Tool
EV Charging Load Simulator
Monte Carlo simulation of a real EV fleet's charging behavior — combined with your site's existing load — to get P50/P90/P95/P99 peak demand, overload probability, and a safe fleet size at your grid connection.
In plain terms
A more realistic version of the Capacity Planner. Instead of one estimate, it imagines thousands of different days — each time, every car arrives at a random time with a random battery level and stays for a random duration, like rolling dice — then adds up what total electricity demand would look like. Doing this thousands of times shows not just a typical day, but how bad the rare bad days could get.
Fleet & site
01 / inputSets typical arrival/dwell/SOC ranges below — all adjustable under "Advanced".
Converted to 285 kW at cos φ = 0.95.
A simplified, repeating typical-day shape scaled to this peak — not measured data. For a real site, run it through the Load Profile Analyzer first.
Dynamic load management
Caps total EV draw to available headroom each interval
Configure the fleet and site on the left, then run the simulation to see peak demand percentiles, overload probability, and a safe fleet size.
How this is built: each simulated day, every vehicle independently samples an arrival time, dwell duration and starting state of charge from the triangular distributions set above, then charges from arrival until it either reaches its target state of charge or departs — whichever comes first. A day is modeled as one repeating 24-hour cycle (a session starting at 22:00 with a 9-hour dwell wraps to depart at 07:00 on the same cycle), not a specific calendar date, so this shows a representative day's risk, not week-to-week or seasonal variation. The "safe fleet size" figure is found by re-running the simulation at different vehicle counts (fixed seed, so the search is comparing like with like) until the 95th-percentile peak just fits the stated capacity — P95, not the absolute worst case, is a standard, risk-informed choice for this kind of planning, not a guarantee that overload is impossible.
Further reading
- M. Muratori, "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, 2018 — stochastic, arrival/dwell/SOC-based modeling of uncoordinated EV charging demand, the same general approach used here.
- S. B. Bollerslev, P. B. Andersen, T. V. Jensen, M. Marinelli, A. Thingvad, L. Calearo & E. Weckesser, "Coincidence Factors for Domestic EV Charging From Driving and Plug-In Behavior," IEEE Transactions on Transportation Electrification, 2022 — real metered EV charging behavior data, also the source of the coincidence-factor curve used in GRIDRA's EV Charging Capacity Planner.