Digital Twin Model Optimizes Local Energy Tariffs, Cuts Community Costs

A new digital twin method from researchers at the University of Southern Denmark (Kristoffer Christensen, Bo Nørregaard Jørgensen, Zheng Grace Ma) offers an engineering-grounded way to evaluate local collective tariffs in distribution-level energy systems. The approach integrates agent-based modeling of household consumption and generation, virtual aggregation through a shared metering abstraction, and explicit representation of tariff logic within a unified simulation environment. This allows realistic assessment of how tariffs perform under actual behavioral and infrastructural conditions, rather than relying on simplified assumptions.

Applied to the Danish Local Collective Tariff across representative residential energy community configurations—including scenarios with photovoltaic generation, battery storage, and electric vehicle charging—the method showed that aggregation of heterogeneous demand profiles reduces peak coincidence and enables more efficient allocation of tariff components. This leads to measurable cost reductions at the community level. However, the outcomes reveal sensitivity to the temporal alignment of consumption and generation, influencing the degree of cost neutrality across participants. The framework provides a systematic basis for analyzing and comparing tariff structures beyond this specific case, supporting regulators and utilities in designing fairer, more efficient local energy markets.