Synchronization and Rebound Effects in Residential Loads
Increasing fuel prices and capacity investment deferral place an increasing demand for peak reduction from distribution level systems. Residential and commercial devices, such as HVAC systems and water heaters, are increasingly involved in load control programs, and their use may generate synchroniz...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Access Journal of Power and Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10606292/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592840470822912 |
|---|---|
| author | Nora Agah Eve Tsybina Viswadeep Lebakula Justin Hill Jeff Munk Helia Zandi |
| author_facet | Nora Agah Eve Tsybina Viswadeep Lebakula Justin Hill Jeff Munk Helia Zandi |
| author_sort | Nora Agah |
| collection | DOAJ |
| description | Increasing fuel prices and capacity investment deferral place an increasing demand for peak reduction from distribution level systems. Residential and commercial devices, such as HVAC systems and water heaters, are increasingly involved in load control programs, and their use may generate synchronization and rebound effects, such as artificial peaks caused by device optimization. While there have been concerns over device synchronization, few studies quantify the extent of this effect with numerical values. In this study, we attempt to investigate whether control efforts result in device synchronization or rebound effects. We focus on three clustering methods – Ward’s clustering, Euclidean K-means, and Density-based spatial clustering of applications with noise – to evaluate the extent of synchronization of a fleet of water heaters and HVAC systems in Atlanta, Georgia. Our findings show that synchronization and rebound effects are present in the neighborhood’s water heaters, but none were found in the HVAC systems. Further, high usage water heaters are more susceptible to synchronization and rebound effects. |
| format | Article |
| id | doaj-art-2082a31a351743f3ae426a88d0aa4ebf |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-2082a31a351743f3ae426a88d0aa4ebf2025-01-21T00:03:11ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011167668910.1109/OAJPE.2024.343238910606292Synchronization and Rebound Effects in Residential LoadsNora Agah0https://orcid.org/0000-0003-0271-1204Eve Tsybina1https://orcid.org/0000-0001-8131-1828Viswadeep Lebakula2https://orcid.org/0000-0001-5293-5914Justin Hill3https://orcid.org/0000-0003-3329-3367Jeff Munk4Helia Zandi5https://orcid.org/0000-0003-3966-7454Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USAOak Ridge National Laboratory, Oak Ridge, TN, USAOak Ridge National Laboratory, Oak Ridge, TN, USASouthern Company, Birmingham, AL, USANational Renewable Energy Laboratory, Golden, CO, USAOak Ridge National Laboratory, Oak Ridge, TN, USAIncreasing fuel prices and capacity investment deferral place an increasing demand for peak reduction from distribution level systems. Residential and commercial devices, such as HVAC systems and water heaters, are increasingly involved in load control programs, and their use may generate synchronization and rebound effects, such as artificial peaks caused by device optimization. While there have been concerns over device synchronization, few studies quantify the extent of this effect with numerical values. In this study, we attempt to investigate whether control efforts result in device synchronization or rebound effects. We focus on three clustering methods – Ward’s clustering, Euclidean K-means, and Density-based spatial clustering of applications with noise – to evaluate the extent of synchronization of a fleet of water heaters and HVAC systems in Atlanta, Georgia. Our findings show that synchronization and rebound effects are present in the neighborhood’s water heaters, but none were found in the HVAC systems. Further, high usage water heaters are more susceptible to synchronization and rebound effects.https://ieeexplore.ieee.org/document/10606292/Demand responsedirect load controlpeak shiftingwater heaterHVACsynchronization |
| spellingShingle | Nora Agah Eve Tsybina Viswadeep Lebakula Justin Hill Jeff Munk Helia Zandi Synchronization and Rebound Effects in Residential Loads IEEE Open Access Journal of Power and Energy Demand response direct load control peak shifting water heater HVAC synchronization |
| title | Synchronization and Rebound Effects in Residential Loads |
| title_full | Synchronization and Rebound Effects in Residential Loads |
| title_fullStr | Synchronization and Rebound Effects in Residential Loads |
| title_full_unstemmed | Synchronization and Rebound Effects in Residential Loads |
| title_short | Synchronization and Rebound Effects in Residential Loads |
| title_sort | synchronization and rebound effects in residential loads |
| topic | Demand response direct load control peak shifting water heater HVAC synchronization |
| url | https://ieeexplore.ieee.org/document/10606292/ |
| work_keys_str_mv | AT noraagah synchronizationandreboundeffectsinresidentialloads AT evetsybina synchronizationandreboundeffectsinresidentialloads AT viswadeeplebakula synchronizationandreboundeffectsinresidentialloads AT justinhill synchronizationandreboundeffectsinresidentialloads AT jeffmunk synchronizationandreboundeffectsinresidentialloads AT heliazandi synchronizationandreboundeffectsinresidentialloads |