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.