eCite Digital Repository

Optimising low-voltage transformer tap settings in distribution networks

Citation

Paoli, JP and Brinkmann, B and Negnevitsky, M, Optimising low-voltage transformer tap settings in distribution networks, Proceedings of the 29th Australasian Universities Power Engineering Conference, AUPEC 2019, 26-29 November 2019, Fiji, pp. 1-6. (2019) [Refereed Conference Paper]


Preview
PDF
Restricted - Request a copy
368Kb
  

Copyright Statement

Copyright IEEE

Official URL: https://ieeexplore.ieee.org/document/9084570

DOI: doi:10.1109/AUPEC48547.2019.211792

Abstract

In this paper, a practical method of determining the optimal tap setting of no-load distribution tap-changing transformers is proposed. The uptake of distributed energy resources impacts the risk of distribution systems violating voltage constraints. Setting no-load transformer tap settings appropriately can mitigate some of this risk, but changing these taps requires an outage to the customer and must be infrequent. Hence, the optimisation of these tap settings must consider loading for at least a whole year to account for seasonal variation. An evolution strategy is used to determine these settings based on an average loading case. The performance of this method is measured with a normalised objective function. Monte Carlo simulations are used to determine the probability that the network voltages on the secondary side of the transformer terminals violate the required voltage constraints once this optimal set of taps is established. This algorithm was tested on a real distribution feeder, and generates a sufficientlyoptimal set of taps without significant computation time. Furthermore, it can provide information about areas of a given distribution system that may require augmentation from a network planning perspective as more distributed resources are gradually introduced.

Item Details

Item Type:Refereed Conference Paper
Keywords:no-load tap-changing transformers, distribution network utilisation, evolution strategy, optimisation, network planning
Research Division:Engineering
Research Group:Electrical engineering
Research Field:Electrical energy generation (incl. renewables, excl. photovoltaics)
Objective Division:Energy
Objective Group:Energy efficiency
Objective Field:Industrial energy efficiency
UTAS Author:Paoli, JP (Mr Joshua Paoli)
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:137464
Year Published:2019
Web of Science® Times Cited:1
Deposited By:Engineering
Deposited On:2020-02-14
Last Modified:2021-01-25
Downloads:0

Repository Staff Only: item control page