ev_profiles#

popo.profiles.synthetic.ev_profiles(stats, n, rng, index, charger_kw=7.4, efficiency=0.9)#

One charging profile per EV, in kW, sampled from session statistics.

Each EV charges on a randomly drawn subset of days. On a charging day the plug-in time and delivered energy are drawn from the empirical distributions and the vehicle draws a constant charger_kw until the energy is met, which is how an uncontrolled domestic charger behaves.

Parameters:

  • stats (EVStats) – Session statistics to sample from, e.g. from ev_stats().

  • n (int) – Number of vehicles to generate.

  • rng (Generator) – Seeded generator.

  • index (DatetimeIndex) – Target calendar; must be on a half-hourly grid (48 steps/day).

  • charger_kw (float) – Constant charging power once plugged in.

  • efficiency (float) – Charger efficiency; delivered energy is divided by this to get energy drawn from the grid.

Return type:

DataFrame

Returns:

A frame of shape (len(index), n), columns EV1…``EVn``, in kW.

On this page