A Digital Twin-Based Method for Evaluating Local Collective Tariffs in Distribution-Level Energy Systems

Can Neighbors Share a Single Electricity Bill?

As households install solar panels and electric vehicles, traditional electricity pricing is changing. Most current systems use volumetric tariffs (charges based solely on the amount of energy consumed). These often fail to account for how local energy sharing changes the physical load on the grid.

Researchers are now exploring "local collective tariffs" (LCT). In this system, a group of neighbors shares a collective electricity bill. The goal is to incentivize people to consume energy when neighbors are producing it locally. This process aims to smooth out spikes in demand. A new study from the University of Southern Denmark uses a "digital twin"—a high-fidelity virtual replica of a real-world energy system—to test this. The authors ask whether these collective agreements actually save money or create new winners and losers.

Testing the neutrality of local energy sharing

The core question driving this research is whether a localized tariff can achieve "cost neutrality." Regulators often design these schemes so the collective group pays roughly the same as they would under standard individual billing. The authors specifically investigated if the Danish LCT produces similar annual network-related costs as the existing Tariff Model 3.0. This test assumes consumer behavior remains unchanged.

Beyond mere neutrality, the study explores how economic outcomes shift as the group changes. The researchers examined how sensitive savings are to the number of participants. They also looked at the mix of technologies, such as the ratio of solar panel owners to electric vehicle (EV) users.

The limitations of idealized models

Historically, evaluating new electricity tariffs has relied on optimization-based models. These are mathematical formulations that solve for the absolute best possible outcome. However, they often assume "idealized behavior." This means they treat consumers as perfect actors who always move to minimize costs. Such models often lack the randomness of real life or the physical constraints of the grid.

Older methods often ignore the operational realities of how a transformer handles fluctuating loads. This creates a gap between regulatory theory and engineering reality. There was a need for a method that could simulate the interaction between physical infrastructure, user behavior, and complex tariff logic.

Simulating a neighborhood's energy ecosystem

To bridge this gap, the authors developed a digital twin-based framework. They applied it to a real residential area in Strib, Denmark. The simulation included 126 consumers connected to a 400 kVA transformer. Instead of just modeling power flow, the researchers built a socio-technical ecosystem.

This included "agents" representing individual households with unique consumption patterns. They added a "virtual meter" to aggregate the group's total imports and exports. Finally, they included an "Energy Sharing Consultant" to manage the math of splitting the bills.

The researchers ran eight distinct scenarios to stress-test the system. They compared standard individual billing against various LCT configurations. They also introduced "shocks," such as increasing solar panel use or adding 23 electric vehicles. As seen in, the virtual meter tracks the total load.

It also calculates a "rolling peak"—an average of the highest consumption values over a moving window. This peak is a critical driver of the LCT cost.

Aggregation beats the individual peak

The study finds that the LCT does not actually maintain cost neutrality. Instead, it provides a systematic reduction in both total and average electricity expenses. The authors report that in a comparison between the baseline (S1) and full LCT participation (S2), total expenses dropped from -492,752 DKK to -320,202 DKK. This represents a significant saving for the entire community.

The reason for this saving is "demand aggregation." By grouping diverse consumption profiles together, the community-level peak demand becomes lower. This happens because not everyone peaks at the same time. This reduces the group's exposure to expensive, peak-based tariff components.

The researchers also found that benefits scale with technology. Higher solar penetration (S6) led to the lowest mean user expenses. While electric vehicles increase the total load, the LCT helps mitigate the economic impact. It smoothes the impact through collective settlement, as illustrated in .

Implications for the electrified grid

The findings suggest that collective tariff settlement could support residential electrification. If the LCT offsets the costs of EV charging through aggregation, it provides a financial cushion for the transition.

However, the paper highlights a crucial trade-off. The system is highly sensitive to group composition. A single high-consumption user can disproportionately raise the collective peak. This increases the shared power charge for everyone else. This introduces significant "fairness" concerns regarding cost redistribution.

If these results generalize, the implication for grid operators is clear. Tariff design is just as important as physical hardware. A well-designed tariff can act as a virtual stabilizer for the grid. Future research could introduce "smart" elements like battery storage to see if active management amplifies these gains.

Figures from the paper