Charging of plug-in electric vehicles: Stochastic modelling of load demand within domestic grids

On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production

“…[2,[40][41][42][43][44][45][46][47][48][49][50][51][52]. EV charging can be modeled using predefined time periods for charging [45,49], charging only after all trips of the day made with the vehicle [42,43,46,48], charging only at home [2] and generally opportunistic charging at various locations [5].…”

“…[2,[40][41][42][43][44][45][46][47][48][49][50][51][52]. EV charging can be modeled using predefined time periods for charging [45,49], charging only after all trips of the day made with the vehicle [42,43,46,48], charging only at home [2] and generally opportunistic charging at various locations [5]. Models for EV charging can be deterministic [43,44], stochastic with the use of Monte Carlo simulations [42] or Markov-chains [2,46,53] or distributions [5,48,52,[54][55][56].…”

Statistical modeling of Electric Vehicle electricity consumption in the Victorian EV Trial, Australia

“…However, some researchers recognized the importance of statistical modeling to explicitly accounting for the inherent uncertainties (e.g., Pina et al (2014), Soares et al (2011)). Based on the survey data in Tehran, Iran, Pashajavid and Golkar (2012) used probabilistic modeling to consider home arrival time, daily travelled distance, and home departure time as random variables. Similarly, Trovao and Jorge (2011) modeled EV's power demand and the number of changing events as normal distributions, Qian et al (2011) fitted the daily travel distance and EV battery's initial state-of-charge to lognormal distributions, and Alizadeh et al (2014) modeled PHEV charge duration as a clipped lognormal distribution.…”

Sensitivity of electric vehicles demand profile to the batteries departure state-of-charge

“…Regarding various uncertainties in PEVs, demand modeling of this vehicular load is carried out via probabilistic and stochastic methodologies [20][21][22]. The random variables considered in demand estimation of PEVs are their arrival time, travelled distance and departure time.…”

“…Therefore, multivariate Probability Density Functions (PDF) should be utilized in order to consider this correlation. In case the associated RVs do not follow identical distributions, copula functions are applied to fit the appropriate joint distributions to the datasets [22][23][24][25].…”