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Generalized approach to assess and characterise the impact of solar PV on LV networks

Almeida, Dilini, Abeysinghe Herath Mudiyanselage, Sathsara Madumali, Ekanayake, Mervyn Parakrama, Godaliyadda, Roshan Indika, Ekanayake, Janaka and Pasupuleti, Jagadeesh 2020. Generalized approach to assess and characterise the impact of solar PV on LV networks. International Journal of Electrical Power and Energy Systems 121 , 106058. 10.1016/j.ijepes.2020.106058

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Abstract

Many of the studies which analyse the impact of solar photovoltaic (PV) on low voltage distribution networks (LVDNs) are based on sample networks or synthetic networks such as IEEE test cases. Therefore, the conclusions drawn in these studies are often specific to the study cases, limiting their applicability for generalization. This paper proposes a methodology that can generate a multitude of network topologies that have statistically similar characteristics to a selected cohort of existing networks. Furthermore, a stochastic evaluation based on the Monte Carlo technique is utilized to analyse the impacts of solar PV on generated LVDN models. A case study was conducted on ten networks that were generated statistically similar to existing urban networks to allow for a more generalized study. A total of 1000 Monte Carlo simulations for each network was carried out to accurately identify the most representative parameters that reflect the voltage rise and voltage unbalance in the considered LVDN cohort. Two parameters, namely: the momentum of the PV capacity and the mean absolute deviation, were identified from the case studies as the most representative parameters to analyse the impact of voltage rise and voltage unbalance factor. Thereafter a generalized framework based on these two parameters was derived to determine the impact of PV connections to the selected cohort of networks. This framework facilitates an efficient process for the utility supplier to determine the impact of incorporating new PV connections without the need for extensive studies.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0142-0615
Date of Acceptance: 29 March 2020
Last Modified: 24 Sep 2020 10:17
URI: http://orca.cf.ac.uk/id/eprint/135055

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