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EVERGi > EV charging hub design tool 


EV charging hub design tool


 

Figuur EV charging hub design toolThe EV charging hub design tool is a python-based framework that allows to easily calculate optimal numbers of EV chargers and solar installation size for small (a couple of chargers) and large (up to hundreds of chargers) charging hubs. The tool optimizes the required number of chargers and size of the PV installation based on the mobility needs and technical constraints of your site or business.

The tool displays figures and graphs for the defined objectives (such as total-cost of ownership (TCO) and net present value (NPV)) which are easily interpretable and serve to make decisions on investments for charging hubs and PV installations.

1. Simulation tool

Coming soon

2. What it does for you

The design tool will help you:

  • Select the right number of chargers (cost, serviceability) for your mobility demand

  • Select the right (cost-effective) size of PV installation

  • Understand energy costs of introducing electric vehicle charging into your system

  • Quantify the benefits of smart charging

3. How does it work?

The process works in three steps:

  • Step 1: mapping of the current situation of your business or site. This includes the mobility needs, technical constraints as well as geospatial information (e.g. location, surfaces). This information is used to set-up the simulation and optimization of the design tool. 

  • Step 2: optimization algorithms run iterative simulations for various configurations of the charging hub based on pre-defined design variables (such as number of chargers and size of the PV installation) to come to an optimal solution based on the pre-defined objectives

  • Step 3: results are stored and displayed in figures and graphs which are easily interpretable

The unique set-up of this tool is the combination of multiple techniques to calculate accurate cost calculations:

  • The integration of a Charging Session Simulator that produces charging sessions distributed in time, energy, and detention time using profiles specifically for your mobility demand.

  • The integration of the smart charging scheduler that allows to include realistic achievable smart charging into the simulation

The improvement in accuracy is achieved by using actual time-based (15 minute resolution) power profiles of all elements (vehicles, building, PV) instead of using averages. This is essential with regards to accurate values for self-consumption and peak powers and all associated costs. In comparison to linear optimization techniques, this optimization set-up is much closer to reality by using actual 15 control decisions instead of perfect foresight optimal decision.

4. Results in practice

In the figures and tables below you can follow the 3 step process of the EV charging hub design tool.

Step 1: mapping the current situation and construction of driver profiles and transition scenarios

Using clustering techniques on arrival time, detention time, and energy demand (Figure 4) driver profiles are constructed (Table 1) specific for your situation.

Figuur EV smart charging scheduler
Table 1
EV smart charging scheduler
Figure 4

Transition scenarios from ICE (internal combustion engine) to EV are drafted in correspondence to your plans (Table 2)

 

Figuur EV charging hub design tool
Table 2

 

Step 2: the tool applies state-of-the-art optimization techniques to calculate the result.

Step 3: The tool generates diagrams to evaluate the cost-effectiveness of your solution. Below you find the results for scenarios 1 and scenario 3 of table 2.

Figuur 5 EV charging hub design tool
Figure 5
Figure 6 EV charging hub design tool
Figure 6

Figure 5 and Figure 6 show the TCO for scenario 1 and scenario 3 respectively with and without applying smart charging with the SCS. The TCO includes the investment cost of the EV chargers and PV installation, as well as all the energy costs over a period of 15 years. The diagrams show decreasing TCO with increasing sizes of PV until a treshold size where costs rice again due to overinvestments in PV. Both figures show applying smart charging on top of installing PV allow additional savings of similar magnitude and optimal PV size increases, which benefits the self-consumption and, ultimately, your environmental footprint as well (Figure 7 and Figure 8).

Figure 7 EV charging hub design tool
Figure 7
Figure 8 EV charging hub design tool
Figure 8

 


EV smart charging scheduler


 

Figuur EV smart charging schedulerThe 'Electric Vehicle Smart Charging Scheduler', developed by the EVERGI team of the Vrije Universiteit Brussel, is a predictive power control algorithm that has a first application in the operational design and management of uni- & bi-directional charging for electric vehicles. 

The Scheduler defines the quarter-hourly charging power for individual chargers on sites equipped with PV, various loads and energy storage. It's a centralized control algorithm that optimizes the charging cost and peak powers. The scheduler uses a model predictive control (MPC) algorithm and forecasted values for PV production, aggregated load, and charging demand.

1. What it does for you

The scheduler will help you to:Figuur EV smart charging scheduler

  • Reduce your energy bill through:

    • Exploiting variable energy prices

    • Avoiding peak powers and costs

    • Increasing self-consumption of your solar installation

  • Avoid grid connection reinforcements or grid connection overloading

  • Increase self-sufficiency

while maintaining driving range requirements for the driver.

2. How does it work?

The smart charging scheduler (SCS) uses driver inputs (additional driving range and departure time) combined with power measurements of your electricity meter and solar installation to calculate the charging power of your EV chargers.

The SCS has a build-in EV model for charging and uses state-of-the-art techniques to predict the future behavior of your solar installation and building to optimize the charging process for your EV. Self-learning algorithms will adapt predictions with measurements of behavior over time.

3. Results in practice

We highlight a common case of a medium sized office building with a PV installation (with 57kWp solar) where we add 12 EV chargers (with maximum 22kW charging power).

Figure 1 EV smart chagring scheduler
                       Figure 1

Average daily power consumption will increase for an office building with PV when adding electric vehicles

Adding electric vehicles to an office building with PV can increase morning peak consumption without exploiting the potential of the PV generation during the day (Figure 1). This will result in additional energy consumption from the grid and higher peak cost.

By applying the smart scharging scheduler, peak powers demands and energy consumption from the grid can be reduced through spreading your chargers over time in an intelligent way and maximizing self-consumption. This reduces your energy bill (Figure 2) and avoids overloading of your grid connection (Figure 3). If vehicle-to-grid (V2G is at hand, bi-directional power flow between your vehicle enhances that effect.

Figure 2 EV smart charging scheduler                  Figure 2
Figure 3 EV smart charging scheduler  Figure 3

 

News

March 23, 2022
News item 1 EV smart charging scheduler Smart Village Lab

Live testing of the EVERGi smart charging scheduler at the Smart Village Lab of the Green Energy Park!