Slovenia is using smart meters, dynamic network tariffs and automated electric-vehicle charging as a test bed for flexibility-market participation. Recent pilots indicate that EV charging can be shifted to cheaper periods while maintaining fleet operations. The national regulator has also flagged a risk that simultaneous responses to the same price signal could create a new electricity-demand peak.
From time-of-use signals to coordinated consumption
Slovenia’s regulator-identified challenge is pushing market design beyond conventional time-of-use tariffs. The shift being tested is a model where consumption responds at the same time to electricity prices, network conditions and operational requirements. The commercial focus is expanding from selling electricity to selling optimisation decisions for when vehicles charge, when buildings consume electricity, and when industrial equipment operates.
Suppliers, aggregators and energy-management companies can use optimisation services where regulations allow. Slovenia’s infrastructure base includes widespread smart-meter deployment that provides granular consumption information. That data can be used to establish customer baselines, forecast flexible demand and verify whether agreed reductions or shifts in electricity consumption occurred.
EV fleets as an early flexibility portfolio
Pilot results involving Avantcar and Kolektor sETup show that fleet charging can be optimised against electricity-market conditions while maintaining vehicle availability. The approach relies on the difference between when a vehicle connects to a charger and when it must be ready. A vehicle connected for eight hours may require only two or three hours of actual charging.
Aggregators can use the connection-to-departure window to move electricity demand, and across hundreds or thousands of vehicles those windows form a larger flexible portfolio. For fleet operators, the immediate effect described in the pilots is lower energy procurement costs. For aggregators, the same flexibility could later be offered into balancing or local distribution-network markets where regulations permit.
Avoiding new peaks from automated responses
Slovenia’s national flexibility assessment highlights limits of simple price optimisation. If electricity or network tariffs become cheaper after a certain hour, automated chargers may all react simultaneously. Instead of lowering system pressure, thousands of vehicles could begin charging together and create a new night-time peak.
The assessment also notes similar risks for heat pumps, electric boilers and other automated loads. It describes a change in programme design from first-generation demand-side efforts that aimed to move consumption away from peak hours. A second generation is required to prevent too many flexible devices from moving in the same direction at the same time, which requires more granular signals.
Dynamic network tariffs and location-aware constraints
Dynamic network pricing is presented as one solution for location-aware control. Traditional electricity tariffs primarily indicate when electricity is expensive, while a more sophisticated network tariff can also signal when particular parts of the grid are constrained. The distinction becomes more important as distributed solar, EVs, heat pumps and other flexible assets connect to distribution networks.
The material describes how one megawatt of additional consumption may be beneficial in a location with high local solar production and available network capacity. The same megawatt could worsen congestion elsewhere. Future optimisation therefore shifts from only asking when electricity is cheapest to asking when and where the power system has capacity for additional consumption.
Smart-meter data, software layers and market participation
Slovenia’s advanced metering infrastructure provides energy-service providers with consumption data needed to answer capacity questions. Detailed meter data can show when customers consume electricity, how predictable that consumption is, and how much could potentially be moved. The examples given include identifying flexible pumps, compressors and cooling systems for industrial users, heating, cooling or ventilation for commercial buildings, and connection duration plus required energy before departure for EV fleets.
The described approach uses software to combine hundreds of individual profiles into a portfolio large enough to participate in electricity markets. This creates a commercial layer around meter-data analytics, automated demand response, consumption forecasting and flexibility verification. In this model, the valuable infrastructure is not only the meter but also the software that converts meter readings into an asset the system can dispatch.
Automated flexibility using wholesale prices and grid inputs
A longer-term model described for Slovenia involves platforms that examine multiple inputs simultaneously. A fleet-management platform could consider wholesale electricity prices, network tariffs, local grid conditions, balancing-market revenues and each vehicle’s charging requirement. An industrial energy-management system could apply similar calculations to production equipment.
The customer sets operational boundaries while software decides when electricity should be consumed within those limits. This is described as turning electricity flexibility into an automated service rather than a behavioural response to cheaper night-time tariffs. Aggregators are positioned as intermediaries that combine thousands of small assets, forecast availability and sell resulting flexibility to parties that need it.
Implications for Southeast Europe’s next electricity business
The model described in Slovenia is presented as having implications beyond its borders 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 once equipment exists.
The next stage suggested is centred on interaction between smart-meter data, dynamic tariffs and automated consumption. EVs are cited as an early use case, with the same infrastructure potentially coordinating 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 identified is a digital layer capable of determining when controllable assets should consume electricity and turning resulting flexibility into revenue.