Slovenia is testing how smart meters, dynamic network tariffs and automated electric-vehicle charging can make electricity consumers act as flexible power-market assets. Recent pilots indicate that EV charging schedules can be shifted toward cheaper periods while maintaining fleet operations. The national regulator has also flagged a second issue: if many customers respond to the same price signal at the same time, flexibility can create a new electricity-demand peak.
According to the regulator’s identified tension, the market is moving beyond conventional time-of-use tariffs. Consumption is expected to respond at the same time to electricity prices, network conditions and operational requirements. This shift is linked to a commercial model in which suppliers, aggregators and energy-management companies sell optimisation in addition to electricity.
In that model, optimisation covers decisions on when vehicles charge, when buildings consume electricity and when industrial equipment operates. Slovenia’s infrastructure is described as already supporting parts of this approach through widespread smart-meter deployment. The granular consumption data can be used to establish customer baselines, forecast flexible demand and verify whether agreed reductions or shifts in consumption occurred.
The meter is therefore presented as more than billing equipment. It is described as part of the infrastructure needed to run a flexibility market. EV fleets are highlighted as an early test case for this setup.
EV fleet pilots and operational constraints
Pilot results involving Avantcar and Kolektor sETup are cited as showing that fleet charging can be optimised against electricity-market conditions while maintaining vehicle availability. The underlying principle is based on the difference between connection time and required readiness time. A vehicle connected for eight hours may require only two or three hours of actual charging.
That gap creates an aggregator window in which electricity demand can be moved. Across hundreds or thousands of vehicles, these windows are described as forming a significant flexible electricity portfolio. For fleet operators, the immediate benefit cited is lower energy procurement costs.
For aggregators, the same flexibility could be offered into balancing or local distribution-network markets where regulations permit. The pilots are described as transforming an EV fleet from a passive electricity consumer into a potential grid resource. The next section addresses how price-driven automation can affect system peaks.
Limits of price-only flexibility signals
Slovenia’s national flexibility assessment highlights limitations of simple price optimisation. If electricity or network tariffs become cheaper after a certain hour, automated chargers may react simultaneously. Instead of reducing system pressure, thousands of vehicles could begin charging together and create a new night-time peak.
The assessment also notes that similar risks apply to heat pumps, electric boilers and other automated loads. It describes a change in programme design requirements between two generations of demand-side measures. The first generation focused on shifting consumption away from peak hours.
The second generation is described as needing to prevent too many flexible devices from moving in exactly the same direction at exactly the same time. That requirement is linked to the need for more granular signals rather than broad price changes alone.
Dynamic network tariffs and location-aware optimisation
Dynamic network pricing is presented as one possible solution for providing more granular signals. Traditional electricity tariffs are described as primarily indicating when electricity is expensive. A more sophisticated network tariff can also signal when particular parts of the grid are constrained.
The distinction is described as becoming more important as distributed solar, EVs, heat pumps and other flexible assets expand across distribution networks. The assessment gives an example in which one megawatt of additional consumption may be beneficial where local solar production is high and network capacity exists. It also notes that the same megawatt could worsen congestion elsewhere.
As a result, future optimisation is described as not relying only on when electricity is cheapest. It increasingly involves when and where the power system has capacity for additional consumption. This shift is characterised as creating a different market structure than price-only optimisation.
Smart-meter data, software orchestration and market participation
Slovenia’s advanced metering infrastructure is described as giving energy-service providers access to consumption data needed for location-aware optimisation. Detailed meter data can show when customers consume electricity, how predictable that consumption is and how much could potentially be moved. The examples include industrial flexible pumps, compressors, cooling systems or production processes.
For commercial buildings, the data can relate to heating, cooling or ventilation patterns. For EV fleets, it can support determining how long vehicles remain connected and how much energy each requires before departure. Software is then described as combining hundreds of individual profiles into a portfolio large enough to participate in electricity markets.
This approach creates a commercial layer around meter-data analytics, automated demand response, consumption forecasting and flexibility verification. The infrastructure is described as extending beyond meters because software converts meter readings into an asset that the electricity system can dispatch.
The longer-term model is described as becoming more sophisticated through platform-level calculations across multiple inputs. A fleet-management platform could examine wholesale electricity prices, network tariffs, local grid conditions and balancing-market revenues alongside each vehicle’s charging requirement. An industrial energy-management system could apply similar calculations to production equipment.
In both cases, customers are described as setting operational boundaries while software decides when electricity should be consumed within those limits. The resulting service is described as automated rather than dependent on behavioural responses to cheaper night-time tariffs.
The role of aggregators is also described in this framework. Aggregators can combine thousands of small assets, forecast their availability and sell resulting flexibility to parties that need it. Buyers are described as potentially including suppliers, transmission operators and distribution companies.
Implications for Southeast Europe’s broader market build-out
The model’s implications are described as extending beyond Slovenia because Southeast European countries are investing heavily 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 after deployment.
The larger commercial question identified is what markets can be built once equipment exists. Slovenia indicates that the next stage will focus on interaction between smart-meter data, dynamic tariffs and automated consumption across controllable loads such as commercial buildings, industrial processes, heat pumps and electric boilers.
This creates an electricity-market business model with relatively little new generation capacity because the asset base already exists in terms of controllable demand resources. What is described as missing is the digital layer capable of determining when those assets should consume electricity and turning resulting flexibility into revenue.