Introduction

This interactive tool simulates a set of (low voltage) charging profiles for either Charging Stations or EVs. The simulation produces a set of n charging profiles for either public, work or home locations. It gives insight into the expected impact on the low-voltage grid for a certain scenario. By entering desired simulation properties such as the number of profiles, max EV power, and max CS power, the model will simulate a set of profiles. By using real world charging session data, charging profiles are being simulated while correcting for distribution over time and seasonality.

The tool includes two different kinds of power charging profiles, a regular profile and a "Smart Charging" profile that aims to reduce grid load during a predefined peak period. For the regular profile a constant available power of 11 kW per charging point and 17,25 kW per charging station is assumed throughout the day. In this scenario an EV starts charging immediately when connected. In the standard (static) Smart Charging profile, the available power is reduced from 11 kW to 4 kW between 5 p.m. and 11 p.m., whereafter the available power is increased again with 1 kW per hour until it reaches 11 kW at 6 a.m. However, the Smart Charging profile is configurable to meet the specific demands of the target location.

The charging profiles are based on the output of a simulation model. The first step of the model is to determine a yearly demand. This step is different between EVs and charging stations. For the charging profiles for EVs, a yearly energy demand is sampled based on historical data and predictions (from previous outlooks) for kilometrage and EV usage. For charging profiles on charging station level, a yearly energy demand is based on the distribution of yearly energy demand of charging stations as seen in charging sessiondata. For each type of profile, the yearly energy demand is distributed over all weeks of the simulated year. Then, for each week, charging sessions are sampled that fit the sampled power demand of that week. Each sampled session is then converted into a sequence of intervals spanning the start to end time of the session and the predicted power draw is added to each interval. Finally, corrections are applied to all intervals in case charging station capacity is exceeded due to connection limits or smart charging. This is done by moving overcapacity to later intervals (while vehicles are still connected). This process is repeated for the number of profiles that is requested.

Knowledge and innovation center ElaadNL researches and tests smart and sustainable charging of electric vehicles.

Low-Voltage Profiles Generator is licensed under CC BY-NC-ND 4.0

Introduction

Yearly Demand (kWh)

Yearly Demand (kWh)

Urbanization

Annual charging profile (aggregated)

Daily charging profile (Percentiles)

Charging point capacity per interval