Data-Driven Modeling of Smart Power Cell Dynamics at the Transmission System Interconnection

Abstract

Smart Power Cells (SPCs) are controllable distribution grid sections that coordinate local flexibility to support higher-level grid operation. However, integrating detailed SPC simulations into higher-level coordination frameworks, such as transmission system controllers, is limited by their high computational cost. This paper presents a data-driven approach to model the dynamic behavior of an SPC at the TSO–SPC interconnection. A high-fidelity MATLAB/Simulink model is used to generate data for learning the system response to active and reactive power reference signals. A Long Short-Term Memory (LSTM) neural network is employed to approximate the aggregated SPC behavior. The results show that the main power flow dynamics are captured with low prediction errors while requiring considerably less computational effort than the high-fidelity simulation. This enables the approach, with R2 ≈ 96%, to serve as a baseline for further investigations of computationally efficient frameworks for SPC behavior studies.