From Energy Transition Pathways to Measurement Requirements: A Scenario-Based Study of Low-Voltage Grids

Abstract

Increasing penetration of electric vehicles, heat pumps, and rooftop photovoltaics is creating thermal and voltage stress in low-voltage distribution grids. This work links three German energy transition pathways (2025-2045) with state estimation performance requirements, evaluated on two SimBench reference networks across three equipment quality levels (good, medium, poor) and three VDE Forum Netztechnik/Netzbetrieb (VDE FNN) measurement constellations that differ in the availability of transformer and feeder-level instrumentation. Congestion is caused exclusively by transformer overloading and voltage-band violations. No individual line exceeds its thermal rating. Equipment quality is the primary factor: under good equipment, congestion remains nearly absent through 2045 (1/26 scenarios), under medium equipment it emerges from 2035 (10/26), under poor equipment from 2025 (25/26), reaching 208 % peak transformer loading. Without transformer instrumentation, voltage estimation errors remain at 6-35% regardless of smart meter penetration. Adding a single transformer measurement reduces errors by a factor of 3 to 24, achieving median errors below 1.1% under poor equipment. Per-feeder measurements achieve comparable accuracy and outperform the transformer-only configuration under poor equipment in rural networks (0.8% vs. 1.1%). In urban networks under poor and medium equipment, transformer and feeder-level instrumentation meet the VDE FNN voltage accuracy target without requiring customer-side sensors. These findings motivate prioritizing transformer instrumentation as an effective first step for grid observability and supplementing the current consumption-driven metering rollout with risk-based deployment criteria linked to local congestion exposure.

Figures (11)

• Figure 1: Development of congestion frequency in urban and rural grids by year, energy transition pathway, and equipment level. Bars show the fraction of all 35 040 time steps per year in which at least one component overload occurs, split into hard congestion events (solid, loading $>$110 % or voltage $<$0.90 pu) and grey-zone periods (hatched, 100--110 % or 0.90--0.95 pu).
• Figure 2: Component-level limit violations during congestion periods, by equipment quality level (rows: medium, poor) and network type (columns: rural, urban). For each year, two grouped bars show the pathway-averaged fraction of congestion periods with transformer loading $>$100 % (blue) and fraction of congestion periods with at least one bus voltage $<$0.95 p.u. or $>$1.05 p.u. (red). Individual pathway values are overlaid as markers ($\circ$ Agora, $\square$ Fraunhofer, $\triangle$ Fed. Gov.) to indicate inter-pathway spread. No line overloading ($>$100 %) occurs in any scenario (maximum line loading: 98.6 %). Empty bars indicate years without congestion events at that equipment level.
• Figure 3: Distribution of maximum transformer loading during congestion periods, by year, equipment level, and area type. Box plots show the distribution across all scenarios per year, with separate panels for each equipment level (good/medium/poor) and network type (rural/urban). Red dashed line: 100 % overload threshold. Empty panels indicate years with no congestion events at that equipment level.
• Figure 4: Spatial progression of congestion in the rural SimBench LV grid (1-LV-rural3, 129 buses) under the Fraunhofer pathway (poor equipment), each panel showing the worst-case congestion period for that scenario.
• Figure 5: Seasonal distribution of congestion events by month, area type, and equipment level (good equipment omitted due to near-zero congestion). Each bar shows the percentage of quarter-hour time steps in that month containing at least one congestion event, averaged across the three energy transition pathways. Bars are colored by target year (2025--2045).