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An improved statistical time series method for voltage violation quantification in residential distribution network with small wind turbines and batte

Author(s): Chao Long*, Donald M. Hepburn, Mohamed E.A. Farrag, Chengke Zhou
School of Engineering and Built Environment, Glasgow Caledonian University, Cowcaddens Road, UK. G4 0BA
International Journal of Smart Grid and Clean Energy, vol. 3, no. 1, January 2014: pp. 29-36
ISSN: 2315-4462
Digital Object Identifier: 10.12720/sgce.3.1.29-36

Abstract: The probabilistic power flow method has previously been used for voltage violation quantification in residential distribution networks (RDNs) with small wind turbines (SWTs) and battery electric vehicles (BEVs). An improved statistical time series approach is developed in this paper to account for factors which impact on system conformity. In addition to consideration of variations in wind speed over time and BEV charging, using a statistical time series approach, the novel method takes into account variations in system load caused by the randomness of load switching by customers. The method has been applied to a generic UK distribution network. Results show that the proposed method provides a closer indication of daily probability distribution of voltage violations in a RDN, based on physical data, than the previous statistical time series approach.
Keywords: Battery electric vehicles, probability distribution, small wind turbines, time series, voltage violation

Full Paper.pdf

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