The number of photovoltaic (PV) installations is increasing. Within 2024, an additional 16.2 GW of PV capacity was installed

in Germany. In total, the majority of installed systems are connected to the low-voltage grid and mounted on rooftops. This

development marks a structural shift from passive, radial grids to networks characterized by volatile renewable generation. As a

result, low-voltage grids experience increasing bidirectional power flows and more variable net injections at the grid connection

points. Since PV is expected to remain a central pillar of the transition toward a carbon-free energy system and in meeting

the 1.5°C climate target, associated issues - such as grid congestion and voltage band violations - are expected to intensify.

Consequently, accurate forecasting of the grid state is essential for distribution system operators (DSOs) to enable proactive and

preventive grid management. This work presents a real-time system for nowcasting the state of low-voltage distribution grids

using open-source approaches, developed and validated with reference to a real German distribution grid in Hittistetten. The

grid model is kindly provided by the local DSO, Stadtwerke Ulm/Neu-Ulm Netze GmbH, to the Smart Grids Research Group

as a representative test area, with measurement data available from various grid components. The objective is to demonstrate a

forward-looking methodology that enables DSOs to maintain reliable and stable grid operation under increasing PV penetration.

The core of the system is a solar radiation forecast based on data from the Meteosat Second Generation (MSG) satellite. This feeds

into a physical PV power model to generate feed-in forecasts for the grid area. A complementary load forecast is generated using

modified standard load profiles. These feed-in and load forecasts are combined and processed through a load flow algorithm to

determine the future grid state. Because the load profiles are based on annual aggregates, the load forecast is further refined using

real-time measurements of transformer utilization. The complete model chain is comprehensively validated using measurement

data from the operational grid. All components of the model show strong correlation with observed data and low prediction

errors. The system provides accurate PV generation forecasts and, at an aggregated level, reliable grid status predictions for two

out of three transformers in the test network. This study demonstrates that data-driven nowcasting of the low-voltage grid state is

not only technically feasible but also represents a key contribution to ensuring grid stability amid the evolving dynamics of the

energy system, ultimately enabling DSOs to perform active grid control.