices |
| topic | Bulk power system residential air conditioning coordinated command and control cloud connected distributed generation |
| url | https://ieeexplore.ieee.org/document/9328163/ |
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