Author ORCID Identifier

https://orcid.org/0000-0002-5839-6779

Date Available

12-18-2025

Year of Publication

2025

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Engineering

Department/School/Program

Electrical Engineering

Faculty

Yuan Liao

Abstract

Rising power demand calls for electric distribution systems to manage peak load. One option is reducing feeder voltage, which lowers voltage-dependent load demand and may also reduce energy usage. The technique, known as Conservation Voltage Reduction (CVR), may operate independently or within a volt/var control system. This dissertation examines CVR factor calculation using measurements collected at the substation, and proposes a curve-fitting and artificial neural network method to estimate active power losses using input active power, reactive power, and substation voltage. As utilities integrate more inverter-based resources (IBRs) to support increasing demand, rapid voltage fluctuations arise due to the intermittent behavior of these resources. Conventional mechanical devices such as on-load tap changers (OLTCs) and capacitor banks (CBs) would require frequent switching to mitigate these fluctuations, increasing operational and maintenance costs. Smart inverter functions within IBRs offer a more flexible means of voltage control. Accordingly, this dissertation introduces a dual layer optimization method that leverages IBR capabilities to limit rapid voltage changes, reduce device operations, lower power demand, and decrease inverter aging associated with reactive power support. With growing system size and complexity, reliable protection is essential to ensure safe operation. Testing relays directly on the network is costly and offers limited fault scenarios. Hardware-in-the-Loop (HIL) testing provides an efficient alternative by enabling numerous simulated faults in a realistic closed-loop environment. This dissertation presents HIL testing of protective relays using a sample distribution system modeled on a Real Time Digital Simulator (RTDS), offering guidance for replicating or extending the work.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2025.608

Funding Information

This PhD dissertation was supported by the following organizations:

  1. Louisville Gas & Electric and Kentucky Utilities (LG&E and KU), part of the PPL Corporation family of companies, from year 2021 to 2024.
  2. Office of Naval Research, USA, under award number N00014-21-1-2972, for year 2023.
  3. U.S. Department of Energy’s Office of Electricity under the award Number DE-OE0000989 for year 2025.

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