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A new approach to voltage management in unbalanced low voltage networks using demand response and OLTC considering consumer preference

Rahman, M.M., Arefi, A., Shafiullah, GM. and Hettiwatte, S. (2018) A new approach to voltage management in unbalanced low voltage networks using demand response and OLTC considering consumer preference. International Journal of Electrical Power & Energy Systems, 99 . pp. 11-27.

Link to Published Version: https://doi.org/10.1016/j.ijepes.2017.12.034
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Abstract

Voltage unbalance and magnitude violations under normal operating conditions have become main power quality problems in many low voltage (LV) distribution networks. Maintaining the voltage level in an LV network within the standard limits is the main constraining factor in increasing the network hosting ability for rooftop photovoltaic (PV). This study presents a new effective method for voltage management in unbalanced distribution networks through the implementation of optimal residential demand response (DR) and on-load tap changers (OLTCs). The proposed method minimises the compensation costs of voltage management (cost of DR and network loss), while prioritises the consumer consumption preferences for minimising their comfort level violations. A modified particle swarm optimisation algorithm (MPSO) is utilised to identify the optimal switching combination of household appliances and OLTC tap positions for the network voltage management. The proposed method is comprehensively examined on a real three-phase four-wire Australian LV network with considerable unbalanced and distributed generations. Several scenarios are investigated for improving the network voltage magnitude and unbalance considering individual and coordinated operations of DR and OLTCs (three phase tap control and independent phase tap control). Simulation results show that the coordinated approach of DR and OLTC, especially, DR integrated with OLTC independent phase tap control effectively improves the network voltage and increases the PV hosting capacity.

Publication Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Elsevier Limited
Copyright: © 2017 Elsevier Ltd.
URI: http://researchrepository.murdoch.edu.au/id/eprint/39927
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