Asset Congestion and Voltage Management in Large-Scale MV-LV Networks With Solar PV

Abstract

The rapid uptake of residential solar photovoltaic (PV) systems in LV networks is resulting in reverse power flows that can reach the upstream MV network. Given the scale of MV networks, supplying thousands of customers through hundreds of LV transformers, distribution companies will require cost-effective and scalable solutions. This work proposes an adaptive control strategy to manage asset congestion and voltages in both MV and LV networks leveraging existing controllable devices and without network models. For asset congestion, a bottom-up approach is adopted using smart meter data and irradiance to estimate the maximum allowable PV generation. For voltages, the target of the MV on-load tap changer is updated according to smart meter data. The proposed control strategy, which does not require any electrical model information, is assessed on a real large-scale Australian MV-LV network (4,600+ customers, 79 LV transformers) considering realistic, high-resolution, time-series smart meter and generation data. To cater for load and generation uncertainties, a time-series Monte Carlo analysis is applied considering different PV penetrations. It is demonstrated that the proposed electrical model-free control strategy effectively mitigates all voltage and congestions issues across the MV-LV network, making it an attractive scalable alternative to reinforcements.