Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load

Paevere, Phillip; Higgins, Andrew; Ren, Zhengen; Horn, Mark E. T.; Grozev, George; McNamara, Cheryl

doi:10.1007/s11625-013-0235-3

Cited by 52 publications

(23 citation statements)

References 20 publications

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“…Since electric vehicle uptake is projected to be much greater in urban than in rural settings [8], we chose a typical suburban network in northern Melbourne containing 112 houses as a case study. An LV grid model was developed in MATLAB SimPowerSystems.…”

Section: B Grid Model and Load Flowmentioning

confidence: 99%

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

Hoog

Thomas

Müenzel

et al. 2013

2013 IEEE Power &Amp; Energy Society General Meeting

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Abstract-The expected rise of electric vehicles will lead to significant additional demand on low voltage (LV) distribution systems. Uncontrolled charging could lead to problems such as thermal overload of transformers and lines, voltage deviation, harmonics, and phase unbalance. We propose two electric vehicle charging algorithms, one centralized and one distributed, and compare their performance in simulations that use real vehicle data, on a model based on a real LV network in northern Melbourne, Australia. Our experiments confirm that the locations of the vehicles in the network are an important factor in predicting adverse effects. Furthermore, our coordinated charging solutions allow penetrations of electric vehicles approximately 3-6 times higher than is possible using uncoordinated charging, in our network.

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Section: B Grid Model and Load Flowmentioning

confidence: 99%

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

Hoog

Thomas

Müenzel

et al. 2013

2013 IEEE Power &Amp; Energy Society General Meeting

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“…Clement-Nyns et al [7] base their analysis on extrapolation of non-EV car usage in Belgium. Paevere et al [8] model the spatio-temporal impact of EV load based on a linked suite 80 of models of future EV uptake, their travel and charging/discharging models.…”

Section: Related Workmentioning

confidence: 99%

Quantitive analysis of electric vehicle flexibility: A data-driven approach

Sadeghianpourhamami

Refa

Strobbe

et al. 2018

International Journal of Electrical Power & Energy Systems

156

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“…Other significant EV trials were conducted in Victoria and Western Australia. Several organisations investigated different aspects of integration of EVs, including the CSIRO Electric Driveway project (Paevere et al 2014). …”

Section: The Physical Network Datasetsmentioning

confidence: 99%

Knowledge-Mining the Australian Smart Grid Smart City Data: A Statistical-Neural Approach to Demand-Response Analysis

Motlagh

Foliente

Grozev

2015

Lecture Notes in Geoinformation and Cartography

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Large scale field trials of smart grid technologies provide important insights as they capture the complex interdependencies of all the key variables, including consumer behaviours, which are needed for their effective evaluation. We present the Australian Smart Grid Smart City program and describe its big data using a narrative approach to hasten understanding and further analyses by others. Then we present a novel statistical-neural approach to maximise knowledge extraction from large datasets of diurnal load profiles, and demonstrate its use in evaluating the effectiveness of two cost-reflective product offerings, a Network-type and a Retail-type product bundle. The methods of analyses include Principal Component Analysis and Self-Organising Mapping. The results for the mid-winter electricity consumption profiles of participating households in July 2013 in New South Wales showed consumption behaviour changes with up to 12 % reduction in relative peak demand at 700 households who accepted the offerings compared to the control group. The resultant load factor of the high consuming outliers improved by about 18 % under demand-response compared to the control group. The featurebased classifier also revealed which behavioural components change due to users' demand-response activities; results compared favourably with third party consumer survey results.O. Motlagh (&) Á G. Foliente Á G. Grozev CSIRO Land and Water,

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Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load

Cited by 52 publications

References 20 publications

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

Electric vehicle charging and grid constraints: Comparing distributed and centralized approaches

Quantitive analysis of electric vehicle flexibility: A data-driven approach

Knowledge-Mining the Australian Smart Grid Smart City Data: A Statistical-Neural Approach to Demand-Response Analysis

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