Archived
This content is available here for research, reference, and/or recordkeeping.
Abstract
With significant increase in EV adoption expected in the near future and the associated impacts on power systems, the effect of volt/var optimization (VVO) as the next step to conservation voltage reduction (CVR), requires reevaluation. Implementation of a cluster-based VVO control strategy employs a novel approach with machine learning (ML) load forecasting to reduce device adjustments through k-means clustering of contiguous time steps with similar active power load in which a single adjustment would be sufficient. The cluster-based VVO method is tested on a complex real world utility distribution feeder with 2,018 nodes, 8.65MW peak load, 9 capacitor banks (CBs), and a load tap changer (LTC) at the substation transformer. Performance of the cluster-based VVO method for the circuit with high forecasted EV penetration is tested with comparison to a baseline case. Total reduction in energy consumption of 1.7% and 1.9% in the expected range of 1-4% with minimal tap changes over 24 hours was achieved by the cluster-based VVO method with and without EV charging, respectfully.
Document Type
Conference Proceeding
Publication Date
Summer 6-2025
Digital Object Identifier (DOI)
10.1109/ITEC63604.2025.11097985
Repository Citation
Poore, Steven B.; Fischer, Grant M.; Alden, Rosemary E.; Jones, Evan S.; Patrick, Aron; and Ionel, Dan M., "Cluster-based Volt/Var Optimization on a Utility Distribution Feeder with Forecasted EV Penetration" (2025). Electrical and Computer Engineering Graduate Research. 33.
https://uknowledge.uky.edu/ece_gradpub/33
Notes/Citation Information
Poore, S. B., Fischer, G. M., Alden, R. E., Jones, E. S., Patrick, A., and Ionel, D. M., "Cluster-based Volt/Var Optimization on a Utility Distribution Feeder with Forecasted EV Penetration," Proceedings, IEEE Transportation Electrification Conference & Expo (ITEC), Anaheim, CA, doi: 10.1109/ITEC63604.2025.11097985, 5p (Jun 2025)