Multi-Feature Dynamic Reconstruction of Photovoltaic Systems with Battery Storage for Real-Time Grid Monitoring

Real-time grid monitoring of photovoltaic (PV) systems with battery storage requires continuous access to voltage, current, and power states. Traditional simulation models obtain these responses by calculating switching events, which limits the scale of real-time simulation. This paper proposes a Multi-Feature Dynamic Reconstruction (MFDR) method that combines environmental inputs, averaged converter states, and frequency-domain electrical variables. On the DC side, PV output is calculated from irradiance and temperature, while the bidirectional battery converter is represented by a low-frequency reconstruction model. On the AC side, dynamic phasors are used to convert the grid-connected inverter into a frequency-domain Norton equivalent for reconstructing its port voltage and current responses. Controller hardware-in-the-loop tests are conducted under different operating conditions. The reported peak and normalized tracking errors of the evaluated transient quantities remain below 3%. In the four-core IEEE 118-bus case, the average per-core CPU utilization decreases from 70.43% to 41.08%, while the maximum step execution time decreases from 38 μs to 27 μs. The model with 1069 state variables also operates within the fixed 50 μs simulation step. The results show that the MFDR method reduces the computational demand of real-time grid monitoring while retaining the voltage, current, and power responses.