Slovenia is using smart meters, dynamic network tariffs and automated electric-vehicle charging to test how electricity consumers can become flexible power-market assets. Recent pilots indicate that EV charging can be shifted to cheaper periods while maintaining fleet operations. The national regulator has also flagged a risk where thousands of customers could respond to the same price signal at the same time, creating a new demand peak.
Pilots show shifting EV load and the risk of synchronized demand
Slovenian pilots involving Avantcar and Kolektor sETup reported that fleet charging can be optimized against electricity-market conditions while keeping vehicle availability. The approach relies on the difference between when a vehicle connects to a charger and when it must be ready, creating a window for moving electricity demand. A vehicle connected for eight hours may require only two or three hours of actual charging, and aggregated across hundreds or thousands of vehicles this can form a significant flexible portfolio.
For fleet operators, the immediate effect reported in the pilots is lower energy procurement costs. For aggregators, the same flexibility could be offered into balancing or local distribution-network markets where regulations permit. Slovenia’s national flexibility assessment highlights that simple price optimization can be limited if cheaper electricity or network tariffs occur after a certain hour and automated chargers react simultaneously.
In that scenario, thousands of vehicles could start charging together and create a new night-time peak rather than reducing system pressure. The same synchronization risk is described for heat pumps, electric boilers and other automated loads. The shift in challenge is from moving demand away from peak hours to preventing too many flexible devices from changing consumption in the same direction at the same time.
Dynamic network tariffs and smart-meter data for location-aware optimization
Dynamic network pricing is presented as one possible solution to add grid constraints to the signals received by customers. Traditional electricity tariffs are described as primarily indicating when electricity is expensive, while more sophisticated network tariffs can also indicate when parts of the grid are constrained. This distinction becomes relevant as distributed solar, EVs, heat pumps and other flexible assets expand across distribution networks.
The source describes how one megawatt of additional consumption may be beneficial in a location with high local solar production and available network capacity, while the same megawatt could worsen congestion elsewhere. As a result, future optimization is framed as depending not only on when electricity is cheapest but also on when and where the power system has capacity for additional consumption. This is described as a fundamentally different market requirement.
Slovenia’s advanced metering infrastructure provides energy-service providers with consumption data needed to address these location- and capacity-related questions. Detailed meter data can show when customers consume electricity, how predictable that consumption is, and how much could potentially be moved. For industrial users this can involve identifying flexible pumps, compressors, cooling systems or production processes; for commercial buildings it can involve heating, cooling or ventilation; for EV fleets it involves connection duration and required energy before departure.
Software can combine hundreds of individual profiles into a portfolio large enough to participate in electricity markets. The commercial layer described in the source includes meter-data analytics, automated demand response, consumption forecasting and flexibility verification. In this model, the valuable infrastructure extends beyond metering hardware to software that converts meter readings into an asset the system can dispatch.
Automated flexibility using wholesale prices, network tariffs and balancing revenues
A longer-term model described for Slovenia involves platforms that evaluate multiple inputs at once, including wholesale electricity prices, network tariffs, local grid conditions and balancing-market revenues alongside each vehicle’s charging requirement. An industrial energy-management system is described as performing similar calculations for production equipment. In both cases, customers set operational boundaries within which consumption decisions are made.
The source describes software as deciding when electricity should be consumed inside those limits, turning flexibility into an automated service rather than a behavioural response to cheaper night-time tariffs. Aggregators are also described as combining thousands of small assets, forecasting availability and selling resulting flexibility to parties that need it. Buyers could include suppliers as well as transmission operators and distribution companies.
Broader Southeast European context for smart meters and controllable loads
The model is described as having implications beyond Slovenia because Southeast European countries are investing in smart-meter infrastructure while EV charging, electric heating and distributed generation increase flexible demand connected to distribution networks. Most investments are still discussed in terms of equipment deployment rather than market design. The larger question identified is what markets can be built once the equipment exists.
The next stage described centers on interaction between smart-meter data, dynamic tariffs and automated consumption. EVs are identified as an early use case, with the same infrastructure expected to coordinate commercial buildings, industrial processes, heat pumps, electric boilers and other controllable loads. The business model described relies on existing assets with limited new generation capacity.
The remaining requirement described is a digital layer able to determine when controllable assets should consume electricity and convert resulting flexibility into revenue.