A Ljubljana demonstration involving Avant Car and Kolektor sETup used automated scheduling to shift electric-vehicle charging into lower-cost electricity periods while keeping vehicle charging requirements met. The trial ran for two months and covered six charging stations. It relied on day-ahead electricity-price signals to determine when vehicles should charge.
Before optimisation, actual charging costs were 18.9% above the theoretical least-cost scenario. During the demonstration, the gap narrowed to 5.99%, an improvement of around 68% relative to alignment with the cheapest available charging periods.
From fleet charging to controllable electricity demand
The commercial significance described in the project extends beyond cheaper electricity. EV fleets are characterised by large electrical loads, predictable periods when vehicles are connected, and flexibility over when charging occurs. A vehicle may require a certain amount of electricity before its next journey, but it does not typically need to consume every kilowatt-hour immediately after plugging in.
The time between connection and departure creates a flexibility window that software can monetise. A fleet-management platform can determine which vehicles need immediate charging, which can wait, and how much aggregate consumption can be moved between different electricity-market periods. Charging is therefore presented as an optimisation problem rather than a simple transaction between charger and vehicle.
For fleet operators, the immediate benefit is reduced electricity cost. For aggregators and electricity suppliers, the larger opportunity is combining hundreds or thousands of chargers into a controllable portfolio. A fleet with hundreds of vehicles could potentially move several megawatts of electricity demand from one period to another without changing the transport service delivered to customers.
Market signals, network constraints and scaling issues
The Slovenian demonstration focused primarily on day-ahead price optimisation, shifting charging sessions toward less expensive periods, particularly after midnight. The project reported that automated optimisation narrowed the difference between actual charging cost and the theoretical optimum.
Larger-scale adoption is described as creating a risk if thousands of vehicles receive the same price signal and move charging into the same cheap hour, potentially creating a new demand peak. It is also noted that what is optimal for the electricity consumer may not be optimal for the network.
The next generation of smart charging is described as requiring more than time-of-use tariffs. Charging algorithms are expected to consider at least two signals simultaneously: the wholesale price of electricity and the physical condition of the local network. A third signal may come from balancing or flexibility markets, allowing an EV fleet to respond differently depending on which service has the highest value.
In one hour, an EV fleet could charge more aggressively when wholesale electricity is inexpensive. Later it could reduce charging to relieve a distribution constraint, or adjust demand when a system operator needs balancing flexibility. This approach is described as turning a charging portfolio into a virtual power-system asset.
Centrally managed fleets and infrastructure procurement
The business model is described as particularly attractive for centrally managed fleets such as car-sharing operators, delivery companies, municipal fleets, taxis, corporate vehicles, buses and logistics companies. These operators are said to typically have better information about vehicle schedules than individual residential customers, which supports forecasting of charging flexibility.
A fleet operator may know which vehicles must leave at 06:00, which will remain parked until noon, and how much energy each one requires. An optimiser can use those constraints to determine the cheapest or most valuable charging schedule automatically. In this setup, the physical charger is described as only one part of the service.
The value may also sit in the software layer controlling thousands of chargers. This creates opportunities for companies providing fleet-management systems, aggregation platforms, automated trading, charging optimisation and electricity-market access. It could also change how companies procure charging infrastructure by shifting evaluation from hardware price and maximum power toward a platform’s ability to optimise electricity cost and earn flexibility revenue over an equipment’s lifetime.
Flexibility services before V2G
Vehicle-to-grid technology could extend opportunities by allowing electricity to flow back from EV batteries. However, bidirectional charging is described as not necessary for the first stage of the market because controlling when vehicles consume electricity already creates substantial flexibility.
The Slovenian demonstration is described as small compared with the scale required for a liquid national flexibility market. The results indicate that smart charging of a shared fleet can reduce the gap to optimal charging costs under real operating conditions.
As EV adoption grows, unmanaged charging risks becoming another source of peak electricity demand. Managed fleets are presented as offering an alternative where electric vehicles become one of the largest controllable loads for power systems.
For electricity companies and fleet operators, it is stated that the value of an EV fleet may increasingly include not only kilometres travelled but also flexibility created during hours when vehicles are standing still.