CVR Study and Active Power Loss Estimation Based on Analytical and ANN Method

Gaurav Yadav, Yuan Liao, Nicholas Jewell, Dan M. Ionel

Research output: Contribution to journalArticlepeer-review

Abstract

Conservation through voltage reduction (CVR) aims to reduce the peak load and energy savings in electric power systems and is being deployed at various utilities. The effectiveness of the CVR program depends on the load characteristics, i.e., the sensitivity of the load to voltage variation, and voltage regulation device settings. In the current literature, there is a lack of discussion on the CVR factor calculation using different measurements, and there is a lack of method for active power loss estimation using substation measurements. This paper provides insights into CVR factor calculation based on the measurements captured at the substation and those at the load location. This paper also proposes a new method based on curve fitting and artificial neural network to estimate the active power loss using input active power, input reactive power and input voltage at the sub-station. The CVR comparison study conducted in this paper helps in understanding the factors af-fecting CVR factor and may provide guidance in CVR implementation and impact assessment. The proposed loss estimation method sheds light on the impacts of CVR in terms of load and loss reduc-tion. The results based on simulation studies using the IEEE 13-bus and 34-bus systems are reported in this paper, noting that the proposed methods are applicable to larger systems, as long as the required measurements at the substation are available. Future research includes testing and refining the methods using large IEEE and utility distribution systems and considering the stochastic nature of the CVR factor with changing load and voltage regulator control schemes.

Original languageEnglish
Article number4689
JournalEnergies
Volume15
Issue number13
DOIs
StatePublished - Jul 1 2022

Bibliographical note

Funding Information:
Funding: This research was funded by LG&E and KU, Louisville, Kentucky, USA.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • ZIP load model
  • artificial neural network
  • conservation through voltage reduction
  • curve fitting
  • electric power systems
  • network power loss

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Fuel Technology
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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