A Data‐Driven Prototyping Framework for Control Strategies in Low‐Voltage Grids: Application to Smart EV Charging

This paper presents a prototyping framework that supports decisions on the design and deployment of prosumer‐oriented control strategies in low‐voltage (LV) grids. Unlike existing approaches that focus either on operational detail or on scalable planning studies, the framework combines conceptualisation, planning‐oriented and operational modelling within a single data‐driven process. It structures strategy development and establishes a KPI basis that captures grid‐related, customer‐related and practical aspects, thereby supporting data‐informed assessments of control strategies, such as smart electric vehicle (EV) charging, and their implications for grid planning and operation. A data‐driven case study on private EV charging illustrates the capabilities of the framework. Several practically relevant charging strategies are developed and assessed across a large set of LV grids using complementary simulation methods. In the case study, grid‐oriented strategies yield favourable outcomes, reducing the occurrence of critical grid situations significantly, for example, undervoltages by up to 50%. Price‐based concepts with grid‐dependent behaviour can also mitigate load peaks when parameterised appropriately. These strategies also contribute to delaying grid reinforcement and extension. A simplified bidirectional variant, focusing on local voltage‐based active power adjustments, provides initial insights into the grid‐supportive potential of vehicle‐to‐grid approaches. The case study demonstrates how the framework supports multiperspective KPI‐based evaluation and thereby strengthens decision‐making for the integration of prosumers into LV grids and for data‐driven smart charging concepts.